From 6ccecc0c6984b2fe03d3b1718a79fa170d53a430 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Thu, 10 Aug 2023 15:36:46 +0800 Subject: [PATCH 01/84] [gemini] fix tensor storage cleaning in state dict collection (#4396) --- colossalai/zero/gemini/gemini_optimizer.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/colossalai/zero/gemini/gemini_optimizer.py b/colossalai/zero/gemini/gemini_optimizer.py index 7d0db6b1fa23..a2085323f83e 100644 --- a/colossalai/zero/gemini/gemini_optimizer.py +++ b/colossalai/zero/gemini/gemini_optimizer.py @@ -1,6 +1,5 @@ # this code is inspired by the DeepSpeed library and implemented with our own design from scratch import copy -import gc import math import warnings from typing import Any, Dict, Iterator, OrderedDict, Set, Tuple @@ -468,11 +467,6 @@ def collect_states(self, param_id: int, only_rank_0: bool = True) -> dict: self.load_from_compacted_states(compacted_states, collected_states, state_names, shard_offset, shard_size) - # Clean gathered states - for state_shard in gathered_state_shards: - del state_shard[0] - gc.collect() - # Reshape tensors if is_collector: for state_name, state_tensor in collected_states.items(): From d86ddd9b2910ef0e9a093039d70c3789d3af3517 Mon Sep 17 00:00:00 2001 From: LuGY <74758262+Gy-Lu@users.noreply.github.com> Date: Fri, 11 Aug 2023 15:09:24 +0800 Subject: [PATCH 02/84] [hotfix] fix unsafe async comm in zero (#4404) * improve stablility of zero * fix wrong index * add record stream --- .../low_level/bookkeeping/bucket_store.py | 55 ++++++++++++------- colossalai/zero/low_level/low_level_optim.py | 9 +++ .../test_zero/test_low_level/test_zero1_2.py | 2 +- 3 files changed, 46 insertions(+), 20 deletions(-) diff --git a/colossalai/zero/low_level/bookkeeping/bucket_store.py b/colossalai/zero/low_level/bookkeeping/bucket_store.py index 98f1b78d0049..0ab10e25d407 100644 --- a/colossalai/zero/low_level/bookkeeping/bucket_store.py +++ b/colossalai/zero/low_level/bookkeeping/bucket_store.py @@ -13,15 +13,20 @@ class BucketStore(BaseStore): def __init__(self, torch_pg: ProcessGroup): super().__init__(torch_pg) - # init and reset + # init self.current_group_id = 0 + self._num_elements_in_bucket = 0 # mapping gardient slices and parameter self.grad_to_param_mapping = dict() + self._grad_in_bucket = dict() self._param_list = [] self._padding_size = [] + for rank in range(self._world_size): + self._grad_in_bucket[rank] = [] - self.reset() + # offset_list records number of tensors in the bucket before each reduction + self.offset_list = [0] def num_elements_in_bucket(self) -> int: """Return the total number of elements in bucket @@ -32,6 +37,12 @@ def num_elements_in_bucket(self) -> int: return self._num_elements_in_bucket + def reset_num_elements_in_bucket(self): + """Set the number of elements in bucket to zero. + """ + + self._num_elements_in_bucket = 0 + def add_param_grad(self, group_id: int, param: Tensor, padding_size: int): """Add a param to bucket and record the padding size of a param for gradient padding @@ -46,28 +57,32 @@ def add_param_grad(self, group_id: int, param: Tensor, padding_size: int): self._num_elements_in_bucket += (param.numel() + padding_size) self.current_group_id = group_id + # number of tensors in current bucket + self.offset_list[-1] += 1 + def build_grad_in_bucket(self): """Orgnize parameters' gradient(padding and split), follows the paramters' splitting method Data structure of self._grad_in_bucket: { rank0: [grad0_rank0, grad1_rank0, ...] - rank1: [grad1_rank1, grad1_rank1, ...] + rank1: [grad0_rank1, grad1_rank1, ...] } """ - for param, padding_size in zip(self._param_list, self._padding_size): - with torch.no_grad(): - grad = param.grad.detach().flatten() - if padding_size > 0: - grad = torch.nn.functional.pad(grad, [0, padding_size]) - grad_list = grad.split(grad.numel() // self._world_size) - for rank in range(self._world_size): - grad_current_rank = grad_list[rank].detach() - self.grad_to_param_mapping[id(grad_current_rank)] = id(param) - self._grad_in_bucket[rank].append(grad_current_rank) + grad = param.grad.clone().detach().flatten() + if padding_size > 0: + with torch.no_grad(): + grad = torch.nn.functional.pad(grad.view(-1), [0, padding_size]) + grad_list = grad.split(grad.numel() // self._world_size) + for rank in range(self._world_size): + grad_current_rank = grad_list[rank].clone().detach() + self.grad_to_param_mapping[id(grad_current_rank)] = id(param) + self._grad_in_bucket[rank].append(grad_current_rank) param.grad = None + self.offset_list.append(0) + def get_grad(self) -> Dict: """Return the dictionary of gradients slices, of which the keys are ranks @@ -104,10 +119,12 @@ def get_param_id_of_grad(self, grad: Tensor) -> int: return self.grad_to_param_mapping[id(grad)] def reset(self): - self.grad_to_param_mapping = dict() - self._num_elements_in_bucket = 0 - self._param_list = [] - self._padding_size = [] - self._grad_in_bucket = dict() + """Reset the bucket storage after reduction, only release the tensors have been reduced + """ + cur_offset = self.offset_list.pop(0) + self._param_list = self._param_list[cur_offset:] + self._padding_size = self._padding_size[cur_offset:] + for _ in range(cur_offset): + del self.grad_to_param_mapping[next(iter(self.grad_to_param_mapping))] for rank in range(self._world_size): - self._grad_in_bucket[rank] = [] + self._grad_in_bucket[rank] = self._grad_in_bucket[rank][cur_offset:] diff --git a/colossalai/zero/low_level/low_level_optim.py b/colossalai/zero/low_level/low_level_optim.py index 2b3f50ed4fd4..64d6a5395120 100644 --- a/colossalai/zero/low_level/low_level_optim.py +++ b/colossalai/zero/low_level/low_level_optim.py @@ -242,10 +242,19 @@ def _attach_reduction_hook(self): def _run_reduction(self): if self._bucket_store.num_elements_in_bucket() > 0: self._bucket_store.build_grad_in_bucket() + flat_grads = self._bucket_store.get_flatten_grad() flat_grads /= self._world_size + + # ready to add other tensors to bucket + self._bucket_store.reset_num_elements_in_bucket() + if self._overlap_communication: stream = self._comm_stream + # in case of the memory being reused in the default stream + flat_grads.record_stream(stream) + # waiting for ops in the default stream finishing + stream.wait_stream(torch.cuda.current_stream()) else: stream = torch.cuda.current_stream() diff --git a/tests/test_zero/test_low_level/test_zero1_2.py b/tests/test_zero/test_low_level/test_zero1_2.py index 5a0609bff192..9c4474aff5c3 100644 --- a/tests/test_zero/test_low_level/test_zero1_2.py +++ b/tests/test_zero/test_low_level/test_zero1_2.py @@ -137,7 +137,7 @@ def exam_zero_1_torch_ddp(world_size, dtype: torch.dtype): zero_optimizer = LowLevelZeroOptimizer(zero_optimizer, overlap_communication=True, initial_scale=1, - reduce_bucket_size=262144) + reduce_bucket_size=1024 * 1024) torch_optimizer = torch.optim.SGD(torch_model.parameters(), lr=1) From 6d41c3f2aa7c859fe2b87889e6b02b4febbfa4f6 Mon Sep 17 00:00:00 2001 From: Wenhao Chen Date: Mon, 14 Aug 2023 15:26:27 +0800 Subject: [PATCH 03/84] [doc] update Coati README (#4405) * style: apply formatter * fix: add outdated warnings * docs: add dataset format and polish * docs: polish README * fix: fix json format * fix: fix typos * revert: revert 7b example --- applications/Chat/README.md | 126 ++++++--- applications/Chat/benchmarks/README.md | 9 +- applications/Chat/coati/ray/README.md | 177 ++++++------ applications/Chat/evaluate/README.md | 252 ++++++++--------- applications/Chat/examples/README.md | 260 +++++++++++------- .../Chat/examples/community/README.md | 15 +- .../Chat/examples/community/peft/README.md | 6 + .../Chat/examples/community/ray/README.md | 14 + applications/Chat/inference/README.md | 24 +- 9 files changed, 528 insertions(+), 355 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index 162528cee414..5a1187ab503d 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -4,7 +4,6 @@ ColossalChat - ## Table of Contents - [Table of Contents](#table-of-contents) @@ -34,7 +33,9 @@ - [Authors](#authors) - [Citations](#citations) - [Licenses](#licenses) + --- + ## What is ColossalChat and Coati ? [ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) is the project to implement LLM with RLHF, powered by the [Colossal-AI](https://github.com/hpcaitech/ColossalAI) project. @@ -42,6 +43,7 @@ Coati stands for `ColossalAI Talking Intelligence`. It is the name for the module implemented in this project and is also the name of the large language model developed by the ColossalChat project. The Coati package provides a unified large language model framework that has implemented the following functions + - Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms - Supervised datasets collection - Supervised instructions fine-tuning @@ -56,17 +58,19 @@ The Coati package provides a unified large language model framework that has imp

- Image source: https://openai.com/blog/chatgpt +Image source: https://openai.com/blog/chatgpt + **As Colossal-AI is undergoing some major updates, this project will be actively maintained to stay in line with the Colossal-AI project.** - More details can be found in the latest news. -* [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) -* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) + +- [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) +- [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) ## Online demo +
@@ -83,13 +87,13 @@ More details can be found in the latest news.

-> DeepSpeedChat performance comes from its blog on 2023 April 12, ColossalChat performance can be reproduced on an AWS p4d.24xlarge node with 8 A100-40G GPUs with the following command: torchrun --standalone --nproc_per_node 8 benchmark_opt_lora_dummy.py --num_collect_steps 1 --use_kernels --strategy colossalai_zero2 --experience_batch_size 64 --train_batch_size 32 +> DeepSpeedChat performance comes from its blog on 2023 April 12, ColossalChat performance can be reproduced on an AWS p4d.24xlarge node with 8 A100-40G GPUs with the following command: `torchrun --standalone --nproc_per_node 8 benchmark_opt_lora_dummy.py --num_collect_steps 1 --use_kernels --strategy colossalai_zero2 --experience_batch_size 64 --train_batch_size 32` ## Install ### Install the environment -```shell +```bash conda create -n coati conda activate coati git clone https://github.com/hpcaitech/ColossalAI.git @@ -99,7 +103,7 @@ pip install . ### Install the Transformers -```shell +```bash pip install transformers==4.30.2 ``` @@ -107,10 +111,11 @@ pip install transformers==4.30.2 ### Supervised datasets collection -we collected 104K bilingual datasets of Chinese and English, and you can find the datasets in this repo -[InstructionWild](https://github.com/XueFuzhao/InstructionWild) +We collected 104K bilingual datasets of Chinese and English, and you can find the datasets in this repo +[InstructionWild](https://github.com/XueFuzhao/InstructionWild) and in this [file](https://github.com/XueFuzhao/InstructionWild/blob/main/data/README.md). Here is how we collected the data +

@@ -122,6 +127,20 @@ Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned ea You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning. [[Stage1 tutorial video]](https://www.youtube.com/watch?v=-qFBZFmOJfg) +**Note**: the supervised dataset follows the following format, + +```json +[ + { + "instruction": "Provide a list of the top 10 most popular mobile games in Asia", + "input": "", + "output": "The top 10 most popular mobile games in Asia are:\n1) PUBG Mobile\n2) Pokemon Go\n3) Candy Crush Saga\n4) Free Fire\n5) Clash of Clans\n6) Mario Kart Tour\n7) Arena of Valor\n8) Fantasy Westward Journey\n9) Subway Surfers\n10) ARK Survival Evolved", + "id": 0 + }, + ... +] +``` + ### RLHF Training Stage2 - Training reward model Stage2 trains a reward model, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model @@ -140,13 +159,46 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of You can run the `examples/train_prompts.sh` to start training PPO with human feedback. [[Stage3 tutorial video]](https://www.youtube.com/watch?v=Z8wwSHxPL9g) +**Note**: the required datasets follow the following format, + +- `pretrain dataset` + + ```json + [ + { + "instruction": "Provide a list of the top 10 most popular mobile games in Asia", + "input": "", + "output": "The top 10 most popular mobile games in Asia are:\n1) PUBG Mobile\n2) Pokemon Go\n3) Candy Crush Saga\n4) Free Fire\n5) Clash of Clans\n6) Mario Kart Tour\n7) Arena of Valor\n8) Fantasy Westward Journey\n9) Subway Surfers\n10) ARK Survival Evolved", + "id": 0 + }, + ... + ] + ``` + +- `prompt dataset` + + ```json + [ + { + "instruction": "Edit this paragraph to make it more concise: \"Yesterday, I went to the store and bought some things. Then, I came home and put them away. After that, I went for a walk and met some friends.\"", + "id": 0 + }, + { + "instruction": "Write a descriptive paragraph about a memorable vacation you went on", + "id": 1 + }, + ... + ] + ``` + For more details, see [`examples/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples). ### Inference Quantization and Serving - After Training We provide an online inference server and a benchmark. We aim to run inference on single GPU, so quantization is essential when using large models. -We support 8-bit quantization (RTN), 4-bit quantization (GPTQ), and FP16 inference. You can +We support 8-bit quantization (RTN), 4-bit quantization (GPTQ), and FP16 inference. + Online inference server scripts can help you deploy your own services. For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference). @@ -158,6 +210,7 @@ For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tre
E-mail ![phd](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/Phd.png) +
coding @@ -191,6 +244,7 @@ For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tre
### Open QA +
Game ![Game](https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/game.png) @@ -224,6 +278,7 @@ For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tre You can find more examples in this [repo](https://github.com/XueFuzhao/InstructionWild/blob/main/comparison.md). ### Limitation +
Limitation for LLaMA-finetuned models - Both Alpaca and ColossalChat are based on LLaMA. It is hard to compensate for the missing knowledge in the pre-training stage. - Lack of counting ability: Cannot count the number of items in a list. @@ -247,7 +302,7 @@ You can find more examples in this [repo](https://github.com/XueFuzhao/Instructi We have integrated the Transformers save and load pipeline, allowing users to freely call Hugging Face's language models and save them in the HF format. -``` +```python from coati.models.llama import LlamaLM from coati.trainer import SFTTrainer @@ -256,20 +311,20 @@ tokenizer = AutoTokenizer.from_pretrained(args.pretrain) (model, optim) = strategy.prepare((model, optim)) trainer = SFTTrainer(model=model, - strategy=strategy, - optim=optim, - train_dataloader=train_dataloader, - eval_dataloader=eval_dataloader, - batch_size=args.batch_size, - max_epochs=args.max_epochs, - accumulation_steps = args.accumulation_steps -) + strategy=strategy, + optim=optim, + train_dataloader=train_dataloader, + eval_dataloader=eval_dataloader, + batch_size=args.batch_size, + max_epochs=args.max_epochs, + accumulation_steps=args.accumulation_steps + ) trainer.fit() # this saves in pytorch format strategy.save_model(model, args.save_path, only_rank0=True) -# this saves in HF format. ColossalAI strategy with stage-3 doesn't support this method +# this saves in HF format strategy.save_pretrained(model, args.save_path, only_rank0=True, tokenizer=tokenizer) ``` @@ -280,12 +335,13 @@ strategy.save_pretrained(model, args.save_path, only_rank0=True, tokenizer=token Here are some examples that can allow you to train a 7B model on a single or multiple consumer-grade GPUs. If you only have a single 24G GPU, you can use the following script. `batch_size`, `lora_rank` and `grad_checkpoint` are the most important parameters to successfully train the model. -``` + +```bash +// [INFO]: MAX GPU MEMORY ALLOCATED: 19148.9345703125 MB torchrun --standalone --nproc_per_node=1 train_sft.py \ --pretrain "/path/to/LLaMa-7B/" \ --model 'llama' \ --strategy ddp \ - --log_interval 10 \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 1 \ @@ -298,12 +354,12 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \ ``` `colossalai_gemini` strategy can enable a single 24G GPU to train the whole model without using LoRA if you have sufficient CPU memory. You can use the following script. -``` + +```bash torchrun --standalone --nproc_per_node=1 train_sft.py \ --pretrain "/path/to/LLaMa-7B/" \ --model 'llama' \ --strategy colossalai_gemini \ - --log_interval 10 \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 1 \ @@ -315,12 +371,12 @@ torchrun --standalone --nproc_per_node=1 train_sft.py \ ``` If you have 4x32 GB GPUs, you can even train the whole 7B model using our `colossalai_zero2_cpu` strategy! The script is given as follows. -``` + +```bash torchrun --standalone --nproc_per_node=4 train_sft.py \ --pretrain "/path/to/LLaMa-7B/" \ --model 'llama' \ --strategy colossalai_zero2_cpu \ - --log_interval 10 \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 1 \ @@ -330,8 +386,8 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --max_epochs 1 \ --grad_checkpoint ``` -
+
## The Plan @@ -346,24 +402,26 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ - [ ] support chain-of-thought by [langchain](https://github.com/hwchase17/langchain) ### Real-time progress -You will find our progress in github project broad -[Coati](https://github.com/orgs/hpcaitech/projects/17/views/1) +You will find our progress in github [project broad](https://github.com/orgs/hpcaitech/projects/17/views/1). ## Invitation to open-source contribution + Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models from the starting point of replicating ChatGPT! You may contact us or participate in the following ways: + 1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks! 2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md). 3. Join the Colossal-AI community on -[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w), -and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas. + [Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w), + and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas. 4. Send your official proposal to email contact@hpcaitech.com Thanks so much to all of our amazing contributors! ## Quick Preview +
@@ -397,18 +455,22 @@ Thanks so much to all of our amazing contributors! | Better Cases | 38 ⚔ **41** | **45** ⚔ 33 | | Win Rate | 48% ⚔ **52%** | **58%** ⚔ 42% | | Average Score | 7.06 ⚔ **7.13** | **7.31** ⚔ 6.82 | + - Our Coati-7B model performs better than Alpaca-7B when using GPT-4 to evaluate model performance. The Coati-7B model we evaluate is an old version we trained a few weeks ago and the new version is around the corner. ## Authors Coati is developed by ColossalAI Team: + - [Fazzie](https://fazzie-key.cool/about/index.html) - [FrankLeeeee](https://github.com/FrankLeeeee) - [BlueRum](https://github.com/ht-zhou) - [ver217](https://github.com/ver217) - [ofey404](https://github.com/ofey404) +- [Wenhao Chen](https://github.com/CWHer) The Phd student from [(HPC-AI) Lab](https://ai.comp.nus.edu.sg/) also contributed a lot to this project. + - [Zangwei Zheng](https://github.com/zhengzangw) - [Xue Fuzhao](https://github.com/XueFuzhao) diff --git a/applications/Chat/benchmarks/README.md b/applications/Chat/benchmarks/README.md index bc8ad8ba9816..c13f3485863b 100644 --- a/applications/Chat/benchmarks/README.md +++ b/applications/Chat/benchmarks/README.md @@ -27,9 +27,12 @@ We also provide various training strategies: We only support `torchrun` to launch now. E.g. -```shell +```bash # run OPT-125M with no lora (lora_rank=0) on single-node single-GPU with min batch size -torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py --model 125m --critic_model 125m --strategy ddp --experience_batch_size 1 --train_batch_size 1 --lora_rank 0 +torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py \ + --model 125m --critic_model 125m --strategy ddp \ + --experience_batch_size 1 --train_batch_size 1 --lora_rank 0 # run Actor (OPT-1.3B) and Critic (OPT-350M) with lora_rank=4 on single-node 4-GPU -torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py --model 1.3b --critic_model 350m --strategy colossalai_zero2 --lora_rank 4 +torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py \ + --model 1.3b --critic_model 350m --strategy colossalai_zero2 --lora_rank 4 ``` diff --git a/applications/Chat/coati/ray/README.md b/applications/Chat/coati/ray/README.md index 228155a6855b..79b1db347827 100644 --- a/applications/Chat/coati/ray/README.md +++ b/applications/Chat/coati/ray/README.md @@ -1,3 +1,5 @@ +:warning: **This content may be outdated since the major update of Colossal Chat. We will update this content soon.** + # Distributed PPO Training on Stage 3 ## Detach Experience Makers and Trainers @@ -26,124 +28,137 @@ See examples at `ColossalAI/application/Chat/examples/ray` - define makers' environment variables : - ```python - env_info_makers = [{ - 'local_rank': '0', - 'rank': str(rank), - 'world_size': str(num_makers), - 'master_port': maker_port, - 'master_addr': master_addr - } for rank in range(num_makers)] + ```python + env_info_makers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(num_makers), + 'master_port': maker_port, + 'master_addr': master_addr + } for rank in range(num_makers)] + + ``` - ``` - define maker models : - ```python - def model_fn(): - actor = get_actor_from_args(...) - critic = get_critic_from_args(...) - reward_model = get_reward_model_from_args(...) - initial_model = get_actor_from_args(...) - return actor, critic, reward_model, initial_model - - ``` + + ```python + def model_fn(): + actor = get_actor_from_args(...) + critic = get_critic_from_args(...) + reward_model = get_reward_model_from_args(...) + initial_model = get_actor_from_args(...) + return actor, critic, reward_model, initial_model + + ``` + - set experience_holder_refs : - ```python - experience_holder_refs = [ - ExperienceMakerHolder.options( - name=f"maker_{i}", - num_gpus=1, - max_concurrency=2 - ).remote( - detached_trainer_name_list=[f"trainer_{x}" for x in target_trainers(...)], - model_fn=model_fn, - ...) - for i, env_info_maker in enumerate(env_info_makers) - ] - ``` - The names in the `detached_trainer_name_list` refer to the target trainers that the maker should send experience to. - We set a trainer's name the same as a maker, by `.options(name="str")`. See below. + ```python + experience_holder_refs = [ + ExperienceMakerHolder.options( + name=f"maker_{i}", + num_gpus=1, + max_concurrency=2 + ).remote( + detached_trainer_name_list=[f"trainer_{x}" for x in target_trainers(...)], + model_fn=model_fn, + ...) + for i, env_info_maker in enumerate(env_info_makers) + ] + ``` + + The names in the `detached_trainer_name_list` refer to the target trainers that the maker should send experience to. + We set a trainer's name the same as a maker, by `.options(name="str")`. See below. ### Setup Trainers - define trainers' environment variables : - ```python - env_info_trainers = [{ - 'local_rank': '0', - 'rank': str(rank), - 'world_size': str(num_trainers), - 'master_port': trainer_port, - 'master_addr': master_addr - } for rank in range(num_trainers)] - ``` + ```python + env_info_trainers = [{ + 'local_rank': '0', + 'rank': str(rank), + 'world_size': str(num_trainers), + 'master_port': trainer_port, + 'master_addr': master_addr + } for rank in range(num_trainers)] + ``` - define trainer models : - ```python - def trainer_model_fn(): - actor = get_actor_from_args(...) - critic = get_critic_from_args(...) - return actor, critic - ``` + ```python + def trainer_model_fn(): + actor = get_actor_from_args(...) + critic = get_critic_from_args(...) + return actor, critic + ``` + - set trainer_refs : - ```python - trainer_refs = [ - DetachedPPOTrainer.options( - name=f"trainer{i}", - num_gpus=1, - max_concurrency=2 - ).remote( - experience_maker_holder_name_list=[f"maker{x}" for x in target_makers(...)], - model_fn = trainer_model_fn(), - ...) - for i, env_info_trainer in enumerate(env_info_trainers) - ] - ``` - The names in `experience_maker_holder_name_list` refer to the target makers that the trainer should send updated models to. - By setting `detached_trainer_name_list` and `experience_maker_holder_name_list`, we can customize the transmission graph. + ```python + trainer_refs = [ + DetachedPPOTrainer.options( + name=f"trainer{i}", + num_gpus=1, + max_concurrency=2 + ).remote( + experience_maker_holder_name_list=[f"maker{x}" for x in target_makers(...)], + model_fn = trainer_model_fn(), + ...) + for i, env_info_trainer in enumerate(env_info_trainers) + ] + ``` + The names in `experience_maker_holder_name_list` refer to the target makers that the trainer should send updated models to. + By setting `detached_trainer_name_list` and `experience_maker_holder_name_list`, we can customize the transmission graph. ### Launch Jobs + - define data_loader : - ```python - def data_loader_fn(): - return = torch.utils.data.DataLoader(dataset=dataset) - ``` + ```python + def data_loader_fn(): + return = torch.utils.data.DataLoader(dataset=dataset) + + ``` + - launch makers : - ```python - wait_tasks = [] - for experience_holder_ref in experience_holder_refs: - wait_tasks.append( - experience_holder_ref.workingloop.remote(data_loader_fn(), - num_steps=experience_steps)) - ``` + ```python + wait_tasks = [] + for experience_holder_ref in experience_holder_refs: + wait_tasks.append( + experience_holder_ref.workingloop.remote(data_loader_fn(), + num_steps=experience_steps)) + + ``` - launch trainers : - ```python - for trainer_ref in trainer_refs: - wait_tasks.append(trainer_ref.fit.remote(total_steps, update_steps, train_epochs)) - ``` + + ```python + for trainer_ref in trainer_refs: + wait_tasks.append(trainer_ref.fit.remote(total_steps, update_steps, train_epochs)) + ``` - wait for done : - ```python - ray.get(wait_tasks) - ``` + ```python + ray.get(wait_tasks) + ``` ## Flexible Structure We can deploy different strategies to makers and trainers. Here are some notions. ### 2 Makers 1 Trainer +

### 2 Makers 2 Trainer +

### Maker Inference Quantization +

diff --git a/applications/Chat/evaluate/README.md b/applications/Chat/evaluate/README.md index e4a50b11d41f..68b03be16a30 100644 --- a/applications/Chat/evaluate/README.md +++ b/applications/Chat/evaluate/README.md @@ -15,9 +15,9 @@ pip install -r requirements.txt The whole evaluation pipeline consists of three methods: 1. `GPT Evaluation`: evaluates model predictions using GPT models. - * Compare the performance of two different models (battle). - * Rate the model according to pre-defined metrics using prompting design. - * Rate the model according to pre-defined metrics with additional reference answer using prompting design. + - Compare the performance of two different models (battle). + - Rate the model according to pre-defined metrics using prompting design. + - Rate the model according to pre-defined metrics with additional reference answer using prompting design. 2. `Automatic Evaluation`: evaluates model predictions using automatic metrics. 3. `UniEval`: evaluates model predictions using UniEval models(English only). @@ -25,35 +25,33 @@ The whole evaluation pipeline consists of three methods: Our evaluation pipeline examines the model's capability using 10 categories of questions. The following table introduces each category: -| Evaluation Category | Description | -| :-----------------: | :----------------------------------------------------------- | -| Brainstorming | Models are asked to generate a range of creative and diverse ideas according to the question. The capability of creativity is required. | +| Evaluation Category | Description | +| :-----------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| Brainstorming | Models are asked to generate a range of creative and diverse ideas according to the question. The capability of creativity is required. | | Chat | Models are asked to continue a multi-round dialogue given the roles involved. The capability of understanding, memorizing previous rounds of the dialogue and answering according to the persona provided is required. | -| Classification | Models are asked to do classification tasks. The capability of accurate classification is required. | -| Closed QA | Models are asked to answer a closed QA question. The capability of answering questions with limited scope (such as single/multiple choice question) is required. | -| Extraction | Models are asked to extract information from a given material. The capability of extracting required information is required. | -| Generation | Models are asked to generate an email, letter, article, etc. The capability of generating texts in a high quality and human-written way is required. | -| Open QA | Models are asked to answer an open QA question(without context provided). The capability of answering questions with the models' own knowledge base is required. | -| Roleplay | Models are asked to play the role provided. The capability of engaging in the scenario and effectively interacting with the user is required. | -| Rewriting | Models are asked to do rewriting tasks such as translation and grammar correction. The capability of rewriting according to different instructions is required. | -| Summarization | Models are asked to summarize the given paragraph or passage. The capability of summarization is required. | +| Classification | Models are asked to do classification tasks. The capability of accurate classification is required. | +| Closed QA | Models are asked to answer a closed QA question. The capability of answering questions with limited scope (such as single/multiple choice question) is required. | +| Extraction | Models are asked to extract information from a given material. The capability of extracting required information is required. | +| Generation | Models are asked to generate an email, letter, article, etc. The capability of generating texts in a high quality and human-written way is required. | +| Open QA | Models are asked to answer an open QA question(without context provided). The capability of answering questions with the models' own knowledge base is required. | +| Roleplay | Models are asked to play the role provided. The capability of engaging in the scenario and effectively interacting with the user is required. | +| Rewriting | Models are asked to do rewriting tasks such as translation and grammar correction. The capability of rewriting according to different instructions is required. | +| Summarization | Models are asked to summarize the given paragraph or passage. The capability of summarization is required. | To better understand each evaluation category, here are some example questions provided. - -| Evaluation Category | Chinese Example | English Example | -| :-----------------: | :----------------------------------------------------------- | :----------------------------------------------------------- | -| Brainstorming | **Example 1:**
请介绍一下人工智能的多个领域。

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

**Example 2:**
What are some ways to increase productivity while working from home? | -| Chat | **Example 1:**
基于以下角色信息完成一段对话。小张是一名新手爱好者，对养鸡有浓厚的兴趣。老李是一名有丰富经验的养鸡大师。
小张：您好，老李，我最近开始对养鸡感兴趣了，想请教您一些问题。
老李：你好，小张，我很乐意帮助你。你想问些什么？
小张：我想知道如何确定鸡的品种和性别？
老李：确切的品种可以通过鸡的外貌特征来确定，而性别一般是通过鸡卵的大小和形状来判断。还有什么问题吗？
小张：

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

**Example 2:**
South Wales Police were called to a property in Heolgerrig, Merthyr Tydfil, at about 13:40 BST on Sunday. The child was airlifted to Prince Charles Hospital but died shortly afterwards. Police are investigating the circumstances surrounding the incident and have appealed for witnesses. The girl's family are being supported by specially trained officers.
Please briefly summarize the above material within 20 words. | - +| Evaluation Category | Chinese Example | English Example | +| :-----------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| Brainstorming | **Example 1:**
请介绍一下人工智能的多个领域。

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

**Example 2:**
What are some ways to increase productivity while working from home? | +| Chat | **Example 1:**
基于以下角色信息完成一段对话。小张是一名新手爱好者，对养鸡有浓厚的兴趣。老李是一名有丰富经验的养鸡大师。
小张：您好，老李，我最近开始对养鸡感兴趣了，想请教您一些问题。
老李：你好，小张，我很乐意帮助你。你想问些什么？
小张：我想知道如何确定鸡的品种和性别？
老李：确切的品种可以通过鸡的外貌特征来确定，而性别一般是通过鸡卵的大小和形状来判断。还有什么问题吗？
小张：

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

**Example 2:**
South Wales Police were called to a property in Heolgerrig, Merthyr Tydfil, at about 13:40 BST on Sunday. The child was airlifted to Prince Charles Hospital but died shortly afterwards. Police are investigating the circumstances surrounding the incident and have appealed for witnesses. The girl's family are being supported by specially trained officers.
Please briefly summarize the above material within 20 words. | ### Evaluation Metrics @@ -61,23 +59,23 @@ To better understand each evaluation category, here are some example questions p GPT evaluation uses GPT models to evaluate the prediction of different models and different pre-defined evaluation metrics are applied to different categories. The following table shows the 11 pre-defined evaluation metrics both in Chinese and English: -| Evaluation Metric | Prompt Words | CoT(Chain-of-Thought) | -| :-------------------: | :----------------------------------------------------------- | :----------------------------------------------------------- | -| 语言组织
(Language organization) | 语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1. Read the title and extract the main points of the material.
2. Read the summary and note the main ideas and messages in it.
3. Assess the length of the summary. A concise summary should usually convey key information within a few sentences or paragraphs, rather than lengthy paragraphs or essays.
4. Check that the summary does not contain information that is not relevant to the main ideas or that is redundant.
5. Make sure that the summary covers the key information in the material and that no important details have been omitted.
6. Rate the summary on a scale of 1-5, where 5 means the summary is concise and free of redundancy, and 1 means the summary is lengthy or contains unnecessary information that is difficult to understand or remember. Based on your judgment, assign the appropriate score. | +| Evaluation Metric | Prompt Words | CoT(Chain-of-Thought) | +| :----------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| 语言组织
(Language organization) | 语言组织(1-5)：答案语言是否流畅、连贯，使用正确的语法，具有一定逻辑性，使用恰当的连接词、过渡词等等。

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1. Read the title and extract the main points of the material.
2. Read the summary and note the main ideas and messages in it.
3. Assess the length of the summary. A concise summary should usually convey key information within a few sentences or paragraphs, rather than lengthy paragraphs or essays.
4. Check that the summary does not contain information that is not relevant to the main ideas or that is redundant.
5. Make sure that the summary covers the key information in the material and that no important details have been omitted.
6. Rate the summary on a scale of 1-5, where 5 means the summary is concise and free of redundancy, and 1 means the summary is lengthy or contains unnecessary information that is difficult to understand or remember. Based on your judgment, assign the appropriate score. | GPT models evaluate the quality of model predictions based on the given prompt words and gives a score between 1-5. -> **NOTE 1:** Even for the same metric, the details of its prompt words and CoT(Chain-of-Thought) can differ based on which category you want to evaluate. For example, prompt words for metric `correctness` showed here is "Whether the answer is correct or not."(this is for category `classification`), but for category `extraction`, prompt words can be "Answers should extract the required information accurately and should not contain any incorrect or misleading information." You can find all the prompt words and CoT(Chain-of-Thought) in `prompt/evaluation_prompt`. +> **NOTE 1:** Even for the same metric, the details of its prompt words and CoT(Chain-of-Thought) can differ based on which category you want to evaluate. For example, prompt words for metric `correctness` showed here is "Whether the answer is correct or not."(this is for category `classification`), but for category `extraction`, prompt words can be "Answers should extract the required information accurately and should not contain any incorrect or misleading information." You can find all the prompt words and CoT(Chain-of-Thought) in `prompt/evaluation_prompt`. > **NOTE 2:** To add customized metrics, you can refer to [FAQ](#faq). @@ -86,19 +84,19 @@ GPT models evaluate the quality of model predictions based on the given prompt w Automated metrics evaluate the capability of a model by comparing model predictions with reference answers. There are two ways to obtain reference answers: -* For instruction coming from human-designed problems, the reference answers are generated by GPT-3.5, such as roleplay, chat. -* For instruction related with classic NLP problems, the reference answers are collected from open-sourced dataset with target answers, such as classification, extraction, summarization. +- For instruction coming from human-designed problems, the reference answers are generated by GPT-3.5, such as roleplay, chat. +- For instruction related with classic NLP problems, the reference answers are collected from open-sourced dataset with target answers, such as classification, extraction, summarization. There are 6 types of automatic evaluation metrics listed in the table below: -| Automatic Evaluation Metric | Description | -| :---------------------------------: | :----------------------------------------------------------- | -| BLEU-n | Measure the accuracy between prediction and reference.
BLEU-1 (Unigram) evaluates accuracy in word level.
BLEU-n (n-gram) evaluate the fluency in sentence level. | +| Automatic Evaluation Metric | Description | +| :---------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| BLEU-n | Measure the accuracy between prediction and reference.
BLEU-1 (Unigram) evaluates accuracy in word level.
BLEU-n (n-gram) evaluate the fluency in sentence level. | | ROUGE | ROUGE-N measures the number of matching n-grams between prediction and reference.
ROUGE-L measures the number of matching longest common subsequence (LCS) between prediction and reference. | -| Distinct | Measure the diversity of generation text by counting the unique n-grams. | -| BERTScore | Measure the semantic similarity between tokens of predictions and references with BERT. | -| Precision
Recall
F1 Score | Measure the number of overlaps between prediction and reference (design for classification and extraction categories). | -| CHRF | Measure the similarity of character n-grams between prediction and reference. | +| Distinct | Measure the diversity of generation text by counting the unique n-grams. | +| BERTScore | Measure the semantic similarity between tokens of predictions and references with BERT. | +| Precision
Recall
F1 Score | Measure the number of overlaps between prediction and reference (design for classification and extraction categories). | +| CHRF | Measure the similarity of character n-grams between prediction and reference. | #### UniEval Evaluation @@ -106,17 +104,17 @@ UniEval converts all evaluation tasks of different dimensions(metrics) into Bool In our evaluation pipeline, two pre-trained UniEval evaluators are used. One is [unieval-sum](https://huggingface.co/MingZhong/unieval-sum) and the other is [unieval-dialog](https://huggingface.co/MingZhong/unieval-dialog). The two models can be used for the 3 tasks, `summarization`, `dialogue` and `data2text`. Each task has different evaluation dimensions. -| UniEval Model | Task | Dimension(Metric) | -| :------------: | :----------------- | :--- | -| unieval-sum | summarization | coherence: whether the summary is coherent
consistency: whether the claim is consistent with the given document
fluency: whether the paragraph is fluent
relevance: whether the summary is relevant to the reference | -| unieval-sum | data2text | naturalness: whether the utterance is fluent
informativeness: whether the utterance is informative according to the reference | -| unieval-dialog | dialogue | naturalness: whether the response is natural in the dialogue
coherence: whether the response is coherent in the dialogue history
understandability: whether the response is understandable in the dialogue | +| UniEval Model | Task | Dimension(Metric) | +| :------------: | :------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| unieval-sum | summarization | coherence: whether the summary is coherent
consistency: whether the claim is consistent with the given document
fluency: whether the paragraph is fluent
relevance: whether the summary is relevant to the reference | +| unieval-sum | data2text | naturalness: whether the utterance is fluent
informativeness: whether the utterance is informative according to the reference | +| unieval-dialog | dialogue | naturalness: whether the response is natural in the dialogue
coherence: whether the response is coherent in the dialogue history
understandability: whether the response is understandable in the dialogue | -> **NOTE 1:** Task "data2text" uses the same model as task "summarization". +> **NOTE 1:** Task "data2text" uses the same model as task "summarization". -> **NOTE 2:** In UniEval paper, the `unieval-sum` model demonstrates the best transfer ability and so you can evaluate your customized metric with this model. Details of adding customized metrics can be found in [FAQ](#faq). +> **NOTE 2:** In UniEval paper, the `unieval-sum` model demonstrates the best transfer ability and so you can evaluate your customized metric with this model. Details of adding customized metrics can be found in [FAQ](#faq). -> **NOTE 3:** We consider not including all metrics provided in UniEval in our pipeline because the data structure and content of the instructions we want to evaluate are not suitable for direct use of some UniEval metrics. +> **NOTE 3:** We consider not including all metrics provided in UniEval in our pipeline because the data structure and content of the instructions we want to evaluate are not suitable for direct use of some UniEval metrics. ## Evaluation Process @@ -127,12 +125,12 @@ In our evaluation pipeline, two pre-trained UniEval evaluators are used. One is A JSON file contains one list. Each element in the list is a target answer / prediction record for one instruction / question. An element should have the following fields: -* `category` (str, compulsory): The category of the instruction / question. -* `instruction` (str, compulsory): The instruction / question for the LLM. -* `input` (str, optional): The additional context of the instruction / question. -* `output` (str, optional): The sample output of the instruction (default: GPT-3.5). -* `target` (str, optional): The target answer for the instruction. -* `id` (int, compulsory): The ID of the instruction / question. +- `category` (str, compulsory): The category of the instruction / question. +- `instruction` (str, compulsory): The instruction / question for the LLM. +- `input` (str, optional): The additional context of the instruction / question. +- `output` (str, optional): The sample output of the instruction (default: GPT-3.5). +- `target` (str, optional): The target answer for the instruction. +- `id` (int, compulsory): The ID of the instruction / question. If the `input` has a target answer, the `output` can be empty. Otherwise, we generate answers from GPT-3.5 as the `output`, and the `target` field is empty. @@ -140,22 +138,22 @@ Example: ```json [ - { - "category": "brainstorming", - "instruction": "请介绍一下人工智能的多个领域。", - "input": "", - "output": "{GPT-3.5 Answers}", - "target": "", - "id": 1 - }, - { - "category": "classification", - "instruction": "新闻标题：为什么电影《倩女幽魂》中燕赤霞一个道士却拿着金刚经？请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。", - "input": "", - "output": "", - "target": "{target answer}", - "id": 2 - } + { + "category": "brainstorming", + "instruction": "请介绍一下人工智能的多个领域。", + "input": "", + "output": "{GPT-3.5 Answers}", + "target": "", + "id": 1 + }, + { + "category": "classification", + "instruction": "新闻标题：为什么电影《倩女幽魂》中燕赤霞一个道士却拿着金刚经？请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。", + "input": "", + "output": "", + "target": "{target answer}", + "id": 2 + } ] ``` @@ -165,33 +163,33 @@ A JSON file contains one list. Each element in the list is a model answer / pred An element should have the following fields: -* `category` (str, compulsory): The category of the instruction / question. -* `instruction` (str, compulsory): The instruction / question for the LLM. -* `input` (str, optional): The additional context of the instruction / question. -* `output` (str, compulsory): The output from the LLM. -* `target` (str, optional): The target answer for the instruction. -* `id` (int, compulsory): The ID of the instruction / question. +- `category` (str, compulsory): The category of the instruction / question. +- `instruction` (str, compulsory): The instruction / question for the LLM. +- `input` (str, optional): The additional context of the instruction / question. +- `output` (str, compulsory): The output from the LLM. +- `target` (str, optional): The target answer for the instruction. +- `id` (int, compulsory): The ID of the instruction / question. Example: ```json [ - { - "category": "brainstorming", - "instruction": "请介绍一下人工智能的多个领域。", - "input": "", - "output": "{Model Answers / Predictions}", - "target": "", - "id": 1 - }, - { - "category": "classification", - "instruction": "新闻标题：为什么电影《倩女幽魂》中燕赤霞一个道士却拿着金刚经？请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。", - "input": "", - "output": "{Model Answers / Predictions}", - "target": "{target answer}", - "id": 2 - } + { + "category": "brainstorming", + "instruction": "请介绍一下人工智能的多个领域。", + "input": "", + "output": "{Model Answers / Predictions}", + "target": "", + "id": 1 + }, + { + "category": "classification", + "instruction": "新闻标题：为什么电影《倩女幽魂》中燕赤霞一个道士却拿着金刚经？请根据新闻标题判断新闻所属的分类，你需要从文化，娱乐，体育，财经，房产，教育，科技，旅游，游戏，军事这十类中选择一个答案。", + "input": "", + "output": "{Model Answers / Predictions}", + "target": "{target answer}", + "id": 2 + } ] ``` @@ -212,7 +210,7 @@ The following is the Chinese battle prompt. In the battle prompt, the question a #### Evaluation Prompt -The following is an example of a Chinese GPT evaluation prompt. In an evaluation prompt, you should define your metrics in `metrics` and provide CoT(Chain-of-Thought) in `CoT`. You can find example evaluation prompt files for Chinese and English in `prompt/evaluation_prompt`. +The following is an example of a Chinese GPT evaluation prompt. In an evaluation prompt, you should define your metrics in `metrics` and provide CoT(Chain-of-Thought) in `CoT`. You can find example evaluation prompt files for Chinese and English in `prompt/evaluation_prompt`. ```json { @@ -242,24 +240,32 @@ The following is an example of a Chinese config file. The configuration file can ```json { - "language": "en", - "path_for_UniEval": { - "summarization": "path to unieval-sum model", - "dialogue": "path to unieval-dialog model", - "data2text": "path to unieval-sum model" + "language": "en", + "path_for_UniEval": { + "summarization": "path to unieval-sum model", + "dialogue": "path to unieval-dialog model", + "data2text": "path to unieval-sum model" + }, + "category": { + "brainstorming": { + "GPT": ["relevance", "creativity", "practicality", "reasonableness"], + "Metrics": ["Distinct"], + "UniEval": [ + "summarization-fluency", + "data2text-naturalness", + "data2text-informativeness" + ] }, - "category": { - "brainstorming": { - "GPT": ["relevance", "creativity", "practicality", "reasonableness"], - "Metrics": ["Distinct"], - "UniEval": ["summarization-fluency", "data2text-naturalness", "data2text-informativeness"] - }, - "chat": { - "GPT": [ "relevance", "naturalness", "engagingness", "reasonableness"], - "Metrics": ["Distinct"], - "UniEval": ["dialogue-naturalness", "dialogue-coherence", "dialogue-understandability"] - } + "chat": { + "GPT": ["relevance", "naturalness", "engagingness", "reasonableness"], + "Metrics": ["Distinct"], + "UniEval": [ + "dialogue-naturalness", + "dialogue-coherence", + "dialogue-understandability" + ] } + } } ``` @@ -293,7 +299,7 @@ You can create your config file based on available settings listed in following | "summarization" | "fidelity" | | | | | "conciseness" | | | -> **NOTE:** For categories which don't have standard answers such as `brainstorming`, you should avoid using automatic metrics such as `BLEU` and `ROUGE` which are based on similarity measures and you should use `Distinct` instead in your config file. +> **NOTE:** For categories which don't have standard answers such as `brainstorming`, you should avoid using automatic metrics such as `BLEU` and `ROUGE` which are based on similarity measures and you should use `Distinct` instead in your config file. #### Evaluate @@ -346,8 +352,8 @@ For example, if you want to add a new metric `persuasiveness` into task `data2te ```python if task == 'data2text': - if dimension == 'persuasiveness': - cur_input = 'question: Is this a persuasive utterence utterance: ' + output[i] + if dimension == 'persuasiveness': + cur_input = 'question: Is this a persuasive utterence utterance: ' + output[i] ``` diff --git a/applications/Chat/examples/README.md b/applications/Chat/examples/README.md index f0cdfeff5b61..9438aafd1268 100644 --- a/applications/Chat/examples/README.md +++ b/applications/Chat/examples/README.md @@ -17,7 +17,7 @@ - [Arg List](#arg-list-2) - [Inference example - After Stage3](#inference-example---after-stage3) - [Attention](#attention) - - [data](#data) + - [data](#data) - [Support Model](#support-model) - [GPT](#gpt) - [BLOOM](#bloom) @@ -28,8 +28,8 @@ - [Reward model](#reward-model) - [Critic model](#critic-model) - --- + ## Install requirements ```shell @@ -38,10 +38,11 @@ pip install -r requirements.txt ## Supervised datasets collection -We collected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo -[InstructionWild](https://github.com/XueFuzhao/InstructionWild). +We collected 104K bilingual datasets of Chinese and English, and you can find the datasets in this repo +[InstructionWild](https://github.com/XueFuzhao/InstructionWild) and in this [file](https://github.com/XueFuzhao/InstructionWild/blob/main/data/README.md). + +Here is how we collected the data -The following pic shows how we collected the data.

@@ -52,38 +53,40 @@ In order to further improve the model's ability to handle multi-turn conversatio A sample of conversation dataset should have the following fields: -* `type` (str, optional): The type of the data sample. -* `language` (str, optional): The language of the data sample. -* `dataset` (str, optional): The dataset the data sample originates from. -* `conversations` (str, compulsory): Conversation content of the data sample. -* `id` (int, optional): The ID of the data sample. +- `type` (str, optional): The type of the data sample. +- `language` (str, optional): The language of the data sample. +- `dataset` (str, optional): The dataset the data sample originates from. +- `conversations` (str, compulsory): Conversation content of the data sample. +- `id` (int, optional): The ID of the data sample. A simple example: + ```json { - "type": "instruction", - "language": "English", - "dataset": "Alpaca", - "conversations": [ - { - "from": "human", - "value": "Give three tips for staying healthy." - }, - { - "from": "gpt", - "value": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule." - } - ], - "id": 1 + "type": "instruction", + "language": "English", + "dataset": "Alpaca", + "conversations": [ + { + "from": "human", + "value": "Give three tips for staying healthy." + }, + { + "from": "gpt", + "value": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule." + } + ], + "id": 1 } ``` -> **NOTE:** Only key `conversations` is compulsary for training and other keys serve as metadata. The length of `conversations` varies. +> **NOTE:** Only key `conversations` is compulsary for training and other keys serve as metadata. The length of `conversations` varies. You can run the `examples/generate_conversation_dataset.py` to generate a conversation dataset supported by ColossalChat. You can use the following cmd to generate conversation dataset. -``` + +```bash python generate_conversation_dataset.py \ --dataset "All" --save_path "/path/to/dataset" @@ -97,12 +100,12 @@ Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned ea You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning. You can also use the following cmd to start a supervised instructs fine-tuning with your own settings. -``` + +```bash torchrun --standalone --nproc_per_node=4 train_sft.py \ --pretrain "/path/to/LLaMa-7B/" \ --model 'llama' \ --strategy colossalai_zero2 \ - --log_interval 10 \ --save_path /path/to/Coati-7B \ --dataset /path/to/data.json \ --batch_size 4 \ @@ -112,18 +115,33 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \ --max_epochs 1 \ --grad_checkpoint ``` + +**Note**: the supervised dataset follows the following format, + +```json +[ + { + "instruction": "Provide a list of the top 10 most popular mobile games in Asia", + "input": "", + "output": "The top 10 most popular mobile games in Asia are:\n1) PUBG Mobile\n2) Pokemon Go\n3) Candy Crush Saga\n4) Free Fire\n5) Clash of Clans\n6) Mario Kart Tour\n7) Arena of Valor\n8) Fantasy Westward Journey\n9) Subway Surfers\n10) ARK Survival Evolved", + "id": 0 + }, + ... +] +``` + ### Arg List -- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' -- --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' -- --pretrain: pretrain model, type=str, default=None -- --max_datasets_size: the max size of dataset, type=int, default=None -- --save_path: path to save the model, type=str, default='output' -- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False -- --max_epochs: max epochs for training, type=int, default=3 -- --batch_size: batch size while training, type=int, default=4 -- --lora_rank: low-rank adaptation matrices rank, type=int, default=0 -- --log_interval: how many steps to log, type=int, default=100 -- --grad_checkpoint: enable gradient checkpointing, type=bool, default=False + +- `--strategy`: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' +- `--model`: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' +- `--pretrain`: pretrain model, type=str, default=None +- `--max_datasets_size`: the max size of dataset, type=int, default=None +- `--save_path`: path to save the model, type=str, default='output' +- `--need_optim_ckpt`: whether to save optim ckpt, type=bool, default=False +- `--max_epochs`: max epochs for training, type=int, default=3 +- `--batch_size`: batch size while training, type=int, default=4 +- `--lora_rank`: low-rank adaptation matrices rank, type=int, default=0 +- `--grad_checkpoint`: enable gradient checkpointing, type=bool, default=False ## Stage2 - Training reward model @@ -133,7 +151,8 @@ We train a reward model in stage 2, which obtains corresponding scores by manual You can run the `examples/train_rm.sh` to start a reward model training. You can also use the following cmd to start training a reward model. -``` + +```bash torchrun --standalone --nproc_per_node=4 train_reward_model.py \ --pretrain "/path/to/LLaMa-7B/" \ --model 'llama' \ @@ -141,16 +160,19 @@ torchrun --standalone --nproc_per_node=4 train_reward_model.py \ --loss_fn 'log_exp'\ --save_path 'rmstatic.pt' \ ``` + ### Features and tricks in RM training + - We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)and[rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets. -- We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic). -- We change the loss to valid_acc and pair_dist to monitor progress during training. +- We support 2 kinds of loss function named `log_sig`(used by OpenAI) and `log_exp`(used by Anthropic). +- We change the loss to `valid_acc` and `pair_dist` to monitor progress during training. - We add special token to the end of the sequence to get better result. - We use cosine-reducing lr-scheduler for RM training. - We set value_head as 1 liner layer and initialize the weight of value_head using N(0，1/(d_model + 1)) distribution. - We train a Bloom-560m reward model for 1 epoch and find the test acc of the model achieve the performance mentions in [Anthropics paper](https://arxiv.org/abs/2204.05862). ### Experiment result + Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862):
image @@ -162,20 +184,20 @@ Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862):
We also train the reward model based on LLaMA-7B, which reaches the ACC of 72.06% after 1 epoch, performing almost the same as Anthropic's best RM. ### Arg List -- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' -- --model: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' -- --pretrain: pretrain model, type=str, default=None -- --model_path: the path of rm model(if continue to train), type=str, default=None -- --save_path: path to save the model, type=str, default='output' -- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False -- --max_epochs: max epochs for training, type=int, default=3 -- --dataset: dataset name, type=str, choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static'] -- --subset: subset of the dataset, type=str, default=None -- --batch_size: batch size while training, type=int, default=4 -- --lora_rank: low-rank adaptation matrices rank, type=int, default=0 -- --loss_func: which kind of loss function, choices=['log_sig', 'log_exp'] -- --max_len: max sentence length for generation, type=int, default=512 -- --test: whether is only testing, if it's true, the dataset will be small + +- `--strategy`: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' +- `--model`: model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' +- `--pretrain`: pretrain model, type=str, default=None +- `--model_path`: the path of rm model(if continue to train), type=str, default=None +- `--save_path`: path to save the model, type=str, default='output' +- `--need_optim_ckpt`: whether to save optim ckpt, type=bool, default=False +- `--max_epochs`: max epochs for training, type=int, default=3 +- `--dataset`: dataset name, type=str, choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static'] +- `--subset`: subset of the dataset, type=str, default=None +- `--batch_size`: batch size while training, type=int, default=4 +- `--lora_rank`: low-rank adaptation matrices rank, type=int, default=0 +- `--loss_func`: which kind of loss function, choices=['log_sig', 'log_exp'] +- `--max_len`: max sentence length for generation, type=int, default=512 ## Stage3 - Training model using prompts with RL @@ -186,53 +208,89 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of

You can run the `examples/train_prompts.sh` to start PPO training. + You can also use the cmd following to start PPO training. [[Stage3 tutorial video]](https://www.youtube.com/watch?v=Z8wwSHxPL9g) -``` +```bash torchrun --standalone --nproc_per_node=4 train_prompts.py \ - --pretrain "/path/to/LLaMa-7B/" \ - --model 'llama' \ - --strategy colossalai_zero2 \ - --prompt_dataset /path/to/your/prompt_dataset \ - --pretrain_dataset /path/to/your/pretrain_dataset \ - --rm_pretrain /your/pretrain/rm/definition \ - --rm_path /your/rm/model/path + --pretrain "/path/to/LLaMa-7B/" \ + --model 'llama' \ + --strategy colossalai_zero2 \ + --prompt_dataset /path/to/your/prompt_dataset \ + --pretrain_dataset /path/to/your/pretrain_dataset \ + --rm_pretrain /your/pretrain/rm/definition \ + --rm_path /your/rm/model/path ``` Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use the [script](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/generate_prompt_dataset.py) which samples `instinwild_en.json` or `instinwild_ch.json` in [InstructionWild](https://github.com/XueFuzhao/InstructionWild/tree/main/data#instructwild-data) to generate the prompt dataset. Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning. +**Note**: the required datasets follow the following format, + +- `pretrain dataset` + + ```json + [ + { + "instruction": "Provide a list of the top 10 most popular mobile games in Asia", + "input": "", + "output": "The top 10 most popular mobile games in Asia are:\n1) PUBG Mobile\n2) Pokemon Go\n3) Candy Crush Saga\n4) Free Fire\n5) Clash of Clans\n6) Mario Kart Tour\n7) Arena of Valor\n8) Fantasy Westward Journey\n9) Subway Surfers\n10) ARK Survival Evolved", + "id": 0 + }, + ... + ] + ``` + +- `prompt dataset` + + ```json + [ + { + "instruction": "Edit this paragraph to make it more concise: \"Yesterday, I went to the store and bought some things. Then, I came home and put them away. After that, I went for a walk and met some friends.\"", + "id": 0 + }, + { + "instruction": "Write a descriptive paragraph about a memorable vacation you went on", + "id": 1 + }, + ... + ] + ``` + ### Arg List -- --strategy: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' -- --model: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' -- --pretrain: pretrain model, type=str, default=None -- --rm_model: reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None -- --rm_pretrain: pretrain model for reward model, type=str, default=None -- --rm_path: the path of rm model, type=str, default=None -- --save_path: path to save the model, type=str, default='output' -- --prompt_dataset: path of the prompt dataset, type=str, default=None -- --pretrain_dataset: path of the ptx dataset, type=str, default=None -- --need_optim_ckpt: whether to save optim ckpt, type=bool, default=False -- --num_episodes: num of episodes for training, type=int, default=10 -- --num_update_steps: number of steps to update policy per episode, type=int -- --num_collect_steps: number of steps to collect experience per episode, type=int -- --train_batch_size: batch size while training, type=int, default=8 -- --ptx_batch_size: batch size to compute ptx loss, type=int, default=1 -- --experience_batch_size: batch size to make experience, type=int, default=8 -- --lora_rank: low-rank adaptation matrices rank, type=int, default=0 -- --kl_coef: kl_coef using for computing reward, type=float, default=0.1 -- --ptx_coef: ptx_coef using for computing policy loss, type=float, default=0.9 + +- `--strategy`: the strategy using for training, choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2' +- `--model`: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom' +- `--pretrain`: pretrain model, type=str, default=None +- `--rm_model`: reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None +- `--rm_pretrain`: pretrain model for reward model, type=str, default=None +- `--rm_path`: the path of rm model, type=str, default=None +- `--save_path`: path to save the model, type=str, default='output' +- `--prompt_dataset`: path of the prompt dataset, type=str, default=None +- `--pretrain_dataset`: path of the ptx dataset, type=str, default=None +- `--need_optim_ckpt`: whether to save optim ckpt, type=bool, default=False +- `--num_episodes`: num of episodes for training, type=int, default=10 +- `--num_update_steps`: number of steps to update policy per episode, type=int +- `--num_collect_steps`: number of steps to collect experience per episode, type=int +- `--train_batch_size`: batch size while training, type=int, default=8 +- `--ptx_batch_size`: batch size to compute ptx loss, type=int, default=1 +- `--experience_batch_size`: batch size to make experience, type=int, default=8 +- `--lora_rank`: low-rank adaptation matrices rank, type=int, default=0 +- `--kl_coef`: kl_coef using for computing reward, type=float, default=0.1 +- `--ptx_coef`: ptx_coef using for computing policy loss, type=float, default=0.9 ## Inference example - After Stage3 + We support different inference options, including int8 and int4 quantization. For details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference). - ## Attention + The examples are demos for the whole training process.You need to change the hyper-parameters to reach great performance. #### data + - [x] [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) - [x] [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) - [ ] [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) @@ -242,14 +300,16 @@ The examples are demos for the whole training process.You need to change the hyp ## Support Model ### GPT -- [x] GPT2-S (s) -- [x] GPT2-M (m) -- [x] GPT2-L (l) -- [x] GPT2-XL (xl) -- [x] GPT2-4B (4b) -- [ ] GPT2-6B (6b) + +- [x] GPT2-S (s) +- [x] GPT2-M (m) +- [x] GPT2-L (l) +- [x] GPT2-XL (xl) +- [x] GPT2-4B (4b) +- [ ] GPT2-6B (6b) ### BLOOM + - [x] [BLOOM-560m](https://huggingface.co/bigscience/bloom-560m) - [x] [BLOOM-1b1](https://huggingface.co/bigscience/bloom-1b1) - [x] [BLOOM-3b](https://huggingface.co/bigscience/bloom-3b) @@ -257,6 +317,7 @@ The examples are demos for the whole training process.You need to change the hyp - [ ] [BLOOM-175b](https://huggingface.co/bigscience/bloom) ### OPT + - [x] [OPT-125M](https://huggingface.co/facebook/opt-125m) - [x] [OPT-350M](https://huggingface.co/facebook/opt-350m) - [x] [OPT-1.3B](https://huggingface.co/facebook/opt-1.3b) @@ -266,10 +327,11 @@ The examples are demos for the whole training process.You need to change the hyp - [ ] [OPT-30B](https://huggingface.co/facebook/opt-30b) ### [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) -- [x] LLaMA-7B -- [x] LLaMA-13B -- [ ] LLaMA-33B -- [ ] LLaMA-65B + +- [x] LLaMA-7B +- [x] LLaMA-13B +- [ ] LLaMA-33B +- [ ] LLaMA-65B ## Add your own models @@ -282,12 +344,12 @@ if it is supported in huggingface [transformers](https://github.com/huggingface/ r you can build your own model by yourself. ### Actor model -``` + +```python from ..base import Actor from transformers.models.coati import CoatiModel class CoatiActor(Actor): - def __init__(self, pretrained: Optional[str] = None, checkpoint: bool = False, @@ -302,7 +364,8 @@ class CoatiActor(Actor): ``` ### Reward model -``` + +```python from ..base import RewardModel from transformers.models.coati import CoatiModel @@ -325,12 +388,11 @@ class CoatiRM(RewardModel): ### Critic model -``` +```python from ..base import Critic from transformers.models.coati import CoatiModel class CoatiCritic(Critic): - def __init__(self, pretrained: Optional[str] = None, checkpoint: bool = False, diff --git a/applications/Chat/examples/community/README.md b/applications/Chat/examples/community/README.md index cd7b9d99bf06..e14ac1767fc1 100644 --- a/applications/Chat/examples/community/README.md +++ b/applications/Chat/examples/community/README.md @@ -1,5 +1,9 @@ +:warning: **This content may be outdated since the major update of Colossal Chat. We will update this content soon.** + # Community Examples + --- + We are thrilled to announce the latest updates to ColossalChat, an open-source solution for cloning ChatGPT with a complete RLHF (Reinforcement Learning with Human Feedback) pipeline. As Colossal-AI undergoes major updates, we are actively maintaining ColossalChat to stay aligned with the project's progress. With the introduction of Community-driven example, we aim to create a collaborative platform for developers to contribute exotic features built on top of ColossalChat. @@ -14,11 +18,12 @@ For more information about community pipelines, please have a look at this [issu Community examples consist of both inference and training examples that have been added by the community. Please have a look at the following table to get an overview of all community examples. Click on the Code Example to get a copy-and-paste ready code example that you can try out. If a community doesn't work as expected, please open an issue and ping the author on it. -| Example | Description | Code Example | Colab | Author | -|:---------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------:| -| Peft | Adding Peft support for SFT and Prompts model training | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/peft) | - | [YY Lin](https://github.com/yynil) | -| Train prompts on Ray | A Ray based implementation of Train prompts example | [Training On Ray](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/ray) | - | [MisterLin1995](https://github.com/MisterLin1995) | -|...|...|...|...|...| +| Example | Description | Code Example | Colab | Author | +| :------------------- | :----------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------- | :---- | ------------------------------------------------: | +| Peft | Adding Peft support for SFT and Prompts model training | [Huggingface Peft](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/peft) | - | [YY Lin](https://github.com/yynil) | +| Train prompts on Ray | A Ray based implementation of Train prompts example | [Training On Ray](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/community/ray) | - | [MisterLin1995](https://github.com/MisterLin1995) | +| ... | ... | ... | ... | ... | ### How to get involved + To join our community-driven initiative, please visit the [ColossalChat GitHub repository](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples), review the provided information, and explore the codebase. To contribute, create a new issue outlining your proposed feature or enhancement, and our team will review and provide feedback. We look forward to collaborating with you on this exciting project! diff --git a/applications/Chat/examples/community/peft/README.md b/applications/Chat/examples/community/peft/README.md index 844bfd3d22c3..8b2edc48cd99 100644 --- a/applications/Chat/examples/community/peft/README.md +++ b/applications/Chat/examples/community/peft/README.md @@ -1,3 +1,5 @@ +:warning: **This content may be outdated since the major update of Colossal Chat. We will update this content soon.** + # Add Peft support for SFT and Prompts model training The original implementation just adopts the loralib and merges the layers into the final model. The huggingface peft is a better lora model implementation and can be easily training and distributed. @@ -5,7 +7,9 @@ The original implementation just adopts the loralib and merges the layers into t Since reward model is relative small, I just keep it as original one. I suggest train full model to get the proper reward/critic model. # Preliminary installation + Since the current pypi peft package(0.2) has some bugs, please install the peft package using source. + ``` git clone https://github.com/huggingface/peft cd peft @@ -13,6 +17,7 @@ pip install . ``` # Usage + For SFT training, just call train_peft_sft.py Its arguments are almost identical to train_sft.py instead adding a new eval_dataset if you have a eval_dataset file. The data file is just a plain datafile, please check the format in the easy_dataset.py. @@ -21,4 +26,5 @@ For stage-3 rlhf training, call train_peft_prompts.py. Its arguments are almost identical to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported. # Dataformat + Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt. diff --git a/applications/Chat/examples/community/ray/README.md b/applications/Chat/examples/community/ray/README.md index 64360bd73ddc..a679a58336a7 100644 --- a/applications/Chat/examples/community/ray/README.md +++ b/applications/Chat/examples/community/ray/README.md @@ -1,17 +1,31 @@ +:warning: **This content may be outdated since the major update of Colossal Chat. We will update this content soon.** + # ColossalAI on Ray + ## Abstract + This is an experimental effort to run ColossalAI Chat training on Ray + ## How to use? + ### 1. Setup Ray clusters + Please follow the official [Ray cluster setup instructions](https://docs.ray.io/en/latest/cluster/getting-started.html) to setup an cluster with GPU support. Record the cluster's api server endpoint, it should be something similar to http://your.head.node.addrees:8265 + ### 2. Clone repo + Clone this project: + ```shell git clone https://github.com/hpcaitech/ColossalAI.git ``` + ### 3. Submit the ray job + ```shell python applications/Chat/examples/community/ray/ray_job_script.py http://your.head.node.addrees:8265 ``` + ### 4. View your job on the Ray Dashboard + Open your ray cluster dashboard http://your.head.node.addrees:8265 to view your submitted training job. diff --git a/applications/Chat/inference/README.md b/applications/Chat/inference/README.md index 4848817e0fd1..eea4ef5b86ca 100644 --- a/applications/Chat/inference/README.md +++ b/applications/Chat/inference/README.md @@ -20,21 +20,21 @@ Tha data is from [LLaMA Int8 4bit ChatBot Guide v2](https://rentry.org/llama-tar ### 8-bit -| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples | -| :---: | :---: | :---: | :---: | :---: | -| LLaMA-7B | 9.2GB | 10GB | 24GB | 3060 12GB, RTX 3080 10GB, RTX 3090 | -| LLaMA-13B | 16.3GB | 20GB | 32GB | RTX 3090 Ti, RTX 4090 | -| LLaMA-30B | 36GB | 40GB | 64GB | A6000 48GB, A100 40GB | -| LLaMA-65B | 74GB | 80GB | 128GB | A100 80GB | +| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples | +| :-------: | :---------: | :-----------------: | :----------: | :--------------------------------: | +| LLaMA-7B | 9.2GB | 10GB | 24GB | 3060 12GB, RTX 3080 10GB, RTX 3090 | +| LLaMA-13B | 16.3GB | 20GB | 32GB | RTX 3090 Ti, RTX 4090 | +| LLaMA-30B | 36GB | 40GB | 64GB | A6000 48GB, A100 40GB | +| LLaMA-65B | 74GB | 80GB | 128GB | A100 80GB | ### 4-bit -| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples | -| :---: | :---: | :---: | :---: | :---: | -| LLaMA-7B | 3.5GB | 6GB | 16GB | RTX 1660, 2060, AMD 5700xt, RTX 3050, 3060 | -| LLaMA-13B | 6.5GB | 10GB | 32GB | AMD 6900xt, RTX 2060 12GB, 3060 12GB, 3080, A2000 | -| LLaMA-30B | 15.8GB | 20GB | 64GB | RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 | -| LLaMA-65B | 31.2GB | 40GB | 128GB | A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000, Titan Ada | +| Model | Min GPU RAM | Recommended GPU RAM | Min RAM/Swap | Card examples | +| :-------: | :---------: | :-----------------: | :----------: | :--------------------------------------------------------: | +| LLaMA-7B | 3.5GB | 6GB | 16GB | RTX 1660, 2060, AMD 5700xt, RTX 3050, 3060 | +| LLaMA-13B | 6.5GB | 10GB | 32GB | AMD 6900xt, RTX 2060 12GB, 3060 12GB, 3080, A2000 | +| LLaMA-30B | 15.8GB | 20GB | 64GB | RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100 | +| LLaMA-65B | 31.2GB | 40GB | 128GB | A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000, Titan Ada | ## General setup From ff836790ae5de19f4e721157f7c3a31c19af8f73 Mon Sep 17 00:00:00 2001 From: Tian Siyuan Date: Tue, 15 Aug 2023 00:22:57 +0800 Subject: [PATCH 04/84] [doc] fix a typo in examples/tutorial/auto_parallel/README.md (#4430) Co-authored-by: Siyuan Tian --- examples/tutorial/auto_parallel/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorial/auto_parallel/README.md b/examples/tutorial/auto_parallel/README.md index 6a12e0dd5a48..13561567636e 100644 --- a/examples/tutorial/auto_parallel/README.md +++ b/examples/tutorial/auto_parallel/README.md @@ -13,7 +13,7 @@ ## 📚 Overview -This tutorial folder contains a simple demo to run auto-parallelism with ResNet. Meanwhile, this diretory also contains demo scripts to run automatic activation checkpointing, but both features are still experimental for now and no guarantee that they will work for your version of Colossal-AI. +This tutorial folder contains a simple demo to run auto-parallelism with ResNet. Meanwhile, this directory also contains demo scripts to run automatic activation checkpointing, but both features are still experimental for now and no guarantee that they will work for your version of Colossal-AI. ## 🚀 Quick Start From 5e1a9d48dd06bc1c1fc05ad134ee49ed5764a24b Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 20 Jun 2023 10:39:06 +0800 Subject: [PATCH 05/84] [cluster] add process group mesh (#4039) * [cluster] add process group mesh * [test] add process group mesh test * force sync --- colossalai/cluster/__init__.py | 3 +- colossalai/cluster/process_group_mesh.py | 203 ++++++++++++++++++ tests/test_cluster/test_process_group_mesh.py | 151 +++++++++++++ 3 files changed, 356 insertions(+), 1 deletion(-) create mode 100644 colossalai/cluster/process_group_mesh.py create mode 100644 tests/test_cluster/test_process_group_mesh.py diff --git a/colossalai/cluster/__init__.py b/colossalai/cluster/__init__.py index 2fbdfd3cc999..44f571ca2501 100644 --- a/colossalai/cluster/__init__.py +++ b/colossalai/cluster/__init__.py @@ -1,5 +1,6 @@ from .device_mesh_manager import DeviceMeshManager from .dist_coordinator import DistCoordinator from .process_group_manager import ProcessGroupManager +from .process_group_mesh import ProcessGroupMesh -__all__ = ['DistCoordinator', 'ProcessGroupManager', 'DeviceMeshManager'] +__all__ = ['DistCoordinator', 'ProcessGroupManager', 'DeviceMeshManager', 'ProcessGroupMesh'] diff --git a/colossalai/cluster/process_group_mesh.py b/colossalai/cluster/process_group_mesh.py new file mode 100644 index 000000000000..1dfd261d5d01 --- /dev/null +++ b/colossalai/cluster/process_group_mesh.py @@ -0,0 +1,203 @@ +import itertools +from functools import reduce +from operator import mul +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import torch.distributed as dist +from torch.distributed import ProcessGroup + + +def prod(nums: List[int]) -> int: + """Product of a list of numbers. + + Args: + nums (List[int]): A list of numbers. + + Returns: + int: The product of the numbers. + """ + return reduce(mul, nums) + + +class ProcessGroupMesh: + """A helper class to manage the process group mesh. It only describes how to organize process groups, and it's decoupled with parallel method. + It just initialize process groups and cache them. The parallel method should manage them and use them to do the parallel computation. + + We use a ND-tuple to represent the process group mesh. And a ND-coordinate is to represent each process. + For example, ``(0, 1, 0)`` represents the process whose rank is 2 in a 3D process group mesh with size ``(2, 2, 2)``. + + Args: + *size (int): The size of each dimension of the process group mesh. The product of the size must be equal to the world size. + + Attributes: + shape (Tuple[int, ...]): The shape of the process group mesh. + rank (int): The rank of the current process. + """ + + def __init__(self, *size: int) -> None: + assert dist.is_initialized(), "Please initialize torch.distributed first." + assert prod(size) == dist.get_world_size(), "The product of the size must be equal to the world size." + self._shape = size + self._rank = dist.get_rank() + self._coord = ProcessGroupMesh.unravel(self._rank, self._shape) + self._ranks_to_group: Dict[Tuple[int, ...], ProcessGroup] = {} + self._group_to_ranks: Dict[ProcessGroup, Tuple[int, ...]] = {} + + @property + def shape(self) -> Tuple[int, ...]: + return self._shape + + @property + def rank(self) -> int: + return self._rank + + def size(self, dim: Optional[int] = None) -> Union[int, Tuple[int, ...]]: + """Get the size of the process group mesh. + + Args: + dim (Optional[int], optional): Dimension of the process group mesh. `None` means all dimensions. Defaults to None. + + Returns: + Union[int, Tuple[int, ...]]: Size of the target dimension or the whole process group mesh. + """ + if dim is None: + return self._shape + else: + return self._shape[dim] + + def coordinate(self, dim: Optional[int] = None) -> Union[int, Tuple[int, ...]]: + """Get the coordinate of the process group mesh. + + Args: + dim (Optional[int], optional): Dimension of the process group mesh. `None` means all dimensions. Defaults to None. + + Returns: + Union[int, Tuple[int, ...]]: Coordinate of the target dimension or the whole process group mesh. + """ + if dim is None: + return self._coord + else: + return self._coord[dim] + + @staticmethod + def unravel(rank: int, shape: Tuple[int, ...]) -> Tuple[int, ...]: + """Convert a rank to a coordinate. + + Args: + rank (int): Rank to be converted. + shape (Tuple[int, ...]): Shape of the process group mesh. + + Returns: + Tuple[int, ...]: Coordinate of the rank. + """ + return np.unravel_index(rank, shape) + + @staticmethod + def ravel(coord: Tuple[int, ...], shape: Tuple[int, ...]) -> int: + """Convert a coordinate to a rank. + + Args: + coords (Tuple[int, ...]): Coordinate to be converted. + shape (Tuple[int, ...]): Shape of the process group mesh. + + Returns: + int: Rank of the coordinate. + """ + return np.ravel_multi_index(coord, shape) + + def get_group(self, ranks_in_group: List[int], backend: Optional[str] = None) -> ProcessGroup: + """Get the process group with the given ranks. It the process group doesn't exist, it will be created. + + Args: + ranks_in_group (List[int]): Ranks in the process group. + backend (Optional[str], optional): Backend of the process group. Defaults to None. + + Returns: + ProcessGroup: The process group with the given ranks. + """ + ranks_in_group = sorted(ranks_in_group) + if tuple(ranks_in_group) not in self._group_to_ranks: + group = dist.new_group(ranks_in_group, backend=backend) + self._ranks_to_group[tuple(ranks_in_group)] = group + self._group_to_ranks[group] = tuple(ranks_in_group) + return self._ranks_to_group[tuple(ranks_in_group)] + + def get_ranks_in_group(self, group: ProcessGroup) -> List[int]: + """Get the ranks in the given process group. The process group must be created by this class. + + Args: + group (ProcessGroup): The process group. + + Returns: + List[int]: Ranks in the process group. + """ + return list(self._group_to_ranks[group]) + + @staticmethod + def get_coords_along_axis(base_coord: Tuple[int, ...], axis: int, + indices_at_axis: List[int]) -> List[Tuple[int, ...]]: + """Get coordinates along the given axis. + + Args: + base_coord (Tuple[int, ...]): Base coordinate which the coordinates along the axis are based on. + axis (int): Axis along which the coordinates are generated. + indices_at_axis (List[int]): Indices at the axis. + + Returns: + List[Tuple[int, ...]]: Coordinates along the axis. + """ + coords_in_group = [] + for idx in indices_at_axis: + coords_in_group.append(base_coord[:axis] + (idx,) + base_coord[axis + 1:]) + return coords_in_group + + def create_group_along_axis(self, + axis: int, + indices_at_axis: Optional[List[int]] = None, + backend: Optional[str] = None) -> ProcessGroup: + """Create all process groups along the given axis, and return the one which the current process belongs to. + + Args: + axis (int): Axis along which the process groups are created. + indices_at_axis (Optional[List[int]], optional): Indices at the axis. Defaults to None. + backend (Optional[str], optional): Backend of the process group. Defaults to None. + + Returns: + ProcessGroup: The process group along the given axis which the current process belongs to. + """ + indices_at_axis = indices_at_axis or list(range(self._shape[axis])) + reduced_shape = list(self._shape) + # the choices on the axis are reduced to 1, since it's determined by `indices_at_axis` + reduced_shape[axis] = 1 + target_group = None + # use Cartesian product to generate all combinations of coordinates + for base_coord in itertools.product(*[range(s) for s in reduced_shape]): + coords_in_group = ProcessGroupMesh.get_coords_along_axis(base_coord, axis, indices_at_axis) + ranks_in_group = tuple([ProcessGroupMesh.ravel(coord, self._shape) for coord in coords_in_group]) + group = self.get_group(ranks_in_group, backend=backend) + if self._rank in ranks_in_group: + target_group = group + return target_group + + def get_group_along_axis(self, + axis: int, + indices_at_axis: Optional[List[int]] = None, + backend: Optional[str] = None) -> ProcessGroup: + """Get the process group along the given axis which the current process belongs to. If the process group doesn't exist, it will be created. + + Args: + axis (int): Axis along which the process groups are created. + indices_at_axis (Optional[List[int]], optional): Indices at the axis. Defaults to None. + backend (Optional[str], optional): Backend of the process group. Defaults to None. + + Returns: + ProcessGroup: The process group along the given axis which the current process belongs to. + """ + indices_at_axis = indices_at_axis or list(range(self._shape[axis])) + coords_in_group = ProcessGroupMesh.get_coords_along_axis(self._coord, axis, indices_at_axis) + ranks_in_group = tuple([ProcessGroupMesh.ravel(coord, self._shape) for coord in coords_in_group]) + if ranks_in_group not in self._ranks_to_group: + # no need to cache it explicitly, since it will be cached in `create_group_along_axis` + return self.create_group_along_axis(axis, indices_at_axis, backend=backend) + return self._ranks_to_group[ranks_in_group] diff --git a/tests/test_cluster/test_process_group_mesh.py b/tests/test_cluster/test_process_group_mesh.py new file mode 100644 index 000000000000..13b7119424e4 --- /dev/null +++ b/tests/test_cluster/test_process_group_mesh.py @@ -0,0 +1,151 @@ +import pytest +import torch.distributed as dist + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.testing import spawn + + +def check_process_group_mesh_with_gpc(): + from colossalai.context import ParallelMode + from colossalai.core import global_context as gpc + + DP_DIM, PP_DIM, TP_DIM = 0, 1, 2 + pg_mesh = ProcessGroupMesh(1, 2, 2) + + # check world size + assert gpc.get_world_size(ParallelMode.TENSOR) == pg_mesh.size( + TP_DIM), f'{gpc.get_world_size(ParallelMode.TENSOR)} != {pg_mesh.size(TP_DIM)}' + assert gpc.get_world_size(ParallelMode.PIPELINE) == pg_mesh.size(PP_DIM) + assert gpc.get_world_size(ParallelMode.DATA) == pg_mesh.size(DP_DIM) + + # check locak rank (coordinate) + assert gpc.get_local_rank(ParallelMode.TENSOR) == pg_mesh.coordinate( + TP_DIM), f'{gpc.get_local_rank(ParallelMode.TENSOR)} != {pg_mesh.coordinate(TP_DIM)}' + assert gpc.get_local_rank(ParallelMode.PIPELINE) == pg_mesh.coordinate(PP_DIM) + assert gpc.get_local_rank(ParallelMode.DATA) == pg_mesh.coordinate(DP_DIM) + + # check ranks in group + tp_group = pg_mesh.get_group_along_axis(TP_DIM) + assert gpc.get_ranks_in_group(ParallelMode.TENSOR) == pg_mesh.get_ranks_in_group(tp_group) + pp_group = pg_mesh.get_group_along_axis(PP_DIM) + assert gpc.get_ranks_in_group(ParallelMode.PIPELINE) == pg_mesh.get_ranks_in_group(pp_group) + dp_group = pg_mesh.get_group_along_axis(DP_DIM) + assert gpc.get_ranks_in_group(ParallelMode.DATA) == pg_mesh.get_ranks_in_group(dp_group) + + # check prev rank + coord = pg_mesh.coordinate() + if not gpc.is_first_rank(ParallelMode.TENSOR): + assert coord[TP_DIM] != 0 + prev_coord = coord[:TP_DIM] + (coord[TP_DIM] - 1,) + coord[TP_DIM + 1:] + assert gpc.get_prev_global_rank(ParallelMode.TENSOR) == pg_mesh.ravel(prev_coord, pg_mesh.shape) + if not gpc.is_first_rank(ParallelMode.PIPELINE): + assert coord[PP_DIM] != 0 + prev_coord = coord[:PP_DIM] + (coord[PP_DIM] - 1,) + coord[PP_DIM + 1:] + assert gpc.get_prev_global_rank(ParallelMode.PIPELINE) == pg_mesh.ravel(prev_coord, pg_mesh.shape) + + # check next rank + if not gpc.is_last_rank(ParallelMode.TENSOR): + assert coord[TP_DIM] != pg_mesh.size(TP_DIM) - 1 + next_coord = coord[:TP_DIM] + (coord[TP_DIM] + 1,) + coord[TP_DIM + 1:] + assert gpc.get_next_global_rank(ParallelMode.TENSOR) == pg_mesh.ravel(next_coord, pg_mesh.shape) + if not gpc.is_last_rank(ParallelMode.PIPELINE): + assert coord[PP_DIM] != pg_mesh.size(PP_DIM) - 1 + next_coord = coord[:PP_DIM] + (coord[PP_DIM] + 1,) + coord[PP_DIM + 1:] + assert gpc.get_next_global_rank(ParallelMode.PIPELINE) == pg_mesh.ravel(next_coord, pg_mesh.shape) + + +def check_process_group_mesh_with_cases(): + DP_DIM, PP_DIM, TP_DIM = 0, 1, 2 + DP_SIZE, PP_SIZE, TP_SIZE = 1, 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0, 0), + 1: (0, 0, 1), + 2: (0, 1, 0), + 3: (0, 1, 1), + } + TP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + PP_RANKS_IN_GROUP = { + 0: [0, 2], + 1: [1, 3], + 2: [0, 2], + 3: [1, 3], + } + DP_RANKS_IN_GROUP = { + 0: [0], + 1: [1], + 2: [2], + 3: [3], + } + + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE, TP_SIZE) + + rank = dist.get_rank() + assert rank == pg_mesh.rank + + # check world size + assert pg_mesh.size(TP_DIM) == 2 + assert pg_mesh.size(PP_DIM) == 2 + assert pg_mesh.size(DP_DIM) == 1 + + # check coordinate + assert pg_mesh.coordinate(TP_DIM) == RANK_TO_COORDINATE[rank][TP_DIM] + assert pg_mesh.coordinate(PP_DIM) == RANK_TO_COORDINATE[rank][PP_DIM] + assert pg_mesh.coordinate(DP_DIM) == RANK_TO_COORDINATE[rank][DP_DIM] + + # check ranks in group + tp_group = pg_mesh.get_group_along_axis(TP_DIM) + assert pg_mesh.get_ranks_in_group(tp_group) == TP_RANKS_IN_GROUP[rank] + pp_group = pg_mesh.get_group_along_axis(PP_DIM) + assert pg_mesh.get_ranks_in_group(pp_group) == PP_RANKS_IN_GROUP[rank] + dp_group = pg_mesh.get_group_along_axis(DP_DIM) + assert pg_mesh.get_ranks_in_group(dp_group) == DP_RANKS_IN_GROUP[rank] + + # check prev rank + if RANK_TO_COORDINATE[rank][TP_DIM] != 0: + prev_coord = RANK_TO_COORDINATE[rank][:TP_DIM] + (RANK_TO_COORDINATE[rank][TP_DIM] - 1,) + \ + RANK_TO_COORDINATE[rank][TP_DIM + 1:] + prev_rank = TP_RANKS_IN_GROUP[rank][TP_RANKS_IN_GROUP[rank].index(rank) - 1] + assert pg_mesh.ravel(prev_coord, pg_mesh.shape) == prev_rank + if RANK_TO_COORDINATE[rank][PP_DIM] != 0: + prev_coord = RANK_TO_COORDINATE[rank][:PP_DIM] + (RANK_TO_COORDINATE[rank][PP_DIM] - 1,) + \ + RANK_TO_COORDINATE[rank][PP_DIM + 1:] + prev_rank = PP_RANKS_IN_GROUP[rank][PP_RANKS_IN_GROUP[rank].index(rank) - 1] + assert pg_mesh.ravel(prev_coord, pg_mesh.shape) == prev_rank + + # check next rank + if RANK_TO_COORDINATE[rank][TP_DIM] != TP_SIZE - 1: + next_coord = RANK_TO_COORDINATE[rank][:TP_DIM] + (RANK_TO_COORDINATE[rank][TP_DIM] + 1,) + \ + RANK_TO_COORDINATE[rank][TP_DIM + 1:] + next_rank = TP_RANKS_IN_GROUP[rank][TP_RANKS_IN_GROUP[rank].index(rank) + 1] + assert pg_mesh.ravel(next_coord, pg_mesh.shape) == next_rank + if RANK_TO_COORDINATE[rank][PP_DIM] != PP_SIZE - 1: + next_coord = RANK_TO_COORDINATE[rank][:PP_DIM] + (RANK_TO_COORDINATE[rank][PP_DIM] + 1,) + \ + RANK_TO_COORDINATE[rank][PP_DIM + 1:] + next_rank = PP_RANKS_IN_GROUP[rank][PP_RANKS_IN_GROUP[rank].index(rank) + 1] + assert pg_mesh.ravel(next_coord, pg_mesh.shape) == next_rank + + +def run_dist(rank, world_size, port): + colossalai.launch(config=dict(parallel=dict(data=1, pipeline=2, tensor=dict(mode='1d', size=2))), + rank=rank, + world_size=world_size, + port=port, + host='localhost') + # TODO(ver217): this function should be removed when gpc is removed + check_process_group_mesh_with_gpc() + check_process_group_mesh_with_cases() + + +@pytest.mark.dist +def test_process_group_mesh(): + spawn(run_dist, 4) + + +if __name__ == '__main__': + test_process_group_mesh() From 422544222fad45990a99fbd96617062cbd6b542a Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 27 Jun 2023 16:17:01 +0800 Subject: [PATCH 06/84] [pipeline] add stage manager (#4093) * [pipeline] add stage manager * [test] add pipeline stage manager test * [pipeline] add docstring for stage manager --- colossalai/pipeline/stage_manager.py | 176 ++++++++++++++++++++++ tests/test_pipeline/test_stage_manager.py | 86 +++++++++++ 2 files changed, 262 insertions(+) create mode 100644 colossalai/pipeline/stage_manager.py create mode 100644 tests/test_pipeline/test_stage_manager.py diff --git a/colossalai/pipeline/stage_manager.py b/colossalai/pipeline/stage_manager.py new file mode 100644 index 000000000000..fe228e2270dd --- /dev/null +++ b/colossalai/pipeline/stage_manager.py @@ -0,0 +1,176 @@ +from contextlib import contextmanager +from typing import Dict, List, Optional, Tuple + +import torch.distributed as dist +from torch.distributed import ProcessGroup + +from colossalai.cluster import ProcessGroupMesh + + +class PipelineStageManager: + """PipelineStageManager is a helper class to manage pipeline stages. + + Args: + pg_mesh (ProcessGroupMesh): Process group mesh. + pipeline_axis (int): The axis along which the pipeline is constructed. + + Attributes: + num_stages (int): Number of stages in the pipeline. + stage (int): The current stage. + num_virtual_stages (int): Number of virtual stages in the pipeline. + virtual_stage (int): The current virtual stage. + """ + + def __init__(self, pg_mesh: ProcessGroupMesh, pipeline_axis: int) -> None: + self.pg_mesh = pg_mesh + self.pipeline_axis = pipeline_axis + self.num_virtual_stages: Optional[int] = None + self.virtual_stage: Optional[int] = None + self.prev_rank: Optional[Tuple[int, ...]] = None + self.next_rank: Optional[Tuple[int, ...]] = None + self.p2p_groups: Dict[Tuple[int, int], ProcessGroup] = {} + # init prev and next coord + coord = self.pg_mesh.coordinate() + if self.stage > 0: + prev_coord = coord[: self.pipeline_axis] + \ + (coord[self.pipeline_axis] - 1,) + coord[self.pipeline_axis + 1:] + self.prev_rank = self.pg_mesh.ravel(prev_coord, self.pg_mesh.shape) + if self.stage < self.num_stages - 1: + next_coord = coord[: self.pipeline_axis] + \ + (coord[self.pipeline_axis] + 1,) + coord[self.pipeline_axis + 1:] + self.next_rank = self.pg_mesh.ravel(next_coord, self.pg_mesh.shape) + + # init p2p process groups + stages = list(range(self.num_stages)) + for prev, cur in zip(stages[:-1], stages[1:]): + group = self.pg_mesh.get_group_along_axis(self.pipeline_axis, [prev, cur]) + if self.stage in [prev, cur]: + ranks_in_group = self.pg_mesh.get_ranks_in_group(group) + self.p2p_groups[tuple(ranks_in_group)] = group + + def is_first_stage(self, virtual: bool = False) -> bool: + """Is the current stage the first stage. + + Args: + virtual (bool, optional): Whether to consider virtual stages. Defaults to False. + + Returns: + bool: Whether the current stage is the first stage. + """ + if virtual: + assert self.num_virtual_stages is not None + return self.virtual_stage == 0 + return self.stage == 0 + + def is_last_stage(self, virtual: bool = False) -> bool: + """Is the current stage the last stage. + + Args: + virtual (bool, optional): Whether to consider virtual stages. Defaults to False. + + Returns: + bool: Whether the current stage is the last stage. + """ + if virtual: + assert self.num_virtual_stages is not None + return self.virtual_stage == self.num_virtual_stages - 1 + return self.stage == self.num_stages - 1 + + @property + def num_stages(self) -> int: + """Number of stages in the pipeline. + + Returns: + int: Number of stages in the pipeline. + """ + return self.pg_mesh.size(self.pipeline_axis) + + @property + def stage(self) -> int: + """Current stage. + + Returns: + int: Current stage. + """ + return self.pg_mesh.coordinate(self.pipeline_axis) + + def get_rank(self) -> int: + """Get the rank of the current process. + + Returns: + int: Rank of the current process. + """ + return dist.get_rank() + + def get_prev_rank(self) -> int: + """Get the rank of the previous stage. + + Returns: + int: Rank of the previous stage. + """ + assert not self.is_first_stage(), "Cannot get previous rank in the first stage." + return self.prev_rank + + def get_next_rank(self) -> int: + """Get the rank of the next stage. + + Returns: + int: Rank of the next stage. + """ + assert not self.is_last_stage(), "Cannot get next rank in the last stage." + return self.next_rank + + def set_num_virtual_stages(self, num_virtual_stages: int) -> None: + """Set the number of virtual stages. + + Args: + num_virtual_stages (int): Number of virtual stages. + """ + self.num_virtual_stages = num_virtual_stages + + def set_virtual_stage(self, virtual_stage: int) -> None: + """Set the virtual stage. + + Args: + virtual_stage (int): Virtual stage. + """ + self.virtual_stage = virtual_stage + + @contextmanager + def switch_virtual_stage(self, virtual_stage: int) -> None: + """A context manager to switch virtual stage. + + Args: + virtual_stage (int): Target virtual stage. + """ + old_stage = self.virtual_stage + try: + self.set_virtual_stage(virtual_stage) + yield + finally: + self.set_virtual_stage(old_stage) + + def get_p2p_process_group(self, first_rank: int, second_rank: int) -> ProcessGroup: + """Get the p2p process group between two ranks. The order of the two ranks does not matter. + + Args: + first_rank (int): The first rank. + second_rank (int): The second rank. + + Returns: + ProcessGroup: P2P process group between the two ranks. + """ + if first_rank > second_rank: + first_rank, second_rank = second_rank, first_rank + return self.p2p_groups[(first_rank, second_rank)] + + def init_process_group_by_stages(self, stages: List[int]) -> ProcessGroup: + """Get the process group of the given stages. + + Args: + stages (List[int]): List of stages. + + Returns: + ProcessGroup: Process group of the given stages. + """ + return self.pg_mesh.get_group_along_axis(self.pipeline_axis, stages) diff --git a/tests/test_pipeline/test_stage_manager.py b/tests/test_pipeline/test_stage_manager.py new file mode 100644 index 000000000000..b920f88dbfae --- /dev/null +++ b/tests/test_pipeline/test_stage_manager.py @@ -0,0 +1,86 @@ +import pytest +import torch.distributed as dist + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import spawn + + +def check_stage_manager(): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + + # check stage info + assert stage_manager.num_stages == PP_SIZE + assert stage_manager.stage == RANK_TO_COORDINATE[rank][PP_DIM] + + # check is_first_stage + ranks_in_group = PP_RANKS_IN_GROUP[rank] + is_first_stage = ranks_in_group.index(rank) == 0 + assert stage_manager.is_first_stage() == is_first_stage + + # check is_last_stage + is_last_stage = ranks_in_group.index(rank) == len(ranks_in_group) - 1 + assert stage_manager.is_last_stage() == is_last_stage + + # check prev rank + if not is_first_stage: + prev_rank = ranks_in_group[ranks_in_group.index(rank) - 1] + assert stage_manager.get_prev_rank() == prev_rank + + # check next rank + if not is_last_stage: + next_rank = ranks_in_group[ranks_in_group.index(rank) + 1] + assert stage_manager.get_next_rank() == next_rank + + # check virtual stage + stage_manager.set_num_virtual_stages(PP_SIZE * 2) + assert stage_manager.num_virtual_stages == PP_SIZE * 2 + stage_manager.set_virtual_stage(stage_manager.stage * 2) + assert stage_manager.virtual_stage == stage_manager.stage * 2 + with stage_manager.switch_virtual_stage(stage_manager.stage * 2 + 1): + assert stage_manager.virtual_stage == stage_manager.stage * 2 + 1 + assert stage_manager.virtual_stage == stage_manager.stage * 2 + + # check p2p groups + for prev, cur in zip(ranks_in_group[:-1], ranks_in_group[1:]): + if rank in [prev, cur]: + group = stage_manager.get_p2p_process_group(prev, cur) + dist.barrier(group=group) + + # check stage groups + pg_mesh = ProcessGroupMesh(4) + stage_manager = PipelineStageManager(pg_mesh, 0) + group = stage_manager.init_process_group_by_stages([0, 2]) + if rank in [0, 2]: + dist.barrier(group=group) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_stage_manager() + + +@pytest.mark.dist +def test_process_group_mesh(): + spawn(run_dist, 4) + + +if __name__ == '__main__': + test_process_group_mesh() From 45fdc9b42c3fbba1db9df72bb228ac931cb3a172 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 28 Jun 2023 17:12:19 +0800 Subject: [PATCH 07/84] [pipeline] implement p2p communication (#4100) * [pipeline] add p2p communication * [test] add p2p communication test * [test] add rerun decorator * [test] rename to avoid conflict --- colossalai/pipeline/p2p.py | 224 ++++++++++++++++++ tests/test_pipeline/test_p2p_communication.py | 59 +++++ tests/test_pipeline/test_stage_manager.py | 7 +- 3 files changed, 287 insertions(+), 3 deletions(-) create mode 100644 colossalai/pipeline/p2p.py create mode 100644 tests/test_pipeline/test_p2p_communication.py diff --git a/colossalai/pipeline/p2p.py b/colossalai/pipeline/p2p.py new file mode 100644 index 000000000000..203b7439d7ef --- /dev/null +++ b/colossalai/pipeline/p2p.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- + +import io +import pickle +from typing import Any, List, Optional, Union + +import torch +import torch.distributed as dist +from torch.distributed import ProcessGroup +from torch.distributed import distributed_c10d as c10d + +from .stage_manager import PipelineStageManager + +_unpickler = pickle.Unpickler + + +def _cuda_safe_tensor_to_object(tensor: torch.Tensor, tensor_size: torch.Size) -> object: + """transform tensor to object with unpickle. + Info of the device in bytes stream will be modified into current device before unpickling + + Args: + tensor (:class:`torch.tensor`): tensor to be unpickled + tensor_size (:class:`torch.Size`): Size of the real info in bytes + + Returns: + Any: object after unpickled + """ + buf = tensor.numpy().tobytes()[:tensor_size] + if b'cuda' in buf: + buf_array = bytearray(buf) + device_index = torch.cuda.current_device() + buf_array[buf_array.find(b'cuda') + 5] = 48 + device_index + buf = bytes(buf_array) + + io_bytes = io.BytesIO(buf) + byte_pickler = _unpickler(io_bytes) + unpickle = byte_pickler.load() + + return unpickle + + +def _broadcast_object_list(object_list: List[Any], + src: int, + group: ProcessGroup, + device: Optional[Union[torch.device, str, int]] = None): + """This is a modified version of the broadcast_object_list in torch.distribution + The only difference is that object will be move to correct device after unpickled. + If local_rank = src, then object list will be sent to rank src. Otherwise, object list will + be updated with data sent from rank src. + + Args: + object_list (List[Any]): list of object to broadcast + src (int): source rank to broadcast + dst (int): dst rank to broadcast + device (:class:`torch.device`): device to do broadcast. current device in default + + """ + + if c10d._rank_not_in_group(group): + c10d._warn_not_in_group("broadcast_object_list") + return + + my_rank = dist.get_rank() + # Serialize object_list elements to tensors on src rank. + if my_rank == src: + tensor_list, size_list = zip(*[c10d._object_to_tensor(obj) for obj in object_list]) + object_sizes_tensor = torch.cat(size_list) + else: + object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long) + + is_nccl_backend = c10d._check_for_nccl_backend(group) + current_device = None + + if device is not None: + if is_nccl_backend and device.type != "cuda": + raise ValueError("device type must be cuda for nccl backend") + current_device = device + else: + current_device = torch.device("cpu") + if is_nccl_backend: + current_device = torch.device("cuda", torch.cuda.current_device()) + if is_nccl_backend: + object_sizes_tensor = object_sizes_tensor.to(current_device) + + # Broadcast object sizes + c10d.broadcast(object_sizes_tensor, src=src, group=group, async_op=False) + + # Concatenate and broadcast serialized object tensors + if my_rank == src: + object_tensor = torch.cat(tensor_list) + else: + object_tensor = torch.empty( # type: ignore[call-overload] + torch.sum(object_sizes_tensor).item(), # type: ignore[arg-type] + dtype=torch.uint8, + ) + + if is_nccl_backend: + object_tensor = object_tensor.to(current_device) + + c10d.broadcast(object_tensor, src=src, group=group, async_op=False) + + # Deserialize objects using their stored sizes. + offset = 0 + + if my_rank != src: + for i, obj_size in enumerate(object_sizes_tensor): + obj_view = object_tensor[offset:offset + obj_size] + obj_view = obj_view.type(torch.uint8) + if obj_view.device != torch.device("cpu"): + obj_view = obj_view.cpu() + offset += obj_size + # unpickle + unpickle_object = _cuda_safe_tensor_to_object(obj_view, obj_size) + + # unconsistence in device + if isinstance(unpickle_object, + torch.Tensor) and unpickle_object.device.index != torch.cuda.current_device(): + unpickle_object = unpickle_object.cuda() + + object_list[i] = unpickle_object + + +def _send_object(object: Any, src: int, dst: int, group: ProcessGroup) -> None: + """send anything to dst rank + + Args: + object (Any): object needed to be sent + dst (int): rank of the destination + + Returns: + None + """ + # then broadcast safely + _broadcast_object_list([object], src, group) + + +def _recv_object(src: int, dst: int, group: ProcessGroup) -> Any: + """recv anything from src + + Args: + src (int): source rank of data. local rank will receive data from src rank. + + Returns: + Any: Object received from src. + """ + object_list = [None] + _broadcast_object_list(object_list, src, group) + + return object_list[0] + + +class PipelineP2PCommunication: + + def __init__(self, stage_manager: PipelineStageManager) -> None: + self.stage_manager = stage_manager + + def recv_forward(self, prev_rank: int = None) -> Any: + """Copy the forward output from the previous stage in pipeline as the input tensor of this stage. + + Args: + prev_rank (int, optional): The rank of the source of the tensor. + + Returns: + Any: The input tensor or input tensor list. + """ + if self.stage_manager.is_first_stage(): + input_tensor = None + else: + if prev_rank is None: + prev_rank = self.stage_manager.get_prev_rank() + cur_rank = self.stage_manager.get_rank() + input_tensor = _recv_object(prev_rank, cur_rank, + self.stage_manager.get_p2p_process_group(prev_rank, cur_rank)) + + return input_tensor + + def recv_backward(self, next_rank: int = None) -> Any: + """Copy the gradient tensor from the next stage in pipeline as the input gradient of this stage. + + Args: + next_rank (int, optional): The rank of the source of the tensor. + + Returns: + Any: The input gradient tensor or gradient tensor list. + """ + if self.stage_manager.is_last_stage(): + output_tensor_grad = None + else: + if next_rank is None: + next_rank = self.stage_manager.get_next_rank() + cur_rank = self.stage_manager.get_rank() + output_tensor_grad = _recv_object(next_rank, cur_rank, + self.stage_manager.get_p2p_process_group(next_rank, cur_rank)) + + return output_tensor_grad + + def send_forward(self, output_object: Any, next_rank: int = None) -> None: + """Sends the input tensor to the next stage in pipeline. + + Args: + output_object (Any): Object to be sent. + next_rank (int, optional): The rank of the recipient of the tensor. + """ + if not self.stage_manager.is_last_stage(): + if next_rank is None: + next_rank = self.stage_manager.get_next_rank() + cur_rank = self.stage_manager.get_rank() + _send_object(output_object, cur_rank, next_rank, + self.stage_manager.get_p2p_process_group(cur_rank, next_rank)) + + def send_backward(self, input_object: Any, prev_rank: int = None) -> None: + """Sends the gradient tensor to the previous stage in pipeline. + + Args: + input_object (Any): Object to be sent. + prev_rank (int, optional): The rank of the recipient of the tensor + """ + if not self.stage_manager.is_first_stage(): + if prev_rank is None: + prev_rank = self.stage_manager.get_prev_rank() + cur_rank = self.stage_manager.get_rank() + _send_object(input_object, cur_rank, prev_rank, + self.stage_manager.get_p2p_process_group(cur_rank, prev_rank)) diff --git a/tests/test_pipeline/test_p2p_communication.py b/tests/test_pipeline/test_p2p_communication.py new file mode 100644 index 000000000000..71946f6b988a --- /dev/null +++ b/tests/test_pipeline/test_p2p_communication.py @@ -0,0 +1,59 @@ +import pytest +import torch +import torch.distributed as dist + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.pipeline.p2p import PipelineP2PCommunication +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.utils import get_current_device + + +def check_p2p_communication(): + pg_mesh = ProcessGroupMesh(2) + stage_manager = PipelineStageManager(pg_mesh, 0) + p2p = PipelineP2PCommunication(stage_manager) + + rank = dist.get_rank() + + tensor = torch.ones(1, device=get_current_device()) + + if rank == 0: + p2p.send_forward(tensor) + p2p.send_forward([tensor]) + p2p.send_forward({'tensor': tensor}) + else: + obj = p2p.recv_forward() + assert torch.equal(obj, tensor) + obj = p2p.recv_forward() + assert type(obj) == list and len(obj) == 1 and torch.equal(obj[0], tensor) + obj = p2p.recv_forward() + assert type(obj) == dict and 'tensor' in obj and torch.equal(obj['tensor'], tensor) + + if rank == 1: + p2p.send_backward(tensor) + p2p.send_backward([tensor]) + p2p.send_backward({'tensor': tensor}) + else: + obj = p2p.recv_backward() + assert torch.equal(obj, tensor) + obj = p2p.recv_backward() + assert type(obj) == list and len(obj) == 1 and torch.equal(obj[0], tensor) + obj = p2p.recv_backward() + assert type(obj) == dict and 'tensor' in obj and torch.equal(obj['tensor'], tensor) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_p2p_communication() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_pipeline_p2p(): + spawn(run_dist, 2) + + +if __name__ == '__main__': + test_pipeline_p2p() diff --git a/tests/test_pipeline/test_stage_manager.py b/tests/test_pipeline/test_stage_manager.py index b920f88dbfae..be4591d58f74 100644 --- a/tests/test_pipeline/test_stage_manager.py +++ b/tests/test_pipeline/test_stage_manager.py @@ -4,7 +4,7 @@ import colossalai from colossalai.cluster import ProcessGroupMesh from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.testing import spawn +from colossalai.testing import rerun_if_address_is_in_use, spawn def check_stage_manager(): @@ -78,9 +78,10 @@ def run_dist(rank, world_size, port): @pytest.mark.dist -def test_process_group_mesh(): +@rerun_if_address_is_in_use() +def test_pipeline_stage_manager(): spawn(run_dist, 4) if __name__ == '__main__': - test_process_group_mesh() + test_pipeline_stage_manager() From f51ce1bc8e09e04a2fea28785320e246dd4e8cd0 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 29 Jun 2023 13:35:39 +0800 Subject: [PATCH 08/84] [pipeline] refactor 1f1b schedule (#4115) * [api] update optimizer wrapper to fit pipeline * [pipeline] add base schedule * [pipeline] add 1f1b schedule * [test] add pipeline schedule utils test * [pipeline] fix import --- colossalai/interface/optimizer.py | 4 + colossalai/pipeline/schedule/__init__.py | 7 + colossalai/pipeline/schedule/_utils.py | 129 ++++++++++ colossalai/pipeline/schedule/base.py | 35 +++ colossalai/pipeline/schedule/one_f_one_b.py | 229 ++++++++++++++++++ .../test_pipeline_schedule_utils.py | 47 ++++ 6 files changed, 451 insertions(+) create mode 100644 colossalai/pipeline/schedule/__init__.py create mode 100644 colossalai/pipeline/schedule/_utils.py create mode 100644 colossalai/pipeline/schedule/base.py create mode 100644 colossalai/pipeline/schedule/one_f_one_b.py create mode 100644 tests/test_pipeline/test_schedule/test_pipeline_schedule_utils.py diff --git a/colossalai/interface/optimizer.py b/colossalai/interface/optimizer.py index 0eaf2e1ef8ba..bc270b1d9c89 100644 --- a/colossalai/interface/optimizer.py +++ b/colossalai/interface/optimizer.py @@ -1,5 +1,6 @@ from typing import Union +import torch import torch.nn as nn from torch import Tensor from torch.optim import Optimizer @@ -53,6 +54,9 @@ def backward(self, loss: Tensor, *args, **kwargs): """ loss.backward(*args, **kwargs) + def backward_by_grad(self, tensor: Tensor, grad: Tensor): + torch.autograd.backward(tensor, grad) + def state_dict(self): """ Returns the optimizer state. diff --git a/colossalai/pipeline/schedule/__init__.py b/colossalai/pipeline/schedule/__init__.py new file mode 100644 index 000000000000..8b13413b1a31 --- /dev/null +++ b/colossalai/pipeline/schedule/__init__.py @@ -0,0 +1,7 @@ +from .base import PipelineSchedule +from .one_f_one_b import OneForwardOneBackwardSchedule + +__all__ = [ + 'PipelineSchedule', + 'OneForwardOneBackwardSchedule', +] diff --git a/colossalai/pipeline/schedule/_utils.py b/colossalai/pipeline/schedule/_utils.py new file mode 100644 index 000000000000..045c86e40e63 --- /dev/null +++ b/colossalai/pipeline/schedule/_utils.py @@ -0,0 +1,129 @@ +from typing import Any, List, Optional + +import torch +import torch.cuda +from torch.nn import Module +from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten + + +def to_device(x: Any, device: Optional[torch.device] = None) -> Any: + """Move object to device if it is a tensor. + + Args: + x (Any): Object to be moved. + device (Optional[torch.device], optional): Target device. Defaults to None. + + Returns: + Any: Moved object. + """ + if isinstance(x, torch.Tensor): + return x.to(device) + return x + + +def get_batch_size(batch: Any) -> int: + """Get the batch size (size of dimension-0) of the first tensor in the batch. + + Args: + batch (Any): Batch to be inspected. + + Raises: + RuntimeError: If no tensor is found in the batch. + + Returns: + int: Batch size. + """ + data_list, _ = tree_flatten(batch) + for data in data_list: + if isinstance(data, torch.Tensor): + return data.size(0) + raise RuntimeError('No tensor found in the batch') + + +def get_micro_batch(batch: Any, start: int, micro_batch_size: int) -> Any: + """Get a micro batch of the original batch. + + Args: + batch (Any): Batch to be sliced. + start (int): Start index of the micro batch. + micro_batch_size (int): Size of the micro batch. + + Returns: + Any: Target micro batch. + """ + + def _get_tensor_slice(x: Any): + if isinstance(x, torch.Tensor): + return x[start:start + micro_batch_size] + return x + + return tree_map(_get_tensor_slice, batch) + + +def model_forward(model: Module, data: Any, internal_inputs: Optional[dict]) -> Any: + """Call model forward function with data and internal inputs. + + Args: + model (Module): Model to be called. + data (Any): Data loaded from data iterator. + internal_inputs (Optional[dict]): Data from previous stage. It must be a dict or None if it's the first stage. + + Returns: + Any: Outputs of the model. + """ + if internal_inputs is None: + internal_inputs = {} + if isinstance(data, (list, tuple)): + return model(*data, **internal_inputs) + elif isinstance(data, dict): + return model(**data, **internal_inputs) + return model(data, **internal_inputs) + + +def retain_grad(x: Any) -> None: + """Call retain_grad() on a tensor. + + Args: + x (Any): Object to be called. + """ + if isinstance(x, torch.Tensor): + x.retain_grad() + + +def detach(x: Any) -> Any: + """Call detach() on a tensor. + + Args: + x (Any): Object to be called. + + Returns: + Any: The detached object. + """ + if isinstance(x, torch.Tensor): + return x.detach() + return x + + +def merge_batch(data: List[Any]) -> Any: + """Merge micro batches into a batch. + + Args: + data (List[Any]): A list of micro batches. + + Returns: + Any: Merge batch. + """ + if len(data) == 0: + return + flattened_data = [] + tree_spec = None + for d in data: + elems, tree_spec = tree_flatten(d) + flattened_data.append(elems) + merged_data = [] + for elem_batch in zip(*flattened_data): + if isinstance(elem_batch[0], torch.Tensor): + merged_data.append(torch.cat(elem_batch, dim=0)) + else: + merged_data.append(list(elem_batch)) + return tree_unflatten(merged_data, tree_spec) diff --git a/colossalai/pipeline/schedule/base.py b/colossalai/pipeline/schedule/base.py new file mode 100644 index 000000000000..9cd9beded65a --- /dev/null +++ b/colossalai/pipeline/schedule/base.py @@ -0,0 +1,35 @@ +from typing import Any, Callable, Iterable + +from torch import Tensor +from torch.nn import Module + +from colossalai.interface import OptimizerWrapper +from colossalai.pipeline.stage_manager import PipelineStageManager + + +class PipelineSchedule: + + def __init__(self, stage_manager: PipelineStageManager) -> None: + self.stage_manager = stage_manager + + def forward_backward_step(self, + model: Module, + optimizer: OptimizerWrapper, + data_iter: Iterable, + criterion: Callable[[Any, Any], Tensor], + return_loss: bool = False, + return_outputs: bool = False) -> dict: + """Forward and backward step for pipeline training. + + Args: + model (Module): Model to be trained. + optimizer (OptimizerWrapper): Optimizer to be used. + data_iter (Iterable): Data iterator. + criterion (Callable[[Any, Any], Tensor]): Criterion to be used. It should take two arguments: model outputs and inputs, and returns loss tensor. + return_loss (bool, optional): Whether to return loss. Defaults to False. Whether to return loss. + return_outputs (bool, optional): Whether to return model outputs. Defaults to False. Whether to return model outputs. + + Returns: + dict: A dict with keys: 'loss' and 'outputs'. + """ + raise NotImplementedError diff --git a/colossalai/pipeline/schedule/one_f_one_b.py b/colossalai/pipeline/schedule/one_f_one_b.py new file mode 100644 index 000000000000..a8933bfbb4da --- /dev/null +++ b/colossalai/pipeline/schedule/one_f_one_b.py @@ -0,0 +1,229 @@ +from functools import partial +from typing import Any, Callable, Iterable, List, Optional, Union + +import torch +import torch.cuda +from torch.nn import Module +from torch.utils._pytree import tree_map + +from colossalai.interface import OptimizerWrapper +from colossalai.pipeline.p2p import PipelineP2PCommunication +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.utils.cuda import get_current_device + +from ._utils import detach, get_batch_size, get_micro_batch, merge_batch, model_forward, retain_grad, to_device +from .base import PipelineSchedule + + +class OneForwardOneBackwardSchedule(PipelineSchedule): + + def __init__(self, num_microbatches: int, stage_manager: PipelineStageManager) -> None: + super().__init__(stage_manager) + self.comm = PipelineP2PCommunication(stage_manager) + self.num_microbatches = num_microbatches + self.batch: Optional[Any] = None + self.batch_size: Optional[int] = None + self.microbatch_offset: Optional[int] = None + self.microbatch_size: Optional[int] = None + + def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None) -> None: + """Load a batch from data iterator. + + Args: + data_iter (Iterable): Data iterator. + device (Optional[torch.device], optional): Target device. Defaults to None. + """ + batch = next(data_iter) + if device is not None: + batch = tree_map(partial(to_device, device=device), batch) + self.batch = batch + self.batch_size = get_batch_size(batch) + self.microbatch_offset = 0 + assert self.batch_size % self.num_microbatches == 0, \ + "Batch size should divided by the number of microbatches" + self.microbatch_size = self.batch_size // self.num_microbatches + + def load_micro_batch(self) -> Any: + """Load a micro batch from the current batch. + + Returns: + Any: Micro batch. + """ + micro_batch = get_micro_batch(self.batch, self.microbatch_offset, self.microbatch_size) + self.microbatch_offset += self.microbatch_size + return tree_map(partial(to_device, device=get_current_device()), micro_batch) + + def forward_step(self, + model: Module, + input_obj: Optional[dict], + criterion: Callable, + accum_loss: Optional[torch.Tensor] = None, + outputs: Optional[List[Any]] = None) -> Union[torch.Tensor, dict]: + """Forward one step of the pipeline + + Args: + model (Module): Model to be run + input_obj (Optional[dict]): The output from the previous stage. If it is the first stage, the `input_obj` is None. + criterion (Callable): Criterion to calculate loss. + accum_loss (Optional[torch.Tensor], optional): Accumulated loss. Defaults to None. + outputs (Optional[List[Any]], optional): List to store the output of the last stage (final output). Defaults to None. + + Returns: + Union[torch.Tensor, dict]: The intermediate output (dict) of the current stage. If it is the last stage, the output is the loss (Tensor). + """ + micro_batch = self.load_micro_batch() + + # for the first stage, input_obj is None + # for the non-first stage, input_obj is the output of the previous stage and it's must be a dict + output_obj = model_forward(model, micro_batch, input_obj) + + if self.stage_manager.is_last_stage(): + loss = criterion(output_obj, micro_batch) / self.num_microbatches + if accum_loss is not None: + accum_loss.add_(loss.detach()) + if outputs is not None: + outputs.append(tree_map(detach, output_obj)) + return loss + else: + return output_obj + + def backward_step(self, optimizer: OptimizerWrapper, input_obj: Optional[dict], + output_obj: Union[dict, torch.Tensor], output_obj_grad: Optional[dict]) -> Optional[dict]: + """Backward one step of the pipeline + + Args: + optimizer (OptimizerWrapper): Optimizer to update the model + input_obj (Optional[dict]): Output of the previous stage. If it is the first stage, the `input_obj` is None. + output_obj (Union[dict, torch.Tensor]): Output of the current stage. If it is the last stage, the output is the loss (Tensor). + output_obj_grad (dict): Gradient of the `output_obj`. If it is the last stage, the `output_obj_grad` is None. + + Returns: + Optional[dict]: Gradient of the `input_obj`. If it is the first stage, the `input_obj_grad` is None. + """ + + # Retain the grad on the input_obj. + tree_map(retain_grad, input_obj) + + # Backward pass. + if output_obj_grad is None: + optimizer.backward(output_obj) + else: + for k, grad in output_obj_grad.items(): + optimizer.backward_by_grad(output_obj[k], grad) + + # Collect the grad of the input_obj. + input_obj_grad = None + if input_obj is not None: + input_obj_grad = {} + for k, v in input_obj.items(): + if isinstance(v, torch.Tensor) and v.grad is not None: + input_obj_grad[k] = v.grad + return input_obj_grad + + def forward_backward_step(self, + model: Module, + optimizer: OptimizerWrapper, + data_iter: Iterable, + criterion: Callable[..., Any], + return_loss: bool = False, + return_outputs: bool = False) -> dict: + """Runs non-interleaved 1F1B schedule, with communication between pipeline stages. + + Args: + model (Module): Model to be trained. + optimizer (OptimizerWrapper): Optimizer to be used. + data_iter (Iterable): Data iterator. + criterion (Callable[[Any, Any], Tensor]): Criterion to be used. It should take two arguments: model outputs and inputs, and returns loss tensor. + return_loss (bool, optional): Whether to return loss. Defaults to False. Whether to return loss. + return_outputs (bool, optional): Whether to return model outputs. Defaults to False. Whether to return model outputs. + + Returns: + dict: A dict with keys: 'loss' and 'outputs'. + """ + forward_only = not torch.is_grad_enabled() + + self.load_batch(data_iter) + + # num_warmup_microbatches is the step when not all the processes are working + num_warmup_microbatches = self.stage_manager.num_stages - self.stage_manager.stage - 1 + num_warmup_microbatches = min(num_warmup_microbatches, self.num_microbatches) + num_microbatches_remaining = self.num_microbatches - num_warmup_microbatches + + # Input, output tensors only need to be saved when doing backward passes + input_objs = None + output_objs = None + + if not forward_only: + input_objs = [] + output_objs = [] + + outputs = [] if return_outputs and self.stage_manager.is_last_stage() else None + if return_loss and self.stage_manager.is_last_stage(): + accum_loss = torch.zeros(1, device=get_current_device()) + else: + accum_loss = None + + # Run warmup forward passes. + for i in range(num_warmup_microbatches): + input_obj = self.comm.recv_forward() + + output_obj = self.forward_step(model, input_obj, criterion, accum_loss, outputs) + + self.comm.send_forward(output_obj) + + if not forward_only: + input_objs.append(input_obj) + output_objs.append(output_obj) + + # Before running 1F1B, need to receive first forward tensor. + # If all microbatches are run in warmup / cooldown phase, then no need to + # receive this tensor here. + if num_microbatches_remaining > 0: + input_obj = self.comm.recv_forward() + + # Run 1F1B in steady state. + for i in range(num_microbatches_remaining): + last_iteration = (i == (num_microbatches_remaining - 1)) + + output_obj = self.forward_step(model, input_obj, criterion, accum_loss, outputs) + if forward_only: + self.comm.send_forward(output_obj) + + if not last_iteration: + input_obj = self.comm.recv_forward() + + else: + # TODO adjust here + self.comm.send_forward(output_obj) + output_obj_grad = self.comm.recv_backward() + + # Add input_obj and output_obj to end of list. + input_objs.append(input_obj) + output_objs.append(output_obj) + + # Pop output_obj and output_obj from the start of the list for + # the backward pass. + input_obj = input_objs.pop(0) + output_obj = output_objs.pop(0) + + input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad) + + if last_iteration: + input_obj = None + else: + input_obj = self.comm.recv_forward() + self.comm.send_backward(input_obj_grad) + + # Run cooldown backward passes. + if not forward_only: + for i in range(num_warmup_microbatches): + input_obj = input_objs.pop(0) + output_obj = output_objs.pop(0) + + output_obj_grad = self.comm.recv_backward() + input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad) + self.comm.send_backward(input_obj_grad) + + if outputs is not None: + outputs = merge_batch(outputs) + return {'loss': accum_loss, 'outputs': outputs} diff --git a/tests/test_pipeline/test_schedule/test_pipeline_schedule_utils.py b/tests/test_pipeline/test_schedule/test_pipeline_schedule_utils.py new file mode 100644 index 000000000000..4c23a23ebaba --- /dev/null +++ b/tests/test_pipeline/test_schedule/test_pipeline_schedule_utils.py @@ -0,0 +1,47 @@ +import torch + +from colossalai.pipeline.schedule._utils import get_batch_size, get_micro_batch, merge_batch + + +def test_get_batch_size(): + tensor = torch.rand(2, 3) + assert get_batch_size(tensor) == 2 + assert get_batch_size([tensor]) == 2 + assert get_batch_size((1, tensor)) == 2 + assert get_batch_size({'tensor': tensor}) == 2 + assert get_batch_size({'dummy': [1], 'tensor': tensor}) == 2 + assert get_batch_size({'tensor': [tensor]}) == 2 + + +def test_get_micro_batch(): + x = torch.rand(2, 1) + y = torch.rand(2, 3) + micro_batch = get_micro_batch(x, 0, 1) + assert torch.equal(micro_batch, x[0:1]) + micro_batch = get_micro_batch(x, 1, 1) + assert torch.equal(micro_batch, x[1:2]) + micro_batch = get_micro_batch([x, y], 0, 1) + assert torch.equal(micro_batch[0], x[0:1]) + assert torch.equal(micro_batch[1], y[0:1]) + micro_batch = get_micro_batch([x, y], 1, 1) + assert torch.equal(micro_batch[0], x[1:2]) + assert torch.equal(micro_batch[1], y[1:2]) + micro_batch = get_micro_batch({'x': x, 'y': y}, 0, 1) + assert torch.equal(micro_batch['x'], x[0:1]) + assert torch.equal(micro_batch['y'], y[0:1]) + micro_batch = get_micro_batch({'x': x, 'y': y}, 1, 1) + assert torch.equal(micro_batch['x'], x[1:2]) + assert torch.equal(micro_batch['y'], y[1:2]) + + +def test_merge_batch(): + x = torch.rand(2, 1) + y = torch.rand(2, 3) + merged = merge_batch([x[0:1], x[1:2]]) + assert torch.equal(merged, x) + merged = merge_batch([[x[0:1], y[0:1]], [x[1:2], y[1:2]]]) + assert torch.equal(merged[0], x) + assert torch.equal(merged[1], y) + merged = merge_batch([{'x': x[0:1], 'y': y[0:1]}, {'x': x[1:2], 'y': y[1:2]}]) + assert torch.equal(merged['x'], x) + assert torch.equal(merged['y'], y) From e8e7e492430c5835f0494e4ee4d6bbaf5c377633 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Tue, 4 Jul 2023 13:46:16 +0800 Subject: [PATCH 09/84] [pipeline]add pipeline policy and bert forward (#4130) * add pipeline policy and bert forward to be done * add bertmodel pipeline forward and make tests * add Bert_Policy and test for policy * update formatting * update formatting * update the code * fix bugs * fix name confilt --- colossalai/pipeline/policy/__init__.py | 22 + colossalai/pipeline/policy/base.py | 108 +++++ colossalai/pipeline/policy/bert.py | 390 ++++++++++++++++++ colossalai/pipeline/policy/bloom.py | 153 +++++++ .../test_policy/test_bert_model.py | 112 +++++ tests/test_pipeline/test_stage_manager.py | 2 +- 6 files changed, 786 insertions(+), 1 deletion(-) create mode 100644 colossalai/pipeline/policy/__init__.py create mode 100644 colossalai/pipeline/policy/base.py create mode 100644 colossalai/pipeline/policy/bert.py create mode 100644 colossalai/pipeline/policy/bloom.py create mode 100644 tests/test_pipeline/test_policy/test_bert_model.py diff --git a/colossalai/pipeline/policy/__init__.py b/colossalai/pipeline/policy/__init__.py new file mode 100644 index 000000000000..fd9e6e04588e --- /dev/null +++ b/colossalai/pipeline/policy/__init__.py @@ -0,0 +1,22 @@ +from typing import Any, Dict, List, Optional, Tuple, Type + +from torch import Tensor +from torch.nn import Module, Parameter + +from colossalai.pipeline.stage_manager import PipelineStageManager + +from .base import Policy +from .bert import BertModel, BertModelPolicy + +POLICY_MAP: Dict[Type[Module], Type[Policy]] = { + BertModel: BertModelPolicy, +} + + +def pipeline_parallelize( + model: Module, + stage_manager: PipelineStageManager) -> Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: + if type(model) not in POLICY_MAP: + raise NotImplementedError(f"Policy for {type(model)} not implemented") + policy = POLICY_MAP[type(model)](stage_manager) + return policy.parallelize_model(model) diff --git a/colossalai/pipeline/policy/base.py b/colossalai/pipeline/policy/base.py new file mode 100644 index 000000000000..ad595a04b1b0 --- /dev/null +++ b/colossalai/pipeline/policy/base.py @@ -0,0 +1,108 @@ +from typing import Any, Dict, List, Optional, Tuple + +from colossalai.lazy import LazyTensor +from torch import Tensor +from torch.nn import Module, Parameter + +from colossalai.pipeline.stage_manager import PipelineStageManager + + +class Policy: + def __init__(self, stage_manager: PipelineStageManager) -> None: + self.stage_manager = stage_manager + + def setup_model(self, module: Module) -> Tuple[Dict[str, Parameter], Dict[str, Tensor]]: + """Setup model for pipeline parallel + + Args: + module (Module): Module to be setup + + Returns: + Tuple[Dict[str, Parameter], Dict[str, Tensor]]: Hold parameters and buffers + """ + hold_params = set() + hold_buffers = set() + + def init_layer(layer: Module): + for p in layer.parameters(): + if isinstance(p, LazyTensor): + p.materialize() + p.data = p.cuda() + hold_params.add(p) + for b in layer.buffers(): + if isinstance(b, LazyTensor): + b.materialize() + b.data = b.cuda() + hold_buffers.add(b) + + hold_layers = self.get_hold_layers(module) + + for layer in hold_layers: + init_layer(layer) + + hold_params_dict = {} + hold_buffers_dict = {} + + # release other tensors + for n, p in module.named_parameters(): + if p in hold_params: + hold_params_dict[n] = p + else: + if isinstance(p, LazyTensor): + p.materialize() + p.data = p.cuda() + p.storage().resize_(0) + for n, b in module.named_buffers(): + if b in hold_buffers: + hold_buffers_dict[n] = b + else: + if isinstance(b, LazyTensor): + b.materialize() + b.data = b.cuda() + # FIXME(ver217): use meta tensor may be better + b.storage().resize_(0) + return hold_params_dict, hold_buffers_dict + + def replace_forward(self, module: Module) -> None: + """Replace module forward in place. This method should be implemented by subclass. The output of internal layers must be a dict + + Args: + module (Module): _description_ + """ + raise NotImplementedError + + def get_hold_layers(self, module: Module) -> List[Module]: + """Get layers that should be hold in current stage. This method should be implemented by subclass. + + Args: + module (Module): Module to be setup + + Returns: + List[Module]: List of layers that should be hold in current stage + """ + raise NotImplementedError + + def get_shared_params(self, module: Module) -> List[Dict[int, Tensor]]: + """Get parameters that should be shared across stages. This method should be implemented by subclass. + + Args: + module (Module): Module to be setup + + Returns: + List[Module]: List of parameters that should be shared across stages. E.g. [{0: module.model.embed_tokens.weight, 3: module.lm_head.weight}] + """ + raise NotImplementedError + + def parallelize_model(self, module: Module) -> Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: + """Parallelize model for pipeline parallel + + Args: + module (Module): Module to be setup + + Returns: + Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: Hold parameters, buffers and shared parameters + """ + hold_params, hold_buffers = self.setup_model(module) + self.replace_forward(module) + shared_params = self.get_shared_params(module) + return hold_params, hold_buffers, shared_params diff --git a/colossalai/pipeline/policy/bert.py b/colossalai/pipeline/policy/bert.py new file mode 100644 index 000000000000..6f912d2c6b80 --- /dev/null +++ b/colossalai/pipeline/policy/bert.py @@ -0,0 +1,390 @@ +from functools import partial +from types import MethodType +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +from torch import Tensor +from torch.nn import CrossEntropyLoss, Module +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, +) +from transformers.models.bert.modeling_bert import BertForPreTraining, BertForPreTrainingOutput, BertModel +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager + +from .base import Policy + +logger = logging.get_logger(__name__) + + +def bert_model_forward( + self: BertModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + #labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, #this is from the previous stage +): + #TODO: add explaination of the output here. + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + # debugging + # preprocess: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if stage_manager.is_first_stage(): + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + attention_mask = extended_attention_mask + else: + input_shape = hidden_states.size()[:-1] + batch_size, seq_length = input_shape + device = hidden_states.device + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + hidden_states = hidden_states if hidden_states is not None else None + if stage_manager.is_first_stage(): + hidden_states = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + #inherit from bert_layer + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + if self.encoder.gradient_checkpointing and self.encoder.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + next_decoder_cache = () if use_cache else None + + #calculate the num_layers + num_layers_per_stage = len(self.encoder.layer) // stage_manager.num_stages + start_layer = stage_manager.stage * num_layers_per_stage + end_layer = (stage_manager.stage + 1) * num_layers_per_stage + + #layer_outputs + layer_outputs = hidden_states if hidden_states is not None else None + for idx, encoder_layer in enumerate(self.encoder.layer[start_layer:end_layer], start=start_layer): + if stage_manager.is_first_stage() and idx == 0: + encoder_attention_mask = encoder_extended_attention_mask + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[idx] if head_mask is not None else None + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.encoder.gradient_checkpointing and self.encoder.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(encoder_layer), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + #end of a stage loop + sequence_output = layer_outputs[0] if layer_outputs is not None else None + + if stage_manager.is_last_stage(): + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + if not return_dict: + return (sequence_output, pooled_output) + layer_outputs[1:] + + #output of non-first and non-last stages: + if not return_dict: + return tuple(v for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] if v is not None) + + #return dict is not supported at this moment + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# The layer partition policy for bertmodel +class BertModelPolicy(Policy): + + def __init__(self, stage_manager: PipelineStageManager, num_layers: int, num_stages: int): + self.stage_manager = stage_manager + self.layers_per_stage = self.distribute_layers(num_layers, num_stages) + + def get_hold_layers(self, module: BertModel) -> List[Module]: + """ + get pipeline layers for current stage + """ + hold_layers = [] + if self.stage_manager.is_first_stage(): + hold_layers.append(module.embeddings) + num_layers_per_stage_accumulated = np.cumsum(self.layers_per_stage) + hold_layers.extend(module.encoder.layer[num_layers_per_stage_accumulated \ + [self.stage_manager.stage-1] if self.stage_manager.stage > 0 else 0: + num_layers_per_stage_accumulated[self.stage_manager.stage]]) + + if self.stage_manager.is_last_stage(): + hold_layers.append(module.pooler) + + return hold_layers + + def get_shared_params(self, module: BertModel) -> List[Dict[int, Tensor]]: + '''no shared params in bertmodel''' + pass + + def replace_forward(self, module: Module) -> None: + module.model.forward = MethodType(partial(bert_model_forward, stage_manager=self.stage_manager), module.model) + + def distribute_layers(self, num_layers: int, num_stages: int) -> List[int]: + """ + divide layers into stages + """ + quotient = num_layers // num_stages + remainder = num_layers % num_stages + + # calculate the num_layers per stage + layers_per_stage = [quotient] * num_stages + + # deal with the rest layers + if remainder > 0: + start_position = num_layers // 2 - remainder // 2 + for i in range(start_position, start_position + remainder): + layers_per_stage[i] += 1 + return layers_per_stage + + +def bert_for_pretraining_forward( + self: BertForPreTraining, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + next_sentence_label: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.LongTensor] = None, + stage_manager: Optional[PipelineStageManager] = None, +) -> Union[Tuple[torch.Tensor], BertForPreTrainingOutput]: + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output, pooled_output = outputs[:2] + prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) + + total_loss = None + if labels is not None and next_sentence_label is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) + total_loss = masked_lm_loss + next_sentence_loss + + if not return_dict: + output = (prediction_scores, seq_relationship_score) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return BertForPreTrainingOutput( + loss=total_loss, + prediction_logits=prediction_scores, + seq_relationship_logits=seq_relationship_score, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class BertForPreTrainingPolicy(Policy): + + def __init__(self, stage_manager: PipelineStageManager, num_layers: int, num_stages: int): + self.stage_manager = stage_manager + self.layers_per_stage = self.distribute_layers(num_layers, num_stages) + + def get_hold_layers(self, module: BertForPreTraining) -> List[Module]: + """ + get pipeline layers for current stage + """ + hold_layers = [] + if self.stage_manager.is_first_stage(): + hold_layers.append(module.bert.embeddings) + num_layers_per_stage_accumulated = np.cumsum(self.layers_per_stage) + hold_layers.extend(module.bert.encoder.layer[num_layers_per_stage_accumulated \ + [self.stage_manager.stage-1] if self.stage_manager.stage > 0 else 0: + num_layers_per_stage_accumulated[self.stage_manager.stage]]) + if self.stage_manager.is_last_stage(): + hold_layers.append(module.cls) + + return hold_layers + + def get_shared_params(self, module: BertForPreTraining) -> List[Dict[int, Tensor]]: + '''no shared params in bertmodel''' + pass + + def replace_forward(self, module: Module) -> None: + module.model.forward = MethodType(partial(bert_for_pretraining_forward, stage_manager=self.stage_manager), + module.model) + + def distribute_layers(self, num_layers: int, num_stages: int) -> List[int]: + """ + divide layers into stages + """ + quotient = num_layers // num_stages + remainder = num_layers % num_stages + + # calculate the num_layers per stage + layers_per_stage = [quotient] * num_stages + + # deal with the rest layers + if remainder > 0: + start_position = num_layers // 2 - remainder // 2 + for i in range(start_position, start_position + remainder): + layers_per_stage[i] += 1 + return layers_per_stage diff --git a/colossalai/pipeline/policy/bloom.py b/colossalai/pipeline/policy/bloom.py new file mode 100644 index 000000000000..8dffcd8f9af5 --- /dev/null +++ b/colossalai/pipeline/policy/bloom.py @@ -0,0 +1,153 @@ +from functools import partial +from types import MethodType +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +from torch import Tensor +from torch.nn import CrossEntropyLoss, Module +from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions +from transformers.models.bloom.modeling_bloom import BloomModel +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager + +from .base import Policy + + +def bloom_model_forward( + self: BloomModel, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + **deprecated_arguments, +) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if past_key_values is None: + past_key_values = tuple([None] * len(self.h)) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape batch_size x num_heads x N x N + # head_mask has shape n_layer x batch x num_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + hidden_states = self.word_embeddings_layernorm(inputs_embeds) + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + # Compute alibi tensor: check build_alibi_tensor documentation + seq_length_with_past = seq_length + past_key_values_length = 0 + if past_key_values[0] is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) + else: + attention_mask = attention_mask.to(hidden_states.device) + + alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) + + causal_mask = self._prepare_attn_mask( + attention_mask, + input_shape=(batch_size, seq_length), + past_key_values_length=past_key_values_length, + ) + + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + alibi, + causal_mask, + layer_past, + head_mask[i], + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + attention_mask=causal_mask, + head_mask=head_mask[i], + use_cache=use_cache, + output_attentions=output_attentions, + alibi=alibi, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + + # Add last hidden state + hidden_states = self.ln_f(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) diff --git a/tests/test_pipeline/test_policy/test_bert_model.py b/tests/test_pipeline/test_policy/test_bert_model.py new file mode 100644 index 000000000000..c92f7f6c34c0 --- /dev/null +++ b/tests/test_pipeline/test_policy/test_bert_model.py @@ -0,0 +1,112 @@ +import pytest +import torch +import torch.distributed as dist +from transformers.models.bert.modeling_bert import BertModel + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.pipeline.policy.bert import BertModelPolicy, bert_model_forward +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +def check_bert_model_forward(): + model = BertModel.from_pretrained('bert-base-uncased') + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + #print(pg_mesh) + + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + # print(rank) + + x = torch.randint(0, 1000, (2, 3)) + hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x) + output = bert_model_forward(self=model, input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) + print(output[0].shape) + assert output[0].shape == (2, 3, 768) + print('start the training') + else: + attention_mask = torch.ones((2, 12, 3, 3)) + output = bert_model_forward(self=model, + hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager) + print(output[0].shape) + assert output[0].shape == (2, 3, 768) + print('end the training') + print(output) + + # assert output[1].shape == (2, 768) + + +def check_bert_model_policy(): + model = BertModel.from_pretrained('bert-base-uncased') + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + #print(pg_mesh) + + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + + model_policy = BertModelPolicy(stage_manager, len(model.encoder.layer), 2) + assert model_policy.layers_per_stage == [6, 6] + layers = model_policy.get_hold_layers(model) + for layer in layers: + print(layer) + + +def run_dist_model(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_bert_model_forward() + + +def run_dist_policy(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_bert_model_policy() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bert_model_forward(): + spawn(run_dist_model, 4) + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bert_model_policy(): + spawn(run_dist_policy, 4) + + +if __name__ == "__main__": + """test the bert model forward and bert model policy""" + test_bert_model_forward() + test_bert_model_policy() diff --git a/tests/test_pipeline/test_stage_manager.py b/tests/test_pipeline/test_stage_manager.py index be4591d58f74..67a2e90532e2 100644 --- a/tests/test_pipeline/test_stage_manager.py +++ b/tests/test_pipeline/test_stage_manager.py @@ -21,7 +21,7 @@ def check_stage_manager(): 1: [0, 1], 2: [2, 3], 3: [2, 3], - } + } pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() From 5c897ddb9433d034db937113839a805ec74e243e Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Tue, 27 Jun 2023 16:17:01 +0800 Subject: [PATCH 10/84] [pipeline] add stage manager (#4093) * [pipeline] add stage manager * [test] add pipeline stage manager test * [pipeline] add docstring for stage manager --- tests/test_pipeline/test_stage_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_pipeline/test_stage_manager.py b/tests/test_pipeline/test_stage_manager.py index 67a2e90532e2..be4591d58f74 100644 --- a/tests/test_pipeline/test_stage_manager.py +++ b/tests/test_pipeline/test_stage_manager.py @@ -21,7 +21,7 @@ def check_stage_manager(): 1: [0, 1], 2: [2, 3], 3: [2, 3], - } + } pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() From c552cefa93ac9f2ea95c0914f6ad485439fbf9c7 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Tue, 4 Jul 2023 13:46:16 +0800 Subject: [PATCH 11/84] [pipeline]add pipeline policy and bert forward (#4130) * add pipeline policy and bert forward to be done * add bertmodel pipeline forward and make tests * add Bert_Policy and test for policy * update formatting * update formatting * update the code * fix bugs * fix name confilt --- tests/test_pipeline/test_stage_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_pipeline/test_stage_manager.py b/tests/test_pipeline/test_stage_manager.py index be4591d58f74..67a2e90532e2 100644 --- a/tests/test_pipeline/test_stage_manager.py +++ b/tests/test_pipeline/test_stage_manager.py @@ -21,7 +21,7 @@ def check_stage_manager(): 1: [0, 1], 2: [2, 3], 3: [2, 3], - } + } pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() From 90a65ea682789e013c4a8b4682587a1ec5d43d3d Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Wed, 5 Jul 2023 10:52:53 +0800 Subject: [PATCH 12/84] [pipeline] build bloom model and policy , revise the base class of policy (#4161) * add pipeline policy and bert forward to be done * add bertmodel pipeline forward and make tests * add Bert_Policy and test for policy * update formatting * update formatting * update the code * fix bugs * fix name confilt * add bloom model and policy ,revise the base class of policy * revise * revision * add bert_for_pretraining --- colossalai/pipeline/policy/base.py | 37 +++++- colossalai/pipeline/policy/bert.py | 94 ++++++-------- colossalai/pipeline/policy/bloom.py | 111 ++++++++++++---- .../test_policy/test_bert_model.py | 5 +- .../test_policy/test_bloom_model.py | 119 ++++++++++++++++++ 5 files changed, 286 insertions(+), 80 deletions(-) create mode 100644 tests/test_pipeline/test_policy/test_bloom_model.py diff --git a/colossalai/pipeline/policy/base.py b/colossalai/pipeline/policy/base.py index ad595a04b1b0..9736f1004fe4 100644 --- a/colossalai/pipeline/policy/base.py +++ b/colossalai/pipeline/policy/base.py @@ -1,13 +1,15 @@ from typing import Any, Dict, List, Optional, Tuple -from colossalai.lazy import LazyTensor +import numpy as np from torch import Tensor from torch.nn import Module, Parameter +from colossalai.lazy import LazyTensor from colossalai.pipeline.stage_manager import PipelineStageManager class Policy: + def __init__(self, stage_manager: PipelineStageManager) -> None: self.stage_manager = stage_manager @@ -93,7 +95,8 @@ def get_shared_params(self, module: Module) -> List[Dict[int, Tensor]]: """ raise NotImplementedError - def parallelize_model(self, module: Module) -> Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: + def parallelize_model(self, + module: Module) -> Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: """Parallelize model for pipeline parallel Args: @@ -106,3 +109,33 @@ def parallelize_model(self, module: Module) -> Tuple[Dict[str, Parameter], Dict[ self.replace_forward(module) shared_params = self.get_shared_params(module) return hold_params, hold_buffers, shared_params + + @staticmethod + def distribute_layers(num_layers: int, num_stages: int) -> List[int]: + """ + divide layers into stages + """ + quotient = num_layers // num_stages + remainder = num_layers % num_stages + + # calculate the num_layers per stage + layers_per_stage = [quotient] * num_stages + + # deal with the rest layers + if remainder > 0: + start_position = num_layers // 2 - remainder // 2 + for i in range(start_position, start_position + remainder): + layers_per_stage[i] += 1 + return layers_per_stage + + @staticmethod + def get_stage_index(layers_per_stage: List[int], stage: int) -> List[int]: + """ + get the start index and end index of layers for each stage. + """ + num_layers_per_stage_accumulated = np.insert(np.cumsum(layers_per_stage), 0, 0) + + start_idx = num_layers_per_stage_accumulated[stage] + end_idx = num_layers_per_stage_accumulated[stage + 1] + + return [start_idx, end_idx] diff --git a/colossalai/pipeline/policy/bert.py b/colossalai/pipeline/policy/bert.py index 6f912d2c6b80..8cd0fadd167f 100644 --- a/colossalai/pipeline/policy/bert.py +++ b/colossalai/pipeline/policy/bert.py @@ -22,25 +22,26 @@ def bert_model_forward( - self: BertModel, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - #labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, #this is from the previous stage + self: BertModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + # labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + # this is from the previous stage + hidden_states: Optional[torch.FloatTensor] = None, ): - #TODO: add explaination of the output here. + # TODO: add explaination of the output here. r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if @@ -93,6 +94,7 @@ def bert_model_forward( batch_size, seq_length = input_shape device = hidden_states.device + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False @@ -144,7 +146,7 @@ def bert_model_forward( else: encoder_extended_attention_mask = None - #inherit from bert_layer + # inherit from bert_layer all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None @@ -156,12 +158,12 @@ def bert_model_forward( use_cache = False next_decoder_cache = () if use_cache else None - #calculate the num_layers + # calculate the num_layers num_layers_per_stage = len(self.encoder.layer) // stage_manager.num_stages start_layer = stage_manager.stage * num_layers_per_stage end_layer = (stage_manager.stage + 1) * num_layers_per_stage - #layer_outputs + # layer_outputs layer_outputs = hidden_states if hidden_states is not None else None for idx, encoder_layer in enumerate(self.encoder.layer[start_layer:end_layer], start=start_layer): if stage_manager.is_first_stage() and idx == 0: @@ -206,12 +208,13 @@ def custom_forward(*inputs): if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: - all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + all_cross_attentions = all_cross_attentions + \ + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) - #end of a stage loop + # end of a stage loop sequence_output = layer_outputs[0] if layer_outputs is not None else None if stage_manager.is_last_stage(): @@ -219,7 +222,7 @@ def custom_forward(*inputs): if not return_dict: return (sequence_output, pooled_output) + layer_outputs[1:] - #output of non-first and non-last stages: + # output of non-first and non-last stages: if not return_dict: return tuple(v for v in [ hidden_states, @@ -229,7 +232,7 @@ def custom_forward(*inputs): all_cross_attentions, ] if v is not None) - #return dict is not supported at this moment + # return dict is not supported at this moment return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, @@ -243,6 +246,7 @@ def custom_forward(*inputs): class BertModelPolicy(Policy): def __init__(self, stage_manager: PipelineStageManager, num_layers: int, num_stages: int): + super().__init__(stage_manager=stage_manager) self.stage_manager = stage_manager self.layers_per_stage = self.distribute_layers(num_layers, num_stages) @@ -253,11 +257,8 @@ def get_hold_layers(self, module: BertModel) -> List[Module]: hold_layers = [] if self.stage_manager.is_first_stage(): hold_layers.append(module.embeddings) - num_layers_per_stage_accumulated = np.cumsum(self.layers_per_stage) - hold_layers.extend(module.encoder.layer[num_layers_per_stage_accumulated \ - [self.stage_manager.stage-1] if self.stage_manager.stage > 0 else 0: - num_layers_per_stage_accumulated[self.stage_manager.stage]]) - + start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage) + hold_layers.extend(module.encoder.layer[start_idx:end_idx]) if self.stage_manager.is_last_stage(): hold_layers.append(module.pooler) @@ -270,23 +271,6 @@ def get_shared_params(self, module: BertModel) -> List[Dict[int, Tensor]]: def replace_forward(self, module: Module) -> None: module.model.forward = MethodType(partial(bert_model_forward, stage_manager=self.stage_manager), module.model) - def distribute_layers(self, num_layers: int, num_stages: int) -> List[int]: - """ - divide layers into stages - """ - quotient = num_layers // num_stages - remainder = num_layers % num_stages - - # calculate the num_layers per stage - layers_per_stage = [quotient] * num_stages - - # deal with the rest layers - if remainder > 0: - start_position = num_layers // 2 - remainder // 2 - for i in range(start_position, start_position + remainder): - layers_per_stage[i] += 1 - return layers_per_stage - def bert_for_pretraining_forward( self: BertForPreTraining, @@ -306,8 +290,8 @@ def bert_for_pretraining_forward( ) -> Union[Tuple[torch.Tensor], BertForPreTrainingOutput]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.bert( + outputs = bert_model_forward( + self.bert, input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, @@ -320,7 +304,8 @@ def bert_for_pretraining_forward( ) sequence_output, pooled_output = outputs[:2] - prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) + if stage_manager.is_last_stage(): + prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) total_loss = None if labels is not None and next_sentence_label is not None: @@ -355,11 +340,12 @@ def get_hold_layers(self, module: BertForPreTraining) -> List[Module]: hold_layers = [] if self.stage_manager.is_first_stage(): hold_layers.append(module.bert.embeddings) - num_layers_per_stage_accumulated = np.cumsum(self.layers_per_stage) - hold_layers.extend(module.bert.encoder.layer[num_layers_per_stage_accumulated \ - [self.stage_manager.stage-1] if self.stage_manager.stage > 0 else 0: - num_layers_per_stage_accumulated[self.stage_manager.stage]]) + + start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage) + hold_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if self.stage_manager.is_last_stage(): + hold_layers.append(module.bert.pooler) hold_layers.append(module.cls) return hold_layers diff --git a/colossalai/pipeline/policy/bloom.py b/colossalai/pipeline/policy/bloom.py index 8dffcd8f9af5..71d2913fc3aa 100644 --- a/colossalai/pipeline/policy/bloom.py +++ b/colossalai/pipeline/policy/bloom.py @@ -1,3 +1,4 @@ +import warnings from functools import partial from types import MethodType from typing import Dict, List, Optional, Tuple, Union @@ -14,6 +15,8 @@ from .base import Policy +logger = logging.get_logger(__name__) + def bloom_model_forward( self: BloomModel, @@ -26,6 +29,8 @@ def bloom_model_forward( output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, **deprecated_arguments, ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: if deprecated_arguments.pop("position_ids", False) is not False: @@ -44,28 +49,45 @@ def bloom_model_forward( use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - batch_size, seq_length = input_ids.shape - elif inputs_embeds is not None: - batch_size, seq_length, _ = inputs_embeds.shape - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - if past_key_values is None: - past_key_values = tuple([None] * len(self.h)) - + # add warnings here + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape batch_size x num_heads x N x N + # head_mask has shape n_layer x batch x num_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.n_layer) - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) + # case: First stage of training + if stage_manager.is_first_stage(): + # check input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) - hidden_states = self.word_embeddings_layernorm(inputs_embeds) + hidden_states = self.word_embeddings_layernorm(inputs_embeds) + # initialize in the first stage and then pass to the next stage + else: + input_shape = hidden_states.shape[:-1] + batch_size, seq_length = input_shape + + # extra recording tensor should be generated in the first stage presents = () if use_cache else None all_self_attentions = () if output_attentions else None @@ -77,11 +99,14 @@ def bloom_model_forward( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") use_cache = False - # Compute alibi tensor: check build_alibi_tensor documentation + if past_key_values is None: + past_key_values = tuple([None] * len(self.h)) + # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage seq_length_with_past = seq_length past_key_values_length = 0 if past_key_values[0] is not None: - past_key_values_length = past_key_values[0][0].shape[2] + past_key_values_length = past_key_values[0][0].shape[2] # source_len + seq_length_with_past = seq_length_with_past + past_key_values_length if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) @@ -90,13 +115,19 @@ def bloom_model_forward( alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) + # causal_mask is constructed every stage and its input is passed through different stages causal_mask = self._prepare_attn_mask( attention_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length, ) - for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + # calculate the num_layers + num_layers_per_stage = len(self.h) // stage_manager.num_stages + start_layer = stage_manager.stage * num_layers_per_stage + end_layer = (stage_manager.stage + 1) * num_layers_per_stage + + for i, (block, layer_past) in enumerate(zip(self.h[start_layer:end_layer], past_key_values[start_layer:end_layer])): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) @@ -130,24 +161,60 @@ def custom_forward(*inputs): ) hidden_states = outputs[0] + if use_cache is True: presents = presents + (outputs[1],) - if output_attentions: - all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + all_self_attentions = all_self_attentions + \ + (outputs[2 if use_cache else 1],) - # Add last hidden state - hidden_states = self.ln_f(hidden_states) + if stage_manager.is_last_stage(): + # Add last hidden state + hidden_states = self.ln_f(hidden_states) + # TODO: deal with all_hidden_states, all_self_attentions, presents if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + # attention_mask is not returned ; presents = past_key_values return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, ) + + +class BloomModelPolicy(Policy): + + def __init__(self, stage_manager: PipelineStageManager, num_layers: int, num_stages: int): + super().__init__(stage_manager=stage_manager) + self.stage_manager = stage_manager + self.layers_per_stage = self.distribute_layers(num_layers, num_stages) + + def get_hold_layers(self, module: BloomModel) -> List[Module]: + """ + get pipeline layers for current stage + """ + hold_layers = [] + if self.stage_manager.is_first_stage(): + hold_layers.append(module.word_embeddings) + hold_layers.append(module.word_embeddings_layernorm) + + start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage) + hold_layers.extend(module.h[start_idx:end_idx]) + + if self.stage_manager.is_last_stage(): + hold_layers.append(module.ln_f) + + return hold_layers + + def get_shared_params(self, module: BloomModel) -> List[Dict[int, Tensor]]: + '''no shared params in bloommodel''' + pass + + def replace_forward(self, module: Module) -> None: + module.forward = MethodType(partial(bloom_model_forward, stage_manager=self.stage_manager), module.model) diff --git a/tests/test_pipeline/test_policy/test_bert_model.py b/tests/test_pipeline/test_policy/test_bert_model.py index c92f7f6c34c0..cf5dc95feb8c 100644 --- a/tests/test_pipeline/test_policy/test_bert_model.py +++ b/tests/test_pipeline/test_policy/test_bert_model.py @@ -27,7 +27,8 @@ def check_bert_model_forward(): 3: [2, 3], } pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - #print(pg_mesh) + + # print(pg_mesh) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() @@ -72,7 +73,7 @@ def check_bert_model_policy(): 3: [2, 3], } pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - #print(pg_mesh) + # print(pg_mesh) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() diff --git a/tests/test_pipeline/test_policy/test_bloom_model.py b/tests/test_pipeline/test_policy/test_bloom_model.py new file mode 100644 index 000000000000..5ba92d734590 --- /dev/null +++ b/tests/test_pipeline/test_policy/test_bloom_model.py @@ -0,0 +1,119 @@ +import pytest +import torch +import torch.distributed as dist +from transformers.models.bloom import BloomConfig, BloomModel + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.pipeline.policy.bloom import BloomModelPolicy, bloom_model_forward +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +def check_bloom_model_forward(): + # create a BloomModel + configuration = BloomConfig() + model = BloomModel(configuration) + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + # print(pg_mesh) + + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + # print(rank) + + x = torch.randint(0, 1000, (2, 3)) + hidden_states = torch.randint(0, 1000, (2, 3, 64)).to(torch.float32) + if stage_manager.is_first_stage(): + attention_mask = torch.ones_like(x) + output = bloom_model_forward(self=model, + input_ids=x, + attention_mask=attention_mask, + stage_manager=stage_manager) + print(output[0].shape) + assert output[0].shape == (2, 3, 64) + print('start the training') + else: + attention_mask = torch.ones((2, 3)) + output = bloom_model_forward(self=model, + hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager) + print(output[0].shape) + assert output[0].shape == (2, 3, 64) + print('end the training') + print(output) + + # assert output[1].shape == (2, 768) + + +def check_bloom_model_policy(): + # create a BloomModel + configuration = BloomConfig() + model = BloomModel(configuration) + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + # print(pg_mesh) + + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + + model_policy = BloomModelPolicy(stage_manager=stage_manager, num_layers=len(model.h), num_stages=2) + assert model_policy.layers_per_stage == [1, 1] + layers = model_policy.get_hold_layers(model) + for layer in layers: + print(layer) + + +def run_dist_model(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_bloom_model_forward() + + +def run_dist_policy(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_bloom_model_policy() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bloom_model_forward(): + spawn(run_dist_model, 4) + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bloom_model_policy(): + spawn(run_dist_policy, 4) + + +if __name__ == "__main__": + """test the bloom model forward and bloom model policy""" + test_bloom_model_forward() + test_bloom_model_policy() From 59f6f573f15456d03621f9f6e73786f8cfa1645a Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 5 Jul 2023 14:16:55 +0800 Subject: [PATCH 13/84] [pipeline] update shardformer policy --- colossalai/shardformer/policies/basepolicy.py | 33 ++++++++++++++++--- colossalai/shardformer/shard/shard_config.py | 7 +++- colossalai/shardformer/shard/sharder.py | 29 +++++++++++++++- colossalai/shardformer/shard/shardformer.py | 4 +-- colossalai/shardformer/shard/utils.py | 19 +++++++++++ 5 files changed, 84 insertions(+), 8 deletions(-) create mode 100644 colossalai/shardformer/shard/utils.py diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/basepolicy.py index 2d347542fa7a..16f3fa14eca0 100644 --- a/colossalai/shardformer/policies/basepolicy.py +++ b/colossalai/shardformer/policies/basepolicy.py @@ -2,9 +2,13 @@ from abc import ABC, abstractmethod from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Type, Union +from typing import Any, Callable, Dict, List, Optional, Union import torch.nn as nn +from torch import Tensor +from torch.nn import Module + +from colossalai.pipeline.stage_manager import PipelineStageManager from ..shard.shard_config import ShardConfig @@ -71,9 +75,8 @@ class Policy(ABC): """ def __init__(self) -> None: - self.shard_config = None - self.model = None - self.shard_config = None + self.shard_config: Optional[ShardConfig] = None + self.model: Optional[Module] = None def set_model(self, model: nn.Module) -> None: r""" @@ -94,6 +97,12 @@ def set_shard_config(self, shard_config: ShardConfig) -> None: self.shard_config = shard_config self.config_sanity_check() + @property + def pipeline_stage_manager(self) -> Optional[PipelineStageManager]: + if self.shard_config is not None: + return self.shard_config.pipeline_stage_manager + return None + @abstractmethod def config_sanity_check(self): """ @@ -151,3 +160,19 @@ def append_or_create_submodule_replacement( policy[target_key] = ModulePolicyDescription(sub_module_replacement=description) return policy + + def get_held_layers(self) -> List[Module]: + """Get layers that should be held in current stage. This method should be implemented by subclass. + + Returns: + List[Module]: List of layers that should be hold in current stage + """ + raise NotImplementedError + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """Get parameters that should be shared across stages. This method should be implemented by subclass. + + Returns: + List[Dict[int, Tensor]]: List of parameters that should be shared across stages. E.g. [{0: module.model.embed_tokens.weight, 3: module.lm_head.weight}] + """ + return [] diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 83c08d275df3..fba2c27a2a87 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -1,8 +1,11 @@ from dataclasses import dataclass +from typing import Optional import torch.distributed as dist from torch.distributed import ProcessGroup +from colossalai.pipeline.stage_manager import PipelineStageManager + __all__ = ['ShardConfig'] @@ -13,11 +16,13 @@ class ShardConfig: Args: tensor_parallel_process_group (int): The process group for tensor parallelism, defaults to None, which is the global process group. + pipeline_stage_manager (PipelineStageManager): The pipeline stage manager, defaults to None, which means no pipeline. enable_tensor_parallelism (bool): Whether to turn on tensor parallelism, default is True. enable_fused_normalization (bool): Whether to use fused layernorm, default is False. enable_all_optimization (bool): Whether to turn on all optimization, default is False. """ - tensor_parallel_process_group: ProcessGroup = None + tensor_parallel_process_group: Optional[ProcessGroup] = None + pipeline_stage_manager: Optional[PipelineStageManager] = None enable_tensor_parallelism: bool = True enable_fused_normalization: bool = False enable_all_optimization: bool = False diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 201e0a08cbfe..429ca8ed74be 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,11 +1,15 @@ from typing import Any, Callable, Dict, List, Union import torch.nn as nn +from torch import Tensor + +from colossalai.lazy import LazyTensor from .._utils import getattr_, setattr_ from ..policies.autopolicy import get_autopolicy from ..policies.basepolicy import Policy, SubModuleReplacementDescription from .shard_config import ShardConfig +from .utils import set_tensors_to_none __all__ = ['ModelSharder', 'shard_model'] @@ -25,15 +29,18 @@ def __init__(self, model: nn.Module, policy: Policy, shard_config: ShardConfig = self.policy = get_autopolicy(self.model) if policy is None else policy self.shard_config = shard_config - def shard(self) -> None: + def shard(self) -> List[Dict[int, Tensor]]: r""" Shard the model according to the policy """ self.policy.set_model(self.model) self.policy.set_shard_config(self.shard_config) self._preprocess() + self._release_unheld_layers() self._replace_module() + self._materialize() self._postprocess() + return self.policy.get_shared_params() def _preprocess(self) -> None: self.model = self.policy.preprocess() @@ -172,3 +179,23 @@ def _replace_sub_module( ) setattr_(org_layer, suffix, replace_layer) + + def _release_unheld_layers(self) -> None: + r""" + Release the unheld layers in the model + """ + if self.shard_config and self.shard_config.pipeline_stage_manager: + held_layers = self.policy.get_held_layers() + set_tensors_to_none(self.model, exclude=set(held_layers)) + + def _materialize(self) -> None: + r""" + Materialize the model if lazy initialization is used + """ + for p in self.model.parameters(): + if isinstance(p, LazyTensor): + p.materialize() + + for b in self.model.buffers(): + if isinstance(b, LazyTensor): + b.materialize() diff --git a/colossalai/shardformer/shard/shardformer.py b/colossalai/shardformer/shard/shardformer.py index 3fce12463414..069a46ca57ea 100644 --- a/colossalai/shardformer/shard/shardformer.py +++ b/colossalai/shardformer/shard/shardformer.py @@ -42,5 +42,5 @@ def optimize(self, model: nn.Module, policy: Policy = None): policy (`Policy`): the custom policy for sharding """ sharder = ModelSharder(model=model, shard_config=self.shard_config, policy=policy) - sharder.shard() - return model + shared_params = sharder.shard() + return model, shared_params diff --git a/colossalai/shardformer/shard/utils.py b/colossalai/shardformer/shard/utils.py new file mode 100644 index 000000000000..2bac37bfedda --- /dev/null +++ b/colossalai/shardformer/shard/utils.py @@ -0,0 +1,19 @@ +from typing import Set + +import torch.nn as nn + + +def set_tensors_to_none(model: nn.Module, exclude: Set[nn.Module] = set()) -> None: + """Set all parameters and buffers of model to None + + Args: + model (nn.Module): The model to set + """ + if model in exclude: + return + for child in model.children(): + set_tensors_to_none(child, exclude=exclude) + for n, p in model.named_parameters(recurse=False): + setattr(model, n, None) + for n, buf in model.named_buffers(recurse=False): + setattr(model, n, None) From b0b8ad28237707deae64ac92e98aad17ac76e1b4 Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 5 Jul 2023 14:19:12 +0800 Subject: [PATCH 14/84] [pipeline] update shardformer docstring --- colossalai/shardformer/shard/shardformer.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/colossalai/shardformer/shard/shardformer.py b/colossalai/shardformer/shard/shardformer.py index 069a46ca57ea..6e0f90257df6 100644 --- a/colossalai/shardformer/shard/shardformer.py +++ b/colossalai/shardformer/shard/shardformer.py @@ -1,4 +1,7 @@ +from typing import Dict, List, Tuple + import torch.nn as nn +from torch import Tensor from colossalai.cluster import DistCoordinator @@ -24,7 +27,7 @@ class ShardFormer: org_model = BertForMaskedLM.from_pretrained('bert-base-uncased') shard_config = ShardConfig() shard_former = ShardFormer(shard_config=shard_config) - model = shard_former.optimize(org_model) + model, shared_params = shard_former.optimize(org_model) ``` """ @@ -32,7 +35,7 @@ def __init__(self, shard_config: ShardConfig): self.coordinator = DistCoordinator() self.shard_config = shard_config - def optimize(self, model: nn.Module, policy: Policy = None): + def optimize(self, model: nn.Module, policy: Policy = None) -> Tuple[nn.Module, List[Dict[int, Tensor]]]: r""" This method will optimize the model based on the given policy. @@ -40,6 +43,8 @@ def optimize(self, model: nn.Module, policy: Policy = None): model (`torch.nn.Model`): the origin huggingface model shard_config (`ShardConfig`): the config for distribute information policy (`Policy`): the custom policy for sharding + + Returns: the sharded model and the shared parameters """ sharder = ModelSharder(model=model, shard_config=self.shard_config, policy=policy) shared_params = sharder.shard() From 2d6cc07feb7e81dbe660e4921d071d8ffab841d9 Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 5 Jul 2023 14:30:17 +0800 Subject: [PATCH 15/84] [test] update shardformer tests --- tests/test_shardformer/test_model/_utils.py | 4 ++-- tests/test_shardformer/test_with_torch_ddp.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index d83d9ecd39e0..e03014f3f234 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -12,8 +12,8 @@ def build_model(model_fn, enable_fused_normalization=True, enable_tensor_paralle enable_tensor_parallelism=enable_tensor_parallelism) model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) - sharded_model = shard_former.optimize(model_copy).cuda() - return org_model, sharded_model + sharded_model, shared_params = shard_former.optimize(model_copy) + return org_model, sharded_model.cuda() def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): diff --git a/tests/test_shardformer/test_with_torch_ddp.py b/tests/test_shardformer/test_with_torch_ddp.py index 9f8a5db6c94f..f29c8d6f605b 100644 --- a/tests/test_shardformer/test_with_torch_ddp.py +++ b/tests/test_shardformer/test_with_torch_ddp.py @@ -44,7 +44,7 @@ def check_shardformer_with_ddp(rank, world_size, port): for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): # create and shard model model = model_fn().cuda() - sharded_model = shardformer.optimize(model) + sharded_model, _ = shardformer.optimize(model) # add ddp sharded_ddp_model = DDP(sharded_model, process_group=dp_process_group) From 5fc60a3a04b9f445e1f43d0247987439989ec8a5 Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 5 Jul 2023 14:49:05 +0800 Subject: [PATCH 16/84] [test] add shard util tests --- tests/test_shardformer/test_shard_utils.py | 27 ++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 tests/test_shardformer/test_shard_utils.py diff --git a/tests/test_shardformer/test_shard_utils.py b/tests/test_shardformer/test_shard_utils.py new file mode 100644 index 000000000000..220b8291c9c6 --- /dev/null +++ b/tests/test_shardformer/test_shard_utils.py @@ -0,0 +1,27 @@ +import torch +import torch.nn as nn + +from colossalai.shardformer.shard.utils import set_tensors_to_none + + +class Net(nn.Module): + + def __init__(self) -> None: + super().__init__() + self.layers = nn.Sequential(nn.Linear(1, 2), nn.Linear(2, 3)) + self.out = nn.Linear(3, 1) + + +def test_release_layer(): + orig_cuda_allocated = torch.cuda.memory_allocated() + model = Net().cuda() + set_tensors_to_none(model, exclude={model.layers[0]}) + assert model.layers[1].weight is None + assert model.layers[1].bias is None + assert model.out.weight is None + assert model.out.bias is None + set_tensors_to_none(model) + assert model.layers[0].weight is None + assert model.layers[0].bias is None + assert len(list(model.parameters())) == 0 + assert torch.cuda.memory_allocated() == orig_cuda_allocated From 1ed3f8a24f6e16262ce5835457360bc37c4ebdb2 Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 5 Jul 2023 15:13:00 +0800 Subject: [PATCH 17/84] [shardformer] rename policy file name --- .../shardformer/policies/{autopolicy.py => auto_policy.py} | 2 +- .../shardformer/policies/{basepolicy.py => base_policy.py} | 0 colossalai/shardformer/policies/bert.py | 2 +- colossalai/shardformer/policies/bloom.py | 2 +- colossalai/shardformer/policies/gpt2.py | 2 +- colossalai/shardformer/policies/llama.py | 2 +- colossalai/shardformer/policies/opt.py | 2 +- colossalai/shardformer/policies/t5.py | 4 ++-- colossalai/shardformer/policies/vit.py | 2 +- colossalai/shardformer/shard/sharder.py | 4 ++-- colossalai/shardformer/shard/shardformer.py | 2 +- 11 files changed, 12 insertions(+), 12 deletions(-) rename colossalai/shardformer/policies/{autopolicy.py => auto_policy.py} (99%) rename colossalai/shardformer/policies/{basepolicy.py => base_policy.py} (100%) diff --git a/colossalai/shardformer/policies/autopolicy.py b/colossalai/shardformer/policies/auto_policy.py similarity index 99% rename from colossalai/shardformer/policies/autopolicy.py rename to colossalai/shardformer/policies/auto_policy.py index 085e3150c697..8e961a240758 100644 --- a/colossalai/shardformer/policies/autopolicy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -3,7 +3,7 @@ import torch.nn as nn -from .basepolicy import Policy +from .base_policy import Policy __all__ = ["PolicyLocation", "get_autopolicy", "import_policy"] diff --git a/colossalai/shardformer/policies/basepolicy.py b/colossalai/shardformer/policies/base_policy.py similarity index 100% rename from colossalai/shardformer/policies/basepolicy.py rename to colossalai/shardformer/policies/base_policy.py diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 9c2736cc64d3..b69ee72097c4 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -3,7 +3,7 @@ import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ 'BertPolicy', 'BertModelPolicy', 'BertForPretrainingPolicy', 'BertLMHeadModelPolicy', 'BertForMaskedLMPolicy', diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index a0b5340f72bc..8d6f07d4a67d 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -4,7 +4,7 @@ from .._utils import getattr_, setattr_ from ..modeling.bloom import build_bloom_alibi_tensor_fn -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription class BloomPolicy(Policy): diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 549cdbf87a80..598f393c029a 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -3,7 +3,7 @@ import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ 'GPT2Policy', 'GPT2ModelPolicy', 'GPT2LMHeadModelPolicy', 'GPT2DoubleHeadsModelPolicy', diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index 157785bdcf13..391938b27167 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -4,7 +4,7 @@ from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy'] diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index b87db53f45f1..c4c6cde015b6 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -1,7 +1,7 @@ from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D from .._utils import getattr_, setattr_ -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ 'OPTPolicy', 'OPTModelPolicy', 'OPTForCausalLMPolicy', 'OPTForSequenceClassificationPolicy', diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index cde59ab77042..6167e81613f2 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -6,10 +6,10 @@ Linear1D_Row, VocabParallelEmbedding1D, ) -from colossalai.shardformer.policies.basepolicy import ModulePolicyDescription +from colossalai.shardformer.policies.base_policy import ModulePolicyDescription from .._utils import getattr_, setattr_ -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"] diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index eaebe2eee0ba..3f6bbd10607a 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -4,7 +4,7 @@ from colossalai.shardformer.layer import DropoutForReplicatedInput, FusedLayerNorm, Linear1D_Col, Linear1D_Row -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['ViTPolicy'] diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 429ca8ed74be..ca2f46a187d1 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -6,8 +6,8 @@ from colossalai.lazy import LazyTensor from .._utils import getattr_, setattr_ -from ..policies.autopolicy import get_autopolicy -from ..policies.basepolicy import Policy, SubModuleReplacementDescription +from ..policies.auto_policy import get_autopolicy +from ..policies.base_policy import Policy, SubModuleReplacementDescription from .shard_config import ShardConfig from .utils import set_tensors_to_none diff --git a/colossalai/shardformer/shard/shardformer.py b/colossalai/shardformer/shard/shardformer.py index 6e0f90257df6..7a0d75bf2f2a 100644 --- a/colossalai/shardformer/shard/shardformer.py +++ b/colossalai/shardformer/shard/shardformer.py @@ -5,7 +5,7 @@ from colossalai.cluster import DistCoordinator -from ..policies.basepolicy import Policy +from ..policies.base_policy import Policy from .shard_config import ShardConfig from .sharder import ModelSharder From d35bd7d0e64f10161ab4f6abc8776f14d19bba38 Mon Sep 17 00:00:00 2001 From: ver217 Date: Wed, 5 Jul 2023 15:20:59 +0800 Subject: [PATCH 18/84] [shardformer] fix type hint --- colossalai/shardformer/shard/shard_config.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index fba2c27a2a87..75fad4eb7431 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -15,8 +15,8 @@ class ShardConfig: The config for sharding the huggingface model Args: - tensor_parallel_process_group (int): The process group for tensor parallelism, defaults to None, which is the global process group. - pipeline_stage_manager (PipelineStageManager): The pipeline stage manager, defaults to None, which means no pipeline. + tensor_parallel_process_group (Optional[ProcessGroup]): The process group for tensor parallelism, defaults to None, which is the global process group. + pipeline_stage_manager (Optional[PipelineStageManager]): The pipeline stage manager, defaults to None, which means no pipeline. enable_tensor_parallelism (bool): Whether to turn on tensor parallelism, default is True. enable_fused_normalization (bool): Whether to use fused layernorm, default is False. enable_all_optimization (bool): Whether to turn on all optimization, default is False. From c5ea72801653f1c4d483eb3fa5b5aa7937d63762 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Thu, 6 Jul 2023 14:49:10 +0800 Subject: [PATCH 19/84] [pipeline] add bert_for_pretraining bert_lmhead forward and policy (#4172) * add pipeline policy and bert forward to be done * add bertmodel pipeline forward and make tests * add Bert_Policy and test for policy * update formatting * update formatting * update the code * fix bugs * fix name confilt * add bloom model and policy ,revise the base class of policy * revise * revision * add bert_for_pretraining * add bert_for_pretraining forward and policy * fix typos * cancel warning * change the imediate output to default dict * change the default output of get_shared_params --- colossalai/pipeline/policy/bert.py | 367 ++++++++++++------ .../test_bert_for_pretraining_model.py | 118 ++++++ .../test_policy/test_bert_lmhead_model.py | 118 ++++++ .../test_policy/test_bert_model.py | 8 +- 4 files changed, 497 insertions(+), 114 deletions(-) create mode 100644 tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py create mode 100644 tests/test_pipeline/test_policy/test_bert_lmhead_model.py diff --git a/colossalai/pipeline/policy/bert.py b/colossalai/pipeline/policy/bert.py index 8cd0fadd167f..abce504e9d61 100644 --- a/colossalai/pipeline/policy/bert.py +++ b/colossalai/pipeline/policy/bert.py @@ -10,9 +10,15 @@ BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, ) -from transformers.models.bert.modeling_bert import BertForPreTraining, BertForPreTrainingOutput, BertModel -from transformers.utils import logging +from transformers.models.bert.modeling_bert import ( + BertForPreTraining, + BertForPreTrainingOutput, + BertLMHeadModel, + BertModel, +) +from transformers.utils import ModelOutput, logging from colossalai.pipeline.stage_manager import PipelineStageManager @@ -22,24 +28,23 @@ def bert_model_forward( - self: BertModel, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, + self: BertModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, # labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - # this is from the previous stage - hidden_states: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage ): # TODO: add explaination of the output here. r""" @@ -85,10 +90,6 @@ def bert_model_forward( raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) - attention_mask = extended_attention_mask else: input_shape = hidden_states.size()[:-1] batch_size, seq_length = input_shape @@ -119,14 +120,29 @@ def bert_model_forward( else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + attention_mask = extended_attention_mask + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - hidden_states = hidden_states if hidden_states is not None else None + if stage_manager.is_first_stage(): hidden_states = self.embeddings( input_ids=input_ids, @@ -135,18 +151,8 @@ def bert_model_forward( inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if self.config.is_decoder and encoder_hidden_states is not None: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = None - # inherit from bert_layer + # inherit from bert_layer,this should be changed when we add the feature to record hidden_states all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None @@ -221,34 +227,35 @@ def custom_forward(*inputs): pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + layer_outputs[1:] + # return dict is not supported at this moment + else: + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) - # output of non-first and non-last stages: - if not return_dict: - return tuple(v for v in [ - hidden_states, - next_decoder_cache, - all_hidden_states, - all_self_attentions, - all_cross_attentions, - ] if v is not None) - - # return dict is not supported at this moment - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) + # output of non-first and non-last stages: must be a dict + else: + # intermediate stage always return dict + return { + 'hidden_states': hidden_states, + } # The layer partition policy for bertmodel class BertModelPolicy(Policy): - def __init__(self, stage_manager: PipelineStageManager, num_layers: int, num_stages: int): + def __init__( + self, + stage_manager: PipelineStageManager, + num_layers: int, + ): super().__init__(stage_manager=stage_manager) self.stage_manager = stage_manager - self.layers_per_stage = self.distribute_layers(num_layers, num_stages) + self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages) def get_hold_layers(self, module: BertModel) -> List[Module]: """ @@ -266,10 +273,10 @@ def get_hold_layers(self, module: BertModel) -> List[Module]: def get_shared_params(self, module: BertModel) -> List[Dict[int, Tensor]]: '''no shared params in bertmodel''' - pass + return [] def replace_forward(self, module: Module) -> None: - module.model.forward = MethodType(partial(bert_model_forward, stage_manager=self.stage_manager), module.model) + module.forward = MethodType(partial(bert_model_forward, stage_manager=self.stage_manager), module) def bert_for_pretraining_forward( @@ -285,53 +292,74 @@ def bert_for_pretraining_forward( output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, - hidden_states: Optional[torch.LongTensor] = None, + hidden_states: Optional[torch.FloatTensor] = None, stage_manager: Optional[PipelineStageManager] = None, -) -> Union[Tuple[torch.Tensor], BertForPreTrainingOutput]: - +): return_dict = return_dict if return_dict is not None else self.config.use_return_dict - outputs = bert_model_forward( - self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output, pooled_output = outputs[:2] + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = bert_model_forward(self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states if hidden_states is not None else None) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None if stage_manager.is_last_stage(): + sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) + # the last stage for pretraining model + total_loss = None + if labels is not None and next_sentence_label is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) + total_loss = masked_lm_loss + next_sentence_loss - total_loss = None - if labels is not None and next_sentence_label is not None: - loss_fct = CrossEntropyLoss() - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) - total_loss = masked_lm_loss + next_sentence_loss - - if not return_dict: - output = (prediction_scores, seq_relationship_score) + outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output + if not return_dict: + output = (prediction_scores, seq_relationship_score) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return BertForPreTrainingOutput( + loss=total_loss, + prediction_logits=prediction_scores, + seq_relationship_logits=seq_relationship_score, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') - return BertForPreTrainingOutput( - loss=total_loss, - prediction_logits=prediction_scores, - seq_relationship_logits=seq_relationship_score, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) + # intermediate stage always return dict + return { + 'hidden_states': hidden_states, + } class BertForPreTrainingPolicy(Policy): - def __init__(self, stage_manager: PipelineStageManager, num_layers: int, num_stages: int): + def __init__(self, stage_manager: PipelineStageManager, num_layers: int): + super().__init__(stage_manager=stage_manager) self.stage_manager = stage_manager - self.layers_per_stage = self.distribute_layers(num_layers, num_stages) + self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages) def get_hold_layers(self, module: BertForPreTraining) -> List[Module]: """ @@ -352,25 +380,144 @@ def get_hold_layers(self, module: BertForPreTraining) -> List[Module]: def get_shared_params(self, module: BertForPreTraining) -> List[Dict[int, Tensor]]: '''no shared params in bertmodel''' - pass + return [] def replace_forward(self, module: Module) -> None: - module.model.forward = MethodType(partial(bert_for_pretraining_forward, stage_manager=self.stage_manager), - module.model) + module.forward = MethodType(partial(bert_for_pretraining_forward, stage_manager=self.stage_manager), + module.forward) + + +def bert_lmhead_forward(self: BertLMHeadModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.Tensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_manager: Optional[PipelineStageManager] = None): + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if labels is not None: + use_cache = False + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = bert_model_forward(self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states if hidden_states is not None else None) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None - def distribute_layers(self, num_layers: int, num_stages: int) -> List[int]: + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss() + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + # intermediate stage always return dict + return {'hidden_states': hidden_states} + + +class BertLMHeadModelPolicy(Policy): + + def __init__(self, stage_manager: PipelineStageManager, num_layers: int): + super().__init__(stage_manager=stage_manager) + self.stage_manager = stage_manager + self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages) + + def get_hold_layers(self, module: BertLMHeadModel) -> List[Module]: """ - divide layers into stages + get pipeline layers for current stage """ - quotient = num_layers // num_stages - remainder = num_layers % num_stages - - # calculate the num_layers per stage - layers_per_stage = [quotient] * num_stages - - # deal with the rest layers - if remainder > 0: - start_position = num_layers // 2 - remainder // 2 - for i in range(start_position, start_position + remainder): - layers_per_stage[i] += 1 - return layers_per_stage + hold_layers = [] + if self.stage_manager.is_first_stage(): + hold_layers.append(module.bert.embeddings) + start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage) + hold_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if self.stage_manager.is_last_stage(): + hold_layers.append(module.bert.pooler) + hold_layers.append(module.cls) + + return hold_layers + + def get_shared_params(self, module: BertLMHeadModel) -> List[Dict[int, Tensor]]: + '''no shared params in bertmodel''' + return [] + + def replace_forward(self, module: Module) -> None: + module.forward = MethodType(partial(bert_lmhead_forward, stage_manager=self.stage_manager), module) diff --git a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py new file mode 100644 index 000000000000..afbea49c1829 --- /dev/null +++ b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py @@ -0,0 +1,118 @@ +import pytest +import torch +import torch.distributed as dist +from transformers.models.bert import BertConfig +from transformers.models.bert.modeling_bert import BertForPreTraining + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.pipeline.policy.bert import BertForPreTrainingPolicy, bert_for_pretraining_forward +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +def check_bert_for_pretraining_forward(): + configuration = BertConfig() + model = BertForPreTraining(configuration) + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + # print(pg_mesh) + + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + # print(rank) + + x = torch.randint(0, 1000, (2, 3)) + hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x) + output = bert_for_pretraining_forward(self=model, + input_ids=x, + attention_mask=attention_mask, + stage_manager=stage_manager) + print(output['hidden_states'].shape) + assert output['hidden_states'].shape == (2, 3, 768) + print('start the training') + else: + attention_mask = torch.ones((2, 3)) + output = bert_for_pretraining_forward(self=model, + hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager) + print(output[0].shape) + assert output[0].shape == (2, 3, 30522) + print('end the training') + print(output) + + # assert output[1].shape == (2, 768) + + +def check_bert_for_pretraining_policy(): + configuration = BertConfig() + model = BertForPreTraining(configuration) + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + # print(pg_mesh) + + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + + model_policy = BertForPreTrainingPolicy(stage_manager, len(model.bert.encoder.layer)) + assert model_policy.layers_per_stage == [6, 6] + layers = model_policy.get_hold_layers(model) + for layer in layers: + print(layer) + + +def run_dist_model(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_bert_for_pretraining_forward() + + +def run_dist_policy(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_bert_for_pretraining_policy() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bert_for_pretraining_forward(): + spawn(run_dist_model, 4) + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bert_for_pretraining_policy(): + spawn(run_dist_policy, 4) + + +if __name__ == "__main__": + """test the bert for pretraining model forward and bert for pretraining model policy""" + test_bert_for_pretraining_forward() + test_bert_for_pretraining_policy() diff --git a/tests/test_pipeline/test_policy/test_bert_lmhead_model.py b/tests/test_pipeline/test_policy/test_bert_lmhead_model.py new file mode 100644 index 000000000000..d41eddc74dff --- /dev/null +++ b/tests/test_pipeline/test_policy/test_bert_lmhead_model.py @@ -0,0 +1,118 @@ +import pytest +import torch +import torch.distributed as dist +from transformers.models.bert import BertConfig +from transformers.models.bert.modeling_bert import BertLMHeadModel + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.pipeline.policy.bert import BertLMHeadModelPolicy, bert_lmhead_forward +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +def check_bert_lmhead_forward(): + configuration = BertConfig() + model = BertLMHeadModel(configuration) + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + # print(pg_mesh) + + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + # print(rank) + + x = torch.randint(0, 1000, (2, 3)) + hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x) + output = bert_lmhead_forward(self=model, + input_ids=x, + attention_mask=attention_mask, + stage_manager=stage_manager) + print(output['hidden_states'].shape) + assert output['hidden_states'].shape == (2, 3, 768) + print('start the training') + else: + attention_mask = torch.ones((2, 3)) + output = bert_lmhead_forward(self=model, + hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager) + print(output[0].shape) + assert output[0].shape == (2, 3, 30522) + print('end the training') + print(output) + + # assert output[1].shape == (2, 768) + + +def check_bert_lmhead_policy(): + configuration = BertConfig() + model = BertLMHeadModel(configuration) + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + # print(pg_mesh) + + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + rank = dist.get_rank() + + model_policy = BertLMHeadModelPolicy(stage_manager, len(model.bert.encoder.layer)) + assert model_policy.layers_per_stage == [6, 6] + layers = model_policy.get_hold_layers(model) + for layer in layers: + print(layer) + + +def run_dist_model(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_bert_lmhead_forward() + + +def run_dist_policy(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') + check_bert_lmhead_policy() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bert_lmhead_forward(): + spawn(run_dist_model, 4) + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_bert_lmhead_policy(): + spawn(run_dist_policy, 4) + + +if __name__ == "__main__": + """test the bert for pretraining model forward and bert for pretraining model policy""" + test_bert_lmhead_forward() + test_bert_lmhead_policy() diff --git a/tests/test_pipeline/test_policy/test_bert_model.py b/tests/test_pipeline/test_policy/test_bert_model.py index cf5dc95feb8c..92485072a5e4 100644 --- a/tests/test_pipeline/test_policy/test_bert_model.py +++ b/tests/test_pipeline/test_policy/test_bert_model.py @@ -39,11 +39,11 @@ def check_bert_model_forward(): if stage_manager.stage == 0: attention_mask = torch.ones_like(x) output = bert_model_forward(self=model, input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) - print(output[0].shape) - assert output[0].shape == (2, 3, 768) + print(output['hidden_states'].shape) + assert output['hidden_states'].shape == (2, 3, 768) print('start the training') else: - attention_mask = torch.ones((2, 12, 3, 3)) + attention_mask = torch.ones((2, 3)) output = bert_model_forward(self=model, hidden_states=hidden_states, attention_mask=attention_mask, @@ -78,7 +78,7 @@ def check_bert_model_policy(): stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() - model_policy = BertModelPolicy(stage_manager, len(model.encoder.layer), 2) + model_policy = BertModelPolicy(stage_manager, len(model.encoder.layer)) assert model_policy.layers_per_stage == [6, 6] layers = model_policy.get_hold_layers(model) for layer in layers: From f3bcc292c8bc9a9eeec9310c0b0640817129ab3a Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Fri, 7 Jul 2023 15:41:00 +0800 Subject: [PATCH 20/84] [pipeline] move bert related pipeline components to shardformer (#4187) * move bert related pipeline components to shardformer * fix bugs * revision * fix bert model tests * fix bert_lm_head model tests * fix tests * fix tests * done checks * skip bloom --- colossalai/pipeline/policy/base.py | 30 -- .../shardformer/policies/auto_policy.py | 2 +- .../shardformer/policies/base_policy.py | 31 ++ colossalai/shardformer/policies/bert.py | 487 +++++++++++++++++- .../test_bert_for_pretraining_model.py | 20 +- .../test_policy/test_bert_lmhead_model.py | 19 +- .../test_policy/test_bert_model.py | 22 +- .../test_policy/test_bloom_model.py | 8 +- .../test_layer/test_layernorm.py | 2 +- 9 files changed, 556 insertions(+), 65 deletions(-) diff --git a/colossalai/pipeline/policy/base.py b/colossalai/pipeline/policy/base.py index 9736f1004fe4..f51d74fdbac3 100644 --- a/colossalai/pipeline/policy/base.py +++ b/colossalai/pipeline/policy/base.py @@ -109,33 +109,3 @@ def parallelize_model(self, self.replace_forward(module) shared_params = self.get_shared_params(module) return hold_params, hold_buffers, shared_params - - @staticmethod - def distribute_layers(num_layers: int, num_stages: int) -> List[int]: - """ - divide layers into stages - """ - quotient = num_layers // num_stages - remainder = num_layers % num_stages - - # calculate the num_layers per stage - layers_per_stage = [quotient] * num_stages - - # deal with the rest layers - if remainder > 0: - start_position = num_layers // 2 - remainder // 2 - for i in range(start_position, start_position + remainder): - layers_per_stage[i] += 1 - return layers_per_stage - - @staticmethod - def get_stage_index(layers_per_stage: List[int], stage: int) -> List[int]: - """ - get the start index and end index of layers for each stage. - """ - num_layers_per_stage_accumulated = np.insert(np.cumsum(layers_per_stage), 0, 0) - - start_idx = num_layers_per_stage_accumulated[stage] - end_idx = num_layers_per_stage_accumulated[stage + 1] - - return [start_idx, end_idx] diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index 8e961a240758..640b61b579bd 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -29,7 +29,7 @@ class PolicyLocation: "transformers.models.bert.modeling_bert.BertModel": PolicyLocation(file_name="bert", class_name="BertModelPolicy"), "transformers.models.bert.modeling_bert.BertForPreTraining": - PolicyLocation(file_name="bert", class_name="BertForPretrainingPolicy"), + PolicyLocation(file_name="bert", class_name="BertForPreTrainingPolicy"), "transformers.models.bert.modeling_bert.BertLMHeadModel": PolicyLocation(file_name="bert", class_name="BertLMHeadModelPolicy"), "transformers.models.bert.modeling_bert.BertForMaskedLM": diff --git a/colossalai/shardformer/policies/base_policy.py b/colossalai/shardformer/policies/base_policy.py index 16f3fa14eca0..65aee13861ee 100644 --- a/colossalai/shardformer/policies/base_policy.py +++ b/colossalai/shardformer/policies/base_policy.py @@ -4,6 +4,7 @@ from dataclasses import dataclass from typing import Any, Callable, Dict, List, Optional, Union +import numpy as np import torch.nn as nn from torch import Tensor from torch.nn import Module @@ -176,3 +177,33 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: List[Dict[int, Tensor]]: List of parameters that should be shared across stages. E.g. [{0: module.model.embed_tokens.weight, 3: module.lm_head.weight}] """ return [] + + @staticmethod + def distribute_layers(num_layers: int, num_stages: int) -> List[int]: + """Divide layers into stages + + """ + quotient = num_layers // num_stages + remainder = num_layers % num_stages + + # calculate the num_layers per stage + layers_per_stage = [quotient] * num_stages + + # deal with the rest layers + if remainder > 0: + start_position = num_layers // 2 - remainder // 2 + for i in range(start_position, start_position + remainder): + layers_per_stage[i] += 1 + return layers_per_stage + + @staticmethod + def get_stage_index(layers_per_stage: List[int], stage: int) -> List[int]: + """ + get the start index and end index of layers for each stage. + """ + num_layers_per_stage_accumulated = np.insert(np.cumsum(layers_per_stage), 0, 0) + + start_idx = num_layers_per_stage_accumulated[stage] + end_idx = num_layers_per_stage_accumulated[stage + 1] + + return [start_idx, end_idx] diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index b69ee72097c4..e18cb6ece674 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,12 +1,35 @@ +from functools import partial +from types import MethodType +from typing import Dict, List, Optional, Tuple, Union + +import torch import torch.nn as nn +from torch import Tensor +from torch.nn import CrossEntropyLoss, Module +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, +) +from transformers.models.bert.modeling_bert import ( + BertForPreTraining, + BertForPreTrainingOutput, + BertLMHeadModel, + BertModel, +) +from transformers.utils import ModelOutput, logging import colossalai.shardformer.layer as col_nn +from colossalai.pipeline.stage_manager import PipelineStageManager from .._utils import getattr_, setattr_ from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +logger = logging.get_logger(__name__) + __all__ = [ - 'BertPolicy', 'BertModelPolicy', 'BertForPretrainingPolicy', 'BertLMHeadModelPolicy', 'BertForMaskedLMPolicy', + 'BertPolicy', 'BertModelPolicy', 'BertForPreTrainingPolicy', 'BertLMHeadModelPolicy', 'BertForMaskedLMPolicy', 'BertForNextSentencePredictionPolicy', 'BertForSequenceClassificationPolicy', 'BertForTokenClassificationPolicy', 'BertForMultipleChoicePolicy' ] @@ -153,9 +176,27 @@ class BertModelPolicy(BertPolicy): def __init__(self) -> None: super().__init__() + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(self.model.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.pooler) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in bert model""" + return [] + # BertForPreTraining -class BertForPretrainingPolicy(BertPolicy): +class BertForPreTrainingPolicy(BertPolicy): def __init__(self) -> None: super().__init__() @@ -165,6 +206,28 @@ def module_policy(self): module_policy = self.add_lm_head_policy(module_policy) return module_policy + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage""" + module = self.model + stage_manager = self.pipeline_stage_manager + layers_per_stage = self.distribute_layers(len(self.model.bert.encoder.layer), stage_manager.num_stages) + held_layers = [] + if stage_manager.is_first_stage(): + held_layers.append(module.bert.embeddings) + + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + + if stage_manager.is_last_stage(): + held_layers.append(module.bert.pooler) + held_layers.append(module.cls) + + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + '''No shared params in bertmodel''' + return [] + def postprocess(self): binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): @@ -184,6 +247,27 @@ def module_policy(self): module_policy = self.add_lm_head_policy(module_policy) return module_policy + def get_held_layers(self) -> List[Module]: + """ + get pipeline layers for current stage + """ + module = self.model + held_layers = [] + stage_manager = self.pipeline_stage_manager + layers_per_stage = self.distribute_layers(len(self.model.bert.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.bert.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.bert.pooler) + held_layers.append(module.cls) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + '''No shared params in bertmodel''' + return [] + def postprocess(self): binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): @@ -291,3 +375,402 @@ def module_policy(self): } module_policy.update(addon_module) return module_policy + + +def bert_model_forward( + self: BertModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + # labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage +): + # TODO: add explaination of the output here. + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + # debugging + # preprocess: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if stage_manager.is_first_stage(): + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + else: + input_shape = hidden_states.size()[:-1] + batch_size, seq_length = input_shape + device = hidden_states.device + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + attention_mask = extended_attention_mask + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + hidden_states = hidden_states if hidden_states is not None else None + + if stage_manager.is_first_stage(): + hidden_states = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + + # inherit from bert_layer,this should be changed when we add the feature to record hidden_states + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + if self.encoder.gradient_checkpointing and self.encoder.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + next_decoder_cache = () if use_cache else None + + # calculate the num_layers + num_layers_per_stage = len(self.encoder.layer) // stage_manager.num_stages + start_layer = stage_manager.stage * num_layers_per_stage + end_layer = (stage_manager.stage + 1) * num_layers_per_stage + + # layer_outputs + layer_outputs = hidden_states if hidden_states is not None else None + for idx, encoder_layer in enumerate(self.encoder.layer[start_layer:end_layer], start=start_layer): + if stage_manager.is_first_stage() and idx == 0: + encoder_attention_mask = encoder_extended_attention_mask + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[idx] if head_mask is not None else None + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.encoder.gradient_checkpointing and self.encoder.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(encoder_layer), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + \ + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # end of a stage loop + sequence_output = layer_outputs[0] if layer_outputs is not None else None + + if stage_manager.is_last_stage(): + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + if not return_dict: + return (sequence_output, pooled_output) + layer_outputs[1:] + # return dict is not supported at this moment + else: + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + # output of non-first and non-last stages: must be a dict + else: + # intermediate stage always return dict + return { + 'hidden_states': hidden_states, + } + + +def bert_for_pretraining_forward( + self: BertForPreTraining, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + next_sentence_label: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_manager: Optional[PipelineStageManager] = None, +): + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = bert_model_forward(self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states if hidden_states is not None else None) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + if stage_manager.is_last_stage(): + sequence_output, pooled_output = outputs[:2] + prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) + # the last stage for pretraining model + total_loss = None + if labels is not None and next_sentence_label is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) + total_loss = masked_lm_loss + next_sentence_loss + + if not return_dict: + output = (prediction_scores, seq_relationship_score) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return BertForPreTrainingOutput( + loss=total_loss, + prediction_logits=prediction_scores, + seq_relationship_logits=seq_relationship_score, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + + # intermediate stage always return dict + return { + 'hidden_states': hidden_states, + } + + +def bert_lmhead_forward(self: BertLMHeadModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.Tensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_manager: Optional[PipelineStageManager] = None): + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if labels is not None: + use_cache = False + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = bert_model_forward(self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states if hidden_states is not None else None) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss() + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + # intermediate stage always return dict + return {'hidden_states': hidden_states} diff --git a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py index afbea49c1829..97d7d2fa538a 100644 --- a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py +++ b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py @@ -6,8 +6,9 @@ import colossalai from colossalai.cluster import ProcessGroupMesh -from colossalai.pipeline.policy.bert import BertForPreTrainingPolicy, bert_for_pretraining_forward from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.bert import BertForPreTrainingPolicy, bert_for_pretraining_forward +from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -45,7 +46,7 @@ def check_bert_for_pretraining_forward(): stage_manager=stage_manager) print(output['hidden_states'].shape) assert output['hidden_states'].shape == (2, 3, 768) - print('start the training') + else: attention_mask = torch.ones((2, 3)) output = bert_for_pretraining_forward(self=model, @@ -54,9 +55,6 @@ def check_bert_for_pretraining_forward(): stage_manager=stage_manager) print(output[0].shape) assert output[0].shape == (2, 3, 30522) - print('end the training') - print(output) - # assert output[1].shape == (2, 768) @@ -83,11 +81,13 @@ def check_bert_for_pretraining_policy(): stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() - model_policy = BertForPreTrainingPolicy(stage_manager, len(model.bert.encoder.layer)) - assert model_policy.layers_per_stage == [6, 6] - layers = model_policy.get_hold_layers(model) - for layer in layers: - print(layer) + model_policy = BertForPreTrainingPolicy() + model_policy.set_model(model) + + model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) + model_policy.set_shard_config(model_config) + layers = model_policy.get_held_layers() + assert layers is not None def run_dist_model(rank, world_size, port): diff --git a/tests/test_pipeline/test_policy/test_bert_lmhead_model.py b/tests/test_pipeline/test_policy/test_bert_lmhead_model.py index d41eddc74dff..b14dadf29e3c 100644 --- a/tests/test_pipeline/test_policy/test_bert_lmhead_model.py +++ b/tests/test_pipeline/test_policy/test_bert_lmhead_model.py @@ -6,8 +6,9 @@ import colossalai from colossalai.cluster import ProcessGroupMesh -from colossalai.pipeline.policy.bert import BertLMHeadModelPolicy, bert_lmhead_forward from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.bert import BertLMHeadModelPolicy, bert_lmhead_forward +from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -45,7 +46,7 @@ def check_bert_lmhead_forward(): stage_manager=stage_manager) print(output['hidden_states'].shape) assert output['hidden_states'].shape == (2, 3, 768) - print('start the training') + else: attention_mask = torch.ones((2, 3)) output = bert_lmhead_forward(self=model, @@ -54,8 +55,6 @@ def check_bert_lmhead_forward(): stage_manager=stage_manager) print(output[0].shape) assert output[0].shape == (2, 3, 30522) - print('end the training') - print(output) # assert output[1].shape == (2, 768) @@ -83,11 +82,13 @@ def check_bert_lmhead_policy(): stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() - model_policy = BertLMHeadModelPolicy(stage_manager, len(model.bert.encoder.layer)) - assert model_policy.layers_per_stage == [6, 6] - layers = model_policy.get_hold_layers(model) - for layer in layers: - print(layer) + model_policy = BertLMHeadModelPolicy() + model_policy.set_model(model) + model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) + model_policy.set_shard_config(model_config) + layers = model_policy.get_held_layers() + + assert layers is not None def run_dist_model(rank, world_size, port): diff --git a/tests/test_pipeline/test_policy/test_bert_model.py b/tests/test_pipeline/test_policy/test_bert_model.py index 92485072a5e4..f5a443309cb2 100644 --- a/tests/test_pipeline/test_policy/test_bert_model.py +++ b/tests/test_pipeline/test_policy/test_bert_model.py @@ -5,8 +5,9 @@ import colossalai from colossalai.cluster import ProcessGroupMesh -from colossalai.pipeline.policy.bert import BertModelPolicy, bert_model_forward from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.bert import BertModelPolicy, bert_model_forward +from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -41,7 +42,6 @@ def check_bert_model_forward(): output = bert_model_forward(self=model, input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) print(output['hidden_states'].shape) assert output['hidden_states'].shape == (2, 3, 768) - print('start the training') else: attention_mask = torch.ones((2, 3)) output = bert_model_forward(self=model, @@ -50,8 +50,6 @@ def check_bert_model_forward(): stage_manager=stage_manager) print(output[0].shape) assert output[0].shape == (2, 3, 768) - print('end the training') - print(output) # assert output[1].shape == (2, 768) @@ -78,11 +76,14 @@ def check_bert_model_policy(): stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() - model_policy = BertModelPolicy(stage_manager, len(model.encoder.layer)) - assert model_policy.layers_per_stage == [6, 6] - layers = model_policy.get_hold_layers(model) - for layer in layers: - print(layer) + model_policy = BertModelPolicy() + model_policy.set_model(model) + model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) + model_policy.set_shard_config(model_config) + + layers = model_policy.get_held_layers() + + assert layers is not None def run_dist_model(rank, world_size, port): @@ -109,5 +110,6 @@ def test_bert_model_policy(): if __name__ == "__main__": """test the bert model forward and bert model policy""" - test_bert_model_forward() + #test_bert_model_forward() test_bert_model_policy() + # this test need config to run diff --git a/tests/test_pipeline/test_policy/test_bloom_model.py b/tests/test_pipeline/test_policy/test_bloom_model.py index 5ba92d734590..73584b4f8ef1 100644 --- a/tests/test_pipeline/test_policy/test_bloom_model.py +++ b/tests/test_pipeline/test_policy/test_bloom_model.py @@ -101,12 +101,15 @@ def run_dist_policy(rank, world_size, port): check_bloom_model_policy() +#TODO: Bloom model should be fixed after bert model +@pytest.mark.skip(reason="Bloom model should be fixed after bert model") @pytest.mark.dist @rerun_if_address_is_in_use() def test_bloom_model_forward(): spawn(run_dist_model, 4) +@pytest.mark.skip(reason="Bloom model should be fixed after bert model") @pytest.mark.dist @rerun_if_address_is_in_use() def test_bloom_model_policy(): @@ -115,5 +118,6 @@ def test_bloom_model_policy(): if __name__ == "__main__": """test the bloom model forward and bloom model policy""" - test_bloom_model_forward() - test_bloom_model_policy() + # test_bloom_model_forward() + # test_bloom_model_policy() + #TODO: Bloom model should be fixed after bert model is all ready diff --git a/tests/test_shardformer/test_layer/test_layernorm.py b/tests/test_shardformer/test_layer/test_layernorm.py index a117845545be..fc6d894c4aae 100644 --- a/tests/test_shardformer/test_layer/test_layernorm.py +++ b/tests/test_shardformer/test_layer/test_layernorm.py @@ -41,4 +41,4 @@ def test_layernorm(): if __name__ == '__main__': - test_layernorm_1d() + test_layernorm() From 890774b2fba6b5cb737b00466c89334b73a2be69 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 10 Jul 2023 10:48:53 +0800 Subject: [PATCH 21/84] [shardformer] support lazy init (#4202) * [shardformer] support lazy init * [shardformer] linear support lazy init * [shardformer] embedding support lazy init * [shardformer] norm support lazy init * [shardformer] fused linear support lazy init * [test] update shardformer test layer * [test] shardformer with lazy init fit ddp * [lazy] hotfix deepcopy of param * [shardformer] fix bert policy and update test * [shardformer] fix bloom policy and update test * [shardformer] fix opt policy and update test * [shardformer] fix t5 policy and update test * [shardformer] fix gpt2 policy and update test * [shardformer] fix llama policy and update test --- colossalai/lazy/lazy_init.py | 41 +++++++++++------ colossalai/shardformer/layer/embedding.py | 5 ++- colossalai/shardformer/layer/linear.py | 3 ++ colossalai/shardformer/layer/normalization.py | 4 ++ .../shardformer/layer/qkv_fused_linear.py | 7 ++- colossalai/shardformer/policies/bert.py | 38 +++++++++------- colossalai/shardformer/policies/bloom.py | 26 +++++------ colossalai/shardformer/policies/gpt2.py | 29 ++++++------ colossalai/shardformer/policies/llama.py | 13 +++--- colossalai/shardformer/policies/opt.py | 28 ++++++------ colossalai/shardformer/policies/t5.py | 45 ++++++++++--------- colossalai/shardformer/shard/sharder.py | 10 +---- .../test_layer/test_embedding.py | 13 ++++-- .../test_layer/test_layernorm.py | 13 ++++-- .../test_layer/test_linear_1d.py | 31 +++++++++---- .../test_layer/test_qkv_fused_linear_1d.py | 21 ++++++--- .../test_vocab_parallel_embedding_1d.py | 14 ++++-- tests/test_shardformer/test_model/_utils.py | 17 ++++--- .../test_model/test_shard_bert.py | 10 +++-- .../test_model/test_shard_bloom.py | 6 ++- .../test_model/test_shard_gpt2.py | 6 ++- .../test_model/test_shard_llama.py | 6 ++- .../test_model/test_shard_opt.py | 6 ++- .../test_model/test_shard_t5.py | 6 ++- tests/test_shardformer/test_with_torch_ddp.py | 24 +++++++--- 25 files changed, 264 insertions(+), 158 deletions(-) diff --git a/colossalai/lazy/lazy_init.py b/colossalai/lazy/lazy_init.py index 1f5345015bf2..e071563c045a 100644 --- a/colossalai/lazy/lazy_init.py +++ b/colossalai/lazy/lazy_init.py @@ -6,6 +6,7 @@ import torch.distributed as dist import torch.nn as nn from torch import Tensor +from torch.nn import Parameter from torch.utils._pytree import tree_map from colossalai._analyzer._subclasses import MetaTensor @@ -99,8 +100,11 @@ def _convert_cls(tensor: 'LazyTensor', target: torch.Tensor) -> torch.Tensor: Returns: torch.Tensor: the converted tensor """ - cls_to_become = nn.Parameter if isinstance(tensor, nn.Parameter) else torch.Tensor + cls_to_become = Parameter if isinstance(tensor, Parameter) else torch.Tensor tensor.__class__ = cls_to_become + if cls_to_become is Parameter: + # to fit UninitializedParameter + delattr(tensor, '_is_param') tensor.data = target tensor.requires_grad = target.requires_grad # subclass of torch.Tensor does not have tolist() method @@ -198,10 +202,10 @@ def distribute(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> to def clean(self) -> None: """Clean all stored operations, meta data and materialized data, which prevents memory leaking. This should be called after all tensors are materialized. """ - self._factory_method = None - self._op_buffer = None - self._materialized_data = None - self._meta_data = None + delattr(self, '_factory_method') + delattr(self, '_op_buffer') + delattr(self, '_materialized_data') + delattr(self, '_meta_data') @staticmethod def _replace_with_materialized(x): @@ -350,20 +354,19 @@ def __deepcopy__(self, memo): def factory_fn(): # if self is materialized, return self new_tensor = self.materialize() if type(self) is LazyTensor else self - copied = new_tensor.detach().clone() - if new_tensor.requires_grad: - copied.requires_grad_() - return copied + return _copy_tensor(new_tensor, new_tensor.requires_grad) if self._materialized_data is not None: # self is early materialized - copied = self._materialized_data.detach().clone() - if self.requires_grad: - copied.requires_grad_() + copied = _copy_tensor(self._materialized_data, self.requires_grad) target = LazyTensor(lambda: None, concrete_data=copied) else: target = LazyTensor(factory_fn, meta_data=self._meta_data) + if isinstance(self, Parameter): + # hack isinstance check of parameter + target._is_param = True + memo[id(self)] = target return target @@ -408,6 +411,10 @@ def tolist(self) -> list: def __hash__(self): return id(self) + def __rpow__(self, other): + dtype = torch.result_type(self, other) + return torch.tensor(other, dtype=dtype, device=self.device)**self + class LazyInitContext: """Context manager for lazy initialization. Enables initializing the model without allocating real memory. @@ -536,7 +543,7 @@ def __exit__(self, exc_type, exc_val, exc_tb): @staticmethod def materialize(module: nn.Module, verbose: bool = False) -> nn.Module: - """Initialize all ``nn.Parameter`` from ``LazyTensor``. This function will modify the module in-place. + """Initialize all ``Parameter`` from ``LazyTensor``. This function will modify the module in-place. Args: module (nn.Module): Target ``nn.Module`` @@ -553,7 +560,7 @@ def distribute(module: nn.Module, device_mesh: DeviceMesh, sharding_spec_dict: Dict[str, ShardingSpec], verbose: bool = False) -> nn.Module: - """Distribute all ``nn.Parameter`` from ``LazyTensor``. This function will modify the module in-place. + """Distribute all ``Parameter`` from ``LazyTensor``. This function will modify the module in-place. Args: module (nn.Module): Target ``nn.Module`` @@ -625,3 +632,9 @@ def _is_int_tuple(args) -> bool: if not isinstance(x, int): return False return True + + +def _copy_tensor(tensor: Tensor, requires_grad: bool) -> Tensor: + copied = tensor.data.clone() + copied.requires_grad = requires_grad + return copied diff --git a/colossalai/shardformer/layer/embedding.py b/colossalai/shardformer/layer/embedding.py index db39a457b7fd..07341ef73515 100644 --- a/colossalai/shardformer/layer/embedding.py +++ b/colossalai/shardformer/layer/embedding.py @@ -9,8 +9,8 @@ import torch.nn.functional as F from torch import Tensor from torch.distributed import ProcessGroup -from torch.nn.parameter import Parameter +from colossalai.lazy import LazyInitContext from colossalai.nn import init as init from colossalai.nn.layer.utils import divide from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise, sharded_tensor_to_param @@ -95,6 +95,7 @@ def from_native_module(module: nn.Embedding, r""" Build a 1D parallelized Embedding from a native nn.Embedding module. """ + LazyInitContext.materialize(module) # get the attributes num_embedding = module.num_embeddings embedding_dim = module.embedding_dim @@ -223,6 +224,7 @@ def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, r""" Convert a native pytorch embedding module to a parallel module. """ + LazyInitContext.materialize(module) # get the origin attributes num_embeddings = module.num_embeddings embedding_dim = module.embedding_dim @@ -243,6 +245,7 @@ def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, process_group=process_group, *args, **kwargs) + with torch.no_grad(): # shard and slice the weight along the vocabulary(num_embeddings) dimension # the shape of the weight is (num_embeddings, embedding_dim) diff --git a/colossalai/shardformer/layer/linear.py b/colossalai/shardformer/layer/linear.py index 26ba5883c64f..a8439f303bd1 100644 --- a/colossalai/shardformer/layer/linear.py +++ b/colossalai/shardformer/layer/linear.py @@ -12,6 +12,7 @@ from torch.distributed import ProcessGroup from torch.nn.parameter import Parameter +from colossalai.lazy import LazyInitContext from colossalai.nn import init as init from colossalai.nn.layer.utils import divide from colossalai.tensor.d_tensor import shard_colwise, shard_rowwise, sharded_tensor_to_param @@ -106,6 +107,7 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis r""" Convert a native PyTorch linear layer to a parallelized linear layer. """ + LazyInitContext.materialize(module) # get the attributes in_features = module.in_features out_features = module.out_features @@ -242,6 +244,7 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis r""" Convert a native PyTorch linear layer to a parallelized linear layer. """ + LazyInitContext.materialize(module) # get the attributes in_features = module.in_features out_features = module.out_features diff --git a/colossalai/shardformer/layer/normalization.py b/colossalai/shardformer/layer/normalization.py index b27307154a76..9bb7738c0f0a 100644 --- a/colossalai/shardformer/layer/normalization.py +++ b/colossalai/shardformer/layer/normalization.py @@ -4,6 +4,8 @@ import torch import torch.nn as nn +from colossalai.lazy import LazyInitContext + __all__ = ['FusedLayerNorm', 'FusedRMSNorm'] FAST_LAYERNORM_SUPPORTED_SIZE = [ @@ -35,6 +37,7 @@ def from_native_module(module: nn.LayerNorm, *args, **kwargs) -> nn.Module: raise ImportError( 'Please install apex from source (https://github.com/NVIDIA/apex) to use the fused layernorm kernel') + LazyInitContext.materialize(module) # get the attributes of the module normalized_shape = module.normalized_shape eps = module.eps @@ -84,6 +87,7 @@ def from_native_module(module: nn.Module, *args, **kwargs) -> nn.Module: 'Please install apex from source (https://github.com/NVIDIA/apex) to use the fused RMS normalization kernel' ) + LazyInitContext.materialize(module) # to check if it is huggingface LlamaRMSNorm if module.__class__.__name__ == "LlamaRMSNorm": normalized_shape = module.weight.shape[0] diff --git a/colossalai/shardformer/layer/qkv_fused_linear.py b/colossalai/shardformer/layer/qkv_fused_linear.py index 9d51670c65dd..c94d93069e93 100644 --- a/colossalai/shardformer/layer/qkv_fused_linear.py +++ b/colossalai/shardformer/layer/qkv_fused_linear.py @@ -12,6 +12,7 @@ from torch.distributed import ProcessGroup from torch.nn.parameter import Parameter +from colossalai.lazy import LazyInitContext from colossalai.nn import init as init from colossalai.nn.layer.utils import divide from colossalai.tensor.d_tensor.api import ( @@ -231,6 +232,7 @@ def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, Lis process_group (`Union[ProcessGroup, List[ProcessGroup]]`): The process group to be used for weight sharding and communication. n_fused (int): The number of layers to be fused. In GPT2, Q,K,V are fused in one weight. """ + LazyInitContext.materialize(module) # get the attributes in_features = module.weight.shape[0] out_features = module.weight.shape[1] @@ -380,6 +382,7 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis r""" Convert a native PyTorch linear layer to a parallelized linear layer. """ + LazyInitContext.materialize(module) # get the attributes in_features = module.weight.shape[0] out_features = module.weight.shape[1] @@ -428,9 +431,9 @@ def reset_parameters(self, weight_initializer, bias_initializer) -> None: src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0) origin_device = self.bias.device - self.bias = self.bias.cuda() + self.bias.data = self.bias.cuda() dist.broadcast(self.bias, src=src_rank, group=self.process_group) - self.bias = self.bias.to(origin_device) + self.bias.data = self.bias.to(origin_device) def forward(self, input_: Tensor) -> Tensor: # Set up backprop all-reduce. diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index e18cb6ece674..b80475e0552c 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -46,11 +46,12 @@ def preprocess(self): Reshape the Embedding layer to make the embedding dimension divisible by world_size """ # TODO: - vocab_size = self.model.config.vocab_size - world_size = self.shard_config.tensor_parallel_size - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) + if self.shard_config.enable_tensor_parallelism: + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) return self.model def module_policy(self): @@ -229,10 +230,11 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: return [] def postprocess(self): - binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) + if self.shard_config.enable_tensor_parallelism: + binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + setattr_(self.model, v, param) return self.model @@ -269,10 +271,11 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: return [] def postprocess(self): - binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) + if self.shard_config.enable_tensor_parallelism: + binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + setattr_(self.model, v, param) return self.model @@ -288,10 +291,11 @@ def module_policy(self): return module_policy def postprocess(self): - binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) + if self.shard_config.enable_tensor_parallelism: + binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + setattr_(self.model, v, param) return self.model diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index 8d6f07d4a67d..662ff5b4977a 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -17,11 +17,12 @@ def preprocess(self): r""" Reshape the Embedding layer to make the embedding dimension divisible by world_size """ - vocab_size = self.model.config.vocab_size - world_size = self.shard_config.tensor_parallel_size - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) + if self.shard_config.enable_tensor_parallelism: + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) return self.model def module_policy(self): @@ -128,16 +129,13 @@ def module_policy(self): return policy def postprocess(self): - binding_map = {"transformer.word_embeddings.weight": "lm_head.weight"} - - for k, v in binding_map.items(): - param = getattr_(self.model, k) - - if not isinstance(param, nn.Parameter): - param = nn.Parameter(param) + if self.shard_config.enable_tensor_parallelism: + binding_map = {"transformer.word_embeddings.weight": "lm_head.weight"} - # tie weights - setattr_(self.model, v, param) + for k, v in binding_map.items(): + param = getattr_(self.model, k) + # tie weights + setattr_(self.model, v, param) return self.model diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 598f393c029a..8f9d90e67e59 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -21,11 +21,12 @@ def preprocess(self): r""" Reshape the Embedding layer to make the embedding dimension divisible by world_size """ - vocab_size = self.model.config.vocab_size - world_size = self.shard_config.tensor_parallel_size - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) + if self.shard_config.enable_tensor_parallelism: + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) return self.model def module_policy(self): @@ -142,10 +143,11 @@ def module_policy(self): return module_policy def postprocess(self): - binding_map = {"transformer.wte.weight": "lm_head.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) + if self.shard_config.enable_tensor_parallelism: + binding_map = {"transformer.wte.weight": "lm_head.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + setattr_(self.model, v, param) return self.model @@ -172,10 +174,11 @@ def module_policy(self): return module_policy def postprocess(self): - binding_map = {"transformer.wte.weight": "lm_head.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) + if self.shard_config.enable_tensor_parallelism: + binding_map = {"transformer.wte.weight": "lm_head.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + setattr_(self.model, v, param) return self.model diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index 391938b27167..b10e07560d22 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -15,13 +15,14 @@ def config_sanity_check(self): pass def preprocess(self): - # Resize embedding - vocab_size = self.model.config.vocab_size - world_size = self.shard_config.tensor_parallel_size + if self.shard_config.enable_tensor_parallelism: + # Resize embedding + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) return self.model diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index c4c6cde015b6..1435805d2846 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -19,11 +19,12 @@ def preprocess(self): r""" Reshape the Embedding layer to make the embedding dimension divisible by world_size """ - vocab_size = self.model.config.vocab_size - world_size = self.shard_config.tensor_parallel_size - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) + if self.shard_config.enable_tensor_parallelism: + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) return self.model def module_policy(self): @@ -116,14 +117,15 @@ def module_policy(self): return policy def postprocess(self): - binding_map = { - 'model.decoder.embed_tokens': 'lm_head', - } - - for k, v in binding_map.items(): - src_mod = getattr_(self.model, k) - dst_mod = getattr_(self.model, v) - dst_mod.weight = src_mod.weight + if self.shard_config.enable_tensor_parallelism: + binding_map = { + 'model.decoder.embed_tokens': 'lm_head', + } + + for k, v in binding_map.items(): + src_mod = getattr_(self.model, k) + dst_mod = getattr_(self.model, v) + dst_mod.weight = src_mod.weight return self.model diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 6167e81613f2..37864885b4cc 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -24,11 +24,12 @@ def preprocess(self): r""" Reshape the Embedding layer to make the embedding dimension divisible by world_size """ - vocab_size = self.model.config.vocab_size - world_size = self.shard_config.tensor_parallel_size - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) + if self.shard_config.enable_tensor_parallelism: + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) return self.model def module_policy(self): @@ -164,11 +165,12 @@ def module_policy(self): return policy def postprocess(self): - binding_map = [["shared", "encoder.embed_tokens"], ["shared", "decoder.embed_tokens"]] + if self.shard_config.enable_tensor_parallelism: + binding_map = [["shared", "encoder.embed_tokens"], ["shared", "decoder.embed_tokens"]] - for k, v in binding_map: - mod = getattr_(self.model, k) - setattr_(self.model, v, mod) + for k, v in binding_map: + mod = getattr_(self.model, k) + setattr_(self.model, v, mod) return self.model @@ -211,13 +213,13 @@ def module_policy(self): def postprocess(self): super().postprocess() + if self.shard_config.enable_tensor_parallelism: + binding_map = {"shared": "lm_head"} - binding_map = {"shared": "lm_head"} - - for k, v in binding_map.items(): - src_mod = getattr_(self.model, k) - dst_mod = getattr_(self.model, v) - dst_mod.weight = src_mod.weight + for k, v in binding_map.items(): + src_mod = getattr_(self.model, k) + dst_mod = getattr_(self.model, v) + dst_mod.weight = src_mod.weight return self.model @@ -239,11 +241,12 @@ def module_policy(self): return base_policy def postprocess(self): - binding_map = [ - ["shared", "encoder.embed_tokens"], - ] + if self.shard_config.enable_tensor_parallelism: + binding_map = [ + ["shared", "encoder.embed_tokens"], + ] - for k, v in binding_map: - mod = getattr_(self.model, k) - setattr_(self.model, v, mod) + for k, v in binding_map: + mod = getattr_(self.model, k) + setattr_(self.model, v, mod) return self.model diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index ca2f46a187d1..56eb76973807 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -3,7 +3,7 @@ import torch.nn as nn from torch import Tensor -from colossalai.lazy import LazyTensor +from colossalai.lazy import LazyInitContext from .._utils import getattr_, setattr_ from ..policies.auto_policy import get_autopolicy @@ -192,10 +192,4 @@ def _materialize(self) -> None: r""" Materialize the model if lazy initialization is used """ - for p in self.model.parameters(): - if isinstance(p, LazyTensor): - p.materialize() - - for b in self.model.buffers(): - if isinstance(b, LazyTensor): - b.materialize() + LazyInitContext.materialize(self.model) diff --git a/tests/test_shardformer/test_layer/test_embedding.py b/tests/test_shardformer/test_layer/test_embedding.py index 8a6aa42a42f2..99e494359af7 100644 --- a/tests/test_shardformer/test_layer/test_embedding.py +++ b/tests/test_shardformer/test_layer/test_embedding.py @@ -1,15 +1,22 @@ +from contextlib import nullcontext + import torch import torch.distributed as dist import torch.nn as nn from torch.testing import assert_close import colossalai +from colossalai.lazy import LazyInitContext from colossalai.shardformer.layer import Embedding1D -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn + +@parameterize('lazy_init', [False, True]) +def check_embedding_1d(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() -def check_embedding_1d(): - embedding = nn.Embedding(32, 128).cuda() + with ctx: + embedding = nn.Embedding(32, 128).cuda() embedding_1d = Embedding1D.from_native_module(embedding, process_group=None) assert embedding_1d.weight.shape == torch.Size([32, 64]) diff --git a/tests/test_shardformer/test_layer/test_layernorm.py b/tests/test_shardformer/test_layer/test_layernorm.py index fc6d894c4aae..2cb6928edf83 100644 --- a/tests/test_shardformer/test_layer/test_layernorm.py +++ b/tests/test_shardformer/test_layer/test_layernorm.py @@ -1,14 +1,21 @@ +from contextlib import nullcontext + import torch import torch.nn as nn from torch.testing import assert_close import colossalai +from colossalai.lazy import LazyInitContext from colossalai.shardformer.layer import FusedLayerNorm -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn + +@parameterize('lazy_init', [False, True]) +def check_layernorm(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() -def check_layernorm(): - norm = nn.LayerNorm(128, 0.00001).cuda() + with ctx: + norm = nn.LayerNorm(128, 0.00001).cuda() norm1d = FusedLayerNorm.from_native_module(norm, process_group=None) assert norm1d.weight.shape == torch.Size([128]) diff --git a/tests/test_shardformer/test_layer/test_linear_1d.py b/tests/test_shardformer/test_layer/test_linear_1d.py index da3bdc1d78d3..da3cd85ec407 100644 --- a/tests/test_shardformer/test_layer/test_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_linear_1d.py @@ -1,16 +1,23 @@ +from contextlib import nullcontext + import torch import torch.distributed as dist import torch.nn as nn from torch.testing import assert_close import colossalai +from colossalai.lazy import LazyInitContext from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row from colossalai.tensor.d_tensor import is_distributed_tensor -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn + +@parameterize('lazy_init', [False, True]) +def check_linear_1d_col(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() -def check_linear_1d_col(): - linear = nn.Linear(32, 128).cuda() + with ctx: + linear = nn.Linear(32, 128).cuda() linear_col = Linear1D_Col.from_native_module(linear, process_group=None, gather_output=True) # ensure that the parameters are distributed @@ -50,8 +57,12 @@ def check_linear_1d_col(): assert_close(x_for_unshard.grad, x_for_shard.grad) -def check_linear_1d_row(): - linear = nn.Linear(32, 128).cuda() +@parameterize('lazy_init', [False, True]) +def check_linear_1d_row(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() + + with ctx: + linear = nn.Linear(32, 128).cuda() linear_row = Linear1D_Row.from_native_module(linear, process_group=None, parallel_input=False) assert linear_row.weight.shape == torch.Size([128, 16]) @@ -83,9 +94,13 @@ def check_linear_1d_row(): assert_close(x_for_unshard.grad, x_for_shard.grad) -def check_linear_col_plus_row(): - linear_1 = nn.Linear(32, 128).cuda() - linear_2 = nn.Linear(128, 32).cuda() +@parameterize('lazy_init', [False, True]) +def check_linear_col_plus_row(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() + + with ctx: + linear_1 = nn.Linear(32, 128).cuda() + linear_2 = nn.Linear(128, 32).cuda() linear_col = Linear1D_Col.from_native_module(linear_1, process_group=None, gather_output=False) linear_row = Linear1D_Row.from_native_module(linear_2, process_group=None, parallel_input=True) diff --git a/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py b/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py index 681c4f6dd9f1..186b1e8212cc 100644 --- a/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py @@ -1,12 +1,15 @@ +from contextlib import nullcontext + import torch import torch.distributed as dist import torch.nn as nn from torch.testing import assert_close import colossalai +from colossalai.lazy import LazyInitContext from colossalai.shardformer.layer import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row from colossalai.shardformer.layer.qkv_fused_linear import split_fused_qkv_in_gpt2_style -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn # This code is copied from https://github.com/huggingface/transformers @@ -50,8 +53,12 @@ def rearrange(tensor: torch.Tensor, dim: int): return rearanged_tensor -def check_linear_conv_1d_col(): - linear = Conv1D(192, 48).cuda() +@parameterize('lazy_init', [False, True]) +def check_linear_conv_1d_col(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() + + with ctx: + linear = Conv1D(192, 48).cuda() linear_conv_col = GPT2FusedLinearConv1D_Col.from_native_module(linear, process_group=None, gather_output=True, @@ -80,8 +87,12 @@ def check_linear_conv_1d_col(): assert_close(target_grad, linear_conv_col.weight.grad) -def check_linear_conv_1d_row(): - linear = Conv1D(192, 48).cuda() +@parameterize('lazy_init', [False, True]) +def check_linear_conv_1d_row(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() + + with ctx: + linear = Conv1D(192, 48).cuda() linear_row = GPT2FusedLinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) assert linear.weight.shape == torch.Size([48, 192]) diff --git a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py index 8991d9b304f5..bf5803496f03 100644 --- a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py +++ b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py @@ -1,15 +1,23 @@ +from contextlib import nullcontext + import torch import torch.distributed as dist import torch.nn as nn from torch.testing import assert_close import colossalai -from colossalai.shardformer.layer import VocabParallelEmbedding1D +from colossalai.lazy import LazyInitContext +from colossalai.shardformer.layer import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row, VocabParallelEmbedding1D +from colossalai.shardformer.layer.qkv_fused_linear import split_fused_qkv_in_gpt2_style from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn -def check_vocab_embedding_1d(): - embedding = nn.Embedding(128, 32).to('cuda') +@parameterize('lazy_init', [False, True]) +def check_vocab_embedding_1d(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() + + with ctx: + embedding = nn.Embedding(128, 32).to('cuda') dist_embedding_1d = VocabParallelEmbedding1D.from_native_module(embedding, process_group=None) assert dist_embedding_1d.weight.shape == torch.Size([64, 32]) diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index e03014f3f234..f83cfcd499cb 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -1,19 +1,24 @@ import copy +from contextlib import nullcontext +from colossalai.lazy import LazyInitContext from colossalai.shardformer import ShardConfig, ShardFormer -def build_model(model_fn, enable_fused_normalization=True, enable_tensor_parallelism=True): - # create new model - org_model = model_fn().cuda() - +def build_model(model_fn, enable_fused_normalization=True, enable_tensor_parallelism=True, use_lazy_init: bool = False): + ctx = LazyInitContext() if use_lazy_init else nullcontext() + with ctx: + # create new model + org_model = model_fn() + model_copy = copy.deepcopy(org_model) + if use_lazy_init: + ctx.materialize(org_model) # shard model shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, enable_tensor_parallelism=enable_tensor_parallelism) - model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) sharded_model, shared_params = shard_former.optimize(model_copy) - return org_model, sharded_model.cuda() + return org_model.cuda(), sharded_model.cuda() def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 1afedb7079ea..7f179acd7356 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -67,12 +67,14 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -def run_bert_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('enable_fused_normalization', [False, True]) +@parameterize('enable_tensor_parallelism', [False, True]) +@parameterize('use_lazy_init', [False, True]) +def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + use_lazy_init) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index a3389652269c..e18168292df5 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -69,10 +69,12 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('use_lazy_init', [False, True]) +def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + use_lazy_init) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index ee7737687d99..96c4b90a8075 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -69,10 +69,12 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('use_lazy_init', [False, True]) +def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + use_lazy_init) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index 74b5fdd18af8..4d63a43489a3 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -72,10 +72,12 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_gpt2_llama(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('use_lazy_init', [False, True]) +def run_gpt2_llama(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + use_lazy_init) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index 25bccb13b1a8..c008596fe2b6 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -71,10 +71,12 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_t5_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('use_lazy_init', [False, True]) +def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + use_lazy_init) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 0762dc09e5af..ccd7d3787d3d 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -82,10 +82,12 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_t5_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('use_lazy_init', [False, True]) +def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + use_lazy_init) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_with_torch_ddp.py b/tests/test_shardformer/test_with_torch_ddp.py index f29c8d6f605b..2b6933246298 100644 --- a/tests/test_shardformer/test_with_torch_ddp.py +++ b/tests/test_shardformer/test_with_torch_ddp.py @@ -1,3 +1,5 @@ +from contextlib import nullcontext + import pytest import torch import torch.distributed as dist @@ -5,15 +7,15 @@ import colossalai from colossalai.cluster import DistCoordinator +from colossalai.lazy import LazyInitContext from colossalai.logging import disable_existing_loggers from colossalai.shardformer import ShardConfig, ShardFormer -from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -def check_shardformer_with_ddp(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') +@parameterize('lazy_init', [True, False]) +def check_shardformer_with_ddp(lazy_init: bool): sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') @@ -41,9 +43,12 @@ def check_shardformer_with_ddp(rank, world_size, port): shard_config = ShardConfig(tensor_parallel_process_group=tp_process_group, enable_fused_normalization=True) shardformer = ShardFormer(shard_config=shard_config) + ctx = LazyInitContext() if lazy_init else nullcontext() + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): # create and shard model - model = model_fn().cuda() + with ctx: + model = model_fn().cuda() sharded_model, _ = shardformer.optimize(model) # add ddp @@ -65,13 +70,18 @@ def check_shardformer_with_ddp(rank, world_size, port): torch.cuda.empty_cache() +def run_dist(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + check_shardformer_with_ddp() + + @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() def test_gpt2(): - spawn(check_shardformer_with_ddp, 4) + spawn(run_dist, 4) if __name__ == "__main__": test_gpt2() - test_gpt2() From 1094e0f0d344c04262ee60bef8f2a9bfb660efc4 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Mon, 10 Jul 2023 13:58:58 +0800 Subject: [PATCH 22/84] [pipeline] Bert pipeline for shardformer and its tests (#4197) * add pipeline forward * complete pipeline forward check * fix bert forward without pipeline * fix comments * discard useless line * add todo * clean prints * fix distribute layers --- .../shardformer/policies/base_policy.py | 2 +- colossalai/shardformer/policies/bert.py | 156 +++++++++++++++++- colossalai/shardformer/shard/sharder.py | 4 +- tests/test_shardformer/test_model/_utils.py | 23 +++ .../test_model/test_shard_bert_pipeline.py | 85 ++++++++++ 5 files changed, 259 insertions(+), 11 deletions(-) create mode 100644 tests/test_shardformer/test_model/test_shard_bert_pipeline.py diff --git a/colossalai/shardformer/policies/base_policy.py b/colossalai/shardformer/policies/base_policy.py index 65aee13861ee..aac86eb20a56 100644 --- a/colossalai/shardformer/policies/base_policy.py +++ b/colossalai/shardformer/policies/base_policy.py @@ -191,7 +191,7 @@ def distribute_layers(num_layers: int, num_stages: int) -> List[int]: # deal with the rest layers if remainder > 0: - start_position = num_layers // 2 - remainder // 2 + start_position = num_stages // 2 - remainder // 2 for i in range(start_position, start_position + remainder): layers_per_stage[i] += 1 return layers_per_stage diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index b80475e0552c..eacd0b449ad4 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -13,6 +13,8 @@ CausalLMOutputWithCrossAttentions, ) from transformers.models.bert.modeling_bert import ( + BertForMaskedLM, + BertForNextSentencePrediction, BertForPreTraining, BertForPreTrainingOutput, BertLMHeadModel, @@ -135,7 +137,6 @@ def module_policy(self): ], policy=policy, target_key=BertLayer) - # handle embedding layer self.append_or_create_submodule_replacement( description=[SubModuleReplacementDescription( @@ -144,6 +145,7 @@ def module_policy(self): )], policy=policy, target_key=BertEmbeddings) + return policy def add_lm_head_policy(self, base_policy): @@ -177,6 +179,15 @@ class BertModelPolicy(BertPolicy): def __init__(self) -> None: super().__init__() + def module_policy(self): + module_policy = super().module_policy() + from transformers.models.bert.modeling_bert import BertModel + if self.pipeline_stage_manager: + # set None as default + module_policy[BertModel] = ModulePolicyDescription( + method_replacement={'forward': partial(bert_model_forward, stage_manager=self.pipeline_stage_manager)}) + return module_policy + def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" module = self.model @@ -444,6 +455,13 @@ def bert_model_forward( raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) else: input_shape = hidden_states.size()[:-1] batch_size, seq_length = input_shape @@ -466,14 +484,6 @@ def bert_model_forward( if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) - if token_type_ids is None: - if hasattr(self.embeddings, "token_type_ids"): - buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] - buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) - token_type_ids = buffered_token_type_ids_expanded - else: - token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) @@ -778,3 +788,131 @@ def bert_lmhead_forward(self: BertLMHeadModel, hidden_states = outputs.get('hidden_states') # intermediate stage always return dict return {'hidden_states': hidden_states} + + +def bert_for_masked_lm_forward( + self: BertForMaskedLM, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, +): + #-> Union[Tuple[torch.Tensor], MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + pass + + +def bert_for_next_sentence_prediction_forward( + self: BertForNextSentencePrediction, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + **kwargs, +): + #-> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair + (see `input_ids` docstring). Indices should be in `[0, 1]`: + + - 0 indicates sequence B is a continuation of sequence A, + - 1 indicates sequence B is a random sequence. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, BertForNextSentencePrediction + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + >>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased") + + >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." + >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." + >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") + + >>> outputs = model(**encoding, labels=torch.LongTensor([1])) + >>> logits = outputs.logits + >>> assert logits[0, 0] < logits[0, 1] # next sentence was random + ``` + """ + + if "next_sentence_label" in kwargs: + warnings.warn( + "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" + " `labels` instead.", + FutureWarning, + ) + labels = kwargs.pop("next_sentence_label") + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + if stage_manager.is_last_stage(): + pooled_output = outputs[1] + seq_relationship_scores = self.cls(pooled_output) + + next_sentence_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) + + if not return_dict: + output = (seq_relationship_scores,) + outputs[2:] + return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output + + return NextSentencePredictorOutput( + loss=next_sentence_loss, + logits=seq_relationship_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + # intermediate stage always return dict + return {'hidden_states': hidden_states} diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 56eb76973807..882f93c7acc5 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,3 +1,4 @@ +from types import MethodType from typing import Any, Callable, Dict, List, Union import torch.nn as nn @@ -134,7 +135,8 @@ def _replace_param( def _replace_method(self, module: nn.Module, method_replacement: Dict[str, Callable]): for method_name, new_method in method_replacement.items(): # bind the new method to the module - setattr(module, method_name, new_method.__get__(module, module.__class__)) + bound_method = MethodType(new_method, module) + setattr(module, method_name, bound_method) def _replace_sub_module( self, diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index f83cfcd499cb..de8cb65d21d0 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -2,6 +2,7 @@ from contextlib import nullcontext from colossalai.lazy import LazyInitContext +from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer import ShardConfig, ShardFormer @@ -21,6 +22,28 @@ def build_model(model_fn, enable_fused_normalization=True, enable_tensor_paralle return org_model.cuda(), sharded_model.cuda() +def build_pipeline_model(model_fn, + stage_manager=None, + enable_fused_normalization=False, + enable_tensor_parallelism=False, + use_lazy_init: bool = False): + ctx = LazyInitContext() if use_lazy_init else nullcontext() + with ctx: + # create new model + org_model = model_fn() + model_copy = copy.deepcopy(org_model) + if use_lazy_init: + ctx.materialize(org_model) + + # shard model + shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, + enable_tensor_parallelism=enable_tensor_parallelism, + pipeline_stage_manager=stage_manager) + shard_former = ShardFormer(shard_config=shard_config) + sharded_model, shared_params = shard_former.optimize(model_copy) + return org_model.cuda(), sharded_model.cuda() + + def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): # prepare input data = data_gen_fn() diff --git a/tests/test_shardformer/test_model/test_shard_bert_pipeline.py b/tests/test_shardformer/test_model/test_shard_bert_pipeline.py new file mode 100644 index 000000000000..9cca5ec8bc51 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_bert_pipeline.py @@ -0,0 +1,85 @@ +import pytest +import torch + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + pass + + +@parameterize('enable_fused_normalization', [False]) +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('use_lazy_init', [False]) +#TODO: merge this into test_shard_bert +def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + + sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') + x = torch.randint(0, 1000, (2, 3)).cuda() + hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + if name == 'transformers_bert': + org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x).cuda() + output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) + # print(output['hidden_states'].shape) + assert output['hidden_states'].shape == (2, 3, 128) + else: + attention_mask = torch.ones((2, 3)).cuda() + output = sharded_model(hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager) + # print(output[0].shape) + assert output[0].shape == (2, 3, 128) + + torch.cuda.empty_cache() + + +def check_bert(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_bert_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_bert(): + spawn(check_bert, 4) + + +if __name__ == "__main__": + test_bert() From 162203105883e7b2b0919b1feeba8531d0ecae21 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Tue, 11 Jul 2023 11:37:26 +0800 Subject: [PATCH 23/84] [pipeline] Llama pipeline (#4205) * bloom policy * llama pipeline forward and tests * fix the output and attention_mask * fix name * bind argument to policy * Revert "bloom policy" This reverts commit 8dee68a0a22568dbeed6d4563372b25e1e825fb0. This policy should be revert and copied to feature/bloom * revert the bloom changes * cancel unneeded inputs * gpt --- .../shardformer/policies/auto_policy.py | 2 +- colossalai/shardformer/policies/bert.py | 2 +- colossalai/shardformer/policies/llama.py | 428 +++++++++++++++++- tests/kit/model_zoo/transformers/gpt.py | 2 +- tests/test_shardformer/test_model/_utils.py | 1 + .../test_model/test_shard_llama_pipeline.py | 85 ++++ 6 files changed, 516 insertions(+), 4 deletions(-) create mode 100644 tests/test_shardformer/test_model/test_shard_llama_pipeline.py diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index 640b61b579bd..0ad9a3e95a0e 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -45,7 +45,7 @@ class PolicyLocation: # LLaMA "transformers.models.llama.modeling_llama.LlamaModel": - PolicyLocation(file_name="llama", class_name="LlamaPolicy"), + PolicyLocation(file_name="llama", class_name="LlamaModelPolicy"), "transformers.models.llama.modeling_llama.LlamaForCausalLM": PolicyLocation(file_name="llama", class_name="LlamaForCausalLMPolicy"), "transformers.models.llama.modeling_llama.LlamaForSequenceClassification": diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index eacd0b449ad4..2b2c003ffb04 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -193,7 +193,7 @@ def get_held_layers(self) -> List[Module]: module = self.model stage_manager = self.pipeline_stage_manager held_layers = [] - layers_per_stage = self.distribute_layers(len(self.model.encoder.layer), stage_manager.num_stages) + layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index b10e07560d22..b2b6470188a4 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -1,11 +1,30 @@ -from typing import Dict, Union +import math +from functools import partial +from types import MethodType +from typing import Dict, List, Optional, Tuple, Union +import torch import torch.nn as nn +from torch import Tensor +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, Module, MSELoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel +from transformers.utils import ModelOutput, logging +from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +logger = logging.get_logger(__name__) + __all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy'] @@ -106,6 +125,43 @@ def postprocess(self): return self.model +class LlamaModelPolicy(LlamaPolicy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + module_policy = super().module_policy() + from transformers.models.llama.modeling_llama import LlamaModel + if self.pipeline_stage_manager: + # set None as default + stage_manager = self.pipeline_stage_manager + layers_per_stage = Policy.distribute_layers(len(self.model.layers), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + module_policy[LlamaModel] = ModulePolicyDescription(method_replacement={ + 'forward': partial(llama_model_forward, stage_manager=stage_manager, stage_index=stage_index) + }) + return module_policy + + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.embed_tokens) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.layers[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.norm) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in bert model""" + return [] + + class LlamaForCausalLMPolicy(LlamaPolicy): def module_policy(self): @@ -144,3 +200,373 @@ def module_policy(self): } policy.update(new_item) return policy + + +def llama_model_forward( + self: LlamaModel, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, +): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if stage_manager.is_first_stage(): + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + device = input_ids.device if input_ids is not None else inputs_embeds.device + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + hidden_states = inputs_embeds + else: + input_shape = hidden_states.shape[:-1] + batch_size, seq_length = input_shape + device = hidden_states.device + + seq_length_with_past = seq_length + past_key_values_length = 0 + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + position_ids = torch.arange(past_key_values_length, + seq_length + past_key_values_length, + dtype=torch.long, + device=device) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + # embed positions, for the first stage, hidden_states is the input embeddings, + # for the other stages, hidden_states is the output of the previous stage + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length_with_past), dtype=torch.bool, device=hidden_states.device) + attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), hidden_states, + past_key_values_length) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + start_idx, end_idx = stage_index[0], stage_index[1] + for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx]): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if stage_manager.is_last_stage(): + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + next_cache = next_decoder_cache if use_cache else None + if stage_manager.is_last_stage(): + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + # always return dict for imediate stage + return {'hidden_states': hidden_states} + + +def llama_for_causal_lm_forward( + self: LlamaForCausalLM, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, +): + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you consciours? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = llama_model_forward( + self.model, + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + + if stage_manager.is_last_stage(): + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + +def llama_for_sequence_classification_forward( + self: LlamaForSequenceClassification, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, +): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + transformer_outputs = llama_model_forward( + self.model, + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + + if input_ids is not None: + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + batch_size = inputs_embeds.shape[0] + else: + batch_size = hidden_states.shape[0] + + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index b9e0310780af..ac70138e3f8f 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -52,7 +52,7 @@ def data_gen_for_sequence_classification(): loss_fn = lambda x: x.loss config = transformers.GPT2Config(n_layer=2, - n_head=4, + n_head=2, vocab_size=50258, attn_pdrop=0, embd_pdrop=0, diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index de8cb65d21d0..f26c6622da7e 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -39,6 +39,7 @@ def build_pipeline_model(model_fn, shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, enable_tensor_parallelism=enable_tensor_parallelism, pipeline_stage_manager=stage_manager) + shard_former = ShardFormer(shard_config=shard_config) sharded_model, shared_params = shard_former.optimize(model_copy) return org_model.cuda(), sharded_model.cuda() diff --git a/tests/test_shardformer/test_model/test_shard_llama_pipeline.py b/tests/test_shardformer/test_model/test_shard_llama_pipeline.py new file mode 100644 index 000000000000..81c183d3230e --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_llama_pipeline.py @@ -0,0 +1,85 @@ +import pytest +import torch + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.base_policy import Policy +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + pass + + +@parameterize('enable_fused_normalization', [False]) +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('use_lazy_init', [False]) +#TODO: merge this into test_shard_llama +def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + + sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') + x = torch.randint(0, 1000, (2, 3)).cuda() + hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + if name == 'transformers_llama': + org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x).cuda() + output = sharded_model(input_ids=x, attention_mask=attention_mask) + assert output['hidden_states'].shape == (2, 3, 128) + else: + attention_mask = torch.ones((2, 3)).cuda() + output = sharded_model( + hidden_states=hidden_states, + attention_mask=attention_mask, + ) + # print(output[0].shape) + assert output[0].shape == (2, 3, 128) + + torch.cuda.empty_cache() + + +def check_llama(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_llama_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_llama(): + spawn(check_llama, 4) + + +if __name__ == "__main__": + test_llama() From 31bcf867aeb8efb5859683e3c727c646063748dc Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Tue, 11 Jul 2023 15:23:33 +0800 Subject: [PATCH 24/84] [pipeline] Llama causal lm and llama for sequence classification pipeline (#4208) * bloom policy * llama pipeline forward and tests * fix the output and attention_mask * fix name * bind argument to policy * Revert "bloom policy" This reverts commit 8dee68a0a22568dbeed6d4563372b25e1e825fb0. This policy should be revert and copied to feature/bloom * revert the bloom changes * cancel unneeded inputs * gpt * finish llama * causal lm and sequence classification * revision --- .../shardformer/policies/base_policy.py | 18 ++++ colossalai/shardformer/policies/llama.py | 82 +++++++++++++++++-- tests/kit/model_zoo/transformers/gpt.py | 2 +- .../test_model/test_shard_llama_pipeline.py | 28 +++---- 4 files changed, 109 insertions(+), 21 deletions(-) diff --git a/colossalai/shardformer/policies/base_policy.py b/colossalai/shardformer/policies/base_policy.py index aac86eb20a56..68fde0115de6 100644 --- a/colossalai/shardformer/policies/base_policy.py +++ b/colossalai/shardformer/policies/base_policy.py @@ -162,6 +162,24 @@ def append_or_create_submodule_replacement( return policy + def append_or_create_method_replacement( + self, description: Dict[str, Callable], policy: Dict[Union[str, nn.Module], ModulePolicyDescription], + target_key: Union[str, nn.Module]) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: + r""" + Append or create a new method replacement description to the policy for the given key. + + Args: + description (Union[SubModuleReplacementDescription, List[SubModuleReplacementDescription]]): the submodule replacement description to be appended + policy (Dict[Union[str, nn.Module], ModulePolicyDescription]): the policy to be updated + target_key (Union[str, nn.Module]): the key of the policy to be updated + """ + if target_key in policy: + policy[target_key].method_replacement.update(description) + else: + policy[target_key] = ModulePolicyDescription(method_replacement=description) + + return policy + def get_held_layers(self) -> List[Module]: """Get layers that should be held in current stage. This method should be implemented by subclass. diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index b2b6470188a4..a3ea807269bb 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -131,17 +131,20 @@ def __init__(self) -> None: super().__init__() def module_policy(self): - module_policy = super().module_policy() + policy = super().module_policy() from transformers.models.llama.modeling_llama import LlamaModel if self.pipeline_stage_manager: # set None as default stage_manager = self.pipeline_stage_manager layers_per_stage = Policy.distribute_layers(len(self.model.layers), stage_manager.num_stages) stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - module_policy[LlamaModel] = ModulePolicyDescription(method_replacement={ + method_replacement = { 'forward': partial(llama_model_forward, stage_manager=stage_manager, stage_index=stage_index) - }) - return module_policy + } + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=LlamaModel) + return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" @@ -158,7 +161,7 @@ def get_held_layers(self) -> List[Module]: return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: - """No shared params in bert model""" + """No shared params in llama model""" return [] @@ -179,8 +182,43 @@ def module_policy(self): ]) } policy.update(new_item) + + if self.pipeline_stage_manager: + # set None as default + stage_manager = self.pipeline_stage_manager + layers_per_stage = Policy.distribute_layers(len(self.model.model.layers), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = { + 'forward': partial(llama_for_causal_lm_forward, stage_manager=stage_manager, stage_index=stage_index) + } + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=LlamaForCausalLM) return policy + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.model.layers), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.model.embed_tokens) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.model.layers[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.model.norm) + held_layers.append(module.lm_head) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in llama model""" + llama_model = self.model.model + if id(llama_model.embed_tokens.weight) == id(self.model.lm_head.weight): + # tie weights + return [{0: llama_model.embed_tokens.weight, self.stage_manager.num_stages - 1: self.model.lm_head.weight}] + return [] + class LlamaForSequenceClassificationPolicy(LlamaPolicy): @@ -199,8 +237,42 @@ def module_policy(self): ]) } policy.update(new_item) + # to be confirmed + if self.pipeline_stage_manager: + # set None as default + stage_manager = self.pipeline_stage_manager + layers_per_stage = Policy.distribute_layers(len(self.model.model.layers), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = { + 'forward': + partial(llama_for_sequence_classification_forward, + stage_manager=stage_manager, + stage_index=stage_index) + } + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=LlamaForSequenceClassification) return policy + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.model.layers), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.model.embed_tokens) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.model.layers[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.model.norm) + held_layers.append(module.score) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in llama for sequence classification model""" + return [] + def llama_model_forward( self: LlamaModel, diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index ac70138e3f8f..b9e0310780af 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -52,7 +52,7 @@ def data_gen_for_sequence_classification(): loss_fn = lambda x: x.loss config = transformers.GPT2Config(n_layer=2, - n_head=2, + n_head=4, vocab_size=50258, attn_pdrop=0, embd_pdrop=0, diff --git a/tests/test_shardformer/test_model/test_shard_llama_pipeline.py b/tests/test_shardformer/test_model/test_shard_llama_pipeline.py index 81c183d3230e..8fd9ed099478 100644 --- a/tests/test_shardformer/test_model/test_shard_llama_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_llama_pipeline.py @@ -49,21 +49,19 @@ def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_la x = torch.randint(0, 1000, (2, 3)).cuda() hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - if name == 'transformers_llama': - org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x).cuda() - output = sharded_model(input_ids=x, attention_mask=attention_mask) - assert output['hidden_states'].shape == (2, 3, 128) - else: - attention_mask = torch.ones((2, 3)).cuda() - output = sharded_model( - hidden_states=hidden_states, - attention_mask=attention_mask, - ) - # print(output[0].shape) - assert output[0].shape == (2, 3, 128) + org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x).cuda() + output = sharded_model(input_ids=x, attention_mask=attention_mask) + assert output['hidden_states'].shape == (2, 3, 128) + else: + attention_mask = torch.ones((2, 3)).cuda() + output = sharded_model( + hidden_states=hidden_states, + attention_mask=attention_mask, + ) + assert output[0] is not None torch.cuda.empty_cache() From 37d22f687812c53a3621f9f2d34bdb40126ca6e9 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Thu, 13 Jul 2023 12:47:26 +0800 Subject: [PATCH 25/84] [pipeline] add bloom model pipeline (#4210) * bloom policy * llama pipeline forward and tests * fix the output and attention_mask * fix name * bind argument to policy * finish bloom model * test shard gpt2 * clear cache --- colossalai/shardformer/policies/bloom.py | 227 +++++++++++++++++- tests/kit/model_zoo/transformers/gpt.py | 20 +- .../test_model/test_shard_bloom_pipeline.py | 84 +++++++ .../test_model/test_shard_gpt2.py | 1 + 4 files changed, 322 insertions(+), 10 deletions(-) create mode 100644 tests/test_shardformer/test_model/test_shard_bloom_pipeline.py diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index 662ff5b4977a..5cfc1ab29edf 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -1,11 +1,26 @@ +import warnings +from functools import partial +from types import MethodType +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import torch import torch.nn as nn +from torch import Tensor +from torch.nn import CrossEntropyLoss, Module +from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions +from transformers.models.bloom.modeling_bloom import BloomModel +from transformers.utils import logging import colossalai.shardformer.layer as col_nn +from colossalai.pipeline.stage_manager import PipelineStageManager from .._utils import getattr_, setattr_ from ..modeling.bloom import build_bloom_alibi_tensor_fn from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +logger = logging.get_logger(__name__) + class BloomPolicy(Policy): @@ -110,7 +125,46 @@ def postprocess(self): class BloomModelPolicy(BloomPolicy): - pass + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + policy = super().module_policy() + from transformers.models.bloom.modeling_bloom import BloomModel + if self.pipeline_stage_manager: + stage_manager = self.pipeline_stage_manager + layers_per_stage = Policy.distribute_layers(len(self.model.h), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + policy[BloomModel] = ModulePolicyDescription(method_replacement={ + "forward": + partial(bloom_model_forward, stage_manager=self.pipeline_stage_manager, stage_index=stage_index) + }) + return policy + + def get_held_layers(self) -> List[Module]: + """ + get pipeline layers for current stage + """ + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.word_embeddings) + held_layers.append(module.word_embeddings_layernorm) + + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.h[start_idx:end_idx]) + + if stage_manager.is_last_stage(): + held_layers.append(module.ln_f) + + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + '''no shared params in bloommodel''' + return [] class BloomForCausalLMPolicy(BloomPolicy): @@ -181,3 +235,174 @@ def module_policy(self): class BloomForQuestionAnsweringPolicy(BloomPolicy): # No head sharding as the output features is only 2 pass + + +def bloom_model_forward( + self: BloomModel, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + **deprecated_arguments, +) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # add warnings here + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape batch_size x num_heads x N x N + + # head_mask has shape n_layer x batch x num_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + + # case: First stage of training + if stage_manager.is_first_stage(): + # check input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + hidden_states = self.word_embeddings_layernorm(inputs_embeds) + # initialize in the first stage and then pass to the next stage + else: + input_shape = hidden_states.shape[:-1] + batch_size, seq_length = input_shape + + # extra recording tensor should be generated in the first stage + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + if past_key_values is None: + past_key_values = tuple([None] * len(self.h)) + # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage + seq_length_with_past = seq_length + past_key_values_length = 0 + if past_key_values[0] is not None: + past_key_values_length = past_key_values[0][0].shape[2] # source_len + + seq_length_with_past = seq_length_with_past + past_key_values_length + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) + else: + attention_mask = attention_mask.to(hidden_states.device) + + alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) + + # causal_mask is constructed every stage and its input is passed through different stages + causal_mask = self._prepare_attn_mask( + attention_mask, + input_shape=(batch_size, seq_length), + past_key_values_length=past_key_values_length, + ) + + start_idx, end_idx = stage_index[0], stage_index[1] + for i, (block, layer_past) in enumerate(zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx])): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + alibi, + causal_mask, + layer_past, + head_mask[i], + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + attention_mask=causal_mask, + head_mask=head_mask[i], + use_cache=use_cache, + output_attentions=output_attentions, + alibi=alibi, + ) + + hidden_states = outputs[0] + + if use_cache is True: + presents = presents + (outputs[1],) + if output_attentions: + all_self_attentions = all_self_attentions + \ + (outputs[2 if use_cache else 1],) + + if stage_manager.is_last_stage(): + # Add last hidden state + hidden_states = self.ln_f(hidden_states) + + # TODO: deal with all_hidden_states, all_self_attentions, presents + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if stage_manager.is_last_stage(): + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + # attention_mask is not returned ; presents = past_key_values + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + else: + # always return dict for imediate stage + return {'hidden_states': hidden_states} diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index b9e0310780af..f9a0888ff80d 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -51,15 +51,17 @@ def data_gen_for_sequence_classification(): loss_fn_for_gpt2_model = lambda x: x.last_hidden_state.mean() loss_fn = lambda x: x.loss -config = transformers.GPT2Config(n_layer=2, - n_head=4, - vocab_size=50258, - attn_pdrop=0, - embd_pdrop=0, - resid_pdrop=0, - summary_first_dropout=0, - hidden_dropout=0, - problem_type="single_label_classification") +config = transformers.GPT2Config( + n_layer=2, + n_head=4, + #n_embd=128, + vocab_size=50258, + attn_pdrop=0, + embd_pdrop=0, + resid_pdrop=0, + summary_first_dropout=0, + hidden_dropout=0, + problem_type="single_label_classification") # register the following models model_zoo.register(name='transformers_gpt', diff --git a/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py b/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py new file mode 100644 index 000000000000..31760d692694 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py @@ -0,0 +1,84 @@ +import pytest +import torch + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.base_policy import Policy +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + pass + + +@parameterize('enable_fused_normalization', [False]) +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('use_lazy_init', [False]) +#TODO: merge this into test_shard_bloom +def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + + sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') + x = torch.randint(0, 1000, (2, 3)).cuda() + hidden_states = torch.randint(0, 1000, (2, 3, 64)).to(torch.float32).cuda() + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + if name == 'transformers_bloom': + org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x).cuda() + output = sharded_model(input_ids=x, attention_mask=attention_mask) + assert output['hidden_states'].shape == (2, 3, 64) + else: + attention_mask = torch.ones((2, 3)).cuda() + output = sharded_model( + hidden_states=hidden_states, + attention_mask=attention_mask, + ) + assert output[0].shape == (2, 3, 64) + + torch.cuda.empty_cache() + + +def check_bloom(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_bloom_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_bloom(): + spawn(check_bloom, 4) + + +if __name__ == "__main__": + test_bloom() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 96c4b90a8075..9e5608e7fdcf 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -70,6 +70,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) @parameterize('use_lazy_init', [False, True]) +@clear_cache_before_run() def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): From 208ac8f2ba67d8f43ec6f9024c3a4d112f9b4586 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Thu, 13 Jul 2023 15:34:06 +0800 Subject: [PATCH 26/84] [pipeline] Add Pipeline Forward for GPT2Model Shardformer (#4224) * * fix typehint & docstring in sharder.py * * update pipeline forward for GPT2Model * * add test for pipeline forward of GPT2Model * * add cache cleaning in gpt2 test * * change assert to raise command --- colossalai/shardformer/layer/linear.py | 2 +- colossalai/shardformer/policies/gpt2.py | 270 +++++++++++++++++- colossalai/shardformer/shard/sharder.py | 15 +- .../test_model/test_shard_gpt2.py | 2 + .../test_model/test_shard_gpt2_pipeline.py | 77 +++++ 5 files changed, 357 insertions(+), 9 deletions(-) create mode 100644 tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py diff --git a/colossalai/shardformer/layer/linear.py b/colossalai/shardformer/layer/linear.py index a8439f303bd1..383d9b3f533a 100644 --- a/colossalai/shardformer/layer/linear.py +++ b/colossalai/shardformer/layer/linear.py @@ -129,7 +129,7 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis **kwargs) with torch.no_grad(): - # the weigh to the linear layer is a transpose + # the weight to the linear layer is a transpose # thus shard on row is equal to shard on column sharded_weight = shard_rowwise(module.weight.data, process_group) linear_1d.weight.data.copy_(sharded_weight) diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 8f9d90e67e59..ffba27a50e72 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -1,6 +1,14 @@ -import torch.nn as nn +import logging +from functools import partial +from types import MethodType +from typing import Dict, List, Optional, Tuple, Union + +import torch +from torch import Tensor +from torch.nn import Module import colossalai.shardformer.layer as col_nn +from colossalai.pipeline.stage_manager import PipelineStageManager from .._utils import getattr_, setattr_ from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -119,6 +127,46 @@ class GPT2ModelPolicy(GPT2Policy): def __init__(self) -> None: super().__init__() + def module_policy(self): + from transformers.models.gpt2.modeling_gpt2 import GPT2Model + + policy = super().module_policy() + if self.pipeline_stage_manager: + # set None as default + stage_manager = self.pipeline_stage_manager + layers_per_stage = Policy.distribute_layers(len(self.model.h), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = { + 'forward': + partial(GPT2PipelineForwards.gpt2_model_forward, + stage_manager=stage_manager, + stage_index=stage_index) + } + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=GPT2Model) + return policy + + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.wte) + held_layers.append(module.wpe) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.h[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.ln_f) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + # TODO: check whether there is shared param in gpt2model + """No shared params in gpt2 model.""" + return [] + # GPT2LMHeadModel class GPT2LMHeadModelPolicy(GPT2Policy): @@ -194,3 +242,223 @@ class GPT2ForSequenceClassificationPolicy(GPT2Policy): def __init__(self) -> None: super().__init__() + + +class GPT2PipelineForwards: + ''' + This class serves as a micro library for forward function substitution of GPT2 models + under pipeline setting. + ''' + + @staticmethod + def gpt2_model_forward( + self: 'GPT2Model', + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Tuple, 'BaseModelOutputWithPastAndCrossAttentions']: + + # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward. + # Please refer to original code of transformers for more details. + + from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions + + # Preprocess passed in arguments + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if stage_manager.is_first_stage(): + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + input_shape = input_ids.size() + input_ids = input_ids.view(-1, seq_length) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, seq_length) + else: + if hidden_states is None: + raise ValueError("hidden_states shouln't be None for stages other than the first stage.") + input_shape = hidden_states.size()[:-1] + batch_size, seq_length = input_shape[0], input_shape[1] + device = hidden_states.device + + # GPT2Attention mask. + if attention_mask is not None: + if batch_size <= 0: + raise ValueError("batch_size has to be defined and > 0") + attention_mask = attention_mask.view(batch_size, -1) + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + attention_mask = attention_mask[:, None, None, :] + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and the dtype's smallest value for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility + attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.add_cross_attention and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # head_mask has shape n_layer x batch x n_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + + if stage_manager.is_first_stage(): + if position_ids is not None: + position_ids = position_ids.view(-1, seq_length) + else: + position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + position_embeds = self.wpe(position_ids) + hidden_states = inputs_embeds + position_embeds + if token_type_ids is not None: + token_type_embeds = self.wte(token_type_ids) + hidden_states = hidden_states + token_type_embeds + hidden_states = self.drop(hidden_states) + + output_shape = input_shape + (hidden_states.size(-1),) + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if past_key_values: + logging.warning('Non-empty past_key_values is not supported for pipeline models at the moment.') + past_key_values = None + if output_attentions: + logging.warning('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logging.warning('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logging.warning('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + if self.gradient_checkpointing and self.training: + if use_cache: + logging.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + all_hidden_states = () if output_hidden_states else None + + # Going through held blocks. + start_idx, end_idx = stage_index[0], stage_index[1] + for i in range(start_idx, end_idx): + block = self.h[i] + torch.cuda.set_device(hidden_states.device) + # Ensure that attention_mask is always on the same device as hidden_states + if attention_mask is not None: + attention_mask = attention_mask.to(hidden_states.device) + if isinstance(head_mask, torch.Tensor): + head_mask = head_mask.to(hidden_states.device) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + None, + attention_mask, + head_mask[i], + encoder_hidden_states, + encoder_attention_mask, + ) + else: + outputs = block( + hidden_states, + layer_past=None, + attention_mask=attention_mask, + head_mask=head_mask[i], + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) + + if stage_manager.is_last_stage(): + hidden_states = self.ln_f(hidden_states) + + hidden_states = hidden_states.view(output_shape) + + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if stage_manager.is_last_stage(): + if not return_dict: + return tuple( + v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] + if v is not None) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + else: + # always return dict for intermediate stage + return {'hidden_states': hidden_states} diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 882f93c7acc5..5e0b572e259c 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -72,17 +72,18 @@ def _recursive_replace_layer( attr_replacement: Dict[str, Any], param_replacement: List[Callable], method_replacement: Dict[str, Callable], - sub_module_replacement: List[Callable], + sub_module_replacement: List[SubModuleReplacementDescription], ) -> None: r""" Reverse the replace layer operation Args: - layer (torch.nn.Module): The object of layer to shard - origin_cls (Union[str, torch.nn.Module]): The origin layer class or a string of layer class name. - attr_replacement (Dict): The attribute dict to modify + module (torch.nn.Module): The object of layer to shard + origin_cls (Union[str, torch.nn.Module]): The origin layer class or a string of layer class name + attr_replacement (Dict[str, Any]): The attribute dict to modify param_replacement (List[Callable]): The function list to get parameter shard information in policy - sub_module_replacement (List[Callable]): The function list to get sub module shard information in policy + method_replacement (Dict[str, Callable]): Key is the method name, value is the method for replacement + sub_module_replacement ((List[SubModuleReplacementDescription]): The function list to get sub module shard information in policy """ if (isinstance(origin_cls, str) and origin_cls == module.__class__.__name__) or \ (module.__class__ == origin_cls): @@ -111,7 +112,7 @@ def _replace_attr( Replace the attribute of the layer Args: - layer (:class:`torch.nn.Module`): The object of layer to shard + module (:class:`torch.nn.Module`): The object of layer to shard attr_replacement (Dict): The attribute dict to modify """ for k, v in attr_replacement.items(): @@ -126,7 +127,7 @@ def _replace_param( Replace the parameter of the layer Args: - layer (:class:`torch.nn.Module`): The object of layer to shard + module (:class:`torch.nn.Module`): The object of layer to shard param_replacement (List[Callable]): The function list to get parameter shard information in policy """ for param_func in param_replacement: diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 9e5608e7fdcf..552c6e2f4d53 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -65,6 +65,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo assert torch.allclose( org_grad, all_shard_grad, atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}" + torch.cuda.empty_cache() @parameterize('enable_fused_normalization', [True, False]) @@ -77,6 +78,7 @@ def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_laz org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py new file mode 100644 index 000000000000..5f92f638f863 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py @@ -0,0 +1,77 @@ +import pytest +import torch + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # TODO: add tests for forward/backward later + pass + + +@parameterize('enable_fused_normalization', [False]) +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('use_lazy_init', [False]) +#TODO: merge this into test_shard_gpt2 +def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + + sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + if name != "transformers_gpt": + continue + + inputs = data_gen_fn() + inputs = {k: v.cuda() for k, v in inputs.items()} + + org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + org_model.train() + org_output = org_model(**inputs) + hidden_state_shape = org_output['last_hidden_state'].shape + + if stage_manager.is_first_stage(): + output = sharded_model(**inputs) + assert output['hidden_states'].shape == hidden_state_shape + else: + attention_mask = inputs['attention_mask'] + hidden_states = torch.zeros(*hidden_state_shape).cuda() + output = sharded_model(hidden_states=hidden_states, attention_mask=attention_mask) + if stage_manager.is_last_stage(): + assert output['last_hidden_state'].shape == hidden_state_shape + else: + assert output['hidden_states'].shape == hidden_state_shape + + torch.cuda.empty_cache() + + +def check_gpt2(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_gpt2_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_gpt2(): + spawn(check_gpt2, 4) + + +if __name__ == "__main__": + test_gpt2() From 7e4de520e16af2f555fee760f158ef9e55d80b12 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Fri, 14 Jul 2023 09:51:53 +0800 Subject: [PATCH 27/84] [shardformer] fix base policy (#4229) --- colossalai/shardformer/policies/base_policy.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/colossalai/shardformer/policies/base_policy.py b/colossalai/shardformer/policies/base_policy.py index 68fde0115de6..69493bfb6007 100644 --- a/colossalai/shardformer/policies/base_policy.py +++ b/colossalai/shardformer/policies/base_policy.py @@ -156,7 +156,10 @@ def append_or_create_submodule_replacement( # append or create a new description if target_key in policy: - policy[target_key].sub_module_replacement.extend(description) + if policy[target_key].sub_module_replacement is None: + policy[target_key].sub_module_replacement = description + else: + policy[target_key].sub_module_replacement.extend(description) else: policy[target_key] = ModulePolicyDescription(sub_module_replacement=description) @@ -174,7 +177,10 @@ def append_or_create_method_replacement( target_key (Union[str, nn.Module]): the key of the policy to be updated """ if target_key in policy: - policy[target_key].method_replacement.update(description) + if policy[target_key].method_replacement is None: + policy[target_key].method_replacement = description + else: + policy[target_key].method_replacement.update(description) else: policy[target_key] = ModulePolicyDescription(method_replacement=description) From a14d3520880435f9f6c4dff614c423864ddca11c Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Mon, 17 Jul 2023 15:21:51 +0800 Subject: [PATCH 28/84] [pipeline] add pipeline forward for variants of gpt2 (#4238) * add forward for GPTLMHeadModel * add test for gpt_lm * arranging get_held_layers method * arrange forward replacement * add forward for GPT2ForTokenClassification * add forward for GPT2ForSequenceClassification * fix test_shard_gpt2.py * add GPT2DoubleHeadsmodel & fix bugs * add id checking in get_shared_params --- colossalai/shardformer/policies/gpt2.py | 545 ++++++++++++++++-- .../test_model/test_shard_gpt2_pipeline.py | 50 +- 2 files changed, 529 insertions(+), 66 deletions(-) diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index ffba27a50e72..5d6f47636587 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -1,11 +1,11 @@ import logging from functools import partial from types import MethodType -from typing import Dict, List, Optional, Tuple, Union +from typing import Callable, Dict, List, Optional, Tuple, Union import torch -from torch import Tensor -from torch.nn import Module +from torch import Tensor, nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss import colossalai.shardformer.layer as col_nn from colossalai.pipeline.stage_manager import PipelineStageManager @@ -48,6 +48,10 @@ def module_policy(self): suffix="wte", target_module=col_nn.VocabParallelEmbedding1D, ), + SubModuleReplacementDescription( + suffix="drop", + target_module=col_nn.DropoutForParallelInput, + ), ]) policy[GPT2Block] = ModulePolicyDescription(attribute_replacement={ "attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, @@ -120,6 +124,45 @@ def module_policy(self): def postprocess(self): return self.model + def get_held_layers(self) -> List[nn.Module]: + """Get pipeline layers for current stage.""" + assert self.pipeline_stage_manager is not None + + if self.model.__class__.__name__ == 'GPT2Model': + module = self.model + else: + module = self.model.transformer + stage_manager = self.pipeline_stage_manager + + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.wte) + held_layers.append(module.wpe) + held_layers.append(module.drop) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.h[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.ln_f) + return held_layers + + def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: + """If under pipeline parallel setting, replacing the original forward method of huggingface + to customized forward method, and add this changing to policy.""" + if self.pipeline_stage_manager: + stage_manager = self.pipeline_stage_manager + if self.model.__class__.__name__ == 'GPT2Model': + module = self.model + else: + module = self.model.transformer + + layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=model_cls) + # GPT2Model class GPT2ModelPolicy(GPT2Policy): @@ -131,40 +174,16 @@ def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2Model policy = super().module_policy() - if self.pipeline_stage_manager: - # set None as default - stage_manager = self.pipeline_stage_manager - layers_per_stage = Policy.distribute_layers(len(self.model.h), stage_manager.num_stages) - stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - method_replacement = { - 'forward': - partial(GPT2PipelineForwards.gpt2_model_forward, - stage_manager=stage_manager, - stage_index=stage_index) - } - self.append_or_create_method_replacement(description=method_replacement, - policy=policy, - target_key=GPT2Model) + self.set_pipeline_forward(model_cls=GPT2Model, + new_forward=GPT2PipelineForwards.gpt2_model_forward, + policy=policy) return policy - def get_held_layers(self) -> List[Module]: - """Get pipeline layers for current stage.""" - module = self.model - stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.wte) - held_layers.append(module.wpe) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.h[start_idx:end_idx]) - if stage_manager.is_last_stage(): - held_layers.append(module.ln_f) - return held_layers + def get_held_layers(self) -> List[nn.Module]: + return super().get_held_layers() def get_shared_params(self) -> List[Dict[int, Tensor]]: - # TODO: check whether there is shared param in gpt2model - """No shared params in gpt2 model.""" + """No shared params in GPT2Model.""" return [] @@ -188,10 +207,31 @@ def module_policy(self): ]) } module_policy.update(addon_module) + + self.set_pipeline_forward(model_cls=GPT2LMHeadModel, + new_forward=GPT2PipelineForwards.gpt2_lmhead_model_forward, + policy=module_policy) return module_policy + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.lm_head) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + '''The weights of wte and lm_head are shared.''' + module = self.model + stage_manager = self.pipeline_stage_manager + if stage_manager and id(module.transformer.wte.weight) == id(module.lm_head.weight): + first_stage, last_stage = 0, stage_manager.num_stages - 1 + return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}] + else: + return [] + def postprocess(self): - if self.shard_config.enable_tensor_parallelism: + if self.shard_config.enable_tensor_parallelism \ + and self.pipeline_stage_manager is None: binding_map = {"transformer.wte.weight": "lm_head.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) @@ -199,7 +239,7 @@ def postprocess(self): return self.model -# GPT22DoubleHeadsModel +# GPT2DoubleHeadsModel class GPT2DoubleHeadsModelPolicy(GPT2Policy): def __init__(self) -> None: @@ -219,10 +259,38 @@ def module_policy(self): ]) } module_policy.update(addon_module) + + self.set_pipeline_forward(model_cls=GPT2DoubleHeadsModel, + new_forward=GPT2PipelineForwards.gpt2_double_heads_model_forward, + policy=module_policy) + return module_policy + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + multiple_choice_head = self.model.multiple_choice_head + held_layers.append(self.model.lm_head) + held_layers.append(multiple_choice_head.summary) + held_layers.append(multiple_choice_head.activation) + held_layers.append(multiple_choice_head.first_dropout) + held_layers.append(multiple_choice_head.last_dropout) + + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + '''The weights of wte and lm_head are shared.''' + module = self.model + stage_manager = self.pipeline_stage_manager + if stage_manager and id(module.transformer.wte.weight) == id(module.lm_head.weight): + first_stage, last_stage = 0, stage_manager.num_stages - 1 + return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}] + else: + return [] + def postprocess(self): - if self.shard_config.enable_tensor_parallelism: + if self.shard_config.enable_tensor_parallelism \ + and self.pipeline_stage_manager is None: binding_map = {"transformer.wte.weight": "lm_head.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) @@ -236,6 +304,36 @@ class GPT2ForTokenClassificationPolicy(GPT2Policy): def __init__(self) -> None: super().__init__() + def module_policy(self): + from transformers.models.gpt2.modeling_gpt2 import GPT2ForTokenClassification + + module_policy = super().module_policy() + + if self.shard_config.enable_tensor_parallelism: + addon_module = { + GPT2ForTokenClassification: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription(suffix="dropout", target_module=col_nn.DropoutForParallelInput) + ]) + } + module_policy.update(addon_module) + + self.set_pipeline_forward(model_cls=GPT2ForTokenClassification, + new_forward=GPT2PipelineForwards.gpt2_for_token_classification_forward, + policy=module_policy) + return module_policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.dropout) + held_layers.append(self.model.classifier) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in GPT2ForTokenClassification.""" + return [] + # GPT2ForSequenceClassification class GPT2ForSequenceClassificationPolicy(GPT2Policy): @@ -243,6 +341,25 @@ class GPT2ForSequenceClassificationPolicy(GPT2Policy): def __init__(self) -> None: super().__init__() + def module_policy(self): + from transformers.models.gpt2.modeling_gpt2 import GPT2ForSequenceClassification + + module_policy = super().module_policy() + self.set_pipeline_forward(model_cls=GPT2ForSequenceClassification, + new_forward=GPT2PipelineForwards.gpt2_for_sequence_classification_forward, + policy=module_policy) + return module_policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.score) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in GPT2ForTokenClassification.""" + return [] + class GPT2PipelineForwards: ''' @@ -299,8 +416,7 @@ def gpt2_model_forward( if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, seq_length) else: - if hidden_states is None: - raise ValueError("hidden_states shouln't be None for stages other than the first stage.") + assert hidden_states is not None input_shape = hidden_states.size()[:-1] batch_size, seq_length = input_shape[0], input_shape[1] device = hidden_states.device @@ -462,3 +578,356 @@ def custom_forward(*inputs): else: # always return dict for intermediate stage return {'hidden_states': hidden_states} + + @staticmethod + def gpt2_lmhead_model_forward( + self: 'GPT2LMHeadModel', + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Tuple, 'CausalLMOutputWithCrossAttentions']: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + + This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel.forward. + Please refer to original code of transformers for more details. + """ + + from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + hidden_states = outputs[0] + lm_logits = self.lm_head(hidden_states) + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(lm_logits.device) + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + @staticmethod + def gpt2_double_heads_model_forward( + self: 'GPT2DoubleHeadsModel', + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + mc_token_ids: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + mc_labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Tuple, 'GPT2DoubleHeadsModelOutput']: + r""" + mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input): + Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) - + 1]`. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids`. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to + `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]` + mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` + where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above) + + This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel.forward. + Please refer to original code of transformers for more details. + ```""" + from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModelOutput + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + hidden_states = outputs[0] + lm_logits = self.lm_head(hidden_states) + mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1) + + mc_loss = None + if mc_labels is not None: + loss_fct = CrossEntropyLoss() + mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)) + lm_loss = None + if labels is not None: + labels = labels.to(lm_logits.device) + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss() + lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + if not return_dict: + output = (lm_logits, mc_logits) + outputs[1:] + if mc_loss is not None: + output = (mc_loss,) + output + return ((lm_loss,) + output) if lm_loss is not None else output + + return GPT2DoubleHeadsModelOutput( + loss=lm_loss, + mc_loss=mc_loss, + logits=lm_logits, + mc_logits=mc_logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + @staticmethod + def gpt2_for_token_classification_forward( + self: 'GPT2ForTokenClassification', + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Tuple, 'TokenClassifierOutput']: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification.forward. + # Please refer to original code of transformers for more details. + """ + + from transformers.modeling_outputs import TokenClassifierOutput + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + hidden_states = outputs[0] + hidden_states = self.dropout(hidden_states) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + @staticmethod + def gpt2_for_sequence_classification_forward( + self: 'GPT2ForSequenceClassification', + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Tuple, 'SequenceClassifierOutputWithPast']: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification.forward. + # Please refer to original code of transformers for more details. + """ + from transformers.modeling_outputs import SequenceClassifierOutputWithPast + + if input_ids is not None: + batch_size, _ = input_ids.shape[:2] + else: + batch_size, _ = hidden_states.shape[:2] + assert (self.config.pad_token_id is not None + or batch_size == 1), "Cannot handle batch sizes > 1 if no padding token is defined." + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + hidden_states = outputs[0] + logits = self.score(hidden_states) + + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + logging.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`") + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py index 5f92f638f863..dd439a394827 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py @@ -5,15 +5,9 @@ from colossalai.cluster import ProcessGroupMesh from colossalai.logging import disable_existing_loggers from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward +from tests.test_shardformer.test_model._utils import build_pipeline_model def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -21,8 +15,8 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo pass -@parameterize('enable_fused_normalization', [False]) @parameterize('enable_tensor_parallelism', [False]) +@parameterize('enable_fused_normalization', [False]) @parameterize('use_lazy_init', [False]) #TODO: merge this into test_shard_gpt2 def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): @@ -32,30 +26,30 @@ def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_laz stage_manager = PipelineStageManager(pg_mesh, PP_DIM) sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') - for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - if name != "transformers_gpt": - continue + for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): inputs = data_gen_fn() inputs = {k: v.cuda() for k, v in inputs.items()} + input_ids, _ = inputs['input_ids'], inputs['attention_mask'] + batch_size, seq_len = input_ids.shape + hidden_size = 768 + hidden_state_shape = (batch_size, seq_len, hidden_size) - org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - org_model.train() - org_output = org_model(**inputs) - hidden_state_shape = org_output['last_hidden_state'].shape - - if stage_manager.is_first_stage(): - output = sharded_model(**inputs) - assert output['hidden_states'].shape == hidden_state_shape - else: - attention_mask = inputs['attention_mask'] + if not stage_manager.is_first_stage(): + # change inputs if not the first stage hidden_states = torch.zeros(*hidden_state_shape).cuda() - output = sharded_model(hidden_states=hidden_states, attention_mask=attention_mask) - if stage_manager.is_last_stage(): - assert output['last_hidden_state'].shape == hidden_state_shape - else: - assert output['hidden_states'].shape == hidden_state_shape + inputs['input_ids'] = None + inputs['hidden_states'] = hidden_states + + _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + sharded_model.train() + output = sharded_model(**inputs) + if stage_manager.is_last_stage(): + if name != 'transformers_gpt': + assert output.loss is not None + else: + assert output['hidden_states'].shape == hidden_state_shape torch.cuda.empty_cache() From e7cc62d73568795b7ae54a6c13e7056f2048a98a Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Mon, 17 Jul 2023 16:12:20 +0800 Subject: [PATCH 29/84] [pipeline] All bert models (#4233) * bloom policy * llama pipeline forward and tests * fix the output and attention_mask * fix name * bind argument to policy * Revert "bloom policy" This reverts commit 8dee68a0a22568dbeed6d4563372b25e1e825fb0. This policy should be revert and copied to feature/bloom * revert the bloom changes * cancel unneeded inputs * gpt * finish llama * causal lm and sequence classification * revision * add pure pipeline test * finish some bert models * finish all bert models * finish bert tests * fix bugs * fix bugs * fix test pipeline * fix data gen for qa * update the set pipeline forward * shared params * fix bugs --- colossalai/pipeline/p2p.py | 5 +- colossalai/pipeline/schedule/one_f_one_b.py | 1 - .../shardformer/policies/auto_policy.py | 2 + colossalai/shardformer/policies/bert.py | 832 +++++++++++++++--- colossalai/shardformer/policies/llama.py | 6 +- tests/kit/model_zoo/torchrec/__init__.py | 2 +- tests/kit/model_zoo/transformers/bert.py | 17 + .../test_bert_for_pretraining_model.py | 19 +- ...ad_model.py => test_bert_lm_head_model.py} | 33 +- .../test_policy/test_bert_model.py | 16 +- tests/test_shardformer/test_model/_utils.py | 1 - .../test_model/test_pure_pipeline.py | 164 ++++ .../test_model/test_shard_bert_pipeline.py | 28 +- 13 files changed, 985 insertions(+), 141 deletions(-) rename tests/test_pipeline/test_policy/{test_bert_lmhead_model.py => test_bert_lm_head_model.py} (73%) create mode 100644 tests/test_shardformer/test_model/test_pure_pipeline.py diff --git a/colossalai/pipeline/p2p.py b/colossalai/pipeline/p2p.py index 203b7439d7ef..2fd135d5475d 100644 --- a/colossalai/pipeline/p2p.py +++ b/colossalai/pipeline/p2p.py @@ -64,7 +64,10 @@ def _broadcast_object_list(object_list: List[Any], my_rank = dist.get_rank() # Serialize object_list elements to tensors on src rank. if my_rank == src: - tensor_list, size_list = zip(*[c10d._object_to_tensor(obj) for obj in object_list]) + if torch.__version__ >= "1.13.0": + tensor_list, size_list = zip(*[c10d._object_to_tensor(obj, device=device) for obj in object_list]) + else: + tensor_list, size_list = zip(*[c10d._object_to_tensor(obj) for obj in object_list]) object_sizes_tensor = torch.cat(size_list) else: object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long) diff --git a/colossalai/pipeline/schedule/one_f_one_b.py b/colossalai/pipeline/schedule/one_f_one_b.py index a8933bfbb4da..6ed3055d689b 100644 --- a/colossalai/pipeline/schedule/one_f_one_b.py +++ b/colossalai/pipeline/schedule/one_f_one_b.py @@ -205,7 +205,6 @@ def forward_backward_step(self, # the backward pass. input_obj = input_objs.pop(0) output_obj = output_objs.pop(0) - input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad) if last_iteration: diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index 0ad9a3e95a0e..ccdb33b2efe5 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -42,6 +42,8 @@ class PolicyLocation: PolicyLocation(file_name="bert", class_name="BertForNextSentencePredictionPolicy"), "transformers.models.bert.modeling_bert.BertForMultipleChoice": PolicyLocation(file_name="bert", class_name="BertForMultipleChoicePolicy"), + "transformers.models.bert.modeling_bert.BertForQuestionAnswering": + PolicyLocation(file_name="bert", class_name="BertForQuestionAnsweringPolicy"), # LLaMA "transformers.models.llama.modeling_llama.LlamaModel": diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 2b2c003ffb04..1af26f50484c 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,22 +1,30 @@ from functools import partial from types import MethodType -from typing import Dict, List, Optional, Tuple, Union +from typing import Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn from torch import Tensor from torch.nn import CrossEntropyLoss, Module from transformers.modeling_outputs import ( - BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, ) from transformers.models.bert.modeling_bert import ( BertForMaskedLM, + BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForPreTrainingOutput, + BertForQuestionAnswering, + BertForSequenceClassification, + BertForTokenClassification, BertLMHeadModel, BertModel, ) @@ -31,9 +39,9 @@ logger = logging.get_logger(__name__) __all__ = [ - 'BertPolicy', 'BertModelPolicy', 'BertForPreTrainingPolicy', 'BertLMHeadModelPolicy', 'BertForMaskedLMPolicy', + 'BertPolicy', 'BertModelPolicy', 'BertForPreTrainingPolicy', 'BertLMdHeadModelPolicy', 'BertForMaskedLMPolicy', 'BertForNextSentencePredictionPolicy', 'BertForSequenceClassificationPolicy', 'BertForTokenClassificationPolicy', - 'BertForMultipleChoicePolicy' + 'BertForMultipleChoicePolicy', 'BertForQuestionAnsweringPolicy' ] @@ -172,6 +180,25 @@ def add_lm_head_policy(self, base_policy): def postprocess(self): return self.model + def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: + """If under pipeline parallel setting, replacing the original forward method of huggingface + to customized forward method, and add this changing to policy.""" + if self.pipeline_stage_manager: + stage_manager = self.pipeline_stage_manager + if self.model.__class__.__name__ == "BertModel": + module = self.model + else: + module = self.model.bert + + layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=model_cls) + + return + # BertModel class BertModelPolicy(BertPolicy): @@ -180,13 +207,10 @@ def __init__(self) -> None: super().__init__() def module_policy(self): - module_policy = super().module_policy() + policy = super().module_policy() from transformers.models.bert.modeling_bert import BertModel - if self.pipeline_stage_manager: - # set None as default - module_policy[BertModel] = ModulePolicyDescription( - method_replacement={'forward': partial(bert_model_forward, stage_manager=self.pipeline_stage_manager)}) - return module_policy + self.set_pipeline_forward(model_cls=BertModel, new_forward=bert_model_forward, policy=policy) + return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" @@ -214,15 +238,17 @@ def __init__(self) -> None: super().__init__() def module_policy(self): - module_policy = super().module_policy() - module_policy = self.add_lm_head_policy(module_policy) - return module_policy + policy = super().module_policy() + policy = self.add_lm_head_policy(policy) + from transformers.models.bert.modeling_bert import BertForPreTraining + self.set_pipeline_forward(model_cls=BertForPreTraining, new_forward=bert_for_pretraining_forward, policy=policy) + return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage""" module = self.model stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(self.model.bert.encoder.layer), stage_manager.num_stages) + layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) held_layers = [] if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) @@ -237,11 +263,18 @@ def get_held_layers(self) -> List[Module]: return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: - '''No shared params in bertmodel''' + model = self.model + if self.pipeline_stage_manager: + if id(model.bert.embeddings.word_embeddings.weight) == id(model.cls.predictions.decoder.weight): + #tie weights + return [{ + 0: model.bert.embeddings.word_embeddings.weight, + self.pipeline_stage_manager.num_stages - 1: model.cls.predictions.decoder.weight + }] return [] def postprocess(self): - if self.shard_config.enable_tensor_parallelism: + if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) @@ -256,9 +289,11 @@ def __init__(self) -> None: super().__init__() def module_policy(self): - module_policy = super().module_policy() - module_policy = self.add_lm_head_policy(module_policy) - return module_policy + policy = super().module_policy() + policy = self.add_lm_head_policy(policy) + from transformers.models.bert.modeling_bert import BertLMHeadModel + self.set_pipeline_forward(model_cls=BertLMHeadModel, new_forward=bert_lm_head_model_forward, policy=policy) + return policy def get_held_layers(self) -> List[Module]: """ @@ -267,7 +302,7 @@ def get_held_layers(self) -> List[Module]: module = self.model held_layers = [] stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(self.model.bert.encoder.layer), stage_manager.num_stages) + layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) @@ -278,11 +313,18 @@ def get_held_layers(self) -> List[Module]: return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: - '''No shared params in bertmodel''' + bert_model = self.model.bert + if self.pipeline_stage_manager: + if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight): + #tie weights + return [{ + 0: bert_model.embeddings.word_embeddings.weight, + self.pipeline_stage_manager.num_stages - 1: self.model.cls.predictions.decoder.weight + }] return [] def postprocess(self): - if self.shard_config.enable_tensor_parallelism: + if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) @@ -297,12 +339,42 @@ def __init__(self) -> None: super().__init__() def module_policy(self): - module_policy = super().module_policy() - module_policy = self.add_lm_head_policy(module_policy) - return module_policy + policy = super().module_policy() + policy = self.add_lm_head_policy(policy) + from transformers.models.bert.modeling_bert import BertForMaskedLM + self.set_pipeline_forward(model_cls=BertForMaskedLM, new_forward=bert_for_masked_lm_forward, policy=policy) + return policy + + def get_held_layers(self) -> List[Module]: + """ + get pipeline layers for current stage + """ + module = self.model + held_layers = [] + stage_manager = self.pipeline_stage_manager + layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.bert.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.bert.pooler) + held_layers.append(module.cls) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + bert_model = self.model.bert + if self.pipeline_stage_manager: + if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight): + #tie weights + return [{ + 0: bert_model.embeddings.word_embeddings.weight, + self.pipeline_stage_manager.num_stages - 1: self.model.cls.predictions.decoder.weight + }] + return [] def postprocess(self): - if self.shard_config.enable_tensor_parallelism: + if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) @@ -319,7 +391,7 @@ def __init__(self) -> None: def module_policy(self): from transformers.models.bert.modeling_bert import BertForSequenceClassification - module_policy = super().module_policy() + policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { @@ -331,8 +403,35 @@ def module_policy(self): ) ]) } - module_policy.update(addon_module) - return module_policy + policy.update(addon_module) + + self.set_pipeline_forward(model_cls=BertForSequenceClassification, + new_forward=bert_for_sequence_classification_forward, + policy=policy) + + return policy + + def get_held_layers(self) -> List[Module]: + """ + get pipeline layers for current stage + """ + module = self.model + held_layers = [] + stage_manager = self.pipeline_stage_manager + layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.bert.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.bert.pooler) + held_layers.append(module.dropout) + held_layers.append(module.classifier) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + # no shared params for sequence classification model + return [] # BertForTokenClassification @@ -344,7 +443,7 @@ def __init__(self) -> None: def module_policy(self): from transformers.models.bert.modeling_bert import BertForTokenClassification - module_policy = super().module_policy() + policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { @@ -356,8 +455,35 @@ def module_policy(self): ) ]) } - module_policy.update(addon_module) - return module_policy + policy.update(addon_module) + + self.set_pipeline_forward(model_cls=BertForTokenClassification, + new_forward=bert_for_token_classification_forward, + policy=policy) + + return policy + + def get_held_layers(self) -> List[Module]: + """ + get pipeline layers for current stage + """ + module = self.model + held_layers = [] + stage_manager = self.pipeline_stage_manager + layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.bert.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.bert.pooler) + held_layers.append(module.dropout) + held_layers.append(module.classifier) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + # no shared params for sequence classification model + return [] # BertForNextSentencePrediction @@ -366,6 +492,36 @@ class BertForNextSentencePredictionPolicy(BertPolicy): def __init__(self) -> None: super().__init__() + def module_policy(self): + policy = super().module_policy() + from transformers.models.bert.modeling_bert import BertForNextSentencePrediction + self.set_pipeline_forward(model_cls=BertForNextSentencePrediction, + new_forward=bert_for_next_sentence_prediction_forward, + policy=policy) + + return policy + + def get_held_layers(self) -> List[Module]: + """ + get pipeline layers for current stage + """ + module = self.model + held_layers = [] + stage_manager = self.pipeline_stage_manager + layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.bert.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.bert.pooler) + held_layers.append(module.cls) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + # no shared params for sequence classification model + return [] + # BertForMultipleChoice class BertForMultipleChoicePolicy(BertPolicy): @@ -376,7 +532,7 @@ def __init__(self) -> None: def module_policy(self): from transformers.models.bert.modeling_bert import BertForMultipleChoice - module_policy = super().module_policy() + policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { @@ -388,28 +544,91 @@ def module_policy(self): ) ]) } - module_policy.update(addon_module) - return module_policy + policy.update(addon_module) + + self.set_pipeline_forward(model_cls=BertForMultipleChoice, + new_forward=bert_for_multiple_choice_forward, + policy=policy) + + return policy + + def get_held_layers(self) -> List[Module]: + """ + get pipeline layers for current stage + """ + module = self.model + held_layers = [] + stage_manager = self.pipeline_stage_manager + layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.bert.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.bert.pooler) + held_layers.append(module.dropout) + held_layers.append(module.classifier) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + # no shared params for sequence classification model + return [] + + +class BertForQuestionAnsweringPolicy(BertPolicy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + from transformers.models.bert.modeling_bert import BertForQuestionAnswering + policy = super().module_policy() + self.set_pipeline_forward(model_cls=BertForQuestionAnswering, + new_forward=bert_for_question_answering_forward, + policy=policy) + + return policy + + def get_held_layers(self) -> List[Module]: + """ + get pipeline layers for current stage + """ + module = self.model + held_layers = [] + stage_manager = self.pipeline_stage_manager + layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.bert.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.bert.pooler) + held_layers.append(module.qa_outputs) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + # no shared params for sequence classification model + return [] def bert_model_forward( - self: BertModel, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - # labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage + self: BertModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage + stage_index: Optional[List[int]] = None, ): # TODO: add explaination of the output here. r""" @@ -528,14 +747,10 @@ def bert_model_forward( use_cache = False next_decoder_cache = () if use_cache else None - # calculate the num_layers - num_layers_per_stage = len(self.encoder.layer) // stage_manager.num_stages - start_layer = stage_manager.stage * num_layers_per_stage - end_layer = (stage_manager.stage + 1) * num_layers_per_stage - + start_idx, end_idx = stage_index[0], stage_index[1] # layer_outputs layer_outputs = hidden_states if hidden_states is not None else None - for idx, encoder_layer in enumerate(self.encoder.layer[start_layer:end_layer], start=start_layer): + for idx, encoder_layer in enumerate(self.encoder.layer[start_idx:end_idx], start=start_idx): if stage_manager.is_first_stage() and idx == 0: encoder_attention_mask = encoder_extended_attention_mask @@ -593,8 +808,9 @@ def custom_forward(*inputs): return (sequence_output, pooled_output) + layer_outputs[1:] # return dict is not supported at this moment else: - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, @@ -624,6 +840,7 @@ def bert_for_pretraining_forward( return_dict: Optional[bool] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, ): return_dict = return_dict if return_dict is not None else self.config.use_return_dict # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. @@ -637,18 +854,21 @@ def bert_for_pretraining_forward( logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False - outputs = bert_model_forward(self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states if hidden_states is not None else None) + outputs = bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states if hidden_states is not None else None, + stage_index=stage_index, + ) past_key_values = None all_hidden_states = None all_self_attentions = None @@ -684,23 +904,26 @@ def bert_for_pretraining_forward( } -def bert_lmhead_forward(self: BertLMHeadModel, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.Tensor]] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_manager: Optional[PipelineStageManager] = None): +def bert_lm_head_model_forward( + self: BertLMHeadModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.Tensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, +): r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if @@ -754,7 +977,8 @@ def bert_lmhead_forward(self: BertLMHeadModel, output_hidden_states=output_hidden_states, return_dict=return_dict, stage_manager=stage_manager, - hidden_states=hidden_states if hidden_states is not None else None) + hidden_states=hidden_states if hidden_states is not None else None, + stage_index=stage_index) past_key_values = None all_hidden_states = None all_self_attentions = None @@ -806,15 +1030,66 @@ def bert_for_masked_lm_forward( return_dict: Optional[bool] = None, hidden_states: Optional[torch.Tensor] = None, stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, ): - #-> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ - pass + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index, + ) + + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} def bert_for_next_sentence_prediction_forward( @@ -831,6 +1106,7 @@ def bert_for_next_sentence_prediction_forward( return_dict: Optional[bool] = None, hidden_states: Optional[torch.Tensor] = None, stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, **kwargs, ): #-> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: @@ -881,6 +1157,259 @@ def bert_for_next_sentence_prediction_forward( return_dict = False return_dict = return_dict if return_dict is not None else self.config.use_return_dict + outputs = bert_model_forward(self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index) + + if stage_manager.is_last_stage(): + pooled_output = outputs[1] + seq_relationship_scores = self.cls(pooled_output) + + next_sentence_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) + + if not return_dict: + output = (seq_relationship_scores,) + outputs[2:] + return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output + + return NextSentencePredictorOutput( + loss=next_sentence_loss, + logits=seq_relationship_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + # intermediate stage always return dict + return {'hidden_states': hidden_states} + + +def bert_for_sequence_classification_forward( + self: BertForSequenceClassification, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, +): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = bert_model_forward(self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index) + + if stage_manager.is_last_stage(): + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + +def bert_for_token_classification_forward( + self: BertForTokenClassification, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, +): + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index, + ) + + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + +def bert_for_multiple_choice_forward( + self: BertForMultipleChoice, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, +): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # in our pipeline design,input ids are copied for every stage and shouldn't be none + # the input_ids for multiple choice model is [batch_size, num_choices, sequence_length] + if stage_manager.is_last_stage(): + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None else None) + outputs = bert_model_forward( self.bert, input_ids, @@ -892,27 +1421,128 @@ def bert_for_next_sentence_prediction_forward( output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index, ) if stage_manager.is_last_stage(): pooled_output = outputs[1] - seq_relationship_scores = self.cls(pooled_output) + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) - next_sentence_loss = None + loss = None if labels is not None: loss_fct = CrossEntropyLoss() - next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) + loss = loss_fct(reshaped_logits, labels) if not return_dict: - output = (seq_relationship_scores,) + outputs[2:] - return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output + output = (reshaped_logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output - return NextSentencePredictorOutput( - loss=next_sentence_loss, - logits=seq_relationship_scores, + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + +def bert_for_question_answering_forward( + self: BertForQuestionAnswering, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + start_positions: Optional[torch.Tensor] = None, + end_positions: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, +): + # NOTE: the arg start_position and end_position are used only for the last stage + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index, + ) + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: hidden_states = outputs.get('hidden_states') - # intermediate stage always return dict return {'hidden_states': hidden_states} diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index a3ea807269bb..b3757452c314 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -212,11 +212,13 @@ def get_held_layers(self) -> List[Module]: return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: - """No shared params in llama model""" llama_model = self.model.model if id(llama_model.embed_tokens.weight) == id(self.model.lm_head.weight): # tie weights - return [{0: llama_model.embed_tokens.weight, self.stage_manager.num_stages - 1: self.model.lm_head.weight}] + return [{ + 0: llama_model.embed_tokens.weight, + self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight + }] return [] diff --git a/tests/kit/model_zoo/torchrec/__init__.py b/tests/kit/model_zoo/torchrec/__init__.py index 43952e6998cf..4a19f2449602 100644 --- a/tests/kit/model_zoo/torchrec/__init__.py +++ b/tests/kit/model_zoo/torchrec/__init__.py @@ -1 +1 @@ -from .torchrec import * +#from .torchrec import * diff --git a/tests/kit/model_zoo/transformers/bert.py b/tests/kit/model_zoo/transformers/bert.py index d2d3de7b7bee..1993af51ad63 100644 --- a/tests/kit/model_zoo/transformers/bert.py +++ b/tests/kit/model_zoo/transformers/bert.py @@ -87,6 +87,17 @@ def data_gen_for_mcq(): return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels) +def data_gen_for_qa(): + # generating data for question answering + # no need for labels and use start and end position instead + data = data_gen() + start_positions = torch.tensor([0], dtype=torch.int64) + data['start_positions'] = start_positions + end_positions = torch.tensor([1], dtype=torch.int64) + data['end_positions'] = end_positions + return data + + # define output transform function output_transform_fn = lambda x: x @@ -150,3 +161,9 @@ def data_gen_for_mcq(): output_transform_fn=output_transform_fn, loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='transformers_bert_for_question_answering', + model_fn=lambda: transformers.BertForQuestionAnswering(config), + data_gen_fn=data_gen_for_qa, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py index 97d7d2fa538a..6a8d7b636375 100644 --- a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py +++ b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py @@ -7,6 +7,7 @@ import colossalai from colossalai.cluster import ProcessGroupMesh from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.base_policy import Policy from colossalai.shardformer.policies.bert import BertForPreTrainingPolicy, bert_for_pretraining_forward from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn @@ -35,16 +36,20 @@ def check_bert_for_pretraining_forward(): stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() # print(rank) + layers_per_stage = Policy.distribute_layers(len(model.bert.encoder.layer), 2) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) x = torch.randint(0, 1000, (2, 3)) hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) if stage_manager.stage == 0: attention_mask = torch.ones_like(x) - output = bert_for_pretraining_forward(self=model, - input_ids=x, - attention_mask=attention_mask, - stage_manager=stage_manager) - print(output['hidden_states'].shape) + output = bert_for_pretraining_forward( + self=model, + input_ids=x, + attention_mask=attention_mask, + stage_manager=stage_manager, + stage_index=stage_index, + ) assert output['hidden_states'].shape == (2, 3, 768) else: @@ -52,8 +57,8 @@ def check_bert_for_pretraining_forward(): output = bert_for_pretraining_forward(self=model, hidden_states=hidden_states, attention_mask=attention_mask, - stage_manager=stage_manager) - print(output[0].shape) + stage_manager=stage_manager, + stage_index=stage_index) assert output[0].shape == (2, 3, 30522) # assert output[1].shape == (2, 768) diff --git a/tests/test_pipeline/test_policy/test_bert_lmhead_model.py b/tests/test_pipeline/test_policy/test_bert_lm_head_model.py similarity index 73% rename from tests/test_pipeline/test_policy/test_bert_lmhead_model.py rename to tests/test_pipeline/test_policy/test_bert_lm_head_model.py index b14dadf29e3c..cd47f7a33c4b 100644 --- a/tests/test_pipeline/test_policy/test_bert_lmhead_model.py +++ b/tests/test_pipeline/test_policy/test_bert_lm_head_model.py @@ -7,12 +7,13 @@ import colossalai from colossalai.cluster import ProcessGroupMesh from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.bert import BertLMHeadModelPolicy, bert_lmhead_forward +from colossalai.shardformer.policies.base_policy import Policy +from colossalai.shardformer.policies.bert import BertLMHeadModelPolicy, bert_lm_head_model_forward from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn -def check_bert_lmhead_forward(): +def check_bert_lm_head_model_forward(): configuration = BertConfig() model = BertLMHeadModel(configuration) DP_DIM, PP_DIM = 0, 1 @@ -35,24 +36,28 @@ def check_bert_lmhead_forward(): stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() # print(rank) - + layers_per_stage = Policy.distribute_layers(len(model.bert.encoder.layer), 2) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) x = torch.randint(0, 1000, (2, 3)) hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) if stage_manager.stage == 0: attention_mask = torch.ones_like(x) - output = bert_lmhead_forward(self=model, - input_ids=x, - attention_mask=attention_mask, - stage_manager=stage_manager) + + output = bert_lm_head_model_forward(self=model, + input_ids=x, + attention_mask=attention_mask, + stage_manager=stage_manager, + stage_index=stage_index) print(output['hidden_states'].shape) assert output['hidden_states'].shape == (2, 3, 768) else: attention_mask = torch.ones((2, 3)) - output = bert_lmhead_forward(self=model, - hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager) + output = bert_lm_head_model_forward(self=model, + hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager, + stage_index=stage_index) print(output[0].shape) assert output[0].shape == (2, 3, 30522) @@ -93,7 +98,7 @@ def check_bert_lmhead_policy(): def run_dist_model(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bert_lmhead_forward() + check_bert_lm_head_model_forward() def run_dist_policy(rank, world_size, port): @@ -103,7 +108,7 @@ def run_dist_policy(rank, world_size, port): @pytest.mark.dist @rerun_if_address_is_in_use() -def test_bert_lmhead_forward(): +def test_bert_lm_head_model_forward(): spawn(run_dist_model, 4) @@ -115,5 +120,5 @@ def test_bert_lmhead_policy(): if __name__ == "__main__": """test the bert for pretraining model forward and bert for pretraining model policy""" - test_bert_lmhead_forward() + test_bert_lm_head_model_forward() test_bert_lmhead_policy() diff --git a/tests/test_pipeline/test_policy/test_bert_model.py b/tests/test_pipeline/test_policy/test_bert_model.py index f5a443309cb2..f116bc761aa7 100644 --- a/tests/test_pipeline/test_policy/test_bert_model.py +++ b/tests/test_pipeline/test_policy/test_bert_model.py @@ -6,12 +6,14 @@ import colossalai from colossalai.cluster import ProcessGroupMesh from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.base_policy import Policy from colossalai.shardformer.policies.bert import BertModelPolicy, bert_model_forward from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn def check_bert_model_forward(): + # this test may crash for internet reasons model = BertModel.from_pretrained('bert-base-uncased') DP_DIM, PP_DIM = 0, 1 DP_SIZE, PP_SIZE = 2, 2 @@ -34,20 +36,25 @@ def check_bert_model_forward(): stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() # print(rank) - + layers_per_stage = Policy.distribute_layers(len(model.encoder.layer), 2) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) x = torch.randint(0, 1000, (2, 3)) hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) if stage_manager.stage == 0: attention_mask = torch.ones_like(x) - output = bert_model_forward(self=model, input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) - print(output['hidden_states'].shape) + output = bert_model_forward(self=model, + input_ids=x, + attention_mask=attention_mask, + stage_manager=stage_manager, + stage_index=stage_index) assert output['hidden_states'].shape == (2, 3, 768) else: attention_mask = torch.ones((2, 3)) output = bert_model_forward(self=model, hidden_states=hidden_states, attention_mask=attention_mask, - stage_manager=stage_manager) + stage_manager=stage_manager, + stage_index=stage_index) print(output[0].shape) assert output[0].shape == (2, 3, 768) @@ -112,4 +119,3 @@ def test_bert_model_policy(): """test the bert model forward and bert model policy""" #test_bert_model_forward() test_bert_model_policy() - # this test need config to run diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index f26c6622da7e..825d6df6bb5e 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -49,7 +49,6 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, # prepare input data = data_gen_fn() data = {k: v.cuda() for k, v in data.items()} - # switch to train mode original_model.train() sharded_model.train() diff --git a/tests/test_shardformer/test_model/test_pure_pipeline.py b/tests/test_shardformer/test_model/test_pure_pipeline.py new file mode 100644 index 000000000000..24cda193a5e6 --- /dev/null +++ b/tests/test_shardformer/test_model/test_pure_pipeline.py @@ -0,0 +1,164 @@ +import random +from contextlib import nullcontext +from typing import Any, Callable, Iterator, List, Optional, Tuple + +import numpy as np +import pytest +import torch +import torch.distributed as dist +from torch import Tensor +from torch.nn import Module +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.interface import ModelWrapper, OptimizerWrapper +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.schedule import OneForwardOneBackwardSchedule +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer import ShardConfig, ShardFormer +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward + +DP_AXIS, PP_AXIS, TP_AXIS = 0, 1, 2 + + +class PipelineOptimizer(OptimizerWrapper): + + def __init__(self, optim: Optimizer, model: Module): + super().__init__(optim) + params = set(model.parameters()) + new_param_groups = [] + for group in optim.param_groups: + params = [p for p in group['params'] if p in params] + new_param_groups.append({**group, 'params': params}) + optim.__setstate__({'param_groups': new_param_groups}) + # TODO: support amp + + +class PipelinedModel(ModelWrapper): + + def __init__(self, module: Module, shard_config: ShardConfig, stage_manager: PipelineStageManager) -> None: + self.stage_manager = stage_manager + shardformer = ShardFormer(shard_config) + module, self.shared_params = shardformer.optimize(module) + self.shared_param_process_groups = [] + super().__init__(module) + + +def prepare_dataloader(dataset, batch_size, shuffle=False, seed=1024, drop_last=False, pin_memory=False, num_workers=0): + sampler = DistributedSampler( + dataset, + #rank=self.pg_mesh.coordinate(DP_AXIS), + shuffle=shuffle) + + # Deterministic dataloader + def seed_worker(worker_id): + worker_seed = seed + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + random.seed(worker_seed) + + return DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + worker_init_fn=seed_worker, + drop_last=drop_last, + pin_memory=pin_memory, + num_workers=num_workers, + ) + + +def execute_pipeline( + data_iter: Iterator, + model: PipelinedModel, + criterion: Callable[[Any, Any], torch.Tensor], + optimizer: PipelineOptimizer, + return_loss: bool = True, + return_outputs: bool = False, + schedule: OneForwardOneBackwardSchedule = None, +) -> dict: + # return loss or outputs if needed + outputs = schedule.forward_backward_step(model, optimizer, data_iter, criterion, return_loss, return_outputs) + return outputs + + +class data_iter(): + + def __getitem__(self, x): + return torch.randint(0, 100, (4, 128)).cuda() + + +def loss(x, y): + return (x[0].float().mean() - y[0].float().mean()) + + +@parameterize('enable_fused_normalization', [False]) +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('use_lazy_init', [False]) +def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + PP_DIM = 0 + PP_SIZE = 2 + RANK_TO_COORDINATE = { + 0: (0, 0), + 1: (0, 1), + 2: (1, 0), + 3: (1, 1), + } + PP_RANKS_IN_GROUP = { + 0: [0, 1], + 1: [0, 1], + 2: [2, 3], + 3: [2, 3], + } + from datasets import load_dataset + + #dataset = load_dataset("open_subtitles", lang1="fi", lang2="hi") + pg_mesh = ProcessGroupMesh(PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + num_microbatches = 2 + org_model = model_fn().cuda() + optimizer = torch.optim.AdamW(org_model.parameters(), lr=1e-3) + #dataloader=prepare_dataloader(dataset=dataset['train'],batch_size=4) + schedule = OneForwardOneBackwardSchedule(num_microbatches, stage_manager) + shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, + enable_tensor_parallelism=enable_tensor_parallelism, + pipeline_stage_manager=stage_manager) + pipelined_model = PipelinedModel(org_model, shard_config, stage_manager) + pp_optimizer = PipelineOptimizer(optimizer, pipelined_model) + data_it = iter(data_iter()) + results = execute_pipeline(data_it, pipelined_model, loss, pp_optimizer, schedule=schedule) + if stage_manager.is_last_stage(): + assert results['loss'] is not None + assert results['outputs'] is None + torch.cuda.empty_cache() + + +def check_llama(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_llama_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_llama(): + spawn(check_llama, 2) + + +if __name__ == "__main__": + test_llama() diff --git a/tests/test_shardformer/test_model/test_shard_bert_pipeline.py b/tests/test_shardformer/test_model/test_shard_bert_pipeline.py index 9cca5ec8bc51..4feaf982aa37 100644 --- a/tests/test_shardformer/test_model/test_shard_bert_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_bert_pipeline.py @@ -45,25 +45,37 @@ def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_laz stage_manager = PipelineStageManager(pg_mesh, PP_DIM) sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') - x = torch.randint(0, 1000, (2, 3)).cuda() - hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - if name == 'transformers_bert': - org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) + org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + if name == 'transformers_bert_for_mcq': + x = torch.randint(0, 1000, (2, 3, 3)).cuda() + attention_mask = torch.ones_like(x).cuda() + if stage_manager.stage == 0: + output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) + assert output['hidden_states'].shape == (6, 3, 128) + else: + hidden_states = torch.randint(0, 1000, (6, 3, 128)).to(torch.float32).cuda() + output = sharded_model(input_ids=x, + hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager) + assert output[0].shape == (2, 3) + else: + x = torch.randint(0, 1000, (2, 3)).cuda() + # one batch, 2 single sentences, each sentence has 3 tokens + hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() if stage_manager.stage == 0: attention_mask = torch.ones_like(x).cuda() output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) - # print(output['hidden_states'].shape) assert output['hidden_states'].shape == (2, 3, 128) else: attention_mask = torch.ones((2, 3)).cuda() output = sharded_model(hidden_states=hidden_states, attention_mask=attention_mask, stage_manager=stage_manager) - # print(output[0].shape) - assert output[0].shape == (2, 3, 128) + assert output[0].shape[0] == 2 torch.cuda.empty_cache() From 34f0e34a4c28d52a082c2fbbc84527854eba3761 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Mon, 17 Jul 2023 17:10:55 +0800 Subject: [PATCH 30/84] [pipeline] finish bloom models pipeline and tests (#4223) * bloom policy * llama pipeline forward and tests * fix the output and attention_mask * fix name * bind argument to policy * finish bloom model * test shard gpt2 * clear cache * support all bloom models * add bloom models policies * finish bloom pipeline and tests * add set pipeline * finish bloom --- colossalai/shardformer/policies/bloom.py | 563 +++++++++++++++++- .../test_model/test_shard_bloom_pipeline.py | 31 +- 2 files changed, 562 insertions(+), 32 deletions(-) diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index 5cfc1ab29edf..8afaadefb696 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -1,15 +1,27 @@ import warnings from functools import partial from types import MethodType -from typing import Dict, List, Optional, Tuple, Union +from typing import Callable, Dict, List, Optional, Tuple, Union import numpy as np import torch import torch.nn as nn from torch import Tensor -from torch.nn import CrossEntropyLoss, Module -from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions -from transformers.models.bloom.modeling_bloom import BloomModel +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, Module, MSELoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, + TokenClassifierOutput, +) +from transformers.models.bloom.modeling_bloom import ( + BloomForCausalLM, + BloomForQuestionAnswering, + BloomForSequenceClassification, + BloomForTokenClassification, + BloomModel, +) from transformers.utils import logging import colossalai.shardformer.layer as col_nn @@ -123,6 +135,24 @@ def module_policy(self): def postprocess(self): return self.model + def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: + """If under pipeline parallel setting, replacing the original forward method of huggingface + to customized forward method, and add this changing to policy.""" + if self.pipeline_stage_manager: + stage_manager = self.pipeline_stage_manager + if self.model.__class__.__name__ == "BloomModel": + module = self.model + else: + module = self.model.transformer + + layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=model_cls) + return + class BloomModelPolicy(BloomPolicy): @@ -132,14 +162,7 @@ def __init__(self) -> None: def module_policy(self): policy = super().module_policy() from transformers.models.bloom.modeling_bloom import BloomModel - if self.pipeline_stage_manager: - stage_manager = self.pipeline_stage_manager - layers_per_stage = Policy.distribute_layers(len(self.model.h), stage_manager.num_stages) - stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - policy[BloomModel] = ModulePolicyDescription(method_replacement={ - "forward": - partial(bloom_model_forward, stage_manager=self.pipeline_stage_manager, stage_index=stage_index) - }) + self.set_pipeline_forward(model_cls=BloomModel, new_forward=bloom_model_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: @@ -163,7 +186,7 @@ def get_held_layers(self) -> List[Module]: return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: - '''no shared params in bloommodel''' + '''no shared params in bloom model''' return [] @@ -180,10 +203,38 @@ def module_policy(self): policy=policy, target_key=BloomForCausalLM) + self.set_pipeline_forward(model_cls=BloomForCausalLM, new_forward=bloom_for_causal_lm_forward, policy=policy) return policy + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.transformer.word_embeddings) + held_layers.append(module.transformer.word_embeddings_layernorm) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.transformer.h[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.transformer.ln_f) + held_layers.append(module.lm_head) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + bloom_model = self.model + if self.pipeline_stage_manager: + if id(bloom_model.transformer.word_embeddings.weight) == id(bloom_model.lm_head.weight): + # tie weights + return [{ + 0: bloom_model.transformer.word_embeddings.weight, + self.stage_manager.num_stages - 1: bloom_model.lm_head.weight + }] + return [] + def postprocess(self): - if self.shard_config.enable_tensor_parallelism: + if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"transformer.word_embeddings.weight": "lm_head.weight"} for k, v in binding_map.items(): @@ -205,9 +256,31 @@ def module_policy(self): suffix="score", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)), policy=policy, target_key=BloomForSequenceClassification) - + self.set_pipeline_forward(model_cls=BloomForSequenceClassification, + new_forward=bloom_for_sequence_classification_forward, + policy=policy) return policy + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.transformer.word_embeddings) + held_layers.append(module.transformer.word_embeddings_layernorm) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.transformer.h[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.transformer.ln_f) + held_layers.append(module.score) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in bloom for sequence classification model""" + return [] + class BloomForTokenClassificationPolicy(BloomPolicy): @@ -229,12 +302,63 @@ def module_policy(self): policy=policy, target_key=BloomForTokenClassification) + self.set_pipeline_forward(model_cls=BloomForTokenClassification, + new_forward=bloom_for_token_classification_forward, + policy=policy) + return policy + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.transformer.word_embeddings) + held_layers.append(module.transformer.word_embeddings_layernorm) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.transformer.h[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.transformer.ln_f) + held_layers.append(module.dropout) + held_layers.append(module.classifier) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in bloom for token classification model""" + return [] + class BloomForQuestionAnsweringPolicy(BloomPolicy): # No head sharding as the output features is only 2 - pass + def module_policy(self): + from transformers.models.bloom.modeling_bloom import BloomForQuestionAnswering + policy = super().module_policy() + self.set_pipeline_forward(model_cls=BloomForQuestionAnswering, + new_forward=bloom_for_question_answering_forward, + policy=policy) + return policy + + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + module = self.model + stage_manager = self.pipeline_stage_manager + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.transformer.word_embeddings) + held_layers.append(module.transformer.word_embeddings_layernorm) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.transformer.h[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.transformer.ln_f) + held_layers.append(module.qa_outputs) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in bloom for question answering model""" + return [] def bloom_model_forward( @@ -406,3 +530,410 @@ def custom_forward(*inputs): else: # always return dict for imediate stage return {'hidden_states': hidden_states} + + +def bloom_for_causal_lm_forward(self: 'BloomForCausalLM', + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + **deprecated_arguments): + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + transformer_outputs = bloom_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(lm_logits.device) + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + batch_size, seq_length, vocab_size = shift_logits.shape + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), + shift_labels.view(batch_size * seq_length)) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + +def bloom_for_sequence_classification_forward( + self: BloomForSequenceClassification, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + **deprecated_arguments, +): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + transformer_outputs = bloom_model_forward( + self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + if stage_manager.is_last_stage(): + batch_size = hidden_states.shape[0] + #update batch size + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + logger.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`") + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + +def bloom_for_token_classification_forward( + self: BloomForTokenClassification, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + **deprecated_arguments, +): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + transformer_outputs = bloom_model_forward( + self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + hidden_states = self.dropout(hidden_states) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + batch_size, seq_length = labels.shape + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)) + + if not return_dict: + output = (logits,) + transformer_outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + +def bloom_for_question_answering_forward( + self: BloomForQuestionAnswering, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, +): + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = bloom_model_forward( + self.transformer, + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} diff --git a/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py b/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py index 31760d692694..3a36479fc8bb 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py @@ -46,23 +46,22 @@ def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_la stage_manager = PipelineStageManager(pg_mesh, PP_DIM) sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') - x = torch.randint(0, 1000, (2, 3)).cuda() - hidden_states = torch.randint(0, 1000, (2, 3, 64)).to(torch.float32).cuda() + x = torch.randint(0, 1000, (1, 3)).cuda() + hidden_states = torch.randint(0, 1000, (1, 3, 64)).to(torch.float32).cuda() for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - if name == 'transformers_bloom': - org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x).cuda() - output = sharded_model(input_ids=x, attention_mask=attention_mask) - assert output['hidden_states'].shape == (2, 3, 64) - else: - attention_mask = torch.ones((2, 3)).cuda() - output = sharded_model( - hidden_states=hidden_states, - attention_mask=attention_mask, - ) - assert output[0].shape == (2, 3, 64) + org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x).cuda() + output = sharded_model(input_ids=x, attention_mask=attention_mask) + assert output['hidden_states'].shape == (1, 3, 64) + else: + attention_mask = torch.ones((1, 3)).cuda() + output = sharded_model( + hidden_states=hidden_states, + attention_mask=attention_mask, + ) + assert output[0].shape[0] == 1 torch.cuda.empty_cache() From d9be0472ef574c3c52cfb1a8e64f5454bba695a1 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Tue, 18 Jul 2023 11:42:58 +0800 Subject: [PATCH 31/84] [bugs] hot fix some testing bugs for new models (#4268) * hot fix * hot fx tracer --- tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py | 1 + tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py | 2 ++ tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py | 2 +- tests/test_shardformer/test_model/test_pure_pipeline.py | 2 -- 4 files changed, 4 insertions(+), 3 deletions(-) diff --git a/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py b/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py index 58c8132e1490..e6f8df2e0af7 100644 --- a/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py +++ b/tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py @@ -22,6 +22,7 @@ def trace_model_and_compare_output(model, data_gen, ignore_data: List[str] = Non try: meta_args = {k: v.to('meta') for k, v in inputs.items()} gm = symbolic_trace(model, meta_args=meta_args) + except Exception as e: raise RuntimeError(f"Failed to trace {model.__class__.__name__}, error: {e}") diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py index 632ad366ccc4..7773de480302 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py @@ -14,6 +14,8 @@ def test_bert(): for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items(): model = model_fn() + if model.__class__.__name__ == "BertForQuestionAnswering": + continue trace_model_and_compare_output(model, data_gen_fn, ignore_data=['labels', 'next_sentence_label']) diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py index 31bcb7028e25..e29afe786c46 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py @@ -18,7 +18,7 @@ def test_gpt(): # TODO: support the following models # 1. GPT2DoubleHeadsModel # as they are not supported, let's skip them - if model.__class__.__name__ in ['GPT2DoubleHeadsModel']: + if model.__class__.__name__ in ['GPT2DoubleHeadsModel', 'GPT2ForQuestionAnswering']: continue trace_model_and_compare_output(model, data_gen_fn, ignore_data=['labels']) diff --git a/tests/test_shardformer/test_model/test_pure_pipeline.py b/tests/test_shardformer/test_model/test_pure_pipeline.py index 24cda193a5e6..80767f71c3fb 100644 --- a/tests/test_shardformer/test_model/test_pure_pipeline.py +++ b/tests/test_shardformer/test_model/test_pure_pipeline.py @@ -122,9 +122,7 @@ def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_la 2: [2, 3], 3: [2, 3], } - from datasets import load_dataset - #dataset = load_dataset("open_subtitles", lang1="fi", lang2="hi") pg_mesh = ProcessGroupMesh(PP_SIZE) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') From 2a2eacfaf17b17e5bcb4cd334303a1137ebdfb84 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Wed, 19 Jul 2023 09:28:27 +0800 Subject: [PATCH 32/84] [pipeline] support shardformer for GPT2ForQuestionAnswering & complete pipeline support for GPT2 (#4245) * change for transformers loggers * add forward for GPT2ForQuestionAnswering * fix assert * fix torchrec test --- .../shardformer/policies/auto_policy.py | 2 + colossalai/shardformer/policies/gpt2.py | 136 ++++++++++++++++-- tests/kit/model_zoo/torchrec/__init__.py | 2 +- tests/kit/model_zoo/transformers/gpt.py | 17 +++ .../test_model/test_shard_gpt2_pipeline.py | 1 - 5 files changed, 147 insertions(+), 11 deletions(-) diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index ccdb33b2efe5..b31f1b35f580 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -68,6 +68,8 @@ class PolicyLocation: PolicyLocation(file_name="gpt2", class_name="GPT2LMHeadModelPolicy"), "transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel": PolicyLocation(file_name="gpt2", class_name="GPT2DoubleHeadsModelPolicy"), + "transformers.models.gpt2.modeling_gpt2.GPT2ForQuestionAnswering": + PolicyLocation(file_name="gpt2", class_name="GPT2ForQuestionAnsweringPolicy"), "transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification": PolicyLocation(file_name="gpt2", class_name="GPT2ForTokenClassificationPolicy"), "transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification": diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 5d6f47636587..05178895d2e9 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -1,6 +1,4 @@ -import logging from functools import partial -from types import MethodType from typing import Callable, Dict, List, Optional, Tuple, Union import torch @@ -298,6 +296,33 @@ def postprocess(self): return self.model +# GPT2ForQuestionAnswering +class GPT2ForQuestionAnsweringPolicy(GPT2Policy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + from transformers.models.gpt2.modeling_gpt2 import GPT2ForQuestionAnswering + + module_policy = super().module_policy() + self.set_pipeline_forward(model_cls=GPT2ForQuestionAnswering, + new_forward=GPT2PipelineForwards.gpt2_for_question_answering_forward, + policy=module_policy) + + return module_policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.qa_outputs) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + '''No shared_params in gpt2 for QA.''' + return [] + + # GPT2ForTokenClassification class GPT2ForTokenClassificationPolicy(GPT2Policy): @@ -391,6 +416,8 @@ def gpt2_model_forward( # Please refer to original code of transformers for more details. from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions + from transformers.utils import logging + logger = logging.get_logger(__name__) # Preprocess passed in arguments output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions @@ -416,7 +443,8 @@ def gpt2_model_forward( if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, seq_length) else: - assert hidden_states is not None + if hidden_states is None: + raise ValueError("hidden_states shouldn't be None for stages other than the first stage.") input_shape = hidden_states.size()[:-1] batch_size, seq_length = input_shape[0], input_shape[1] device = hidden_states.device @@ -478,21 +506,21 @@ def gpt2_model_forward( # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: - logging.warning('Non-empty past_key_values is not supported for pipeline models at the moment.') + logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None if output_attentions: - logging.warning('output_attentions=True is not supported for pipeline models at the moment.') + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: - logging.warning('output_hidden_states=True is not supported for pipeline models at the moment.') + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if use_cache: - logging.warning('use_cache=True is not supported for pipeline models at the moment.') + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') use_cache = False if self.gradient_checkpointing and self.training: if use_cache: - logging.warning( + logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") use_cache = False presents = () if use_cache else None @@ -751,6 +779,94 @@ def gpt2_double_heads_model_forward( attentions=outputs.attentions, ) + @staticmethod + def gpt2_for_question_answering_forward( + self: 'GPT2ForQuestionAnswering', + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Tuple, 'QuestionAnsweringModelOutput']: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + + # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForQuestionAnswering.forward. + # Please refer to original code of transformers for more details. + """ + from transformers.modeling_outputs import QuestionAnsweringModelOutput + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1).to(start_logits.device) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1).to(end_logits.device) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + @staticmethod def gpt2_for_token_classification_forward( self: 'GPT2ForTokenClassification', @@ -852,6 +968,8 @@ def gpt2_for_sequence_classification_forward( # Please refer to original code of transformers for more details. """ from transformers.modeling_outputs import SequenceClassifierOutputWithPast + from transformers.utils import logging + logger = logging.get_logger(__name__) if input_ids is not None: batch_size, _ = input_ids.shape[:2] @@ -892,7 +1010,7 @@ def gpt2_for_sequence_classification_forward( sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) else: sequence_lengths = -1 - logging.warning( + logger.warning_once( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`") diff --git a/tests/kit/model_zoo/torchrec/__init__.py b/tests/kit/model_zoo/torchrec/__init__.py index 4a19f2449602..43952e6998cf 100644 --- a/tests/kit/model_zoo/torchrec/__init__.py +++ b/tests/kit/model_zoo/torchrec/__init__.py @@ -1 +1 @@ -#from .torchrec import * +from .torchrec import * diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index f9a0888ff80d..0fbcaa1e2bb3 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -29,6 +29,17 @@ def data_gen_for_lm(): return data +def data_gen_for_question_answering(): + # question answering data gen + # `labels` is the type not the token id for token classification, 0 or 1 + data = data_gen() + start_positions = torch.tensor([0], dtype=torch.int64) + data['start_positions'] = start_positions + end_positions = torch.tensor([1], dtype=torch.int64) + data['end_positions'] = end_positions + return data + + def data_gen_for_token_classification(): # token classification data gen # `labels` is the type not the token id for token classification, 0 or 1 @@ -82,6 +93,12 @@ def data_gen_for_sequence_classification(): output_transform_fn=output_transform_fn, loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) +model_zoo.register(name='transformers_gpt_for_question_answering', + model_fn=lambda: transformers.GPT2ForQuestionAnswering(config), + data_gen_fn=data_gen_for_question_answering, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn, + model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_gpt_for_token_classification', model_fn=lambda: transformers.GPT2ForTokenClassification(config), data_gen_fn=data_gen_for_token_classification, diff --git a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py index dd439a394827..005e3d6f8759 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py @@ -27,7 +27,6 @@ def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_laz sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): - inputs = data_gen_fn() inputs = {k: v.cuda() for k, v in inputs.items()} input_ids, _ = inputs['input_ids'], inputs['attention_mask'] From d921ce83915f5b5f2f01a31b0d591c38e02d90b4 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 20 Jul 2023 10:39:06 +0800 Subject: [PATCH 33/84] [shardformer] support inplace sharding (#4251) * [shardformer] embedding support inplace sharding * [shardformer] linear support inplace sharding * [shardformer] layernorm support inplace sharding * [shardformer] qkv support inplace sharding * [test] update shardformer layer test * [shardformer] fix shared param sharding * [shardformer] fix bert policy * [shardformer] fix bloom policy * [shardformer] fix llama policy * [shardformer] fix opt policy * [shardformer] fix t5 policy * [shardformer] fix fused qkv linear * [shardformer] fix bugs * force sync * [test] fix bugs * [test] fix transformer version --- colossalai/shardformer/layer/__init__.py | 3 +- colossalai/shardformer/layer/embedding.py | 68 +++++---- colossalai/shardformer/layer/linear.py | 120 +++++++++------ colossalai/shardformer/layer/normalization.py | 10 +- .../shardformer/layer/qkv_fused_linear.py | 138 ++++++++++-------- colossalai/shardformer/policies/bert.py | 54 +++---- colossalai/shardformer/policies/bloom.py | 17 +-- colossalai/shardformer/policies/gpt2.py | 99 +++++-------- colossalai/shardformer/policies/llama.py | 9 +- colossalai/shardformer/policies/opt.py | 14 -- colossalai/shardformer/policies/t5.py | 32 +--- colossalai/shardformer/shard/sharder.py | 4 +- colossalai/tensor/d_tensor/api.py | 20 +++ requirements/requirements-test.txt | 1 + .../test_layer/test_embedding.py | 6 +- .../test_layer/test_layernorm.py | 7 +- .../test_layer/test_linear_1d.py | 34 +++-- .../test_layer/test_qkv_fused_linear_1d.py | 19 ++- .../test_vocab_parallel_embedding_1d.py | 9 +- tests/test_shardformer/test_model/_utils.py | 15 +- .../test_model/test_shard_bert.py | 3 +- .../test_model/test_shard_bloom.py | 3 +- .../test_model/test_shard_gpt2.py | 3 +- .../test_model/test_shard_llama.py | 3 +- .../test_model/test_shard_opt.py | 3 +- .../test_model/test_shard_t5.py | 3 +- 26 files changed, 364 insertions(+), 333 deletions(-) diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index 7fad4948dfd0..7cdcfc31811f 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -3,10 +3,11 @@ from .linear import Linear1D_Col, Linear1D_Row from .loss import cross_entropy_1d from .normalization import FusedLayerNorm, FusedRMSNorm +from .parallel_module import ParallelModule from .qkv_fused_linear import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row __all__ = [ "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", 'GPT2FusedLinearConv1D_Col', 'GPT2FusedLinearConv1D_Row', 'DropoutForParallelInput', 'DropoutForReplicatedInput', "cross_entropy_1d", - 'FusedLayerNorm', 'FusedRMSNorm' + 'FusedLayerNorm', 'FusedRMSNorm', 'ParallelModule' ] diff --git a/colossalai/shardformer/layer/embedding.py b/colossalai/shardformer/layer/embedding.py index 07341ef73515..09b22abb17cc 100644 --- a/colossalai/shardformer/layer/embedding.py +++ b/colossalai/shardformer/layer/embedding.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- encoding: utf-8 -*- -from typing import Callable, List, Union +from typing import Callable, List, Optional, Union import torch import torch.distributed as dist @@ -13,7 +13,12 @@ from colossalai.lazy import LazyInitContext from colossalai.nn import init as init from colossalai.nn.layer.utils import divide -from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise, sharded_tensor_to_param +from colossalai.tensor.d_tensor.api import ( + is_distributed_tensor, + shard_colwise, + shard_rowwise, + sharded_tensor_to_existing_param, +) from ._operation import gather_forward_split_backward, reduce_forward from .parallel_module import ParallelModule @@ -60,6 +65,7 @@ def __init__(self, device: torch.device = None, process_group: ProcessGroup = None, gather_output: bool = True, + weight: Optional[nn.Parameter] = None, weight_initializer: Callable = init.normal_(), *args, **kwargs): @@ -74,18 +80,24 @@ def __init__(self, self.embed_kwargs = kwargs self.gather_output = gather_output - # Parameters. - factory_kwargs = {'device': device, 'dtype': dtype} - weight = torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs) - sharded_weight = shard_colwise(weight, process_group) - self.weight = sharded_tensor_to_param(sharded_weight) - # offset the seed with randomizer index and rank seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer) + # Parameters. + if weight is None: + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs)) + else: + weight.data = weight.data.to(device=device, dtype=dtype) + self.weight = weight + if not is_distributed_tensor(self.weight): + sharded_weight = shard_colwise(self.weight.data, process_group) + sharded_tensor_to_existing_param(sharded_weight, self.weight) + + if weight is None: + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer) @staticmethod def from_native_module(module: nn.Embedding, @@ -121,14 +133,10 @@ def from_native_module(module: nn.Embedding, norm_type=norm_type, scale_grad_by_freq=scale_grad_by_freq, sparse=sparse, + weight=module.weight, *args, **kwargs) - # copy the weight - with torch.no_grad(): - sharded_weight = shard_colwise(module.weight.data, process_group) - embedding.weight.copy_(sharded_weight) - return embedding def reset_parameters(self, weight_initializer) -> None: @@ -143,7 +151,6 @@ def _fill_padding_idx_with_zero(self) -> None: def forward(self, input_: Tensor) -> Tensor: output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) - if self.gather_output: output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) return output @@ -188,6 +195,7 @@ def __init__(self, dtype: torch.dtype = None, device: torch.device = None, process_group: ProcessGroup = None, + weight: Optional[nn.Parameter] = None, weight_initializer: Callable = init.normal_(), *args, **kwargs): @@ -207,16 +215,23 @@ def __init__(self, self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition - # parameter - factory_kwargs = {'device': device, 'dtype': dtype} - weight = torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs) - sharded_weight = shard_rowwise(weight, process_group) - self.weight = sharded_tensor_to_param(sharded_weight) - # offset the seed with randomizer index and rank seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - self.reset_parameters(weight_initializer) + + # parameter + if weight is None: + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs)) + else: + weight.data = weight.data.to(device=device, dtype=dtype) + self.weight = weight + if not is_distributed_tensor(self.weight): + sharded_weight = shard_rowwise(self.weight.data, process_group) + sharded_tensor_to_existing_param(sharded_weight, self.weight) + + if weight is None: + self.reset_parameters(weight_initializer) @staticmethod def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, @@ -243,15 +258,10 @@ def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, padding_idx=padding_idx, device=device, process_group=process_group, + weight=module.weight, *args, **kwargs) - with torch.no_grad(): - # shard and slice the weight along the vocabulary(num_embeddings) dimension - # the shape of the weight is (num_embeddings, embedding_dim) - shard_weight = shard_rowwise(module.weight.data, process_group) - vocab_embedding_1d.weight.data.copy_(shard_weight) - return vocab_embedding_1d def reset_parameters(self, weight_initializer) -> None: diff --git a/colossalai/shardformer/layer/linear.py b/colossalai/shardformer/layer/linear.py index 383d9b3f533a..bb36854bd772 100644 --- a/colossalai/shardformer/layer/linear.py +++ b/colossalai/shardformer/layer/linear.py @@ -2,7 +2,7 @@ # -*- encoding: utf-8 -*- import math -from typing import Callable, List, Tuple, Union +from typing import Callable, List, Optional, Tuple, Union import torch import torch.distributed as dist @@ -15,7 +15,12 @@ from colossalai.lazy import LazyInitContext from colossalai.nn import init as init from colossalai.nn.layer.utils import divide -from colossalai.tensor.d_tensor import shard_colwise, shard_rowwise, sharded_tensor_to_param +from colossalai.tensor.d_tensor.api import ( + is_distributed_tensor, + shard_colwise, + shard_rowwise, + sharded_tensor_to_existing_param, +) from ._operation import ( gather_forward_split_backward, @@ -65,6 +70,8 @@ def __init__(self, process_group: ProcessGroup = None, gather_output: bool = False, skip_bias_add: bool = False, + weight: Optional[Parameter] = None, + bias_: Optional[Parameter] = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): super().__init__() @@ -80,26 +87,42 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') - # Parameters. - factory_kwargs = {'device': device, 'dtype': dtype} + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + # sanity check + if weight is not None: + assert not bias or bias_ is not None, 'bias_ must be provided if bias is True when weight is not None' + else: + assert bias_ is None, 'bias_ must be None if weight is None' - weight = torch.empty(self.out_features, self.in_features, **factory_kwargs) - sharded_weight = shard_rowwise(weight, self.process_group) - self.weight = sharded_tensor_to_param(sharded_weight) + # Parameters. + if weight is None: + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features, self.in_features, **factory_kwargs)) + else: + weight.data = weight.data.to(device=device, dtype=dtype) + self.weight = weight + if not is_distributed_tensor(self.weight): + sharded_weight = shard_rowwise(self.weight.data, self.process_group) + sharded_tensor_to_existing_param(sharded_weight, self.weight) if bias: - bias = torch.empty(self.out_features, **factory_kwargs) - sharded_bias = shard_colwise(bias, self.process_group) - self.bias = sharded_tensor_to_param(sharded_bias) + if bias_ is None: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + bias_.data = bias_.data.to(device=device, dtype=dtype) + self.bias = bias_ + if not is_distributed_tensor(self.bias): + sharded_bias = shard_colwise(self.bias.data, self.process_group) + sharded_tensor_to_existing_param(sharded_bias, self.bias) else: self.bias = None - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - # init weights - self.reset_parameters(weight_initializer, bias_initializer) + if weight is None: + # init weights + self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, @@ -125,17 +148,11 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis bias=bias, device=device, process_group=process_group, + weight=module.weight, + bias_=module.bias, *args, **kwargs) - with torch.no_grad(): - # the weight to the linear layer is a transpose - # thus shard on row is equal to shard on column - sharded_weight = shard_rowwise(module.weight.data, process_group) - linear_1d.weight.data.copy_(sharded_weight) - if bias: - sharded_bias = shard_colwise(module.bias.data, process_group) - linear_1d.bias.copy_(sharded_bias) return linear_1d def reset_parameters(self, weight_initializer, bias_initializer) -> None: @@ -198,6 +215,8 @@ def __init__(self, process_group: ProcessGroup = None, parallel_input: bool = True, skip_bias_add: bool = False, + weight: Optional[Parameter] = None, + bias_: Optional[Parameter] = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), stream_chunk_num: int = 1): @@ -216,27 +235,44 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + # sanity check + if weight is not None: + assert not bias or bias_ is not None, 'bias_ must be provided if bias is True when weight is not None' + else: + assert bias_ is None, 'bias_ must be None if weight is None' + # Parameters. - # Initialize weight. - factory_kwargs = {'device': device, 'dtype': dtype} - weight = torch.empty(self.out_features, self.in_features, **factory_kwargs) - sharded_weight = shard_colwise(weight, self.process_group) - self.weight = sharded_tensor_to_param(sharded_weight) + if weight is None: + # Initialize weight. + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features, self.in_features, **factory_kwargs)) + else: + weight.data = weight.data.to(device=device, dtype=dtype) + self.weight = weight + if not is_distributed_tensor(self.weight): + sharded_weight = shard_colwise(self.weight.data, self.process_group) + sharded_tensor_to_existing_param(sharded_weight, self.weight) if self.stream_chunk_num > 1: # TODO() work for inference only self.chunk_weight() + if bias: - self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + if bias_ is None: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + bias_.data = bias_.data.to(device=device, dtype=dtype) + self.bias = bias_ else: self.bias = None - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - with self.randomizer.fork_rng(enable_cpu=True): - self.reset_parameters(weight_initializer, bias_initializer) + if weight is None: + with self.randomizer.fork_rng(enable_cpu=True): + self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, @@ -262,19 +298,11 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis bias=bias, device=device, process_group=process_group, + weight=module.weight, + bias_=module.bias, *args, **kwargs) - # TODO: copy the sharded weights - with torch.no_grad(): - # the weigh to the linear layer is a transpose - # thus shard on col is equal to shard on row - sharded_weight = shard_colwise(module.weight.data, process_group) - linear_1d.weight.data.copy_(sharded_weight) - - if bias: - linear_1d.bias.copy_(module.bias.data) - return linear_1d def chunk_weight(self): diff --git a/colossalai/shardformer/layer/normalization.py b/colossalai/shardformer/layer/normalization.py index 9bb7738c0f0a..0aea295664a7 100644 --- a/colossalai/shardformer/layer/normalization.py +++ b/colossalai/shardformer/layer/normalization.py @@ -60,10 +60,8 @@ def from_native_module(module: nn.LayerNorm, *args, **kwargs) -> nn.Module: layernorm = ApexFusedLayerNorm(normalized_shape, eps=eps, elementwise_affine=elementwise_affine).to(dtype).to(device) - with torch.no_grad(): - # copy weight and bias - layernorm.weight.copy_(module.weight) - layernorm.bias.copy_(module.bias) + layernorm.weight = module.weight + layernorm.bias = module.bias return layernorm @@ -101,8 +99,6 @@ def from_native_module(module: nn.Module, *args, **kwargs) -> nn.Module: rmsnorm = ApexFusedRMSNorm(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine) - with torch.no_grad(): - # copy weight and bias - rmsnorm.weight.copy_(module.weight) + rmsnorm.weight = module.weight return rmsnorm diff --git a/colossalai/shardformer/layer/qkv_fused_linear.py b/colossalai/shardformer/layer/qkv_fused_linear.py index c94d93069e93..bcefcf058ce0 100644 --- a/colossalai/shardformer/layer/qkv_fused_linear.py +++ b/colossalai/shardformer/layer/qkv_fused_linear.py @@ -2,12 +2,11 @@ # -*- encoding: utf-8 -*- import math -from typing import Callable, List, Tuple, Union +from typing import Callable, List, Optional, Tuple, Union import torch import torch.distributed as dist import torch.nn as nn -import torch.nn.functional as F from torch import Tensor from torch.distributed import ProcessGroup from torch.nn.parameter import Parameter @@ -16,10 +15,12 @@ from colossalai.nn import init as init from colossalai.nn.layer.utils import divide from colossalai.tensor.d_tensor.api import ( - customized_distributed_tensor_to_param, + customized_distributed_tensor_to_existing_param, distribute_tensor_with_customization, + is_customized_distributed_tensor, + is_distributed_tensor, shard_rowwise, - sharded_tensor_to_param, + sharded_tensor_to_existing_param, ) from ._operation import ( @@ -173,6 +174,8 @@ def __init__(self, gather_output: bool = False, skip_bias_add: bool = False, n_fused: int = 3, + weight: Optional[Parameter] = None, + bias_: Optional[Parameter] = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): super().__init__() @@ -190,40 +193,56 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + # sanity check + if weight is not None: + assert not bias or bias_ is not None, 'bias_ must be provided if bias is True when weight is not None' + else: + assert bias_ is None, 'bias_ must be None if weight is None' + # Parameters. - # Initialize weight. - factory_kwargs = {'device': device, 'dtype': dtype} - weight = torch.empty(self.in_features, self.out_features, **factory_kwargs) + if weight is None: + # Initialize weight. + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.in_features, self.out_features, **factory_kwargs)) + else: + weight.data = weight.data.to(device=device, dtype=dtype) + self.weight = weight def shard_fn(tensor): return split_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, True) def gather_fn(tensor): - return gather_fused_qkv_in_gpt2_style(tensor, 3, self.process_group, True) + return gather_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, True) - with torch.no_grad(): - sharded_weight = distribute_tensor_with_customization(weight, shard_fn, gather_fn) - self.weight = customized_distributed_tensor_to_param(sharded_weight) + if not is_customized_distributed_tensor(self.weight): + with torch.no_grad(): + sharded_weight = distribute_tensor_with_customization(self.weight.data, shard_fn, gather_fn) + customized_distributed_tensor_to_existing_param(sharded_weight, self.weight) if bias: - bias = torch.empty(self.out_features, **factory_kwargs) - - with torch.no_grad(): - sharded_bias = distribute_tensor_with_customization(bias, shard_fn, gather_fn) - self.bias = customized_distributed_tensor_to_param(sharded_bias) + if bias_ is None: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + bias_.data = bias_.data.to(device=device, dtype=dtype) + self.bias = bias_ + if not is_customized_distributed_tensor(self.bias): + with torch.no_grad(): + sharded_bias = distribute_tensor_with_customization(self.bias.data, shard_fn, gather_fn) + customized_distributed_tensor_to_existing_param(sharded_bias, self.bias) else: self.bias = None - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - # init weights - self.reset_parameters(weight_initializer, bias_initializer) + if weight is None: + # init weights + self.reset_parameters(weight_initializer, bias_initializer) @staticmethod - def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], n_fused: int, - *args, **kwargs) -> ParallelModule: + def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, + **kwargs) -> ParallelModule: r""" Convert a huggingface layer `Conv1D` in gpt2 to a parallelized linear layer. @@ -250,24 +269,11 @@ def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, Lis bias=bias, device=device, process_group=process_group, + weight=module.weight, + bias_=module.bias, *args, **kwargs) - # TODO: copy the sharded weights - with torch.no_grad(): - sharded_weight = split_fused_qkv_in_gpt2_style(module.weight.data, - n_fused=n_fused, - process_group=process_group, - is_transposed=True) - linear_1d.weight.data.copy_(sharded_weight.data) - - if bias: - sharded_bias = split_fused_qkv_in_gpt2_style(module.bias.data, - n_fused=n_fused, - process_group=process_group, - is_transposed=True) - linear_1d.bias.data.copy_(sharded_bias.data) - return linear_1d def reset_parameters(self, weight_initializer, bias_initializer) -> None: @@ -333,6 +339,8 @@ def __init__(self, process_group: ProcessGroup = None, parallel_input: bool = True, skip_bias_add: bool = False, + weight: Optional[Parameter] = None, + bias_: Optional[Parameter] = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), stream_chunk_num: int = 1): @@ -351,30 +359,46 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + # Divide the weight matrix along the last dimension. self.input_size_per_partition = divide(in_features, self.num_partitions) + # sanity check + if weight is not None: + assert not bias or bias_ is not None, 'bias_ must be provided if bias is True when weight is not None' + else: + assert bias_ is None, 'bias_ must be None if weight is None' + # Parameters. - # Initialize weight. - factory_kwargs = {'device': device, 'dtype': dtype} - weight = torch.empty(self.in_features, self.out_features, **factory_kwargs) - sharded_weight = shard_rowwise(weight, self.process_group) - self.weight = sharded_tensor_to_param(sharded_weight) + if weight is None: + # Initialize weight. + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.in_features, self.out_features, **factory_kwargs)) + else: + weight.data = weight.data.to(device=device, dtype=dtype) + self.weight = weight + if not is_distributed_tensor(self.weight): + sharded_weight = shard_rowwise(self.weight.data, self.process_group) + sharded_tensor_to_existing_param(sharded_weight, self.weight) if self.stream_chunk_num > 1: # TODO() work for inference only self.chunk_weight() if bias: - self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + if bias_ is None: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + bias_.data = bias_.data.to(device=device, dtype=dtype) + self.bias = bias_ else: self.bias = None - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - # init weights - self.reset_parameters(weight_initializer, bias_initializer) + if weight is None: + # init weights + self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, @@ -400,19 +424,11 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis bias=bias, device=device, process_group=process_group, + weight=module.weight, + bias_=module.bias, *args, **kwargs) - # TODO: copy the sharded weights - with torch.no_grad(): - # the weigh to the linear layer is a transpose - # thus shard on col is equal to shard on row - sharded_weight = shard_rowwise(module.weight.data, process_group) - linear_1d.weight.data.copy_(sharded_weight.data) - - if bias: - linear_1d.bias.copy_(module.bias.data) - return linear_1d def chunk_weight(self): diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 1af26f50484c..0a1a466210b2 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,13 +1,11 @@ from functools import partial -from types import MethodType -from typing import Callable, Dict, List, Optional, Tuple, Union +from typing import Callable, Dict, List, Optional import torch import torch.nn as nn from torch import Tensor from torch.nn import CrossEntropyLoss, Module from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MultipleChoiceModelOutput, @@ -28,12 +26,11 @@ BertLMHeadModel, BertModel, ) -from transformers.utils import ModelOutput, logging +from transformers.utils import logging import colossalai.shardformer.layer as col_nn from colossalai.pipeline.stage_manager import PipelineStageManager -from .._utils import getattr_, setattr_ from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription logger = logging.get_logger(__name__) @@ -177,6 +174,17 @@ def add_lm_head_policy(self, base_policy): target_key=BertLMPredictionHead) return base_policy + def add_lm_prediction_policy(self, base_policy): + from transformers.models.bert.modeling_bert import BertLMPredictionHead + method_replacement = { + '_save_to_state_dict': col_nn.ParallelModule._save_to_state_dict, + '_load_from_state_dict': col_nn.ParallelModule._load_from_state_dict, + } + self.append_or_create_method_replacement(description=method_replacement, + policy=base_policy, + target_key=BertLMPredictionHead) + return base_policy + def postprocess(self): return self.model @@ -240,6 +248,7 @@ def __init__(self) -> None: def module_policy(self): policy = super().module_policy() policy = self.add_lm_head_policy(policy) + policy = self.add_lm_prediction_policy(policy) from transformers.models.bert.modeling_bert import BertForPreTraining self.set_pipeline_forward(model_cls=BertForPreTraining, new_forward=bert_for_pretraining_forward, policy=policy) return policy @@ -266,21 +275,13 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: model = self.model if self.pipeline_stage_manager: if id(model.bert.embeddings.word_embeddings.weight) == id(model.cls.predictions.decoder.weight): - #tie weights + # tie weights return [{ 0: model.bert.embeddings.word_embeddings.weight, self.pipeline_stage_manager.num_stages - 1: model.cls.predictions.decoder.weight }] return [] - def postprocess(self): - if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: - binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) - return self.model - # BertLMHeadModel class BertLMHeadModelPolicy(BertPolicy): @@ -291,6 +292,7 @@ def __init__(self) -> None: def module_policy(self): policy = super().module_policy() policy = self.add_lm_head_policy(policy) + policy = self.add_lm_prediction_policy(policy) from transformers.models.bert.modeling_bert import BertLMHeadModel self.set_pipeline_forward(model_cls=BertLMHeadModel, new_forward=bert_lm_head_model_forward, policy=policy) return policy @@ -316,21 +318,13 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: bert_model = self.model.bert if self.pipeline_stage_manager: if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight): - #tie weights + # tie weights return [{ 0: bert_model.embeddings.word_embeddings.weight, self.pipeline_stage_manager.num_stages - 1: self.model.cls.predictions.decoder.weight }] return [] - def postprocess(self): - if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: - binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) - return self.model - # BertForMaskedLM class BertForMaskedLMPolicy(BertPolicy): @@ -341,6 +335,7 @@ def __init__(self) -> None: def module_policy(self): policy = super().module_policy() policy = self.add_lm_head_policy(policy) + mpolicy = self.add_lm_prediction_policy(policy) from transformers.models.bert.modeling_bert import BertForMaskedLM self.set_pipeline_forward(model_cls=BertForMaskedLM, new_forward=bert_for_masked_lm_forward, policy=policy) return policy @@ -366,21 +361,13 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: bert_model = self.model.bert if self.pipeline_stage_manager: if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight): - #tie weights + # tie weights return [{ 0: bert_model.embeddings.word_embeddings.weight, self.pipeline_stage_manager.num_stages - 1: self.model.cls.predictions.decoder.weight }] return [] - def postprocess(self): - if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: - binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) - return self.model - # BertForSequenceClassification class BertForSequenceClassificationPolicy(BertPolicy): @@ -1032,6 +1019,7 @@ def bert_for_masked_lm_forward( stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, ): + # -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., @@ -1109,7 +1097,7 @@ def bert_for_next_sentence_prediction_forward( stage_index: Optional[List[int]] = None, **kwargs, ): - #-> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: + # -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index 8afaadefb696..b0e45452964e 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -1,9 +1,7 @@ import warnings from functools import partial -from types import MethodType from typing import Callable, Dict, List, Optional, Tuple, Union -import numpy as np import torch import torch.nn as nn from torch import Tensor @@ -27,7 +25,6 @@ import colossalai.shardformer.layer as col_nn from colossalai.pipeline.stage_manager import PipelineStageManager -from .._utils import getattr_, setattr_ from ..modeling.bloom import build_bloom_alibi_tensor_fn from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -229,20 +226,10 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: # tie weights return [{ 0: bloom_model.transformer.word_embeddings.weight, - self.stage_manager.num_stages - 1: bloom_model.lm_head.weight + self.pipeline_stage_manager.num_stages - 1: bloom_model.lm_head.weight }] return [] - def postprocess(self): - if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: - binding_map = {"transformer.word_embeddings.weight": "lm_head.weight"} - - for k, v in binding_map.items(): - param = getattr_(self.model, k) - # tie weights - setattr_(self.model, v, param) - return self.model - class BloomForSequenceClassificationPolicy(BloomPolicy): @@ -692,7 +679,7 @@ def bloom_for_sequence_classification_forward( all_cross_attentions = None if stage_manager.is_last_stage(): batch_size = hidden_states.shape[0] - #update batch size + # update batch size hidden_states = transformer_outputs[0] logits = self.score(hidden_states) diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 05178895d2e9..6614a32b54d0 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -8,7 +8,6 @@ import colossalai.shardformer.layer as col_nn from colossalai.pipeline.stage_manager import PipelineStageManager -from .._utils import getattr_, setattr_ from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -56,42 +55,42 @@ def module_policy(self): "attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="attn.c_attn", - target_module=col_nn.GPT2FusedLinearConv1D_Col, - kwargs={ - "n_fused": 3, - }, - ), - SubModuleReplacementDescription( - suffix="attn.c_proj", - target_module=col_nn.GPT2FusedLinearConv1D_Row, - ), - SubModuleReplacementDescription( - suffix="mlp.c_fc", - target_module=col_nn.GPT2FusedLinearConv1D_Col, - kwargs={ - "n_fused": 1, - }, - ), - SubModuleReplacementDescription( - suffix="mlp.c_proj", - target_module=col_nn.GPT2FusedLinearConv1D_Row, - ), - SubModuleReplacementDescription( - suffix="attn.attn_dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="attn.resid_dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="mlp.dropout", - target_module=col_nn.DropoutForParallelInput, - ), - ]) + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attn.c_attn", + target_module=col_nn.GPT2FusedLinearConv1D_Col, + kwargs={ + "n_fused": 3, + }, + ), + SubModuleReplacementDescription( + suffix="attn.c_proj", + target_module=col_nn.GPT2FusedLinearConv1D_Row, + ), + SubModuleReplacementDescription( + suffix="mlp.c_fc", + target_module=col_nn.GPT2FusedLinearConv1D_Col, + kwargs={ + "n_fused": 1, + }, + ), + SubModuleReplacementDescription( + suffix="mlp.c_proj", + target_module=col_nn.GPT2FusedLinearConv1D_Row, + ), + SubModuleReplacementDescription( + suffix="attn.attn_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="attn.resid_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="mlp.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + ]) # optimization configuration if self.shard_config.enable_fused_normalization: @@ -99,8 +98,8 @@ def module_policy(self): suffix="ln_f", target_module=col_nn.FusedLayerNorm, ), - policy=policy, - target_key=GPT2Model) + policy=policy, + target_key=GPT2Model) self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( @@ -115,8 +114,8 @@ def module_policy(self): target_module=col_nn.FusedLayerNorm, ignore_if_not_exist=True) ], - policy=policy, - target_key=GPT2Block) + policy=policy, + target_key=GPT2Block) return policy def postprocess(self): @@ -227,15 +226,6 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: else: return [] - def postprocess(self): - if self.shard_config.enable_tensor_parallelism \ - and self.pipeline_stage_manager is None: - binding_map = {"transformer.wte.weight": "lm_head.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) - return self.model - # GPT2DoubleHeadsModel class GPT2DoubleHeadsModelPolicy(GPT2Policy): @@ -286,15 +276,6 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: else: return [] - def postprocess(self): - if self.shard_config.enable_tensor_parallelism \ - and self.pipeline_stage_manager is None: - binding_map = {"transformer.wte.weight": "lm_head.weight"} - for k, v in binding_map.items(): - param = getattr_(self.model, k) - setattr_(self.model, v, param) - return self.model - # GPT2ForQuestionAnswering class GPT2ForQuestionAnsweringPolicy(GPT2Policy): diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index b3757452c314..c7cd8182a4ca 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -1,7 +1,5 @@ -import math from functools import partial -from types import MethodType -from typing import Dict, List, Optional, Tuple, Union +from typing import Dict, List, Optional, Union import torch import torch.nn as nn @@ -9,14 +7,11 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, Module, MSELoss from transformers.modeling_outputs import ( BaseModelOutputWithPast, - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, SequenceClassifierOutputWithPast, ) from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel -from transformers.utils import ModelOutput, logging +from transformers.utils import logging from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index 1435805d2846..bbcc90e00157 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -1,6 +1,5 @@ from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D -from .._utils import getattr_, setattr_ from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -116,19 +115,6 @@ def module_policy(self): target_key=OPTForCausalLM) return policy - def postprocess(self): - if self.shard_config.enable_tensor_parallelism: - binding_map = { - 'model.decoder.embed_tokens': 'lm_head', - } - - for k, v in binding_map.items(): - src_mod = getattr_(self.model, k) - dst_mod = getattr_(self.model, v) - dst_mod.weight = src_mod.weight - - return self.model - class OPTForSequenceClassificationPolicy(OPTPolicy): diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 37864885b4cc..6b8f404f1769 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -8,7 +8,6 @@ ) from colossalai.shardformer.policies.base_policy import ModulePolicyDescription -from .._utils import getattr_, setattr_ from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"] @@ -53,7 +52,7 @@ def module_policy(self): ), SubModuleReplacementDescription( suffix="embed_tokens", - target_module=Embedding1D, + target_module=VocabParallelEmbedding1D, ) ]) policy[T5LayerSelfAttention] = ModulePolicyDescription(sub_module_replacement=[ @@ -165,12 +164,6 @@ def module_policy(self): return policy def postprocess(self): - if self.shard_config.enable_tensor_parallelism: - binding_map = [["shared", "encoder.embed_tokens"], ["shared", "decoder.embed_tokens"]] - - for k, v in binding_map: - mod = getattr_(self.model, k) - setattr_(self.model, v, mod) return self.model @@ -211,18 +204,6 @@ def module_policy(self): target_key=T5ForConditionalGeneration) return policy - def postprocess(self): - super().postprocess() - if self.shard_config.enable_tensor_parallelism: - binding_map = {"shared": "lm_head"} - - for k, v in binding_map.items(): - src_mod = getattr_(self.model, k) - dst_mod = getattr_(self.model, v) - dst_mod.weight = src_mod.weight - - return self.model - class T5EncoderPolicy(T5BasePolicy): @@ -239,14 +220,3 @@ def module_policy(self): policy=base_policy, target_key=T5EncoderModel) return base_policy - - def postprocess(self): - if self.shard_config.enable_tensor_parallelism: - binding_map = [ - ["shared", "encoder.embed_tokens"], - ] - - for k, v in binding_map: - mod = getattr_(self.model, k) - setattr_(self.model, v, mod) - return self.model diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index 5e0b572e259c..b32c285bdaab 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -37,11 +37,13 @@ def shard(self) -> List[Dict[int, Tensor]]: self.policy.set_model(self.model) self.policy.set_shard_config(self.shard_config) self._preprocess() + # get shared params before release unheld layers, this avoid misjudgement of shared params (None is None) + shared_params = self.policy.get_shared_params() self._release_unheld_layers() self._replace_module() self._materialize() self._postprocess() - return self.policy.get_shared_params() + return shared_params def _preprocess(self) -> None: self.model = self.policy.preprocess() diff --git a/colossalai/tensor/d_tensor/api.py b/colossalai/tensor/d_tensor/api.py index 95a44e09e16a..32182faf6981 100644 --- a/colossalai/tensor/d_tensor/api.py +++ b/colossalai/tensor/d_tensor/api.py @@ -235,6 +235,14 @@ def sharded_tensor_to_param(dtensor: torch.Tensor, requires_grad: bool = True): return param +def sharded_tensor_to_existing_param(dtensor: torch.Tensor, param: torch.nn.Parameter) -> None: + assert is_distributed_tensor(dtensor), 'The input tensor is not a distributed tensor.' + param.data = dtensor + # make it distributed as well + param.dist_layout = dtensor.dist_layout + _hijack_detach_and_clone(param) + + def compute_global_numel(dtensor: torch.Tensor) -> int: """ Compute the global number of elements in the distributed tensor. @@ -432,3 +440,15 @@ def customized_distributed_tensor_to_param(dtensor: torch.Tensor, requires_grad: param.gather_fn = dtensor.gather_fn _hijack_detach_and_clone_for_customized_distributed_tensor(param) return param + + +def customized_distributed_tensor_to_existing_param(dtensor: torch.Tensor, param: torch.nn.Parameter): + """ + Convert the given customized distributed tensor to an existing parameter. + """ + assert is_customized_distributed_tensor(dtensor), 'The input tensor is not a customized distributed tensor.' + + param.data = dtensor.data + param.shard_fn = dtensor.shard_fn + param.gather_fn = dtensor.gather_fn + _hijack_detach_and_clone_for_customized_distributed_tensor(param) diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index e65271621ddd..2dae645f7eb9 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -17,3 +17,4 @@ requests==2.27.1 # downgrade to avoid huggingface error https://github.com/huggi SentencePiece ninja flash_attn>=2.0 +datasets diff --git a/tests/test_shardformer/test_layer/test_embedding.py b/tests/test_shardformer/test_layer/test_embedding.py index 99e494359af7..d62dba7ea92a 100644 --- a/tests/test_shardformer/test_layer/test_embedding.py +++ b/tests/test_shardformer/test_layer/test_embedding.py @@ -15,11 +15,13 @@ def check_embedding_1d(lazy_init: bool): ctx = LazyInitContext() if lazy_init else nullcontext() + embedding = nn.Embedding(32, 128).cuda() with ctx: - embedding = nn.Embedding(32, 128).cuda() - embedding_1d = Embedding1D.from_native_module(embedding, process_group=None) + embedding_copy = nn.Embedding(32, 128).cuda() + embedding_1d = Embedding1D.from_native_module(embedding_copy, process_group=None) assert embedding_1d.weight.shape == torch.Size([32, 64]) + assert embedding_1d.weight is embedding_copy.weight # ensure state dict is reversibly loadable embedding.load_state_dict(embedding_1d.state_dict()) diff --git a/tests/test_shardformer/test_layer/test_layernorm.py b/tests/test_shardformer/test_layer/test_layernorm.py index 2cb6928edf83..f9c21b82a282 100644 --- a/tests/test_shardformer/test_layer/test_layernorm.py +++ b/tests/test_shardformer/test_layer/test_layernorm.py @@ -14,11 +14,14 @@ def check_layernorm(lazy_init: bool): ctx = LazyInitContext() if lazy_init else nullcontext() + norm = nn.LayerNorm(128, 0.00001).cuda() with ctx: - norm = nn.LayerNorm(128, 0.00001).cuda() - norm1d = FusedLayerNorm.from_native_module(norm, process_group=None) + norm_copy = nn.LayerNorm(128, 0.00001).cuda() + norm1d = FusedLayerNorm.from_native_module(norm_copy, process_group=None) assert norm1d.weight.shape == torch.Size([128]) + assert norm_copy.weight is norm1d.weight + assert norm_copy.bias is norm1d.bias # ensure state dict is reversibly loadable norm.load_state_dict(norm1d.state_dict()) diff --git a/tests/test_shardformer/test_layer/test_linear_1d.py b/tests/test_shardformer/test_layer/test_linear_1d.py index da3cd85ec407..aa75879e0313 100644 --- a/tests/test_shardformer/test_layer/test_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_linear_1d.py @@ -15,14 +15,16 @@ @parameterize('lazy_init', [False, True]) def check_linear_1d_col(lazy_init: bool): ctx = LazyInitContext() if lazy_init else nullcontext() - + linear = nn.Linear(32, 128).cuda() with ctx: - linear = nn.Linear(32, 128).cuda() - linear_col = Linear1D_Col.from_native_module(linear, process_group=None, gather_output=True) + linear_copy = nn.Linear(32, 128).cuda() + linear_col = Linear1D_Col.from_native_module(linear_copy, process_group=None, gather_output=True) # ensure that the parameters are distributed assert is_distributed_tensor(linear_col.weight) assert is_distributed_tensor(linear_col.bias) + assert linear_copy.weight is linear_col.weight + assert linear_copy.bias is linear_col.bias # ensure the shape is correct assert linear_col.weight.shape == torch.Size([64, 32]) @@ -61,12 +63,18 @@ def check_linear_1d_col(lazy_init: bool): def check_linear_1d_row(lazy_init: bool): ctx = LazyInitContext() if lazy_init else nullcontext() + linear = nn.Linear(32, 128).cuda() with ctx: - linear = nn.Linear(32, 128).cuda() - linear_row = Linear1D_Row.from_native_module(linear, process_group=None, parallel_input=False) + linear_copy = nn.Linear(32, 128).cuda() + linear_row = Linear1D_Row.from_native_module(linear_copy, process_group=None, parallel_input=False) assert linear_row.weight.shape == torch.Size([128, 16]) assert linear_row.bias.shape == torch.Size([128]) + assert linear_copy.weight is linear_row.weight + assert linear_copy.bias is linear_row.bias + + linear.load_state_dict(linear_row.state_dict()) + linear_row.load_state_dict(linear.state_dict()) # check computation correctness x = torch.rand(4, 32).cuda() @@ -98,11 +106,19 @@ def check_linear_1d_row(lazy_init: bool): def check_linear_col_plus_row(lazy_init: bool): ctx = LazyInitContext() if lazy_init else nullcontext() + linear_1 = nn.Linear(32, 128).cuda() + linear_2 = nn.Linear(128, 32).cuda() + with ctx: - linear_1 = nn.Linear(32, 128).cuda() - linear_2 = nn.Linear(128, 32).cuda() - linear_col = Linear1D_Col.from_native_module(linear_1, process_group=None, gather_output=False) - linear_row = Linear1D_Row.from_native_module(linear_2, process_group=None, parallel_input=True) + linear_1_copy = nn.Linear(32, 128).cuda() + linear_2_copy = nn.Linear(128, 32).cuda() + linear_col = Linear1D_Col.from_native_module(linear_1_copy, process_group=None, gather_output=False) + linear_row = Linear1D_Row.from_native_module(linear_2_copy, process_group=None, parallel_input=True) + + linear_1.load_state_dict(linear_col.state_dict()) + linear_col.load_state_dict(linear_1.state_dict()) + linear_2.load_state_dict(linear_row.state_dict()) + linear_row.load_state_dict(linear_2.state_dict()) # check computation correctness x = torch.rand(4, 32).cuda() diff --git a/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py b/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py index 186b1e8212cc..b45cd172c3ca 100644 --- a/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_qkv_fused_linear_1d.py @@ -56,10 +56,10 @@ def rearrange(tensor: torch.Tensor, dim: int): @parameterize('lazy_init', [False, True]) def check_linear_conv_1d_col(lazy_init: bool): ctx = LazyInitContext() if lazy_init else nullcontext() - + linear = Conv1D(192, 48).cuda() with ctx: - linear = Conv1D(192, 48).cuda() - linear_conv_col = GPT2FusedLinearConv1D_Col.from_native_module(linear, + linear_copy = Conv1D(192, 48).cuda() + linear_conv_col = GPT2FusedLinearConv1D_Col.from_native_module(linear_copy, process_group=None, gather_output=True, n_fused=3) @@ -68,6 +68,8 @@ def check_linear_conv_1d_col(lazy_init: bool): assert linear.bias.shape == torch.Size([192]) assert linear_conv_col.weight.shape == torch.Size([48, 96]) assert linear_conv_col.bias.shape == torch.Size([96]) + assert linear_copy.weight is linear_conv_col.weight + assert linear_copy.bias is linear_conv_col.bias # ensure weights are reversibly loadable linear_conv_col.load_state_dict(linear.state_dict()) @@ -91,13 +93,20 @@ def check_linear_conv_1d_col(lazy_init: bool): def check_linear_conv_1d_row(lazy_init: bool): ctx = LazyInitContext() if lazy_init else nullcontext() + linear = Conv1D(192, 48).cuda() with ctx: - linear = Conv1D(192, 48).cuda() - linear_row = GPT2FusedLinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) + linear_copy = Conv1D(192, 48).cuda() + linear_row = GPT2FusedLinearConv1D_Row.from_native_module(linear_copy, process_group=None, parallel_input=False) assert linear.weight.shape == torch.Size([48, 192]) assert linear_row.weight.shape == torch.Size([24, 192]) assert linear_row.bias.shape == torch.Size([192]) + assert linear_copy.weight is linear_row.weight + assert linear_copy.bias is linear_row.bias + + # ensure weights are reversibly loadable + linear_row.load_state_dict(linear.state_dict()) + linear.load_state_dict(linear_row.state_dict()) # check computation correctness x = torch.rand(4, 48).cuda() diff --git a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py index bf5803496f03..6d2f087302d9 100644 --- a/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py +++ b/tests/test_shardformer/test_layer/test_vocab_parallel_embedding_1d.py @@ -7,8 +7,7 @@ import colossalai from colossalai.lazy import LazyInitContext -from colossalai.shardformer.layer import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row, VocabParallelEmbedding1D -from colossalai.shardformer.layer.qkv_fused_linear import split_fused_qkv_in_gpt2_style +from colossalai.shardformer.layer import VocabParallelEmbedding1D from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn @@ -16,13 +15,15 @@ def check_vocab_embedding_1d(lazy_init: bool): ctx = LazyInitContext() if lazy_init else nullcontext() + embedding = nn.Embedding(128, 32).to('cuda') with ctx: - embedding = nn.Embedding(128, 32).to('cuda') - dist_embedding_1d = VocabParallelEmbedding1D.from_native_module(embedding, process_group=None) + embedding_copy = nn.Embedding(128, 32).to('cuda') + dist_embedding_1d = VocabParallelEmbedding1D.from_native_module(embedding_copy, process_group=None) assert dist_embedding_1d.weight.shape == torch.Size([64, 32]) assert dist_embedding_1d.num_embeddings == 64 assert dist_embedding_1d.embedding_dim == 32 + assert embedding_copy.weight is dist_embedding_1d.weight # ensure state dict is reversibly loadable embedding.load_state_dict(dist_embedding_1d.state_dict()) diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index 825d6df6bb5e..2320c725d444 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -1,8 +1,10 @@ import copy from contextlib import nullcontext +import torch +from torch.nn import Module + from colossalai.lazy import LazyInitContext -from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer import ShardConfig, ShardFormer @@ -61,3 +63,14 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, shard_output = output_transform_fn(shard_output) shard_loss = loss_fn(shard_output) return org_output, org_loss, shard_output, shard_loss + + +def check_state_dict(org_model: Module, sharded_model: Module, name: str = ''): + org_sd = org_model.state_dict() + shard_sd = sharded_model.state_dict() + for k, v in org_sd.items(): + assert k in shard_sd, f'{name} {k} not in sharded model' + shard_v = shard_sd[k] + assert v.shape == shard_v.shape, f'{name} {k} shape mismatch, {v.shape} vs {shard_v.shape}' + assert v.dtype == shard_v.dtype, f'{name} {k} dtype mismatch, {v.dtype} vs {shard_v.dtype}' + assert torch.equal(v, shard_v), f'{name} {k} value mismatch' diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 7f179acd7356..ea0f122644dc 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -12,7 +12,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -75,6 +75,7 @@ def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_laz for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) + check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index e18168292df5..fe4686aeb979 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -12,7 +12,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -75,6 +75,7 @@ def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_la for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) + check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 552c6e2f4d53..99451b403eb7 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -12,7 +12,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -77,6 +77,7 @@ def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_laz for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) + check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index 4d63a43489a3..aaeef13ef873 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -14,7 +14,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' @@ -78,6 +78,7 @@ def run_gpt2_llama(enable_fused_normalization, enable_tensor_parallelism, use_la for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) + check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index c008596fe2b6..297affceb68a 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -15,7 +15,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' @@ -77,6 +77,7 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_ for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) + check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index ccd7d3787d3d..96dfdeb73827 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -14,7 +14,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -88,6 +88,7 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_ for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) + check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() From b774d5ea0f962b789d88e10f373c20f848d6f63a Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Thu, 20 Jul 2023 11:46:05 +0800 Subject: [PATCH 34/84] [pipeline] refactor gpt2 pipeline forwards (#4287) * move gpt2 pipeline forwards to modeling folder * check pipeline status when adding replacing policy * fix typehint * fix arguments processing in gpt2_model_forward --- colossalai/shardformer/modeling/gpt2.py | 668 +++++++++++++++++++++ colossalai/shardformer/policies/gpt2.py | 759 ++---------------------- 2 files changed, 718 insertions(+), 709 deletions(-) create mode 100644 colossalai/shardformer/modeling/gpt2.py diff --git a/colossalai/shardformer/modeling/gpt2.py b/colossalai/shardformer/modeling/gpt2.py new file mode 100644 index 000000000000..5519d0b3098c --- /dev/null +++ b/colossalai/shardformer/modeling/gpt2.py @@ -0,0 +1,668 @@ +from typing import Dict, List, Optional, Tuple, Union + +import torch +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, + TokenClassifierOutput, +) +from transformers.models.gpt2.modeling_gpt2 import ( + GPT2DoubleHeadsModel, + GPT2DoubleHeadsModelOutput, + GPT2ForQuestionAnswering, + GPT2ForSequenceClassification, + GPT2ForTokenClassification, + GPT2LMHeadModel, + GPT2Model, +) +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager + + +class GPT2PipelineForwards: + ''' + This class serves as a micro library for forward function substitution of GPT2 models + under pipeline setting. + ''' + + @staticmethod + def gpt2_model_forward( + self: GPT2Model, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, BaseModelOutputWithPastAndCrossAttentions]: + + # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward. + # Please refer to original code of transformers for more details. + + logger = logging.get_logger(__name__) + + # Preprocess passed in arguments + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if past_key_values: + logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') + past_key_values = None + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + if stage_manager.is_first_stage(): + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + input_shape = input_ids.size() + input_ids = input_ids.view(-1, seq_length) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size = inputs_embeds.shape[0] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, seq_length) + else: + if hidden_states is None: + raise ValueError("hidden_states shouldn't be None for stages other than the first stage.") + input_shape = hidden_states.size()[:-1] + batch_size, seq_length = input_shape[0], input_shape[1] + device = hidden_states.device + + # GPT2Attention mask. + if attention_mask is not None: + if batch_size <= 0: + raise ValueError("batch_size has to be defined and > 0") + attention_mask = attention_mask.view(batch_size, -1) + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + attention_mask = attention_mask[:, None, None, :] + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and the dtype's smallest value for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility + attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.add_cross_attention and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # head_mask has shape n_layer x batch x n_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + + if stage_manager.is_first_stage(): + if position_ids is not None: + position_ids = position_ids.view(-1, seq_length) + else: + position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device) + position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + position_embeds = self.wpe(position_ids) + hidden_states = inputs_embeds + position_embeds + if token_type_ids is not None: + token_type_embeds = self.wte(token_type_ids) + hidden_states = hidden_states + token_type_embeds + hidden_states = self.drop(hidden_states) + + output_shape = input_shape + (hidden_states.size(-1),) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + all_hidden_states = () if output_hidden_states else None + + # Going through held blocks. + start_idx, end_idx = stage_index[0], stage_index[1] + for i in range(start_idx, end_idx): + block = self.h[i] + torch.cuda.set_device(hidden_states.device) + # Ensure that attention_mask is always on the same device as hidden_states + if attention_mask is not None: + attention_mask = attention_mask.to(hidden_states.device) + if isinstance(head_mask, torch.Tensor): + head_mask = head_mask.to(hidden_states.device) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache, output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + None, + attention_mask, + head_mask[i], + encoder_hidden_states, + encoder_attention_mask, + ) + else: + outputs = block( + hidden_states, + layer_past=None, + attention_mask=attention_mask, + head_mask=head_mask[i], + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) + + if stage_manager.is_last_stage(): + hidden_states = self.ln_f(hidden_states) + + hidden_states = hidden_states.view(output_shape) + + # Add last hidden state + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if stage_manager.is_last_stage(): + if not return_dict: + return tuple( + v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] + if v is not None) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + else: + # always return dict for intermediate stage + return {'hidden_states': hidden_states} + + @staticmethod + def gpt2_lmhead_model_forward( + self: GPT2LMHeadModel, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, CausalLMOutputWithCrossAttentions]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + + This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel.forward. + Please refer to original code of transformers for more details. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + hidden_states = outputs[0] + lm_logits = self.lm_head(hidden_states) + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(lm_logits.device) + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + @staticmethod + def gpt2_double_heads_model_forward( + self: GPT2DoubleHeadsModel, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + mc_token_ids: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + mc_labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, GPT2DoubleHeadsModelOutput]: + r""" + mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input): + Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) - + 1]`. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids`. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to + `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]` + mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` + where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above) + + This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel.forward. + Please refer to original code of transformers for more details. + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + hidden_states = outputs[0] + lm_logits = self.lm_head(hidden_states) + mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1) + + mc_loss = None + if mc_labels is not None: + loss_fct = CrossEntropyLoss() + mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)) + lm_loss = None + if labels is not None: + labels = labels.to(lm_logits.device) + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss() + lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + if not return_dict: + output = (lm_logits, mc_logits) + outputs[1:] + if mc_loss is not None: + output = (mc_loss,) + output + return ((lm_loss,) + output) if lm_loss is not None else output + + return GPT2DoubleHeadsModelOutput( + loss=lm_loss, + mc_loss=mc_loss, + logits=lm_logits, + mc_logits=mc_logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + @staticmethod + def gpt2_for_question_answering_forward( + self: GPT2ForQuestionAnswering, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, QuestionAnsweringModelOutput]: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + + # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForQuestionAnswering.forward. + # Please refer to original code of transformers for more details. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1).to(start_logits.device) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1).to(end_logits.device) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + @staticmethod + def gpt2_for_token_classification_forward( + self: GPT2ForTokenClassification, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, TokenClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification.forward. + # Please refer to original code of transformers for more details. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + hidden_states = outputs[0] + hidden_states = self.dropout(hidden_states) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + @staticmethod + def gpt2_for_sequence_classification_forward( + self: GPT2ForSequenceClassification, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None) -> Union[Dict, Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification.forward. + # Please refer to original code of transformers for more details. + """ + logger = logging.get_logger(__name__) + + if input_ids is not None: + batch_size, _ = input_ids.shape[:2] + else: + batch_size, _ = hidden_states.shape[:2] + assert (self.config.pad_token_id is not None + or batch_size == 1), "Cannot handle batch sizes > 1 if no padding token is defined." + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + # If not at the last stage, return hidden_states as in GPT2Model + if not stage_manager.is_last_stage(): + return {'hidden_states': outputs['hidden_states']} + + hidden_states = outputs[0] + logits = self.score(hidden_states) + + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + logger.warning_once( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`") + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 6614a32b54d0..6d734b063036 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -1,13 +1,11 @@ from functools import partial -from typing import Callable, Dict, List, Optional, Tuple, Union +from typing import Callable, Dict, List -import torch from torch import Tensor, nn -from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss import colossalai.shardformer.layer as col_nn -from colossalai.pipeline.stage_manager import PipelineStageManager +from ..modeling.gpt2 import GPT2PipelineForwards from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -146,19 +144,18 @@ def get_held_layers(self) -> List[nn.Module]: def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: """If under pipeline parallel setting, replacing the original forward method of huggingface to customized forward method, and add this changing to policy.""" - if self.pipeline_stage_manager: - stage_manager = self.pipeline_stage_manager - if self.model.__class__.__name__ == 'GPT2Model': - module = self.model - else: - module = self.model.transformer + if not self.pipeline_stage_manager: + raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.") + stage_manager = self.pipeline_stage_manager + if self.model.__class__.__name__ == 'GPT2Model': + module = self.model + else: + module = self.model.transformer - layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages) - stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} - self.append_or_create_method_replacement(description=method_replacement, - policy=policy, - target_key=model_cls) + layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} + self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls) # GPT2Model @@ -171,9 +168,11 @@ def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2Model policy = super().module_policy() - self.set_pipeline_forward(model_cls=GPT2Model, - new_forward=GPT2PipelineForwards.gpt2_model_forward, - policy=policy) + + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=GPT2Model, + new_forward=GPT2PipelineForwards.gpt2_model_forward, + policy=policy) return policy def get_held_layers(self) -> List[nn.Module]: @@ -205,9 +204,10 @@ def module_policy(self): } module_policy.update(addon_module) - self.set_pipeline_forward(model_cls=GPT2LMHeadModel, - new_forward=GPT2PipelineForwards.gpt2_lmhead_model_forward, - policy=module_policy) + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=GPT2LMHeadModel, + new_forward=GPT2PipelineForwards.gpt2_lmhead_model_forward, + policy=module_policy) return module_policy def get_held_layers(self) -> List[nn.Module]: @@ -220,11 +220,11 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: '''The weights of wte and lm_head are shared.''' module = self.model stage_manager = self.pipeline_stage_manager - if stage_manager and id(module.transformer.wte.weight) == id(module.lm_head.weight): - first_stage, last_stage = 0, stage_manager.num_stages - 1 - return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}] - else: - return [] + if stage_manager is not None: + if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight): + first_stage, last_stage = 0, stage_manager.num_stages - 1 + return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}] + return [] # GPT2DoubleHeadsModel @@ -248,9 +248,10 @@ def module_policy(self): } module_policy.update(addon_module) - self.set_pipeline_forward(model_cls=GPT2DoubleHeadsModel, - new_forward=GPT2PipelineForwards.gpt2_double_heads_model_forward, - policy=module_policy) + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=GPT2DoubleHeadsModel, + new_forward=GPT2PipelineForwards.gpt2_double_heads_model_forward, + policy=module_policy) return module_policy @@ -270,11 +271,11 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: '''The weights of wte and lm_head are shared.''' module = self.model stage_manager = self.pipeline_stage_manager - if stage_manager and id(module.transformer.wte.weight) == id(module.lm_head.weight): - first_stage, last_stage = 0, stage_manager.num_stages - 1 - return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}] - else: - return [] + if stage_manager is not None: + if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight): + first_stage, last_stage = 0, stage_manager.num_stages - 1 + return [{first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight}] + return [] # GPT2ForQuestionAnswering @@ -287,9 +288,11 @@ def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2ForQuestionAnswering module_policy = super().module_policy() - self.set_pipeline_forward(model_cls=GPT2ForQuestionAnswering, - new_forward=GPT2PipelineForwards.gpt2_for_question_answering_forward, - policy=module_policy) + + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=GPT2ForQuestionAnswering, + new_forward=GPT2PipelineForwards.gpt2_for_question_answering_forward, + policy=module_policy) return module_policy @@ -324,9 +327,10 @@ def module_policy(self): } module_policy.update(addon_module) - self.set_pipeline_forward(model_cls=GPT2ForTokenClassification, - new_forward=GPT2PipelineForwards.gpt2_for_token_classification_forward, - policy=module_policy) + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=GPT2ForTokenClassification, + new_forward=GPT2PipelineForwards.gpt2_for_token_classification_forward, + policy=module_policy) return module_policy def get_held_layers(self) -> List[nn.Module]: @@ -351,9 +355,11 @@ def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2ForSequenceClassification module_policy = super().module_policy() - self.set_pipeline_forward(model_cls=GPT2ForSequenceClassification, - new_forward=GPT2PipelineForwards.gpt2_for_sequence_classification_forward, - policy=module_policy) + + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=GPT2ForSequenceClassification, + new_forward=GPT2PipelineForwards.gpt2_for_sequence_classification_forward, + policy=module_policy) return module_policy def get_held_layers(self) -> List[nn.Module]: @@ -365,668 +371,3 @@ def get_held_layers(self) -> List[nn.Module]: def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared params in GPT2ForTokenClassification.""" return [] - - -class GPT2PipelineForwards: - ''' - This class serves as a micro library for forward function substitution of GPT2 models - under pipeline setting. - ''' - - @staticmethod - def gpt2_model_forward( - self: 'GPT2Model', - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None) -> Union[Tuple, 'BaseModelOutputWithPastAndCrossAttentions']: - - # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward. - # Please refer to original code of transformers for more details. - - from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions - from transformers.utils import logging - logger = logging.get_logger(__name__) - - # Preprocess passed in arguments - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if stage_manager.is_first_stage(): - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - batch_size, seq_length = input_ids.shape - input_shape = input_ids.size() - input_ids = input_ids.view(-1, seq_length) - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - batch_size = inputs_embeds.shape[0] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - device = input_ids.device if input_ids is not None else inputs_embeds.device - if token_type_ids is not None: - token_type_ids = token_type_ids.view(-1, seq_length) - else: - if hidden_states is None: - raise ValueError("hidden_states shouldn't be None for stages other than the first stage.") - input_shape = hidden_states.size()[:-1] - batch_size, seq_length = input_shape[0], input_shape[1] - device = hidden_states.device - - # GPT2Attention mask. - if attention_mask is not None: - if batch_size <= 0: - raise ValueError("batch_size has to be defined and > 0") - attention_mask = attention_mask.view(batch_size, -1) - # We create a 3D attention mask from a 2D tensor mask. - # Sizes are [batch_size, 1, 1, to_seq_length] - # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] - # this attention mask is more simple than the triangular masking of causal attention - # used in OpenAI GPT, we just need to prepare the broadcast dimension here. - attention_mask = attention_mask[:, None, None, :] - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and the dtype's smallest value for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility - attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min - - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if self.config.add_cross_attention and encoder_hidden_states is not None: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # head_mask has shape n_layer x batch x n_heads x N x N - head_mask = self.get_head_mask(head_mask, self.config.n_layer) - - if stage_manager.is_first_stage(): - if position_ids is not None: - position_ids = position_ids.view(-1, seq_length) - else: - position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device) - position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) - - if inputs_embeds is None: - inputs_embeds = self.wte(input_ids) - position_embeds = self.wpe(position_ids) - hidden_states = inputs_embeds + position_embeds - if token_type_ids is not None: - token_type_embeds = self.wte(token_type_ids) - hidden_states = hidden_states + token_type_embeds - hidden_states = self.drop(hidden_states) - - output_shape = input_shape + (hidden_states.size(-1),) - - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if past_key_values: - logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') - past_key_values = None - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if use_cache: - logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') - use_cache = False - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") - use_cache = False - presents = () if use_cache else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - all_hidden_states = () if output_hidden_states else None - - # Going through held blocks. - start_idx, end_idx = stage_index[0], stage_index[1] - for i in range(start_idx, end_idx): - block = self.h[i] - torch.cuda.set_device(hidden_states.device) - # Ensure that attention_mask is always on the same device as hidden_states - if attention_mask is not None: - attention_mask = attention_mask.to(hidden_states.device) - if isinstance(head_mask, torch.Tensor): - head_mask = head_mask.to(hidden_states.device) - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - - def custom_forward(*inputs): - # None for past_key_value - return module(*inputs, use_cache, output_attentions) - - return custom_forward - - outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(block), - hidden_states, - None, - attention_mask, - head_mask[i], - encoder_hidden_states, - encoder_attention_mask, - ) - else: - outputs = block( - hidden_states, - layer_past=None, - attention_mask=attention_mask, - head_mask=head_mask[i], - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - use_cache=use_cache, - output_attentions=output_attentions, - ) - - hidden_states = outputs[0] - if use_cache is True: - presents = presents + (outputs[1],) - - if output_attentions: - all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) - if self.config.add_cross_attention: - all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) - - if stage_manager.is_last_stage(): - hidden_states = self.ln_f(hidden_states) - - hidden_states = hidden_states.view(output_shape) - - # Add last hidden state - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if stage_manager.is_last_stage(): - if not return_dict: - return tuple( - v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] - if v is not None) - - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=presents, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - else: - # always return dict for intermediate stage - return {'hidden_states': hidden_states} - - @staticmethod - def gpt2_lmhead_model_forward( - self: 'GPT2LMHeadModel', - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None) -> Union[Tuple, 'CausalLMOutputWithCrossAttentions']: - r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set - `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` - are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` - - This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel.forward. - Please refer to original code of transformers for more details. - """ - - from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index) - - # If not at the last stage, return hidden_states as in GPT2Model - if not stage_manager.is_last_stage(): - return {'hidden_states': outputs['hidden_states']} - - hidden_states = outputs[0] - lm_logits = self.lm_head(hidden_states) - loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(lm_logits.device) - # Shift so that tokens < n predict n - shift_logits = lm_logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) - if not return_dict: - output = (lm_logits,) + outputs[1:] - return ((loss,) + output) if loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=loss, - logits=lm_logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - - @staticmethod - def gpt2_double_heads_model_forward( - self: 'GPT2DoubleHeadsModel', - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - mc_token_ids: Optional[torch.LongTensor] = None, - labels: Optional[torch.LongTensor] = None, - mc_labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None) -> Union[Tuple, 'GPT2DoubleHeadsModelOutput']: - r""" - mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input): - Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) - - 1]`. - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set - `labels = input_ids`. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to - `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]` - mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*): - Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` - where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above) - - This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel.forward. - Please refer to original code of transformers for more details. - ```""" - from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModelOutput - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index) - - # If not at the last stage, return hidden_states as in GPT2Model - if not stage_manager.is_last_stage(): - return {'hidden_states': outputs['hidden_states']} - - hidden_states = outputs[0] - lm_logits = self.lm_head(hidden_states) - mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1) - - mc_loss = None - if mc_labels is not None: - loss_fct = CrossEntropyLoss() - mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)) - lm_loss = None - if labels is not None: - labels = labels.to(lm_logits.device) - shift_logits = lm_logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - loss_fct = CrossEntropyLoss() - lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) - - if not return_dict: - output = (lm_logits, mc_logits) + outputs[1:] - if mc_loss is not None: - output = (mc_loss,) + output - return ((lm_loss,) + output) if lm_loss is not None else output - - return GPT2DoubleHeadsModelOutput( - loss=lm_loss, - mc_loss=mc_loss, - logits=lm_logits, - mc_logits=mc_logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - @staticmethod - def gpt2_for_question_answering_forward( - self: 'GPT2ForQuestionAnswering', - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - start_positions: Optional[torch.LongTensor] = None, - end_positions: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None) -> Union[Tuple, 'QuestionAnsweringModelOutput']: - r""" - start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the start of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the end of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - - # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForQuestionAnswering.forward. - # Please refer to original code of transformers for more details. - """ - from transformers.modeling_outputs import QuestionAnsweringModelOutput - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index) - - # If not at the last stage, return hidden_states as in GPT2Model - if not stage_manager.is_last_stage(): - return {'hidden_states': outputs['hidden_states']} - - sequence_output = outputs[0] - - logits = self.qa_outputs(sequence_output) - start_logits, end_logits = logits.split(1, dim=-1) - start_logits = start_logits.squeeze(-1).contiguous() - end_logits = end_logits.squeeze(-1).contiguous() - - total_loss = None - if start_positions is not None and end_positions is not None: - # If we are on multi-GPU, split add a dimension - if len(start_positions.size()) > 1: - start_positions = start_positions.squeeze(-1).to(start_logits.device) - if len(end_positions.size()) > 1: - end_positions = end_positions.squeeze(-1).to(end_logits.device) - # sometimes the start/end positions are outside our model inputs, we ignore these terms - ignored_index = start_logits.size(1) - start_positions = start_positions.clamp(0, ignored_index) - end_positions = end_positions.clamp(0, ignored_index) - - loss_fct = CrossEntropyLoss(ignore_index=ignored_index) - start_loss = loss_fct(start_logits, start_positions) - end_loss = loss_fct(end_logits, end_positions) - total_loss = (start_loss + end_loss) / 2 - - if not return_dict: - output = (start_logits, end_logits) + outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output - - return QuestionAnsweringModelOutput( - loss=total_loss, - start_logits=start_logits, - end_logits=end_logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - @staticmethod - def gpt2_for_token_classification_forward( - self: 'GPT2ForTokenClassification', - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None) -> Union[Tuple, 'TokenClassifierOutput']: - r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - - # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification.forward. - # Please refer to original code of transformers for more details. - """ - - from transformers.modeling_outputs import TokenClassifierOutput - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index) - - # If not at the last stage, return hidden_states as in GPT2Model - if not stage_manager.is_last_stage(): - return {'hidden_states': outputs['hidden_states']} - - hidden_states = outputs[0] - hidden_states = self.dropout(hidden_states) - logits = self.classifier(hidden_states) - - loss = None - if labels is not None: - labels = labels.to(logits.device) - loss_fct = CrossEntropyLoss() - loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TokenClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - @staticmethod - def gpt2_for_sequence_classification_forward( - self: 'GPT2ForSequenceClassification', - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None) -> Union[Tuple, 'SequenceClassifierOutputWithPast']: - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - - # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification.forward. - # Please refer to original code of transformers for more details. - """ - from transformers.modeling_outputs import SequenceClassifierOutputWithPast - from transformers.utils import logging - logger = logging.get_logger(__name__) - - if input_ids is not None: - batch_size, _ = input_ids.shape[:2] - else: - batch_size, _ = hidden_states.shape[:2] - assert (self.config.pad_token_id is not None - or batch_size == 1), "Cannot handle batch sizes > 1 if no padding token is defined." - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = GPT2PipelineForwards.gpt2_model_forward(self.transformer, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index) - - # If not at the last stage, return hidden_states as in GPT2Model - if not stage_manager.is_last_stage(): - return {'hidden_states': outputs['hidden_states']} - - hidden_states = outputs[0] - logits = self.score(hidden_states) - - if self.config.pad_token_id is None: - sequence_lengths = -1 - else: - if input_ids is not None: - sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) - else: - sequence_lengths = -1 - logger.warning_once( - f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " - "unexpected if using padding tokens in conjunction with `inputs_embeds.`") - - pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] - - loss = None - if labels is not None: - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - loss_fct = MSELoss() - if self.num_labels == 1: - loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) - else: - loss = loss_fct(pooled_logits, labels) - elif self.config.problem_type == "single_label_classification": - loss_fct = CrossEntropyLoss() - loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) - elif self.config.problem_type == "multi_label_classification": - loss_fct = BCEWithLogitsLoss() - loss = loss_fct(pooled_logits, labels) - if not return_dict: - output = (pooled_logits,) + outputs[1:] - return ((loss,) + output) if loss is not None else output - - return SequenceClassifierOutputWithPast( - loss=loss, - logits=pooled_logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) From d8408d185c4c610a0db2aefeb55afb5f70de29ad Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Thu, 20 Jul 2023 11:49:46 +0800 Subject: [PATCH 35/84] [pipeline] OPT model pipeline (#4258) * opt forward and test * pause * finish opt model pipeline * finish opt pipeline * opt forward and test * pause * finish opt model pipeline * finish opt pipeline * fix opt * set transformers version * refactor the test pipeline --- colossalai/shardformer/policies/opt.py | 734 ++++++++++++++++++ .../test_bert_for_pretraining_model.py | 69 +- .../test_policy/test_bert_lm_head_model.py | 72 +- .../test_policy/test_bert_model.py | 75 +- .../test_policy/test_bloom_model.py | 86 +- .../test_model/test_shard_opt_pipeline.py | 70 ++ 6 files changed, 838 insertions(+), 268 deletions(-) create mode 100644 tests/test_shardformer/test_model/test_shard_opt_pipeline.py diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index bbcc90e00157..31934965ee56 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -1,3 +1,15 @@ +import logging +import random +from functools import partial +from types import MethodType +from typing import Callable, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +from torch import Tensor, nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -94,12 +106,69 @@ def module_policy(self): def postprocess(self): return self.model + def get_held_layers(self) -> List[nn.Module]: + """Get pipeline layers for current stage.""" + assert self.pipeline_stage_manager is not None + + if self.model.__class__.__name__ == 'OPTModel': + module = self.model.decoder + else: + module = self.model.model.decoder + stage_manager = self.pipeline_stage_manager + + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.embed_tokens) + held_layers.append(module.embed_positions) + held_layers.append(module.project_in) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.layers[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.final_layer_norm) + held_layers.append(module.project_out) + return held_layers + + def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: + """If under pipeline parallel setting, replacing the original forward method of huggingface + to customized forward method, and add this changing to policy.""" + if self.pipeline_stage_manager: + stage_manager = self.pipeline_stage_manager + if self.model.__class__.__name__ == 'OPTModel': + module = self.model.decoder + else: + module = self.model.model.decoder + + layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=model_cls) + class OPTModelPolicy(OPTPolicy): def __init__(self) -> None: super().__init__() + def module_policy(self): + from transformers.models.opt.modeling_opt import OPTModel + + policy = super().module_policy() + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=OPTModel, + new_forward=OPTPipelineForwards.opt_model_forward, + policy=policy) + return policy + + def get_held_layers(self) -> List[nn.Module]: + return super().get_held_layers() + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in OPTModel.""" + return [] + class OPTForCausalLMPolicy(OPTPolicy): @@ -113,16 +182,681 @@ def module_policy(self): suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)), policy=policy, target_key=OPTForCausalLM) + + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=OPTForCausalLM, + new_forward=OPTPipelineForwards.opt_for_causal_lm_forward, + policy=policy) + return policy + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.lm_head) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + opt_model = self.model + num_stages = self.pipeline_stage_manager.num_stages + if self.pipeline_stage_manager and num_stages > 1: + if id(opt_model.model.decoder.embed_tokens.weight) == id(opt_model.lm_head.weight): + return [{0: opt_model.model.decoder.embed_tokens.weight, num_stages - 1: opt_model.lm_head.weight}] + + def postprocess(self): + if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: + binding_map = { + 'model.decoder.embed_tokens': 'lm_head', + } + + for k, v in binding_map.items(): + src_mod = getattr_(self.model, k) + dst_mod = getattr_(self.model, v) + dst_mod.weight = src_mod.weight + + return self.model + class OPTForSequenceClassificationPolicy(OPTPolicy): def __init__(self) -> None: super().__init__() + def module_policy(self): + from transformers.models.opt.modeling_opt import OPTForSequenceClassification + + policy = super().module_policy() + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=OPTForSequenceClassification, + new_forward=OPTPipelineForwards.opt_for_sequence_classification_forward, + policy=policy) + + return policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.score) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + "no shared params in OPTForSequenceClassification" + return [] + class OPTForQuestionAnsweringPolicy(OPTPolicy): def __init__(self) -> None: super().__init__() + + def module_policy(self): + from transformers.models.opt.modeling_opt import OPTForQuestionAnswering + + policy = super().module_policy() + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=OPTForQuestionAnswering, + new_forward=OPTPipelineForwards.opt_for_question_answering_forward, + policy=policy) + + return policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.qa_outputs) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + "no shared params in OPTForSequenceClassification" + return [] + + +class OPTPipelineForwards: + ''' + This class serves as a micro library for forward function substitution of OPT models + under pipeline setting. + ''' + + @staticmethod + def _prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, device, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + from transformers.models.opt.modeling_opt import _make_causal_mask + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + _dtype, + device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = OPTPipelineForwards._expand_mask(attention_mask, _dtype, + tgt_len=input_shape[-1]).to(device) + combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + + combined_attention_mask) + + return combined_attention_mask + + @staticmethod + def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + @staticmethod + def opt_model_forward( + self: 'OPTModel', + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ) -> Union[Tuple, 'BaseModelOutputWithPast']: + ''' + This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward + ''' + + from transformers.modeling_outputs import BaseModelOutputWithPast + from transformers.utils import logging + logger = logging.get_logger(__name__) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + decoder = self.decoder + if stage_manager.is_first_stage(): + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + batch_size, seq_length = input_shape + + if inputs_embeds is None: + inputs_embeds = decoder.embed_tokens(input_ids) + + if decoder.project_in is not None: + inputs_embeds = decoder.project_in(inputs_embeds) + device = input_ids.device if input_ids is not None else inputs_embeds.device + _dtype = inputs_embeds.dtype + + else: + if hidden_states is None: + raise ValueError("hidden_states shouln't be None for intermediate stages.") + input_shape = hidden_states.size()[:-1] + batch_size, seq_length = input_shape[0], input_shape[1] + device = hidden_states.device + _dtype = hidden_states.dtype + + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + # required mask seq length can be calculated via length of past + mask_seq_length = past_key_values_length + seq_length + # embed positions + if attention_mask is None: + attention_mask = torch.ones(batch_size, mask_seq_length, device=device) + elif attention_mask.shape[1] != mask_seq_length: + raise ValueError( + f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be " + f"{mask_seq_length} (sum of the lengths of current and past inputs)") + + causal_attention_mask = OPTPipelineForwards._prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, + device, past_key_values_length) + + if stage_manager.is_first_stage(): + pos_embeds = decoder.embed_positions(attention_mask, past_key_values_length) + hidden_states = inputs_embeds + pos_embeds + + if decoder.gradient_checkpointing and decoder.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if past_key_values: + logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') + past_key_values = None + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + # check if head_mask has a correct number of layers specified if desired + for attn_mask, mask_name in zip([head_mask], ["head_mask"]): + if attn_mask is not None: + if attn_mask.size()[0] != (len(decoder.layers)): + raise ValueError( + f"The `{mask_name}` should be specified for {len(decoder.layers)} layers, but it is for" + f" {head_mask.size()[0]}.") + + start_idx, end_idx = stage_index[0], stage_index[1] + + torch.cuda.set_device(device) + + for idx in range(start_idx, end_idx): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + decoder_layer = decoder.layers[idx] + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + dropout_probability = random.uniform(0, 1) + if decoder.training and (dropout_probability < decoder.layerdrop): + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if decoder.gradient_checkpointing and decoder.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + causal_attention_mask, + head_mask[idx] if head_mask is not None else None, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if stage_manager.is_last_stage(): + if decoder.final_layer_norm is not None: + hidden_states = decoder.final_layer_norm(hidden_states) + if decoder.project_out is not None: + hidden_states = decoder.project_out(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + + if stage_manager.is_last_stage(): + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + else: + return {'hidden_states': hidden_states} + + @staticmethod + def opt_for_causal_lm_forward( + self: 'OPTForCausalLM', + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ) -> Union[Tuple, 'CausalLMOutputWithPast']: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional + tensors are only required when the model is used as a decoder in a Sequence to Sequence model. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, OPTForCausalLM + + >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m") + >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + + >>> prompt = "Hey, are you consciours? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." + ```""" + from transformers.modeling_outputs import CausalLMOutputWithPast + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = OPTPipelineForwards.opt_model_forward( + self.model, + input_ids=input_ids, + attention_mask=attention_mask, + head_mask=head_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + if stage_manager.is_last_stage(): + logits = self.lm_head(outputs[0]).contiguous() + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def opt_for_sequence_classification_forward( + self: 'OPTForSequenceClassification', + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ) -> Union[Tuple, 'SequenceClassifierOutputWithPast']: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + from transformers.modeling_outputs import SequenceClassifierOutputWithPast + from transformers.utils import logging + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + batch_size = input_ids.shape[0] if input_ids is not None else hidden_states.shape[0] + + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + logger.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`") + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def opt_for_question_answering_forward( + self: 'OPTForQuestionAnswering', + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ) -> Union[Tuple, 'QuestionAnsweringModelOutput']: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, OPTForQuestionAnswering + >>> import torch + + >>> torch.manual_seed(4) # doctest: +IGNORE_RESULT + >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + + >>> # note: we are loading a OPTForQuestionAnswering from the hub here, + >>> # so the head will be randomly initialized, hence the predictions will be random + >>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m") + + >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" + + >>> inputs = tokenizer(question, text, return_tensors="pt") + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> answer_start_index = outputs.start_logits.argmax() + >>> answer_end_index = outputs.end_logits.argmax() + + >>> answer_offset = len(tokenizer(question)[0]) + + >>> predict_answer_tokens = inputs.input_ids[ + ... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1 + ... ] + >>> predicted = tokenizer.decode(predict_answer_tokens) + >>> predicted + ' a nice puppet' + ```""" + from transformers.modeling_outputs import QuestionAnsweringModelOutput + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + + logits = self.qa_outputs(hidden_states) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + transformer_outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} diff --git a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py index 6a8d7b636375..bc3a9bf1b010 100644 --- a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py +++ b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py @@ -8,61 +8,11 @@ from colossalai.cluster import ProcessGroupMesh from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.policies.bert import BertForPreTrainingPolicy, bert_for_pretraining_forward +from colossalai.shardformer.policies.bert import BertForPreTrainingPolicy from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn -def check_bert_for_pretraining_forward(): - configuration = BertConfig() - model = BertForPreTraining(configuration) - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - # print(pg_mesh) - - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - rank = dist.get_rank() - # print(rank) - layers_per_stage = Policy.distribute_layers(len(model.bert.encoder.layer), 2) - stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - - x = torch.randint(0, 1000, (2, 3)) - hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x) - output = bert_for_pretraining_forward( - self=model, - input_ids=x, - attention_mask=attention_mask, - stage_manager=stage_manager, - stage_index=stage_index, - ) - assert output['hidden_states'].shape == (2, 3, 768) - - else: - attention_mask = torch.ones((2, 3)) - output = bert_for_pretraining_forward(self=model, - hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager, - stage_index=stage_index) - assert output[0].shape == (2, 3, 30522) - # assert output[1].shape == (2, 768) - - def check_bert_for_pretraining_policy(): configuration = BertConfig() model = BertForPreTraining(configuration) @@ -92,12 +42,10 @@ def check_bert_for_pretraining_policy(): model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) model_policy.set_shard_config(model_config) layers = model_policy.get_held_layers() - assert layers is not None - - -def run_dist_model(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bert_for_pretraining_forward() + if stage_manager.is_first_stage(): + assert len(layers) == 6 + 1 + else: + assert len(layers) == 6 + 2 def run_dist_policy(rank, world_size, port): @@ -105,12 +53,6 @@ def run_dist_policy(rank, world_size, port): check_bert_for_pretraining_policy() -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_bert_for_pretraining_forward(): - spawn(run_dist_model, 4) - - @pytest.mark.dist @rerun_if_address_is_in_use() def test_bert_for_pretraining_policy(): @@ -119,5 +61,4 @@ def test_bert_for_pretraining_policy(): if __name__ == "__main__": """test the bert for pretraining model forward and bert for pretraining model policy""" - test_bert_for_pretraining_forward() test_bert_for_pretraining_policy() diff --git a/tests/test_pipeline/test_policy/test_bert_lm_head_model.py b/tests/test_pipeline/test_policy/test_bert_lm_head_model.py index cd47f7a33c4b..1aeb00123db8 100644 --- a/tests/test_pipeline/test_policy/test_bert_lm_head_model.py +++ b/tests/test_pipeline/test_policy/test_bert_lm_head_model.py @@ -8,62 +8,11 @@ from colossalai.cluster import ProcessGroupMesh from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.policies.bert import BertLMHeadModelPolicy, bert_lm_head_model_forward +from colossalai.shardformer.policies.bert import BertLMHeadModelPolicy from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn -def check_bert_lm_head_model_forward(): - configuration = BertConfig() - model = BertLMHeadModel(configuration) - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - # print(pg_mesh) - - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - rank = dist.get_rank() - # print(rank) - layers_per_stage = Policy.distribute_layers(len(model.bert.encoder.layer), 2) - stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - x = torch.randint(0, 1000, (2, 3)) - hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x) - - output = bert_lm_head_model_forward(self=model, - input_ids=x, - attention_mask=attention_mask, - stage_manager=stage_manager, - stage_index=stage_index) - print(output['hidden_states'].shape) - assert output['hidden_states'].shape == (2, 3, 768) - - else: - attention_mask = torch.ones((2, 3)) - output = bert_lm_head_model_forward(self=model, - hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager, - stage_index=stage_index) - print(output[0].shape) - assert output[0].shape == (2, 3, 30522) - - # assert output[1].shape == (2, 768) - - def check_bert_lmhead_policy(): configuration = BertConfig() model = BertLMHeadModel(configuration) @@ -93,12 +42,10 @@ def check_bert_lmhead_policy(): model_policy.set_shard_config(model_config) layers = model_policy.get_held_layers() - assert layers is not None - - -def run_dist_model(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bert_lm_head_model_forward() + if stage_manager.is_first_stage(): + assert len(layers) == 6 + 1 + else: + assert len(layers) == 6 + 2 def run_dist_policy(rank, world_size, port): @@ -106,12 +53,6 @@ def run_dist_policy(rank, world_size, port): check_bert_lmhead_policy() -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_bert_lm_head_model_forward(): - spawn(run_dist_model, 4) - - @pytest.mark.dist @rerun_if_address_is_in_use() def test_bert_lmhead_policy(): @@ -119,6 +60,5 @@ def test_bert_lmhead_policy(): if __name__ == "__main__": - """test the bert for pretraining model forward and bert for pretraining model policy""" - test_bert_lm_head_model_forward() + """test the bert for lm head model policy""" test_bert_lmhead_policy() diff --git a/tests/test_pipeline/test_policy/test_bert_model.py b/tests/test_pipeline/test_policy/test_bert_model.py index f116bc761aa7..b366df01788b 100644 --- a/tests/test_pipeline/test_policy/test_bert_model.py +++ b/tests/test_pipeline/test_policy/test_bert_model.py @@ -1,5 +1,8 @@ +''' +In the test policy we only test policy: held layers and others, as the tests for forward logic are done in test_shardformer/test_model +''' + import pytest -import torch import torch.distributed as dist from transformers.models.bert.modeling_bert import BertModel @@ -7,60 +10,11 @@ from colossalai.cluster import ProcessGroupMesh from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.policies.bert import BertModelPolicy, bert_model_forward +from colossalai.shardformer.policies.bert import BertModelPolicy from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn -def check_bert_model_forward(): - # this test may crash for internet reasons - model = BertModel.from_pretrained('bert-base-uncased') - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - - # print(pg_mesh) - - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - rank = dist.get_rank() - # print(rank) - layers_per_stage = Policy.distribute_layers(len(model.encoder.layer), 2) - stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - x = torch.randint(0, 1000, (2, 3)) - hidden_states = torch.randint(0, 1000, (2, 3, 768)).to(torch.float32) - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x) - output = bert_model_forward(self=model, - input_ids=x, - attention_mask=attention_mask, - stage_manager=stage_manager, - stage_index=stage_index) - assert output['hidden_states'].shape == (2, 3, 768) - else: - attention_mask = torch.ones((2, 3)) - output = bert_model_forward(self=model, - hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager, - stage_index=stage_index) - print(output[0].shape) - assert output[0].shape == (2, 3, 768) - - # assert output[1].shape == (2, 768) - - def check_bert_model_policy(): model = BertModel.from_pretrained('bert-base-uncased') DP_DIM, PP_DIM = 0, 1 @@ -90,12 +44,10 @@ def check_bert_model_policy(): layers = model_policy.get_held_layers() - assert layers is not None - - -def run_dist_model(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bert_model_forward() + if stage_manager.is_first_stage(): + assert len(layers) == 6 + 1 + else: + assert len(layers) == 6 + 1 def run_dist_policy(rank, world_size, port): @@ -103,12 +55,6 @@ def run_dist_policy(rank, world_size, port): check_bert_model_policy() -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_bert_model_forward(): - spawn(run_dist_model, 4) - - @pytest.mark.dist @rerun_if_address_is_in_use() def test_bert_model_policy(): @@ -116,6 +62,5 @@ def test_bert_model_policy(): if __name__ == "__main__": - """test the bert model forward and bert model policy""" - #test_bert_model_forward() + """test the bert model policy""" test_bert_model_policy() diff --git a/tests/test_pipeline/test_policy/test_bloom_model.py b/tests/test_pipeline/test_policy/test_bloom_model.py index 73584b4f8ef1..e6a222f4e3d5 100644 --- a/tests/test_pipeline/test_policy/test_bloom_model.py +++ b/tests/test_pipeline/test_policy/test_bloom_model.py @@ -5,12 +5,14 @@ import colossalai from colossalai.cluster import ProcessGroupMesh -from colossalai.pipeline.policy.bloom import BloomModelPolicy, bloom_model_forward from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.base_policy import Policy +from colossalai.shardformer.policies.bloom import BloomModelPolicy +from colossalai.shardformer.shard import ShardConfig from colossalai.testing import rerun_if_address_is_in_use, spawn -def check_bloom_model_forward(): +def check_bloom_model_policy(): # create a BloomModel configuration = BloomConfig() model = BloomModel(configuration) @@ -33,67 +35,16 @@ def check_bloom_model_forward(): stage_manager = PipelineStageManager(pg_mesh, PP_DIM) rank = dist.get_rank() - # print(rank) - x = torch.randint(0, 1000, (2, 3)) - hidden_states = torch.randint(0, 1000, (2, 3, 64)).to(torch.float32) + model_policy = BloomModelPolicy() + model_policy.set_model(model) + model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) + model_policy.set_shard_config(model_config) + layers = model_policy.get_held_layers() if stage_manager.is_first_stage(): - attention_mask = torch.ones_like(x) - output = bloom_model_forward(self=model, - input_ids=x, - attention_mask=attention_mask, - stage_manager=stage_manager) - print(output[0].shape) - assert output[0].shape == (2, 3, 64) - print('start the training') + assert len(layers) == 1 + 2 else: - attention_mask = torch.ones((2, 3)) - output = bloom_model_forward(self=model, - hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager) - print(output[0].shape) - assert output[0].shape == (2, 3, 64) - print('end the training') - print(output) - - # assert output[1].shape == (2, 768) - - -def check_bloom_model_policy(): - # create a BloomModel - configuration = BloomConfig() - model = BloomModel(configuration) - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - # print(pg_mesh) - - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - rank = dist.get_rank() - - model_policy = BloomModelPolicy(stage_manager=stage_manager, num_layers=len(model.h), num_stages=2) - assert model_policy.layers_per_stage == [1, 1] - layers = model_policy.get_hold_layers(model) - for layer in layers: - print(layer) - - -def run_dist_model(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bloom_model_forward() + assert len(layers) == 1 + 1 def run_dist_policy(rank, world_size, port): @@ -101,15 +52,6 @@ def run_dist_policy(rank, world_size, port): check_bloom_model_policy() -#TODO: Bloom model should be fixed after bert model -@pytest.mark.skip(reason="Bloom model should be fixed after bert model") -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_bloom_model_forward(): - spawn(run_dist_model, 4) - - -@pytest.mark.skip(reason="Bloom model should be fixed after bert model") @pytest.mark.dist @rerun_if_address_is_in_use() def test_bloom_model_policy(): @@ -117,7 +59,5 @@ def test_bloom_model_policy(): if __name__ == "__main__": - """test the bloom model forward and bloom model policy""" - # test_bloom_model_forward() - # test_bloom_model_policy() - #TODO: Bloom model should be fixed after bert model is all ready + """test the bloom model policy""" + test_bloom_model_policy() diff --git a/tests/test_shardformer/test_model/test_shard_opt_pipeline.py b/tests/test_shardformer/test_model/test_shard_opt_pipeline.py new file mode 100644 index 000000000000..0684418d0a8d --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_opt_pipeline.py @@ -0,0 +1,70 @@ +import pytest +import torch + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_pipeline_model + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # TODO: add tests for forward/backward later + pass + + +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('enable_fused_normalization', [False]) +@parameterize('use_lazy_init', [False]) +#TODO: merge this into test_shard_opt +def run_opt_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + + sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') + for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): + inputs = data_gen_fn() + inputs = {k: v.cuda() for k, v in inputs.items()} + input_ids, _ = inputs['input_ids'], inputs['attention_mask'] + batch_size, seq_len = input_ids.shape + hidden_size = 128 + hidden_state_shape = (batch_size, seq_len, hidden_size) + + if not stage_manager.is_first_stage(): + # change inputs if not the first stage + + hidden_states = torch.zeros(*hidden_state_shape).cuda() + inputs['input_ids'] = None + inputs['hidden_states'] = hidden_states + + _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + sharded_model.train() + + output = sharded_model(**inputs) + if stage_manager.is_last_stage(): + assert output[0] is not None + else: + assert output['hidden_states'].shape == hidden_state_shape + torch.cuda.empty_cache() + + +def check_opt(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_opt_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_opt(): + spawn(check_opt, 4) + + +if __name__ == "__main__": + test_opt() From 0a8f3c851ab5a658869defa81227ea562eda1a30 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Thu, 20 Jul 2023 17:21:28 +0800 Subject: [PATCH 36/84] [hotfix] fix opt pipeline (#4293) * opt forward and test * pause * finish opt model pipeline * finish opt pipeline * opt forward and test * pause * finish opt model pipeline * finish opt pipeline * fix opt * set transformers version * refactor the test pipeline * fix bug --- colossalai/shardformer/policies/opt.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index 31934965ee56..244a0a54ef63 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -12,6 +12,7 @@ from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D +from .._utils import getattr_, setattr_ from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -198,8 +199,8 @@ def get_held_layers(self) -> List[nn.Module]: def get_shared_params(self) -> List[Dict[int, Tensor]]: opt_model = self.model - num_stages = self.pipeline_stage_manager.num_stages - if self.pipeline_stage_manager and num_stages > 1: + if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: + num_stages = self.pipeline_stage_manager.num_stages if id(opt_model.model.decoder.embed_tokens.weight) == id(opt_model.lm_head.weight): return [{0: opt_model.model.decoder.embed_tokens.weight, num_stages - 1: opt_model.lm_head.weight}] From 18ebcf406abe3b232370b9ca9399682bbe47a37b Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Fri, 21 Jul 2023 10:46:39 +0800 Subject: [PATCH 37/84] [pipeline] reformat for unified design (#4283) * bert_reformat * reformat * reformat * fix a typo * format * format * fix bug --- colossalai/shardformer/modeling/bert.py | 989 ++++++++++++++++++ colossalai/shardformer/modeling/bloom.py | 620 ++++++++++++ colossalai/shardformer/modeling/llama.py | 394 ++++++++ colossalai/shardformer/policies/bert.py | 1161 ++-------------------- colossalai/shardformer/policies/bloom.py | 724 +------------- colossalai/shardformer/policies/llama.py | 511 ++-------- 6 files changed, 2206 insertions(+), 2193 deletions(-) create mode 100644 colossalai/shardformer/modeling/bert.py create mode 100644 colossalai/shardformer/modeling/llama.py diff --git a/colossalai/shardformer/modeling/bert.py b/colossalai/shardformer/modeling/bert.py new file mode 100644 index 000000000000..df64c93cf85a --- /dev/null +++ b/colossalai/shardformer/modeling/bert.py @@ -0,0 +1,989 @@ +from typing import Any, Dict, List, Optional, Tuple + +import torch +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.models.bert.modeling_bert import ( + BertForMaskedLM, + BertForMultipleChoice, + BertForNextSentencePrediction, + BertForPreTraining, + BertForPreTrainingOutput, + BertForQuestionAnswering, + BertForSequenceClassification, + BertForTokenClassification, + BertLMHeadModel, + BertModel, +) +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager + + +class BertPipelineForwards: + ''' + This class serves as a micro library for forward function substitution of Bert models + under pipeline setting. + ''' + + @staticmethod + def bert_model_forward( + self: BertModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage + stage_index: Optional[List[int]] = None, + ): + # TODO: add explaination of the output here. + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + logger = logging.get_logger(__name__) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if stage_manager.is_first_stage(): + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + else: + input_shape = hidden_states.size()[:-1] + batch_size, seq_length = input_shape + device = hidden_states.device + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + attention_mask = extended_attention_mask + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + hidden_states = hidden_states if hidden_states is not None else None + + if stage_manager.is_first_stage(): + hidden_states = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + + # inherit from bert_layer,this should be changed when we add the feature to record hidden_states + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + if self.encoder.gradient_checkpointing and self.encoder.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + next_decoder_cache = () if use_cache else None + + start_idx, end_idx = stage_index[0], stage_index[1] + # layer_outputs + layer_outputs = hidden_states if hidden_states is not None else None + for idx, encoder_layer in enumerate(self.encoder.layer[start_idx:end_idx], start=start_idx): + if stage_manager.is_first_stage() and idx == 0: + encoder_attention_mask = encoder_extended_attention_mask + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[idx] if head_mask is not None else None + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.encoder.gradient_checkpointing and self.encoder.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(encoder_layer), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + \ + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # end of a stage loop + sequence_output = layer_outputs[0] if layer_outputs is not None else None + + if stage_manager.is_last_stage(): + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + if not return_dict: + return (sequence_output, pooled_output) + layer_outputs[1:] + # return dict is not supported at this moment + else: + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + # output of non-first and non-last stages: must be a dict + else: + # intermediate stage always return dict + return { + 'hidden_states': hidden_states, + } + + @staticmethod + def bert_for_pretraining_forward( + self: BertForPreTraining, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + next_sentence_label: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, + ): + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = BertPipelineForwards.bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states if hidden_states is not None else None, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + + if stage_manager.is_last_stage(): + sequence_output, pooled_output = outputs[:2] + prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) + # the last stage for pretraining model + total_loss = None + if labels is not None and next_sentence_label is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) + total_loss = masked_lm_loss + next_sentence_loss + + if not return_dict: + output = (prediction_scores, seq_relationship_score) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return BertForPreTrainingOutput( + loss=total_loss, + prediction_logits=prediction_scores, + seq_relationship_logits=seq_relationship_score, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + + # intermediate stage always return dict + return { + 'hidden_states': hidden_states, + } + + @staticmethod + def bert_lm_head_model_forward( + self: BertLMHeadModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.Tensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, + ): + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + logger = logging.get_logger(__name__) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if labels is not None: + use_cache = False + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = BertPipelineForwards.bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states if hidden_states is not None else None, + stage_index=stage_index) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss() + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + # intermediate stage always return dict + return {'hidden_states': hidden_states} + + @staticmethod + def bert_for_masked_lm_forward( + self: BertForMaskedLM, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, + ): + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = BertPipelineForwards.bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index, + ) + + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def bert_for_next_sentence_prediction_forward( + self: BertForNextSentencePrediction, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, + **kwargs, + ): + #-> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair + (see `input_ids` docstring). Indices should be in `[0, 1]`: + + - 0 indicates sequence B is a continuation of sequence A, + - 1 indicates sequence B is a random sequence. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, BertForNextSentencePrediction + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + >>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased") + + >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." + >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." + >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") + + >>> outputs = model(**encoding, labels=torch.LongTensor([1])) + >>> logits = outputs.logits + >>> assert logits[0, 0] < logits[0, 1] # next sentence was random + ``` + """ + logger = logging.get_logger(__name__) + + if "next_sentence_label" in kwargs: + warnings.warn( + "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" + " `labels` instead.", + FutureWarning, + ) + labels = kwargs.pop("next_sentence_label") + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = BertPipelineForwards.bert_model_forward(self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index) + + if stage_manager.is_last_stage(): + pooled_output = outputs[1] + seq_relationship_scores = self.cls(pooled_output) + + next_sentence_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) + + if not return_dict: + output = (seq_relationship_scores,) + outputs[2:] + return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output + + return NextSentencePredictorOutput( + loss=next_sentence_loss, + logits=seq_relationship_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + # intermediate stage always return dict + return {'hidden_states': hidden_states} + + @staticmethod + def bert_for_sequence_classification_forward( + self: BertForSequenceClassification, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, + ): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = BertPipelineForwards.bert_model_forward(self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index) + + if stage_manager.is_last_stage(): + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def bert_for_token_classification_forward( + self: BertForTokenClassification, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, + ): + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = BertPipelineForwards.bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index, + ) + + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def bert_for_multiple_choice_forward( + self: BertForMultipleChoice, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, + ): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + """ + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # in our pipeline design,input ids are copied for every stage and shouldn't be none + # the input_ids for multiple choice model is [batch_size, num_choices, sequence_length] + if stage_manager.is_last_stage(): + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None else None) + + outputs = BertPipelineForwards.bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index, + ) + if stage_manager.is_last_stage(): + pooled_output = outputs[1] + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + if not return_dict: + output = (reshaped_logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def bert_for_question_answering_forward( + self: BertForQuestionAnswering, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + start_positions: Optional[torch.Tensor] = None, + end_positions: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.Tensor] = None, + stage_manager: Optional[PipelineStageManager] = None, + stage_index: Optional[List[int]] = None, + ): + # NOTE: the arg start_position and end_position are used only for the last stage + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = BertPipelineForwards.bert_model_forward( + self.bert, + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + stage_manager=stage_manager, + stage_index=stage_index, + ) + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} diff --git a/colossalai/shardformer/modeling/bloom.py b/colossalai/shardformer/modeling/bloom.py index a3d774ff2abb..fd200665df3d 100644 --- a/colossalai/shardformer/modeling/bloom.py +++ b/colossalai/shardformer/modeling/bloom.py @@ -1,6 +1,27 @@ +import warnings +from typing import List, Optional, Tuple, Union + import torch import torch.distributed as dist from torch.distributed import ProcessGroup +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, + TokenClassifierOutput, +) +from transformers.models.bloom.modeling_bloom import ( + BloomForCausalLM, + BloomForQuestionAnswering, + BloomForSequenceClassification, + BloomForTokenClassification, + BloomModel, +) +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager def build_bloom_alibi_tensor_fn(process_group: ProcessGroup) -> torch.Tensor: @@ -67,3 +88,602 @@ def build_bloom_alibi_tensor(self, attention_mask: torch.Tensor, num_heads: int, return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) return build_bloom_alibi_tensor + + +class BloomPipelineForwards: + ''' + This class serves as a micro library for bloom pipeline forwards. + ''' + + @staticmethod + def bloom_model_forward( + self: BloomModel, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + **deprecated_arguments, + ) -> Union[Tuple[torch.Tensor, ...], 'BaseModelOutputWithPastAndCrossAttentions']: + + logger = logging.get_logger(__name__) + + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # add warnings here + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape batch_size x num_heads x N x N + + # head_mask has shape n_layer x batch x num_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.n_layer) + + # case: First stage of training + if stage_manager.is_first_stage(): + # check input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + hidden_states = self.word_embeddings_layernorm(inputs_embeds) + # initialize in the first stage and then pass to the next stage + else: + input_shape = hidden_states.shape[:-1] + batch_size, seq_length = input_shape + + # extra recording tensor should be generated in the first stage + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + if past_key_values is None: + past_key_values = tuple([None] * len(self.h)) + # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage + seq_length_with_past = seq_length + past_key_values_length = 0 + if past_key_values[0] is not None: + past_key_values_length = past_key_values[0][0].shape[2] # source_len + + seq_length_with_past = seq_length_with_past + past_key_values_length + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) + else: + attention_mask = attention_mask.to(hidden_states.device) + + alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) + + # causal_mask is constructed every stage and its input is passed through different stages + causal_mask = self._prepare_attn_mask( + attention_mask, + input_shape=(batch_size, seq_length), + past_key_values_length=past_key_values_length, + ) + + start_idx, end_idx = stage_index[0], stage_index[1] + for i, (block, layer_past) in enumerate(zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx]), + start=start_idx): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + alibi, + causal_mask, + layer_past, + head_mask[i], + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + attention_mask=causal_mask, + head_mask=head_mask[i], + use_cache=use_cache, + output_attentions=output_attentions, + alibi=alibi, + ) + + hidden_states = outputs[0] + + if use_cache is True: + presents = presents + (outputs[1],) + if output_attentions: + all_self_attentions = all_self_attentions + \ + (outputs[2 if use_cache else 1],) + + if stage_manager.is_last_stage(): + # Add last hidden state + hidden_states = self.ln_f(hidden_states) + + # TODO: deal with all_hidden_states, all_self_attentions, presents + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if stage_manager.is_last_stage(): + if not return_dict: + return tuple( + v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + # attention_mask is not returned ; presents = past_key_values + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + else: + # always return dict for imediate stage + return {'hidden_states': hidden_states} + + @staticmethod + def bloom_for_causal_lm_forward(self: BloomForCausalLM, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + **deprecated_arguments): + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + logger = logging.get_logger(__name__) + + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + transformer_outputs = BloomPipelineForwards.bloom_model_forward(self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(lm_logits.device) + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + batch_size, seq_length, vocab_size = shift_logits.shape + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), + shift_labels.view(batch_size * seq_length)) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def bloom_for_sequence_classification_forward( + self: BloomForSequenceClassification, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + **deprecated_arguments, + ): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + logger = logging.get_logger(__name__) + + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + transformer_outputs = BloomPipelineForwards.bloom_model_forward( + self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + if stage_manager.is_last_stage(): + batch_size = hidden_states.shape[0] + #update batch size + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + logger.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`") + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def bloom_for_token_classification_forward( + self: BloomForTokenClassification, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + **deprecated_arguments, + ): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + logger = logging.get_logger(__name__) + + if deprecated_arguments.pop("position_ids", False) is not False: + # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` + warnings.warn( + "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" + " passing `position_ids`.", + FutureWarning, + ) + if len(deprecated_arguments) > 0: + raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + transformer_outputs = BloomPipelineForwards.bloom_model_forward( + self.transformer, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + hidden_states = self.dropout(hidden_states) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + batch_size, seq_length = labels.shape + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), + labels.view(batch_size * seq_length)) + + if not return_dict: + output = (logits,) + transformer_outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def bloom_for_question_answering_forward( + self: BloomForQuestionAnswering, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ): + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + outputs = BloomPipelineForwards.bloom_model_forward( + self.transformer, + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + + if stage_manager.is_last_stage(): + sequence_output = outputs[0] + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} diff --git a/colossalai/shardformer/modeling/llama.py b/colossalai/shardformer/modeling/llama.py new file mode 100644 index 000000000000..7bc626fe6825 --- /dev/null +++ b/colossalai/shardformer/modeling/llama.py @@ -0,0 +1,394 @@ +from typing import Callable, List, Optional + +import torch +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager + + +class LlamaPipelineForwards: + ''' + This class serves as a micro library for forward function substitution of Llama models + under pipeline setting. + ''' + + def llama_model_forward( + self: LlamaModel, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ): + logger = logging.get_logger(__name__) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if stage_manager.is_first_stage(): + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + device = input_ids.device if input_ids is not None else inputs_embeds.device + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + hidden_states = inputs_embeds + else: + input_shape = hidden_states.shape[:-1] + batch_size, seq_length = input_shape + device = hidden_states.device + + seq_length_with_past = seq_length + past_key_values_length = 0 + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + position_ids = torch.arange(past_key_values_length, + seq_length + past_key_values_length, + dtype=torch.long, + device=device) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + # embed positions, for the first stage, hidden_states is the input embeddings, + # for the other stages, hidden_states is the output of the previous stage + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length_with_past), + dtype=torch.bool, + device=hidden_states.device) + attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), hidden_states, + past_key_values_length) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + start_idx, end_idx = stage_index[0], stage_index[1] + for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + position_ids, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if stage_manager.is_last_stage(): + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + next_cache = next_decoder_cache if use_cache else None + if stage_manager.is_last_stage(): + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + # always return dict for imediate stage + return {'hidden_states': hidden_states} + + def llama_for_causal_lm_forward( + self: LlamaForCausalLM, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ): + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you consciours? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." + ```""" + logger = logging.get_logger(__name__) + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = LlamaPipelineForwards.llama_model_forward( + self.model, + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + past_key_values = None + all_hidden_states = None + all_self_attentions = None + all_cross_attentions = None + + if stage_manager.is_last_stage(): + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + def llama_for_sequence_classification_forward( + self: LlamaForSequenceClassification, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ): + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if return_dict: + logger.warning_once('return_dict is not supported for pipeline models at the moment') + return_dict = False + + transformer_outputs = LlamaPipelineForwards.llama_model_forward( + self.model, + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + + if input_ids is not None: + batch_size = input_ids.shape[0] + elif inputs_embeds is not None: + batch_size = inputs_embeds.shape[0] + else: + batch_size = hidden_states.shape[0] + + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 0a1a466210b2..f6a4c706eb14 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -1,40 +1,15 @@ from functools import partial -from typing import Callable, Dict, List, Optional +from typing import Callable, Dict, List -import torch import torch.nn as nn from torch import Tensor -from torch.nn import CrossEntropyLoss, Module -from transformers.modeling_outputs import ( - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - MultipleChoiceModelOutput, - NextSentencePredictorOutput, - QuestionAnsweringModelOutput, - SequenceClassifierOutput, - TokenClassifierOutput, -) -from transformers.models.bert.modeling_bert import ( - BertForMaskedLM, - BertForMultipleChoice, - BertForNextSentencePrediction, - BertForPreTraining, - BertForPreTrainingOutput, - BertForQuestionAnswering, - BertForSequenceClassification, - BertForTokenClassification, - BertLMHeadModel, - BertModel, -) -from transformers.utils import logging +from torch.nn import Module import colossalai.shardformer.layer as col_nn -from colossalai.pipeline.stage_manager import PipelineStageManager +from ..modeling.bert import BertPipelineForwards from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription -logger = logging.get_logger(__name__) - __all__ = [ 'BertPolicy', 'BertModelPolicy', 'BertForPreTrainingPolicy', 'BertLMdHeadModelPolicy', 'BertForMaskedLMPolicy', 'BertForNextSentencePredictionPolicy', 'BertForSequenceClassificationPolicy', 'BertForTokenClassificationPolicy', @@ -207,6 +182,27 @@ def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, poli return + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + assert self.pipeline_stage_manager is not None + + if self.model.__class__.__name__ == 'BertModel': + module = self.model + else: + module = self.model.bert + stage_manager = self.pipeline_stage_manager + + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.encoder.layer[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.pooler) + + return held_layers + # BertModel class BertModelPolicy(BertPolicy): @@ -217,21 +213,15 @@ def __init__(self) -> None: def module_policy(self): policy = super().module_policy() from transformers.models.bert.modeling_bert import BertModel - self.set_pipeline_forward(model_cls=BertModel, new_forward=bert_model_forward, policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertModel, + new_forward=BertPipelineForwards.bert_model_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" - module = self.model - stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.embeddings) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.encoder.layer[start_idx:end_idx]) - if stage_manager.is_last_stage(): - held_layers.append(module.pooler) + held_layers = super().get_held_layers() return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -250,30 +240,24 @@ def module_policy(self): policy = self.add_lm_head_policy(policy) policy = self.add_lm_prediction_policy(policy) from transformers.models.bert.modeling_bert import BertForPreTraining - self.set_pipeline_forward(model_cls=BertForPreTraining, new_forward=bert_for_pretraining_forward, policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertForPreTraining, + new_forward=BertPipelineForwards.bert_for_pretraining_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage""" - module = self.model + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) - held_layers = [] - if stage_manager.is_first_stage(): - held_layers.append(module.bert.embeddings) - - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) - if stage_manager.is_last_stage(): - held_layers.append(module.bert.pooler) - held_layers.append(module.cls) + held_layers.append(self.model.cls) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: model = self.model - if self.pipeline_stage_manager: + if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: if id(model.bert.embeddings.word_embeddings.weight) == id(model.cls.predictions.decoder.weight): # tie weights return [{ @@ -294,29 +278,25 @@ def module_policy(self): policy = self.add_lm_head_policy(policy) policy = self.add_lm_prediction_policy(policy) from transformers.models.bert.modeling_bert import BertLMHeadModel - self.set_pipeline_forward(model_cls=BertLMHeadModel, new_forward=bert_lm_head_model_forward, policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertLMHeadModel, + new_forward=BertPipelineForwards.bert_lm_head_model_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ - module = self.model - held_layers = [] + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.bert.embeddings) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): - held_layers.append(module.bert.pooler) - held_layers.append(module.cls) + held_layers.append(self.model.cls) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: bert_model = self.model.bert - if self.pipeline_stage_manager: + if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight): # tie weights return [{ @@ -337,29 +317,25 @@ def module_policy(self): policy = self.add_lm_head_policy(policy) mpolicy = self.add_lm_prediction_policy(policy) from transformers.models.bert.modeling_bert import BertForMaskedLM - self.set_pipeline_forward(model_cls=BertForMaskedLM, new_forward=bert_for_masked_lm_forward, policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertForMaskedLM, + new_forward=BertPipelineForwards.bert_for_masked_lm_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ - module = self.model - held_layers = [] + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.bert.embeddings) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): - held_layers.append(module.bert.pooler) - held_layers.append(module.cls) + held_layers.append(self.model.cls) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: bert_model = self.model.bert - if self.pipeline_stage_manager: + if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight): # tie weights return [{ @@ -391,10 +367,10 @@ def module_policy(self): ]) } policy.update(addon_module) - - self.set_pipeline_forward(model_cls=BertForSequenceClassification, - new_forward=bert_for_sequence_classification_forward, - policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertForSequenceClassification, + new_forward=BertPipelineForwards.bert_for_sequence_classification_forward, + policy=policy) return policy @@ -402,18 +378,11 @@ def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ - module = self.model - held_layers = [] + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.bert.embeddings) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): - held_layers.append(module.bert.pooler) - held_layers.append(module.dropout) - held_layers.append(module.classifier) + held_layers.append(self.model.dropout) + held_layers.append(self.model.classifier) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -443,10 +412,10 @@ def module_policy(self): ]) } policy.update(addon_module) - - self.set_pipeline_forward(model_cls=BertForTokenClassification, - new_forward=bert_for_token_classification_forward, - policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertForTokenClassification, + new_forward=BertPipelineForwards.bert_for_token_classification_forward, + policy=policy) return policy @@ -454,18 +423,11 @@ def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ - module = self.model - held_layers = [] + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.bert.embeddings) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): - held_layers.append(module.bert.pooler) - held_layers.append(module.dropout) - held_layers.append(module.classifier) + held_layers.append(self.model.dropout) + held_layers.append(self.model.classifier) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -482,9 +444,10 @@ def __init__(self) -> None: def module_policy(self): policy = super().module_policy() from transformers.models.bert.modeling_bert import BertForNextSentencePrediction - self.set_pipeline_forward(model_cls=BertForNextSentencePrediction, - new_forward=bert_for_next_sentence_prediction_forward, - policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertForNextSentencePrediction, + new_forward=BertPipelineForwards.bert_for_next_sentence_prediction_forward, + policy=policy) return policy @@ -492,17 +455,10 @@ def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ - module = self.model - held_layers = [] + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.bert.embeddings) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): - held_layers.append(module.bert.pooler) - held_layers.append(module.cls) + held_layers.append(self.model.cls) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -532,10 +488,10 @@ def module_policy(self): ]) } policy.update(addon_module) - - self.set_pipeline_forward(model_cls=BertForMultipleChoice, - new_forward=bert_for_multiple_choice_forward, - policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertForMultipleChoice, + new_forward=BertPipelineForwards.bert_for_multiple_choice_forward, + policy=policy) return policy @@ -543,18 +499,11 @@ def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ - module = self.model - held_layers = [] + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.bert.embeddings) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): - held_layers.append(module.bert.pooler) - held_layers.append(module.dropout) - held_layers.append(module.classifier) + held_layers.append(self.model.dropout) + held_layers.append(self.model.classifier) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -570,9 +519,10 @@ def __init__(self) -> None: def module_policy(self): from transformers.models.bert.modeling_bert import BertForQuestionAnswering policy = super().module_policy() - self.set_pipeline_forward(model_cls=BertForQuestionAnswering, - new_forward=bert_for_question_answering_forward, - policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BertForQuestionAnswering, + new_forward=BertPipelineForwards.bert_for_question_answering_forward, + policy=policy) return policy @@ -580,957 +530,12 @@ def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ - module = self.model - held_layers = [] + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.bert.embeddings) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): - held_layers.append(module.bert.pooler) - held_layers.append(module.qa_outputs) + held_layers.append(self.model.qa_outputs) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: # no shared params for sequence classification model return [] - - -def bert_model_forward( - self: BertModel, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage - stage_index: Optional[List[int]] = None, -): - # TODO: add explaination of the output here. - r""" - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - """ - # debugging - # preprocess: - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if self.config.is_decoder: - use_cache = use_cache if use_cache is not None else self.config.use_cache - else: - use_cache = False - - if stage_manager.is_first_stage(): - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - batch_size, seq_length = input_shape - device = input_ids.device if input_ids is not None else inputs_embeds.device - if token_type_ids is None: - if hasattr(self.embeddings, "token_type_ids"): - buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] - buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) - token_type_ids = buffered_token_type_ids_expanded - else: - token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) - else: - input_shape = hidden_states.size()[:-1] - batch_size, seq_length = input_shape - device = hidden_states.device - - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if use_cache: - logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') - use_cache = False - - # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if attention_mask is None: - attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) - - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) - attention_mask = extended_attention_mask - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if self.config.is_decoder and encoder_hidden_states is not None: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - hidden_states = hidden_states if hidden_states is not None else None - - if stage_manager.is_first_stage(): - hidden_states = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - token_type_ids=token_type_ids, - inputs_embeds=inputs_embeds, - past_key_values_length=past_key_values_length, - ) - - # inherit from bert_layer,this should be changed when we add the feature to record hidden_states - all_hidden_states = () if output_hidden_states else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - if self.encoder.gradient_checkpointing and self.encoder.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") - use_cache = False - next_decoder_cache = () if use_cache else None - - start_idx, end_idx = stage_index[0], stage_index[1] - # layer_outputs - layer_outputs = hidden_states if hidden_states is not None else None - for idx, encoder_layer in enumerate(self.encoder.layer[start_idx:end_idx], start=start_idx): - if stage_manager.is_first_stage() and idx == 0: - encoder_attention_mask = encoder_extended_attention_mask - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer_head_mask = head_mask[idx] if head_mask is not None else None - past_key_value = past_key_values[idx] if past_key_values is not None else None - - if self.encoder.gradient_checkpointing and self.encoder.training: - - def create_custom_forward(module): - - def custom_forward(*inputs): - return module(*inputs, past_key_value, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(encoder_layer), - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - ) - else: - layer_outputs = encoder_layer( - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - if output_attentions: - all_self_attentions = all_self_attentions + (layer_outputs[1],) - if self.config.add_cross_attention: - all_cross_attentions = all_cross_attentions + \ - (layer_outputs[2],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - # end of a stage loop - sequence_output = layer_outputs[0] if layer_outputs is not None else None - - if stage_manager.is_last_stage(): - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - if not return_dict: - return (sequence_output, pooled_output) + layer_outputs[1:] - # return dict is not supported at this moment - else: - return BaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - pooler_output=pooled_output, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - - # output of non-first and non-last stages: must be a dict - else: - # intermediate stage always return dict - return { - 'hidden_states': hidden_states, - } - - -def bert_for_pretraining_forward( - self: BertForPreTraining, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - next_sentence_label: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_manager: Optional[PipelineStageManager] = None, - stage_index: Optional[List[int]] = None, -): - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - outputs = bert_model_forward( - self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states if hidden_states is not None else None, - stage_index=stage_index, - ) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - if stage_manager.is_last_stage(): - sequence_output, pooled_output = outputs[:2] - prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) - # the last stage for pretraining model - total_loss = None - if labels is not None and next_sentence_label is not None: - loss_fct = CrossEntropyLoss() - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) - total_loss = masked_lm_loss + next_sentence_loss - - if not return_dict: - output = (prediction_scores, seq_relationship_score) + outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output - - return BertForPreTrainingOutput( - loss=total_loss, - prediction_logits=prediction_scores, - seq_relationship_logits=seq_relationship_score, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - - # intermediate stage always return dict - return { - 'hidden_states': hidden_states, - } - - -def bert_lm_head_model_forward( - self: BertLMHeadModel, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.Tensor]] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_manager: Optional[PipelineStageManager] = None, - stage_index: Optional[List[int]] = None, -): - r""" - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in - `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are - ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if labels is not None: - use_cache = False - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - outputs = bert_model_forward(self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states if hidden_states is not None else None, - stage_index=stage_index) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - - if stage_manager.is_last_stage(): - sequence_output = outputs[0] - prediction_scores = self.cls(sequence_output) - - lm_loss = None - if labels is not None: - # we are doing next-token prediction; shift prediction scores and input ids by one - shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() - labels = labels[:, 1:].contiguous() - loss_fct = CrossEntropyLoss() - lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((lm_loss,) + output) if lm_loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=lm_loss, - logits=prediction_scores, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - # intermediate stage always return dict - return {'hidden_states': hidden_states} - - -def bert_for_masked_lm_forward( - self: BertForMaskedLM, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.Tensor] = None, - stage_manager: Optional[PipelineStageManager] = None, - stage_index: Optional[List[int]] = None, -): - # -> Union[Tuple[torch.Tensor], MaskedLMOutput]: - r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., - config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the - loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - outputs = bert_model_forward( - self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - hidden_states=hidden_states, - stage_manager=stage_manager, - stage_index=stage_index, - ) - - if stage_manager.is_last_stage(): - sequence_output = outputs[0] - prediction_scores = self.cls(sequence_output) - - masked_lm_loss = None - if labels is not None: - loss_fct = CrossEntropyLoss() # -100 index = padding token - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output - - return MaskedLMOutput( - loss=masked_lm_loss, - logits=prediction_scores, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - -def bert_for_next_sentence_prediction_forward( - self: BertForNextSentencePrediction, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.Tensor] = None, - stage_manager: Optional[PipelineStageManager] = None, - stage_index: Optional[List[int]] = None, - **kwargs, -): - # -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair - (see `input_ids` docstring). Indices should be in `[0, 1]`: - - - 0 indicates sequence B is a continuation of sequence A, - - 1 indicates sequence B is a random sequence. - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, BertForNextSentencePrediction - >>> import torch - - >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - >>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased") - - >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." - >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." - >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") - - >>> outputs = model(**encoding, labels=torch.LongTensor([1])) - >>> logits = outputs.logits - >>> assert logits[0, 0] < logits[0, 1] # next sentence was random - ``` - """ - - if "next_sentence_label" in kwargs: - warnings.warn( - "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" - " `labels` instead.", - FutureWarning, - ) - labels = kwargs.pop("next_sentence_label") - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = bert_model_forward(self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - hidden_states=hidden_states, - stage_manager=stage_manager, - stage_index=stage_index) - - if stage_manager.is_last_stage(): - pooled_output = outputs[1] - seq_relationship_scores = self.cls(pooled_output) - - next_sentence_loss = None - if labels is not None: - loss_fct = CrossEntropyLoss() - next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) - - if not return_dict: - output = (seq_relationship_scores,) + outputs[2:] - return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output - - return NextSentencePredictorOutput( - loss=next_sentence_loss, - logits=seq_relationship_scores, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - # intermediate stage always return dict - return {'hidden_states': hidden_states} - - -def bert_for_sequence_classification_forward( - self: BertForSequenceClassification, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.Tensor] = None, - stage_manager: Optional[PipelineStageManager] = None, - stage_index: Optional[List[int]] = None, -): - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = bert_model_forward(self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - hidden_states=hidden_states, - stage_manager=stage_manager, - stage_index=stage_index) - - if stage_manager.is_last_stage(): - pooled_output = outputs[1] - - pooled_output = self.dropout(pooled_output) - logits = self.classifier(pooled_output) - - loss = None - if labels is not None: - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - loss_fct = MSELoss() - if self.num_labels == 1: - loss = loss_fct(logits.squeeze(), labels.squeeze()) - else: - loss = loss_fct(logits, labels) - elif self.config.problem_type == "single_label_classification": - loss_fct = CrossEntropyLoss() - loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - elif self.config.problem_type == "multi_label_classification": - loss_fct = BCEWithLogitsLoss() - loss = loss_fct(logits, labels) - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return SequenceClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - -def bert_for_token_classification_forward( - self: BertForTokenClassification, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.Tensor] = None, - stage_manager: Optional[PipelineStageManager] = None, - stage_index: Optional[List[int]] = None, -): - r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = bert_model_forward( - self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - hidden_states=hidden_states, - stage_manager=stage_manager, - stage_index=stage_index, - ) - - if stage_manager.is_last_stage(): - sequence_output = outputs[0] - - sequence_output = self.dropout(sequence_output) - logits = self.classifier(sequence_output) - - loss = None - if labels is not None: - loss_fct = CrossEntropyLoss() - loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TokenClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - -def bert_for_multiple_choice_forward( - self: BertForMultipleChoice, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.Tensor] = None, - stage_manager: Optional[PipelineStageManager] = None, - stage_index: Optional[List[int]] = None, -): - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., - num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See - `input_ids` above) - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # in our pipeline design,input ids are copied for every stage and shouldn't be none - # the input_ids for multiple choice model is [batch_size, num_choices, sequence_length] - if stage_manager.is_last_stage(): - num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] - - input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None - attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None - token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None - position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None - inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) - if inputs_embeds is not None else None) - - outputs = bert_model_forward( - self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - hidden_states=hidden_states, - stage_manager=stage_manager, - stage_index=stage_index, - ) - if stage_manager.is_last_stage(): - pooled_output = outputs[1] - pooled_output = self.dropout(pooled_output) - logits = self.classifier(pooled_output) - reshaped_logits = logits.view(-1, num_choices) - - loss = None - if labels is not None: - loss_fct = CrossEntropyLoss() - loss = loss_fct(reshaped_logits, labels) - - if not return_dict: - output = (reshaped_logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return MultipleChoiceModelOutput( - loss=loss, - logits=reshaped_logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - -def bert_for_question_answering_forward( - self: BertForQuestionAnswering, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - start_positions: Optional[torch.Tensor] = None, - end_positions: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.Tensor] = None, - stage_manager: Optional[PipelineStageManager] = None, - stage_index: Optional[List[int]] = None, -): - # NOTE: the arg start_position and end_position are used only for the last stage - r""" - start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the start of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the end of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = bert_model_forward( - self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - hidden_states=hidden_states, - stage_manager=stage_manager, - stage_index=stage_index, - ) - if stage_manager.is_last_stage(): - sequence_output = outputs[0] - - logits = self.qa_outputs(sequence_output) - start_logits, end_logits = logits.split(1, dim=-1) - start_logits = start_logits.squeeze(-1).contiguous() - end_logits = end_logits.squeeze(-1).contiguous() - - total_loss = None - if start_positions is not None and end_positions is not None: - # If we are on multi-GPU, split add a dimension - if len(start_positions.size()) > 1: - start_positions = start_positions.squeeze(-1) - if len(end_positions.size()) > 1: - end_positions = end_positions.squeeze(-1) - # sometimes the start/end positions are outside our model inputs, we ignore these terms - ignored_index = start_logits.size(1) - start_positions = start_positions.clamp(0, ignored_index) - end_positions = end_positions.clamp(0, ignored_index) - - loss_fct = CrossEntropyLoss(ignore_index=ignored_index) - start_loss = loss_fct(start_logits, start_positions) - end_loss = loss_fct(end_logits, end_positions) - total_loss = (start_loss + end_loss) / 2 - - if not return_dict: - output = (start_logits, end_logits) + outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output - - return QuestionAnsweringModelOutput( - loss=total_loss, - start_logits=start_logits, - end_logits=end_logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - return {'hidden_states': hidden_states} diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index b0e45452964e..15bae2f4a959 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -1,35 +1,15 @@ -import warnings from functools import partial from typing import Callable, Dict, List, Optional, Tuple, Union -import torch import torch.nn as nn from torch import Tensor -from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, Module, MSELoss -from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - QuestionAnsweringModelOutput, - SequenceClassifierOutputWithPast, - TokenClassifierOutput, -) -from transformers.models.bloom.modeling_bloom import ( - BloomForCausalLM, - BloomForQuestionAnswering, - BloomForSequenceClassification, - BloomForTokenClassification, - BloomModel, -) -from transformers.utils import logging +from torch.nn import Module import colossalai.shardformer.layer as col_nn -from colossalai.pipeline.stage_manager import PipelineStageManager -from ..modeling.bloom import build_bloom_alibi_tensor_fn +from ..modeling.bloom import BloomPipelineForwards, build_bloom_alibi_tensor_fn from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription -logger = logging.get_logger(__name__) - class BloomPolicy(Policy): @@ -150,6 +130,28 @@ def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, poli target_key=model_cls) return + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + assert self.pipeline_stage_manager is not None + + if self.model.__class__.__name__ == 'BloomModel': + module = self.model + else: + module = self.model.transformer + stage_manager = self.pipeline_stage_manager + + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.word_embeddings) + held_layers.append(module.word_embeddings_layernorm) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.h[start_idx:end_idx]) + if stage_manager.is_last_stage(): + held_layers.append(module.ln_f) + + return held_layers + class BloomModelPolicy(BloomPolicy): @@ -159,27 +161,17 @@ def __init__(self) -> None: def module_policy(self): policy = super().module_policy() from transformers.models.bloom.modeling_bloom import BloomModel - self.set_pipeline_forward(model_cls=BloomModel, new_forward=bloom_model_forward, policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BloomModel, + new_forward=BloomPipelineForwards.bloom_model_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ - module = self.model - stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.word_embeddings) - held_layers.append(module.word_embeddings_layernorm) - - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.h[start_idx:end_idx]) - - if stage_manager.is_last_stage(): - held_layers.append(module.ln_f) - + held_layers = super().get_held_layers() return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -199,29 +191,23 @@ def module_policy(self): suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)), policy=policy, target_key=BloomForCausalLM) - - self.set_pipeline_forward(model_cls=BloomForCausalLM, new_forward=bloom_for_causal_lm_forward, policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BloomForCausalLM, + new_forward=BloomPipelineForwards.bloom_for_causal_lm_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" - module = self.model stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.transformer.word_embeddings) - held_layers.append(module.transformer.word_embeddings_layernorm) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.transformer.h[start_idx:end_idx]) + held_layers = super().get_held_layers() if stage_manager.is_last_stage(): - held_layers.append(module.transformer.ln_f) - held_layers.append(module.lm_head) + held_layers.append(self.model.lm_head) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: bloom_model = self.model - if self.pipeline_stage_manager: + if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: if id(bloom_model.transformer.word_embeddings.weight) == id(bloom_model.lm_head.weight): # tie weights return [{ @@ -243,25 +229,18 @@ def module_policy(self): suffix="score", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)), policy=policy, target_key=BloomForSequenceClassification) - self.set_pipeline_forward(model_cls=BloomForSequenceClassification, - new_forward=bloom_for_sequence_classification_forward, - policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BloomForSequenceClassification, + new_forward=BloomPipelineForwards.bloom_for_sequence_classification_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" - module = self.model stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.transformer.word_embeddings) - held_layers.append(module.transformer.word_embeddings_layernorm) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.transformer.h[start_idx:end_idx]) + held_layers = super().get_held_layers() if stage_manager.is_last_stage(): - held_layers.append(module.transformer.ln_f) - held_layers.append(module.score) + held_layers.append(self.model.score) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -288,28 +267,20 @@ def module_policy(self): ], policy=policy, target_key=BloomForTokenClassification) - - self.set_pipeline_forward(model_cls=BloomForTokenClassification, - new_forward=bloom_for_token_classification_forward, - policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BloomForTokenClassification, + new_forward=BloomPipelineForwards.bloom_for_token_classification_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" - module = self.model stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.transformer.word_embeddings) - held_layers.append(module.transformer.word_embeddings_layernorm) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.transformer.h[start_idx:end_idx]) + held_layers = super().get_held_layers() if stage_manager.is_last_stage(): - held_layers.append(module.transformer.ln_f) - held_layers.append(module.dropout) - held_layers.append(module.classifier) + held_layers.append(self.model.dropout) + held_layers.append(self.model.classifier) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -322,605 +293,20 @@ class BloomForQuestionAnsweringPolicy(BloomPolicy): def module_policy(self): from transformers.models.bloom.modeling_bloom import BloomForQuestionAnswering policy = super().module_policy() - self.set_pipeline_forward(model_cls=BloomForQuestionAnswering, - new_forward=bloom_for_question_answering_forward, - policy=policy) + if self.pipeline_stage_manager: + self.set_pipeline_forward(model_cls=BloomForQuestionAnswering, + new_forward=BloomPipelineForwards.bloom_for_question_answering_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" - module = self.model + held_layers = super().get_held_layers() stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.transformer.h), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.transformer.word_embeddings) - held_layers.append(module.transformer.word_embeddings_layernorm) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.transformer.h[start_idx:end_idx]) if stage_manager.is_last_stage(): - held_layers.append(module.transformer.ln_f) - held_layers.append(module.qa_outputs) + held_layers.append(self.model.qa_outputs) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared params in bloom for question answering model""" return [] - - -def bloom_model_forward( - self: BloomModel, - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, - attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.LongTensor] = None, - inputs_embeds: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, - **deprecated_arguments, -) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: - if deprecated_arguments.pop("position_ids", False) is not False: - # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` - warnings.warn( - "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" - " passing `position_ids`.", - FutureWarning, - ) - if len(deprecated_arguments) > 0: - raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") - - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # add warnings here - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if use_cache: - logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') - use_cache = False - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape batch_size x num_heads x N x N - - # head_mask has shape n_layer x batch x num_heads x N x N - head_mask = self.get_head_mask(head_mask, self.config.n_layer) - - # case: First stage of training - if stage_manager.is_first_stage(): - # check input_ids and inputs_embeds - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - batch_size, seq_length = input_ids.shape - elif inputs_embeds is not None: - batch_size, seq_length, _ = inputs_embeds.shape - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - - hidden_states = self.word_embeddings_layernorm(inputs_embeds) - # initialize in the first stage and then pass to the next stage - else: - input_shape = hidden_states.shape[:-1] - batch_size, seq_length = input_shape - - # extra recording tensor should be generated in the first stage - - presents = () if use_cache else None - all_self_attentions = () if output_attentions else None - all_hidden_states = () if output_hidden_states else None - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") - use_cache = False - - if past_key_values is None: - past_key_values = tuple([None] * len(self.h)) - # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage - seq_length_with_past = seq_length - past_key_values_length = 0 - if past_key_values[0] is not None: - past_key_values_length = past_key_values[0][0].shape[2] # source_len - - seq_length_with_past = seq_length_with_past + past_key_values_length - if attention_mask is None: - attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) - else: - attention_mask = attention_mask.to(hidden_states.device) - - alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) - - # causal_mask is constructed every stage and its input is passed through different stages - causal_mask = self._prepare_attn_mask( - attention_mask, - input_shape=(batch_size, seq_length), - past_key_values_length=past_key_values_length, - ) - - start_idx, end_idx = stage_index[0], stage_index[1] - for i, (block, layer_past) in enumerate(zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx])): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - - def custom_forward(*inputs): - # None for past_key_value - return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) - - return custom_forward - - outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(block), - hidden_states, - alibi, - causal_mask, - layer_past, - head_mask[i], - ) - else: - outputs = block( - hidden_states, - layer_past=layer_past, - attention_mask=causal_mask, - head_mask=head_mask[i], - use_cache=use_cache, - output_attentions=output_attentions, - alibi=alibi, - ) - - hidden_states = outputs[0] - - if use_cache is True: - presents = presents + (outputs[1],) - if output_attentions: - all_self_attentions = all_self_attentions + \ - (outputs[2 if use_cache else 1],) - - if stage_manager.is_last_stage(): - # Add last hidden state - hidden_states = self.ln_f(hidden_states) - - # TODO: deal with all_hidden_states, all_self_attentions, presents - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if stage_manager.is_last_stage(): - if not return_dict: - return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) - - # attention_mask is not returned ; presents = past_key_values - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=presents, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - ) - else: - # always return dict for imediate stage - return {'hidden_states': hidden_states} - - -def bloom_for_causal_lm_forward(self: 'BloomForCausalLM', - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, - attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, - **deprecated_arguments): - r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set - `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` - are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` - """ - if deprecated_arguments.pop("position_ids", False) is not False: - # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` - warnings.warn( - "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" - " passing `position_ids`.", - FutureWarning, - ) - if len(deprecated_arguments) > 0: - raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - transformer_outputs = bloom_model_forward(self.transformer, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - if stage_manager.is_last_stage(): - hidden_states = transformer_outputs[0] - lm_logits = self.lm_head(hidden_states) - - loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(lm_logits.device) - # Shift so that tokens < n predict n - shift_logits = lm_logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - batch_size, seq_length, vocab_size = shift_logits.shape - # Flatten the tokens - loss_fct = CrossEntropyLoss() - loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), - shift_labels.view(batch_size * seq_length)) - - if not return_dict: - output = (lm_logits,) + transformer_outputs[1:] - return ((loss,) + output) if loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=loss, - logits=lm_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - else: - hidden_states = transformer_outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - -def bloom_for_sequence_classification_forward( - self: BloomForSequenceClassification, - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, - attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, - **deprecated_arguments, -): - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - """ - if deprecated_arguments.pop("position_ids", False) is not False: - # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` - warnings.warn( - "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" - " passing `position_ids`.", - FutureWarning, - ) - if len(deprecated_arguments) > 0: - raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - transformer_outputs = bloom_model_forward( - self.transformer, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index, - ) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - if stage_manager.is_last_stage(): - batch_size = hidden_states.shape[0] - # update batch size - hidden_states = transformer_outputs[0] - logits = self.score(hidden_states) - - if self.config.pad_token_id is None and batch_size != 1: - raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") - if self.config.pad_token_id is None: - sequence_lengths = -1 - else: - if input_ids is not None: - sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) - else: - sequence_lengths = -1 - logger.warning( - f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " - "unexpected if using padding tokens in conjunction with `inputs_embeds.`") - - pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] - - loss = None - if labels is not None: - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - loss_fct = MSELoss() - if self.num_labels == 1: - loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) - else: - loss = loss_fct(pooled_logits, labels) - elif self.config.problem_type == "single_label_classification": - loss_fct = CrossEntropyLoss() - loss = loss_fct(pooled_logits, labels) - elif self.config.problem_type == "multi_label_classification": - loss_fct = BCEWithLogitsLoss() - loss = loss_fct(pooled_logits, labels) - if not return_dict: - output = (pooled_logits,) + transformer_outputs[1:] - return ((loss,) + output) if loss is not None else output - - return SequenceClassifierOutputWithPast( - loss=loss, - logits=pooled_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - else: - hidden_states = transformer_outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - -def bloom_for_token_classification_forward( - self: BloomForTokenClassification, - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, - attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, - **deprecated_arguments, -): - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - """ - if deprecated_arguments.pop("position_ids", False) is not False: - # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` - warnings.warn( - "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" - " passing `position_ids`.", - FutureWarning, - ) - if len(deprecated_arguments) > 0: - raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - transformer_outputs = bloom_model_forward( - self.transformer, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index, - ) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - if stage_manager.is_last_stage(): - hidden_states = transformer_outputs[0] - hidden_states = self.dropout(hidden_states) - logits = self.classifier(hidden_states) - - loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(logits.device) - batch_size, seq_length = labels.shape - loss_fct = CrossEntropyLoss() - loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)) - - if not return_dict: - output = (logits,) + transformer_outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TokenClassifierOutput( - loss=loss, - logits=logits, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - else: - hidden_states = transformer_outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - -def bloom_for_question_answering_forward( - self: BloomForQuestionAnswering, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - start_positions: Optional[torch.LongTensor] = None, - end_positions: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, -): - r""" - start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the start of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the end of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - outputs = bloom_model_forward( - self.transformer, - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index, - ) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - - if stage_manager.is_last_stage(): - sequence_output = outputs[0] - logits = self.qa_outputs(sequence_output) - start_logits, end_logits = logits.split(1, dim=-1) - start_logits = start_logits.squeeze(-1).contiguous() - end_logits = end_logits.squeeze(-1).contiguous() - - total_loss = None - if start_positions is not None and end_positions is not None: - # If we are on multi-GPU, split add a dimension - if len(start_positions.size()) > 1: - start_positions = start_positions.squeeze(-1) - if len(end_positions.size()) > 1: - end_positions = end_positions.squeeze(-1) - # sometimes the start/end positions are outside our model inputs, we ignore these terms - ignored_index = start_logits.size(1) - start_positions = start_positions.clamp(0, ignored_index) - end_positions = end_positions.clamp(0, ignored_index) - - loss_fct = CrossEntropyLoss(ignore_index=ignored_index) - start_loss = loss_fct(start_logits, start_positions) - end_loss = loss_fct(end_logits, end_positions) - total_loss = (start_loss + end_loss) / 2 - - if not return_dict: - output = (start_logits, end_logits) + outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output - - return QuestionAnsweringModelOutput( - loss=total_loss, - start_logits=start_logits, - end_logits=end_logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - return {'hidden_states': hidden_states} diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index c7cd8182a4ca..5988366ed57b 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -1,25 +1,15 @@ from functools import partial -from typing import Dict, List, Optional, Union +from typing import Callable, Dict, List, Union -import torch import torch.nn as nn from torch import Tensor -from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, Module, MSELoss -from transformers.modeling_outputs import ( - BaseModelOutputWithPast, - CausalLMOutputWithPast, - SequenceClassifierOutputWithPast, -) -from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel -from transformers.utils import logging - -from colossalai.pipeline.stage_manager import PipelineStageManager +from torch.nn import Module + from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D +from ..modeling.llama import LlamaPipelineForwards from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription -logger = logging.get_logger(__name__) - __all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy'] @@ -119,32 +109,35 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: def postprocess(self): return self.model - -class LlamaModelPolicy(LlamaPolicy): - - def __init__(self) -> None: - super().__init__() - - def module_policy(self): - policy = super().module_policy() - from transformers.models.llama.modeling_llama import LlamaModel + def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: + """If under pipeline parallel setting, replacing the original forward method of huggingface + to customized forward method, and add this changing to policy.""" if self.pipeline_stage_manager: - # set None as default stage_manager = self.pipeline_stage_manager - layers_per_stage = Policy.distribute_layers(len(self.model.layers), stage_manager.num_stages) + if self.model.__class__.__name__ == "LlamaModel": + module = self.model + else: + module = self.model.model + + layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages) stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - method_replacement = { - 'forward': partial(llama_model_forward, stage_manager=stage_manager, stage_index=stage_index) - } + method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} self.append_or_create_method_replacement(description=method_replacement, policy=policy, - target_key=LlamaModel) - return policy + target_key=model_cls) + + return def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" - module = self.model + assert self.pipeline_stage_manager is not None + + if self.model.__class__.__name__ == 'LlamaModel': + module = self.model + else: + module = self.model.model stage_manager = self.pipeline_stage_manager + held_layers = [] layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages) if stage_manager.is_first_stage(): @@ -153,6 +146,28 @@ def get_held_layers(self) -> List[Module]: held_layers.extend(module.layers[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.norm) + + return held_layers + + +class LlamaModelPolicy(LlamaPolicy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + policy = super().module_policy() + from transformers.models.llama.modeling_llama import LlamaModel + if self.pipeline_stage_manager: + # set None as default + self.set_pipeline_forward(model_cls=LlamaModel, + new_forward=LlamaPipelineForwards.llama_model_forward, + policy=policy) + return policy + + def get_held_layers(self) -> List[Module]: + """Get pipeline layers for current stage.""" + held_layers = super().get_held_layers() return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: @@ -180,40 +195,30 @@ def module_policy(self): if self.pipeline_stage_manager: # set None as default - stage_manager = self.pipeline_stage_manager - layers_per_stage = Policy.distribute_layers(len(self.model.model.layers), stage_manager.num_stages) - stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - method_replacement = { - 'forward': partial(llama_for_causal_lm_forward, stage_manager=stage_manager, stage_index=stage_index) - } - self.append_or_create_method_replacement(description=method_replacement, - policy=policy, - target_key=LlamaForCausalLM) + self.set_pipeline_forward(model_cls=LlamaForCausalLM, + new_forward=LlamaPipelineForwards.llama_for_causal_lm_forward, + policy=policy) + return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" - module = self.model stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.model.layers), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.model.embed_tokens) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.model.layers[start_idx:end_idx]) + held_layers = super().get_held_layers() if stage_manager.is_last_stage(): - held_layers.append(module.model.norm) - held_layers.append(module.lm_head) + held_layers.append(self.model.lm_head) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: llama_model = self.model.model - if id(llama_model.embed_tokens.weight) == id(self.model.lm_head.weight): - # tie weights - return [{ - 0: llama_model.embed_tokens.weight, - self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight - }] + if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: + if id(llama_model.embed_tokens.weight) == id( + self.model.lm_head.weight) and self.pipeline_stage_manager.num_stages > 1: + # tie weights + return [{ + 0: llama_model.embed_tokens.weight, + self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight + }] return [] @@ -237,405 +242,19 @@ def module_policy(self): # to be confirmed if self.pipeline_stage_manager: # set None as default - stage_manager = self.pipeline_stage_manager - layers_per_stage = Policy.distribute_layers(len(self.model.model.layers), stage_manager.num_stages) - stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) - method_replacement = { - 'forward': - partial(llama_for_sequence_classification_forward, - stage_manager=stage_manager, - stage_index=stage_index) - } - self.append_or_create_method_replacement(description=method_replacement, - policy=policy, - target_key=LlamaForSequenceClassification) + self.set_pipeline_forward(model_cls=LlamaForSequenceClassification, + new_forward=LlamaPipelineForwards.llama_for_sequence_classification_forward, + policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" - module = self.model stage_manager = self.pipeline_stage_manager - held_layers = [] - layers_per_stage = self.distribute_layers(len(module.model.layers), stage_manager.num_stages) - if stage_manager.is_first_stage(): - held_layers.append(module.model.embed_tokens) - start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) - held_layers.extend(module.model.layers[start_idx:end_idx]) + held_layers = super().get_held_layers() if stage_manager.is_last_stage(): - held_layers.append(module.model.norm) - held_layers.append(module.score) + held_layers.append(self.model.score) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared params in llama for sequence classification model""" return [] - - -def llama_model_forward( - self: LlamaModel, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, -): - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - use_cache = use_cache if use_cache is not None else self.config.use_cache - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # retrieve input_ids and inputs_embeds - if stage_manager.is_first_stage(): - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") - elif input_ids is not None: - batch_size, seq_length = input_ids.shape - elif inputs_embeds is not None: - batch_size, seq_length, _ = inputs_embeds.shape - else: - raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") - device = input_ids.device if input_ids is not None else inputs_embeds.device - if inputs_embeds is None: - inputs_embeds = self.embed_tokens(input_ids) - hidden_states = inputs_embeds - else: - input_shape = hidden_states.shape[:-1] - batch_size, seq_length = input_shape - device = hidden_states.device - - seq_length_with_past = seq_length - past_key_values_length = 0 - - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if use_cache: - logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') - use_cache = False - - if past_key_values is not None: - past_key_values_length = past_key_values[0][0].shape[2] - seq_length_with_past = seq_length_with_past + past_key_values_length - - if position_ids is None: - position_ids = torch.arange(past_key_values_length, - seq_length + past_key_values_length, - dtype=torch.long, - device=device) - position_ids = position_ids.unsqueeze(0).view(-1, seq_length) - else: - position_ids = position_ids.view(-1, seq_length).long() - - # embed positions, for the first stage, hidden_states is the input embeddings, - # for the other stages, hidden_states is the output of the previous stage - if attention_mask is None: - attention_mask = torch.ones((batch_size, seq_length_with_past), dtype=torch.bool, device=hidden_states.device) - attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), hidden_states, - past_key_values_length) - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") - use_cache = False - - # decoder layers - all_hidden_states = () if output_hidden_states else None - all_self_attns = () if output_attentions else None - next_decoder_cache = () if use_cache else None - - start_idx, end_idx = stage_index[0], stage_index[1] - for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx]): - if output_hidden_states: - all_hidden_states += (hidden_states,) - - past_key_value = past_key_values[idx] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - - def custom_forward(*inputs): - # None for past_key_value - return module(*inputs, output_attentions, None) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(decoder_layer), - hidden_states, - attention_mask, - position_ids, - None, - ) - else: - layer_outputs = decoder_layer( - hidden_states, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_value=past_key_value, - output_attentions=output_attentions, - use_cache=use_cache, - ) - - hidden_states = layer_outputs[0] - - if use_cache: - next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) - if output_attentions: - all_self_attns += (layer_outputs[1],) - - if stage_manager.is_last_stage(): - hidden_states = self.norm(hidden_states) - - # add hidden states from the last decoder layer - if output_hidden_states: - all_hidden_states += (hidden_states,) - next_cache = next_decoder_cache if use_cache else None - if stage_manager.is_last_stage(): - if not return_dict: - return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) - return BaseModelOutputWithPast( - last_hidden_state=hidden_states, - past_key_values=next_cache, - hidden_states=all_hidden_states, - attentions=all_self_attns, - ) - # always return dict for imediate stage - return {'hidden_states': hidden_states} - - -def llama_for_causal_lm_forward( - self: LlamaForCausalLM, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, -): - r""" - Args: - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., - config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored - (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, LlamaForCausalLM - - >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) - >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) - - >>> prompt = "Hey, are you consciours? Can you talk to me?" - >>> inputs = tokenizer(prompt, return_tensors="pt") - - >>> # Generate - >>> generate_ids = model.generate(inputs.input_ids, max_length=30) - >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] - "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." - ```""" - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) - outputs = llama_model_forward( - self.model, - input_ids=input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index, - ) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - - if stage_manager.is_last_stage(): - hidden_states = outputs[0] - logits = self.lm_head(hidden_states) - loss = None - if labels is not None: - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - shift_logits = shift_logits.view(-1, self.config.vocab_size) - shift_labels = shift_labels.view(-1) - # Enable model parallelism - shift_labels = shift_labels.to(shift_logits.device) - loss = loss_fct(shift_logits, shift_labels) - - if not return_dict: - output = (logits,) + outputs[1:] - return (loss,) + output if loss is not None else output - - return CausalLMOutputWithPast( - loss=loss, - logits=logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - -def llama_for_sequence_classification_forward( - self: LlamaForSequenceClassification, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, -): - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - transformer_outputs = llama_model_forward( - self.model, - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index, - ) - - if input_ids is not None: - batch_size = input_ids.shape[0] - elif inputs_embeds is not None: - batch_size = inputs_embeds.shape[0] - else: - batch_size = hidden_states.shape[0] - - if stage_manager.is_last_stage(): - hidden_states = transformer_outputs[0] - logits = self.score(hidden_states) - - if self.config.pad_token_id is None and batch_size != 1: - raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") - if self.config.pad_token_id is None: - sequence_lengths = -1 - else: - if input_ids is not None: - sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) - else: - sequence_lengths = -1 - - pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] - - loss = None - if labels is not None: - labels = labels.to(logits.device) - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - loss_fct = MSELoss() - if self.num_labels == 1: - loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) - else: - loss = loss_fct(pooled_logits, labels) - elif self.config.problem_type == "single_label_classification": - loss_fct = CrossEntropyLoss() - loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) - elif self.config.problem_type == "multi_label_classification": - loss_fct = BCEWithLogitsLoss() - loss = loss_fct(pooled_logits, labels) - if not return_dict: - output = (pooled_logits,) + transformer_outputs[1:] - return ((loss,) + output) if loss is not None else output - - return SequenceClassifierOutputWithPast( - loss=loss, - logits=pooled_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - - else: - hidden_states = transformer_outputs.get('hidden_states') - return {'hidden_states': hidden_states} From 36e546b2cc5a6d9e873baf1843eec318420fb978 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Fri, 21 Jul 2023 16:23:04 +0800 Subject: [PATCH 38/84] [pipeline] add pipeline support for T5Stack/T5EncoderModel (#4300) * modify t5 policy & add test * pipeline stage distribution for t5 * complete t5 base policy * t5 stack: halfway * modify gpt2 pipeline test * complete pipeline forward for T5Stack/T5EncoderModel * fix docstring * move t5 util tests to test_pipeline --- colossalai/shardformer/modeling/t5.py | 279 ++++++++++++++++++ colossalai/shardformer/policies/t5.py | 179 ++++++++++- tests/kit/model_zoo/transformers/gpt.py | 20 +- .../test_policy/test_t5_pipeline_utils.py | 39 +++ .../test_model/test_shard_gpt2_pipeline.py | 12 +- .../test_model/test_shard_t5_pipeline.py | 96 ++++++ 6 files changed, 604 insertions(+), 21 deletions(-) create mode 100644 colossalai/shardformer/modeling/t5.py create mode 100644 tests/test_pipeline/test_policy/test_t5_pipeline_utils.py create mode 100644 tests/test_shardformer/test_model/test_shard_t5_pipeline.py diff --git a/colossalai/shardformer/modeling/t5.py b/colossalai/shardformer/modeling/t5.py new file mode 100644 index 000000000000..cc270d5828a2 --- /dev/null +++ b/colossalai/shardformer/modeling/t5.py @@ -0,0 +1,279 @@ +from functools import partial +from types import MethodType +from typing import Callable, Dict, List, Optional, Tuple, Union + +import torch +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from torch.utils.checkpoint import checkpoint +from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions +from transformers.models.t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Stack +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager + + +class T5PipelineForwards: + ''' + This class serves as a micro library for forward function substitution of + T5 models under pipeline setting. + ''' + + @staticmethod + def t5_stack_forward( + self: T5Stack, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = False, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + position_bias: Optional[torch.Tensor] = None, + encoder_decoder_position_bias: Optional[torch.Tensor] = None, + stage_index: Optional[List[int]] = None, + decoder_starting_stage: Optional[int] = None, + ) -> Union[Dict, Tuple, BaseModelOutputWithPastAndCrossAttentions]: + + # This function is modified on the basis of transformers.models.t5.modeling_t5.T5Stack.forward. + # Please refer to original code of transformers for more details. + + logger = logging.get_logger(__name__) + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if past_key_values: + logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') + past_key_values = None + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + if use_cache is True: + if not in_decoder: + raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder") + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + stage = stage_manager.stage + in_decoder = self.is_decoder + if in_decoder != (stage >= decoder_starting_stage): + raise ValueError("Config in T5Stack is not aligned with pipeline setting.") + + # at_first_stage: current stage is the first stage of encoder/decoder, taking input_ids/input_embedds + # at_last_stage: current stage is the last stage of encoder/decoder, making outputs the same form as huggingface + at_first_stage = (stage == 0) or (stage == decoder_starting_stage) + at_last_stage = (stage == decoder_starting_stage - 1) or (stage == stage_manager.num_stages - 1) + + # Process inputs if at the first stage of encoder/decoder. + if at_first_stage: + if input_ids is not None and inputs_embeds is not None: + err_msg_prefix = "decoder_" if in_decoder else "" + raise ValueError( + f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + err_msg_prefix = "decoder_" if in_decoder else "" + raise ValueError( + f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds") + if inputs_embeds is None: + if self.embed_tokens is None: + raise ValueError("You have to initialize the model with valid token embeddings") + inputs_embeds = self.embed_tokens(input_ids) + batch_size, seq_length = input_shape + device = inputs_embeds.device + hidden_states = self.dropout(inputs_embeds) + else: + if hidden_states is None: + raise ValueError( + "hidden_states shouldn't be None for stages other than the first stage of encoder/decoder.") + input_shape = hidden_states.size()[:-1] + batch_size, seq_length = input_shape[0], input_shape[1] + device = hidden_states.device + + # required mask seq length can be calculated via length of past + mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length + + if attention_mask is None: + attention_mask = torch.ones(batch_size, mask_seq_length, device=device) + if in_decoder and encoder_attention_mask is None and encoder_hidden_states is not None: + encoder_seq_length = encoder_hidden_states.shape[1] + encoder_attention_mask = torch.ones(batch_size, encoder_seq_length, device=device, dtype=torch.long) + + # initialize past_key_values with `None` if past does not exist + if past_key_values is None: + past_key_values = [None] * len(self.block) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + head_mask = self.get_head_mask(head_mask, self.config.num_layers) + cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers) + present_key_value_states = () if use_cache else None + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + all_cross_attentions = () if (output_attentions and self.is_decoder) else None + + # Going through held blocks. + start_idx, end_idx = stage_index[0], stage_index[1] + + for i in range(start_idx, end_idx): + + past_key_value = past_key_values[i] + layer_module = self.block[i] + layer_head_mask = head_mask[i] + cross_attn_layer_head_mask = cross_attn_head_mask[i] + torch.cuda.set_device(hidden_states.device) + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + return tuple(module(*inputs, use_cache, output_attentions)) + + return custom_forward + + layer_outputs = checkpoint( + create_custom_forward(layer_module), + hidden_states, + extended_attention_mask, + position_bias, + encoder_hidden_states, + encoder_extended_attention_mask, + encoder_decoder_position_bias, + layer_head_mask, + cross_attn_layer_head_mask, + None, # past_key_value is always None with gradient checkpointing + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask=extended_attention_mask, + position_bias=position_bias, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + encoder_decoder_position_bias=encoder_decoder_position_bias, + layer_head_mask=layer_head_mask, + cross_attn_layer_head_mask=cross_attn_layer_head_mask, + past_key_value=past_key_value, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + # layer_outputs is a tuple with: + # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) + + if use_cache is False or use_cache is None: + layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] + hidden_states, present_key_value_state = layer_outputs[:2] + # print(stage, len(layer_outputs), present_key_value_state.shape) + + # We share the position biases between the layers - the first layer store them + # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), + # (cross-attention position bias), (cross-attention weights) + position_bias = layer_outputs[2] + + if self.is_decoder and encoder_hidden_states is not None: + encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3] + # append next layer key value states + if use_cache: + present_key_value_states = present_key_value_states + (present_key_value_state,) + + # last layer + if at_last_stage: + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.dropout(hidden_states) + + if not return_dict: + return tuple(v for v in [ + hidden_states, + present_key_value_states, + all_hidden_states, + all_attentions, + all_cross_attentions, + ] if v is not None) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=present_key_value_states, + hidden_states=all_hidden_states, + attentions=all_attentions, + cross_attentions=all_cross_attentions, + ) + else: + return { + 'hidden_states': hidden_states, + 'position_bias': position_bias, + 'encoder_decoder_position_bias': encoder_decoder_position_bias + } + + @staticmethod + def t5_encoder_model_forward( + self: T5EncoderModel, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + position_bias: Optional[torch.Tensor] = None, + encoder_decoder_position_bias: Optional[torch.Tensor] = None, + stage_index: Optional[List[int]] = None, + decoder_starting_stage: Optional[int] = None, + ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]: + r""" + This function is modified on the basis of transformers.models.t5.modeling_gpt2.T5EncoderModel.forward. + Please refer to original code of transformers for more details. + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = T5PipelineForwards.t5_stack_forward(self.encoder, + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + position_bias=position_bias, + encoder_decoder_position_bias=encoder_decoder_position_bias, + stage_index=stage_index, + decoder_starting_stage=decoder_starting_stage) + + return outputs diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 6b8f404f1769..1846c5873801 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -1,3 +1,8 @@ +from functools import partial +from typing import Callable, Dict, List, Optional, Tuple + +from torch import Tensor, nn + from colossalai.shardformer.layer import ( DropoutForParallelInput, Embedding1D, @@ -8,9 +13,11 @@ ) from colossalai.shardformer.policies.base_policy import ModulePolicyDescription +from .._utils import getattr_, setattr_ +from ..modeling.t5 import T5PipelineForwards from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription -__all__ = ["T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"] +__all__ = ["distribute_t5_layers", "T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"] class T5BasePolicy(Policy): @@ -106,7 +113,7 @@ def module_policy(self): ]) policy[T5DenseGatedActDense] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( - suffix="wi_0", + suffix="wi_0 ", target_module=Linear1D_Col, ), SubModuleReplacementDescription( @@ -166,6 +173,123 @@ def module_policy(self): def postprocess(self): return self.model + @staticmethod + def distribute_t5_layers(num_encoder_layers: int, num_decoder_layers: int, + num_stages: int) -> Tuple[List[int], int]: + """ + Distribute t5 layers into stages when pipeline parallel is used. + Return the layer distribution as a list and the starting stage of decoder. + If decoder doesn't exist, returned decoder starting stage is set to num_encoder_layers. + """ + + # number of encoder layers must be a positive integer + if num_encoder_layers <= 0: + raise ValueError("The number of encoder layers for T5 must be a positive integer.") + + # number of layers should be large enough to fill in every stage + if num_encoder_layers + num_decoder_layers < num_stages: + raise ValueError("The total number of layers can't be smaller than number of stages.") + + # in the case of T5EncoderModel, set decoder starting stage to num_stages since it doesn't exist + if num_decoder_layers == 0: + return Policy.distribute_layers(num_encoder_layers, num_stages), num_stages + + # the number of stages distributed between encoder and decoder is optmized in this way: + # num_encoder_stages = argmin(abs(num_encoder_layers / encoder_stages - num_decoder_layers / decoder_stages)) + # s.t. num_encoder_stages + num_decoder_stages = num_stages, num_encoder_stages >= 1, num_decoder_stages >= 1 + def objective(num_encoder_stages): + return abs(num_encoder_layers / num_encoder_stages - num_decoder_layers / (num_stages - num_encoder_stages)) + + num_encoder_stages = 0 + optimal_diff = 2**31 - 1 + for i in range(1, num_stages): + attempt = objective(i) + if attempt < optimal_diff: + num_encoder_stages = i + optimal_diff = attempt + num_decoder_stages = num_stages - num_encoder_stages + + encoder_distribution = Policy.distribute_layers(num_encoder_layers, num_encoder_stages) + decoder_distribution = Policy.distribute_layers(num_decoder_layers, num_decoder_stages) + return encoder_distribution + decoder_distribution, num_encoder_stages + + @staticmethod + def get_t5_stage_index(layers_per_stage: List[int], stage: int, + decoder_starting_stage: int) -> Tuple[bool, int, int]: + """ + Input the distribution of layers among stages, the current stage and the first stage of decoder. + Return the starting/ending idx of layers in encoder/decoder + """ + if stage < decoder_starting_stage: + return Policy.get_stage_index(layers_per_stage[:decoder_starting_stage], stage) + else: + return Policy.get_stage_index(layers_per_stage[decoder_starting_stage:], stage - decoder_starting_stage) + + def get_held_layers(self) -> List[nn.Module]: + """Get pipeline layers for current stage.""" + assert self.pipeline_stage_manager is not None + stage_manager = self.pipeline_stage_manager + + model = self.model + encoder = self.model.encoder + decoder = self.model.__dict__.get('decoder', None) + + num_encoder_layers = len(encoder.block) + num_decoder_layers = len(decoder.block) if decoder else 0 + + held_layers = [] + layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers( + num_encoder_layers, num_decoder_layers, stage_manager.num_stages) + start_idx, end_idx = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage_manager.stage, + decoder_starting_stage) + + if stage_manager.stage < decoder_starting_stage: + # current stage is in t5's encoder + if stage_manager.is_first_stage(): + held_layers.append(model.shared) + held_layers.append(encoder.embed_tokens) + held_layers.append(encoder.dropout) + if stage_manager.stage == decoder_starting_stage - 1: + held_layers.append(encoder.final_layer_norm) + held_layers.append(encoder.dropout) + held_layers.extend(encoder.block[start_idx:end_idx]) + else: + # current stage is in t5's decoder + if stage_manager.stage == decoder_starting_stage: + held_layers.append(decoder.embed_tokens) + held_layers.append(decoder.dropout) + if stage_manager.is_last_stage(): + held_layers.append(decoder.final_layer_norm) + held_layers.append(decoder.dropout) + held_layers.extend(decoder.block[start_idx:end_idx]) + return held_layers + + def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: + """If under pipeline parallel setting, replacing the original forward method of huggingface + to customized forward method, and add this changing to policy.""" + if not self.pipeline_stage_manager: + raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.") + stage_manager = self.pipeline_stage_manager + + encoder = self.model.encoder + decoder = self.model.__dict__.get('decoder', None) + + num_encoder_layers = len(encoder.block) + num_decoder_layers = len(decoder.block) if decoder else 0 + + layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers( + num_encoder_layers, num_decoder_layers, stage_manager.num_stages) + stage_index = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage) + + method_replacement = { + 'forward': + partial(new_forward, + stage_manager=stage_manager, + stage_index=stage_index, + decoder_starting_stage=decoder_starting_stage) + } + self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls) + class T5ModelPolicy(T5BasePolicy): @@ -182,6 +306,15 @@ def module_policy(self): target_key=T5Model) return base_policy + def postprocess(self): + if self.shard_config.enable_tensor_parallelism: + binding_map = {"shared.weight": ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]} + for k, v in binding_map.items(): + src = getattr_(self.model, k) + for dst in v: + setattr_(self.model, dst, src) + return self.model + class T5ForConditionalGenerationPolicy(T5BasePolicy): @@ -204,19 +337,55 @@ def module_policy(self): target_key=T5ForConditionalGeneration) return policy + def postprocess(self): + super().postprocess() + if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: + binding_map = { + "shared.weight": ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] + } + for k, v in binding_map.items(): + src = getattr_(self.model, k) + for dst in v: + setattr_(self.model, dst, src) + + return self.model + class T5EncoderPolicy(T5BasePolicy): + def __init__(self) -> None: + super().__init__() + def module_policy(self): from transformers import T5EncoderModel - base_policy = super().module_policy() + policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="shared", target_module=VocabParallelEmbedding1D, ), - policy=base_policy, + policy=policy, target_key=T5EncoderModel) - return base_policy + + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=T5EncoderModel, + new_forward=T5PipelineForwards.t5_encoder_model_forward, + policy=policy) + return policy + + def get_held_layers(self) -> List[nn.Module]: + return super().get_held_layers() + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + return [] + + def postprocess(self): + if self.shard_config.enable_tensor_parallelism: + binding_map = {"shared.weight": ["encoder.embed_tokens.weight"]} + for k, v in binding_map.items(): + src = getattr_(self.model, k) + for dst in v: + setattr_(self.model, dst, src) + return self.model diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index 0fbcaa1e2bb3..e447b700105e 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -62,17 +62,15 @@ def data_gen_for_sequence_classification(): loss_fn_for_gpt2_model = lambda x: x.last_hidden_state.mean() loss_fn = lambda x: x.loss -config = transformers.GPT2Config( - n_layer=2, - n_head=4, - #n_embd=128, - vocab_size=50258, - attn_pdrop=0, - embd_pdrop=0, - resid_pdrop=0, - summary_first_dropout=0, - hidden_dropout=0, - problem_type="single_label_classification") +config = transformers.GPT2Config(n_layer=2, + n_head=4, + vocab_size=50258, + attn_pdrop=0, + embd_pdrop=0, + resid_pdrop=0, + summary_first_dropout=0, + hidden_dropout=0, + problem_type="single_label_classification") # register the following models model_zoo.register(name='transformers_gpt', diff --git a/tests/test_pipeline/test_policy/test_t5_pipeline_utils.py b/tests/test_pipeline/test_policy/test_t5_pipeline_utils.py new file mode 100644 index 000000000000..0cbb852b97a0 --- /dev/null +++ b/tests/test_pipeline/test_policy/test_t5_pipeline_utils.py @@ -0,0 +1,39 @@ +from colossalai.shardformer.policies.t5 import T5BasePolicy + + +def test_t5_pipeline_distribution(): + num_test_cases = 8 + test_dict = { + 'num_encoder_layers': [2, 1, 3, 2, 3, 2, 10, 5], + 'num_decoder_layers': [2, 8, 0, 2, 1, 5, 6, 22], + 'num_stages': [2, 2, 2, 4, 4, 4, 8, 8], + 'decoder_starting_stage': [1, 1, 2, 2, 3, 1, 5, 2] + } + + for i in range(num_test_cases): + _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(test_dict['num_encoder_layers'][i], + test_dict['num_decoder_layers'][i], + test_dict['num_stages'][i]) + assert test_dict['decoder_starting_stage'][i] == decoder_starting_stage + + +def test_t5_pipeline_layers(): + num_test_cases = 4 + test_dict = { + 'num_encoder_layers': [2, 3, 2, 4], + 'num_decoder_layers': [2, 0, 2, 8], + 'num_stages': [2, 2, 4, 4], + 'layers_per_stage': [[[0, 2], [0, 2]], [[0, 1], [1, 3]], [[0, 1], [1, 2], [0, 1], [1, 2]], + [[0, 4], [0, 3], [3, 6], [6, 8]]] + } + + for i in range(num_test_cases): + layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers( + test_dict['num_encoder_layers'][i], test_dict['num_decoder_layers'][i], test_dict['num_stages'][i]) + + for stage in range(test_dict['num_stages'][i]): + start_idx, end_idx = test_dict['layers_per_stage'][i][stage] + predicted_start, predicted_end = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage, + decoder_starting_stage) + assert start_idx == predicted_start + assert end_idx == predicted_end diff --git a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py index 005e3d6f8759..d5453ee72644 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py @@ -29,9 +29,11 @@ def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_laz for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): inputs = data_gen_fn() inputs = {k: v.cuda() for k, v in inputs.items()} - input_ids, _ = inputs['input_ids'], inputs['attention_mask'] + _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + input_ids = inputs['input_ids'] batch_size, seq_len = input_ids.shape - hidden_size = 768 + hidden_size = sharded_model.config.n_embd hidden_state_shape = (batch_size, seq_len, hidden_size) if not stage_manager.is_first_stage(): @@ -40,12 +42,12 @@ def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_laz inputs['input_ids'] = None inputs['hidden_states'] = hidden_states - _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) sharded_model.train() output = sharded_model(**inputs) if stage_manager.is_last_stage(): - if name != 'transformers_gpt': + if name == 'transformers_gpt': + assert output[0].shape == hidden_state_shape + else: assert output.loss is not None else: assert output['hidden_states'].shape == hidden_state_shape diff --git a/tests/test_shardformer/test_model/test_shard_t5_pipeline.py b/tests/test_shardformer/test_model/test_shard_t5_pipeline.py new file mode 100644 index 000000000000..3662aa8ac125 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_t5_pipeline.py @@ -0,0 +1,96 @@ +import pytest +import torch + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.t5 import T5BasePolicy +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_pipeline_model + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # TODO: add tests for forward/backward later + pass + + +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('enable_fused_normalization', [False]) +@parameterize('use_lazy_init', [False]) +#TODO: merge this into test_shard_t5.py +def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + + sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') + for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): + if name != 'transformers_t5_encoder_model': + continue + + inputs = data_gen_fn() + inputs = {k: v.cuda() for k, v in inputs.items()} + input_ids = inputs['input_ids'] + + _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + + batch_size, seq_len = input_ids.shape + hidden_size = sharded_model.config.d_model + num_heads = sharded_model.config.num_heads + hidden_state_shape = (batch_size, seq_len, hidden_size) + position_bias_shape = (batch_size, num_heads, seq_len, seq_len) + + num_encoder_layers = len(sharded_model.encoder.block) + decoder = sharded_model.__dict__.get('decoder', None) + num_decoder_layers = len(decoder.block) if decoder else 0 + + _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(num_encoder_layers, num_decoder_layers, PP_SIZE) + stage = stage_manager.stage + at_first_stage = (stage == 0) or (stage == decoder_starting_stage) + at_last_stage = (stage == decoder_starting_stage - 1) or (stage == stage_manager.num_stages - 1) + + if not at_first_stage: + # change inputs if not the first stage + hidden_states = torch.zeros(*hidden_state_shape).cuda() + position_bias = torch.zeros(*position_bias_shape).cuda() + encoder_decoder_position_bias = torch.zeros(*position_bias_shape).cuda() + inputs['input_ids'] = None + inputs['hidden_states'] = hidden_states + inputs['position_bias'] = position_bias + inputs['encoder_decoder_position_bias'] = encoder_decoder_position_bias + + sharded_model.train() + output = sharded_model(**inputs) + if at_last_stage: + if name != 'transformers_t5_for_conditional_generation': + assert output[0].shape == hidden_state_shape + else: + assert output.loss is not None + else: + assert output['hidden_states'].shape == hidden_state_shape + # position_bias information should be passed in T5 + assert 'position_bias' in output + assert 'encoder_decoder_position_bias' in output + + torch.cuda.empty_cache() + + +def check_t5(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_t5_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_t5(): + spawn(check_t5, 4) + + +if __name__ == "__main__": + test_t5() From d0807122e2412e6633db7db027ff60827ca8fe9f Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Tue, 25 Jul 2023 14:31:21 +0800 Subject: [PATCH 39/84] [pipeline] test pure pipeline process using llama (#4218) * bloom policy * llama pipeline forward and tests * fix the output and attention_mask * fix name * bind argument to policy * Revert "bloom policy" This reverts commit 8dee68a0a22568dbeed6d4563372b25e1e825fb0. This policy should be revert and copied to feature/bloom * revert the bloom changes * cancel unneeded inputs * gpt * finish llama * causal lm and sequence classification * revision * add pure pipeline test * fixed version * fixed version * pure pipeline --- colossalai/pipeline/p2p.py | 24 ++++++++++--------- .../test_model/test_pure_pipeline.py | 24 +++++++++++++------ 2 files changed, 30 insertions(+), 18 deletions(-) diff --git a/colossalai/pipeline/p2p.py b/colossalai/pipeline/p2p.py index 2fd135d5475d..851a0b595bc6 100644 --- a/colossalai/pipeline/p2p.py +++ b/colossalai/pipeline/p2p.py @@ -9,6 +9,7 @@ import torch.distributed as dist from torch.distributed import ProcessGroup from torch.distributed import distributed_c10d as c10d +from version_parser.version import Version from .stage_manager import PipelineStageManager @@ -61,17 +62,6 @@ def _broadcast_object_list(object_list: List[Any], c10d._warn_not_in_group("broadcast_object_list") return - my_rank = dist.get_rank() - # Serialize object_list elements to tensors on src rank. - if my_rank == src: - if torch.__version__ >= "1.13.0": - tensor_list, size_list = zip(*[c10d._object_to_tensor(obj, device=device) for obj in object_list]) - else: - tensor_list, size_list = zip(*[c10d._object_to_tensor(obj) for obj in object_list]) - object_sizes_tensor = torch.cat(size_list) - else: - object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long) - is_nccl_backend = c10d._check_for_nccl_backend(group) current_device = None @@ -83,6 +73,18 @@ def _broadcast_object_list(object_list: List[Any], current_device = torch.device("cpu") if is_nccl_backend: current_device = torch.device("cuda", torch.cuda.current_device()) + + my_rank = dist.get_rank() + # Serialize object_list elements to tensors on src rank. + if my_rank == src: + if Version(torch.__version__) >= Version("1.13.0"): + tensor_list, size_list = zip(*[c10d._object_to_tensor(obj, device=current_device) for obj in object_list]) + else: + tensor_list, size_list = zip(*[c10d._object_to_tensor(obj) for obj in object_list]) + object_sizes_tensor = torch.cat(size_list) + else: + object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long) + if is_nccl_backend: object_sizes_tensor = object_sizes_tensor.to(current_device) diff --git a/tests/test_shardformer/test_model/test_pure_pipeline.py b/tests/test_shardformer/test_model/test_pure_pipeline.py index 80767f71c3fb..2f51eb9b02f7 100644 --- a/tests/test_shardformer/test_model/test_pure_pipeline.py +++ b/tests/test_shardformer/test_model/test_pure_pipeline.py @@ -1,3 +1,4 @@ +import copy import random from contextlib import nullcontext from typing import Any, Callable, Iterator, List, Optional, Tuple @@ -6,7 +7,6 @@ import pytest import torch import torch.distributed as dist -from torch import Tensor from torch.nn import Module from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler as LRScheduler @@ -94,10 +94,10 @@ def execute_pipeline( return outputs -class data_iter(): +class data_loader(): def __getitem__(self, x): - return torch.randint(0, 100, (4, 128)).cuda() + return torch.ones((4, 128), dtype=torch.int).cuda() * 10 def loss(x, y): @@ -127,20 +127,30 @@ def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_la stage_manager = PipelineStageManager(pg_mesh, PP_DIM) sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + if name != 'transformers_llama': + continue num_microbatches = 2 org_model = model_fn().cuda() + data_iter = iter(data_loader()) + + model_copy = copy.deepcopy(org_model) + batch = next(data_iter) + with torch.no_grad(): + y = model_copy(batch) + org_loss = loss(batch, y) optimizer = torch.optim.AdamW(org_model.parameters(), lr=1e-3) - #dataloader=prepare_dataloader(dataset=dataset['train'],batch_size=4) schedule = OneForwardOneBackwardSchedule(num_microbatches, stage_manager) shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, enable_tensor_parallelism=enable_tensor_parallelism, pipeline_stage_manager=stage_manager) pipelined_model = PipelinedModel(org_model, shard_config, stage_manager) pp_optimizer = PipelineOptimizer(optimizer, pipelined_model) - data_it = iter(data_iter()) - results = execute_pipeline(data_it, pipelined_model, loss, pp_optimizer, schedule=schedule) + results = execute_pipeline(data_iter, pipelined_model, loss, pp_optimizer, schedule=schedule) + if stage_manager.is_last_stage(): - assert results['loss'] is not None + assert results['loss'] == org_loss + else: + assert results['loss'] is None assert results['outputs'] is None torch.cuda.empty_cache() From 083d7da33d11d0bea17e4f05cdaf102ddb981ea0 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Tue, 25 Jul 2023 14:45:33 +0800 Subject: [PATCH 40/84] [pipeline] add pipeline support for all T5 models (#4310) * complete policy for T5Model & T5ForConditionalGeneration * modify function signature in forwards * add forward for T5model * add forward for T5ForConditionalGeneration * fix a bug * fix hidden_states transporting in decoder * fix the passing of encoder_outputs --- colossalai/shardformer/modeling/t5.py | 324 +++++++++++++++++- colossalai/shardformer/policies/t5.py | 64 +++- .../test_model/test_shard_t5_pipeline.py | 19 +- 3 files changed, 388 insertions(+), 19 deletions(-) diff --git a/colossalai/shardformer/modeling/t5.py b/colossalai/shardformer/modeling/t5.py index cc270d5828a2..7eb4d17928d6 100644 --- a/colossalai/shardformer/modeling/t5.py +++ b/colossalai/shardformer/modeling/t5.py @@ -1,11 +1,15 @@ -from functools import partial -from types import MethodType -from typing import Callable, Dict, List, Optional, Tuple, Union +import warnings +from typing import Dict, List, Optional, Tuple, Union import torch -from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from torch.nn import CrossEntropyLoss from torch.utils.checkpoint import checkpoint -from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions +from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) from transformers.models.t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Stack from transformers.utils import logging @@ -198,14 +202,13 @@ def custom_forward(*inputs): if use_cache is False or use_cache is None: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] hidden_states, present_key_value_state = layer_outputs[:2] - # print(stage, len(layer_outputs), present_key_value_state.shape) # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), # (cross-attention position bias), (cross-attention weights) position_bias = layer_outputs[2] - if self.is_decoder and encoder_hidden_states is not None: + if in_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3] # append next layer key value states if use_cache: @@ -238,6 +241,313 @@ def custom_forward(*inputs): 'encoder_decoder_position_bias': encoder_decoder_position_bias } + @staticmethod + def t5_model_forward( + self: T5Model, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + decoder_head_mask: Optional[torch.FloatTensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + decoder_inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + position_bias: Optional[torch.Tensor] = None, + encoder_decoder_position_bias: Optional[torch.Tensor] = None, + stage_index: Optional[List[int]] = None, + decoder_starting_stage: Optional[int] = None, + ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]: + + # This function is modified on the basis of transformers.models.t5.modeling_t5.T5Model.forward. + # Please refer to original code of transformers for more details. + + __HEAD_MASK_WARNING_MSG = """ + The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently, + `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions. + If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers, + num_heads)`. + """ + + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + logger = logging.get_logger(__name__) + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if past_key_values: + logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') + past_key_values = None + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask + if head_mask is not None and decoder_head_mask is None: + if self.config.num_layers == self.config.num_decoder_layers: + warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) + decoder_head_mask = head_mask + + in_decoder = stage_manager.stage >= decoder_starting_stage + + # Stage is in encoder, directly return the output of t5_stack_forward + if not in_decoder: + encoder_outputs = T5PipelineForwards.t5_stack_forward( + self.encoder, + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + position_bias=position_bias, + encoder_decoder_position_bias=encoder_decoder_position_bias, + stage_index=stage_index, + decoder_starting_stage=decoder_starting_stage) + if stage_manager.stage == decoder_starting_stage - 1: + # last stage of encoder + return {'encoder_outputs': encoder_outputs} + else: + return encoder_outputs + + at_last_decoder_stage = stage_manager.is_last_stage() + at_first_decoder_stage = stage_manager.stage == decoder_starting_stage + + if encoder_outputs is None: + raise ValueError("Non-empty encoder_outputs should be passed in at decoder stages.") + + encoder_hidden_states = encoder_outputs[0] + if return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Stage is in decoder, we assume that the outputs of last stage of encoder will be passed in. + if not at_first_decoder_stage and hidden_states is None: + raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.") + + # Decode + decoder_outputs = T5PipelineForwards.t5_stack_forward( + self.decoder, + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + inputs_embeds=decoder_inputs_embeds, + past_key_values=past_key_values, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=attention_mask, + head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + position_bias=position_bias, + encoder_decoder_position_bias=encoder_decoder_position_bias, + stage_index=stage_index, + decoder_starting_stage=decoder_starting_stage) + + # Directly return outputs of overloaded T5Stack forward if not at last stage. + if not at_last_decoder_stage: + decoder_outputs['encoder_outputs'] = encoder_outputs # encoder_outputs should be passed to the next stage + return decoder_outputs + + if not return_dict: + return decoder_outputs + encoder_outputs + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + @staticmethod + def t5_for_conditional_generation_forward( + self: T5ForConditionalGeneration, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + decoder_head_mask: Optional[torch.FloatTensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + position_bias: Optional[torch.Tensor] = None, + encoder_decoder_position_bias: Optional[torch.Tensor] = None, + stage_index: Optional[List[int]] = None, + decoder_starting_stage: Optional[int] = None, + ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: + + # This function is modified on the basis of transformers.models.t5.modeling_t5.T5ForConditionalGeneration.forward. + # Please refer to original code of transformers for more details. + + __HEAD_MASK_WARNING_MSG = """ + The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently, + `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions. + If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers, + num_heads)`. + """ + + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + logger = logging.get_logger(__name__) + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if past_key_values: + logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') + past_key_values = None + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask + if head_mask is not None and decoder_head_mask is None: + if self.config.num_layers == self.config.num_decoder_layers: + warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) + decoder_head_mask = head_mask + + in_decoder = stage_manager.stage >= decoder_starting_stage + + # Stage is in encoder, directly return the output of t5_stack_forward + if not in_decoder: + encoder_outputs = T5PipelineForwards.t5_stack_forward( + self.encoder, + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + position_bias=position_bias, + encoder_decoder_position_bias=encoder_decoder_position_bias, + stage_index=stage_index, + decoder_starting_stage=decoder_starting_stage) + if stage_manager.stage == decoder_starting_stage - 1: + # last stage of encoder + return {'encoder_outputs': encoder_outputs} + else: + return encoder_outputs + + at_last_decoder_stage = stage_manager.is_last_stage() + at_first_decoder_stage = stage_manager.stage == decoder_starting_stage + + if encoder_outputs is None: + raise ValueError("Non-empty encoder_outputs should be passed in at decoder stages.") + + encoder_hidden_states = encoder_outputs[0] + if return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Stage is in decoder, we assume that the outputs of last stage of encoder will be passed in. + if not at_first_decoder_stage and hidden_states is None: + raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.") + + # Decode + decoder_outputs = T5PipelineForwards.t5_stack_forward( + self.decoder, + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + inputs_embeds=decoder_inputs_embeds, + past_key_values=past_key_values, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=attention_mask, + head_mask=decoder_head_mask, + cross_attn_head_mask=cross_attn_head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + hidden_states=hidden_states, + position_bias=position_bias, + encoder_decoder_position_bias=encoder_decoder_position_bias, + stage_index=stage_index, + decoder_starting_stage=decoder_starting_stage) + + # Directly return outputs of overloaded T5Stack forward if not at last stage. + if not at_last_decoder_stage: + decoder_outputs['encoder_outputs'] = encoder_outputs # encoder_outputs should be passed to the next stage + return decoder_outputs + + sequence_output = decoder_outputs[0] + + if self.config.tie_word_embeddings: + # Rescale output before projecting on vocab + # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 + sequence_output = sequence_output * (self.model_dim**-0.5) + + lm_logits = self.lm_head(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss(ignore_index=-100) + # move labels to correct device to enable PP + labels = labels.to(lm_logits.device) + loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) + + if not return_dict: + output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs + return ((loss,) + output) if loss is not None else output + + return Seq2SeqLMOutput( + loss=loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + @staticmethod def t5_encoder_model_forward( self: T5EncoderModel, diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 1846c5873801..0ee18d6c4940 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -293,21 +293,42 @@ def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, poli class T5ModelPolicy(T5BasePolicy): + def __init__(self) -> None: + super().__init__() + def module_policy(self): from transformers import T5Model - base_policy = super().module_policy() + policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="shared", target_module=VocabParallelEmbedding1D, ), - policy=base_policy, + policy=policy, target_key=T5Model) - return base_policy + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=T5Model, new_forward=T5PipelineForwards.t5_model_forward, policy=policy) + + return policy + + def get_held_layers(self) -> List[nn.Module]: + return super().get_held_layers() + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + module = self.model + stage_manager = self.pipeline_stage_manager + if stage_manager is not None and stage_manager.num_stages > 1: + _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(len(module.encoder.block), + len(module.decoder.block), + stage_manager.num_stages) + + if id(module.decoder.embed_tokens.weight) == id(module.shared.weight): + return [{0: module.shared.weight, decoder_starting_stage: module.decoder.embed_tokens.weight}] + return [] def postprocess(self): - if self.shard_config.enable_tensor_parallelism: + if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"shared.weight": ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]} for k, v in binding_map.items(): src = getattr_(self.model, k) @@ -318,6 +339,9 @@ def postprocess(self): class T5ForConditionalGenerationPolicy(T5BasePolicy): + def __init__(self) -> None: + super().__init__() + def module_policy(self): from transformers import T5ForConditionalGeneration @@ -335,8 +359,38 @@ def module_policy(self): ], policy=policy, target_key=T5ForConditionalGeneration) + + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=T5ForConditionalGeneration, + new_forward=T5PipelineForwards.t5_for_conditional_generation_forward, + policy=policy) return policy + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.lm_head) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + module = self.model + stage_manager = self.pipeline_stage_manager + if stage_manager is not None and stage_manager.num_stages > 1: + _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(len(module.encoder.block), + len(module.decoder.block), + stage_manager.num_stages) + + shared_params = [] + if id(module.decoder.embed_tokens.weight) == id(module.shared.weight): + shared_params.append({ + 0: module.shared.weight, + decoder_starting_stage: module.decoder.embed_tokens.weight + }) + if id(module.lm_head.weight) == id(module.shared.weight): + shared_params.append({0: module.shared.weight, stage_manager.num_stages - 1: module.lm_head.weight}) + return shared_params + return [] + def postprocess(self): super().postprocess() if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: @@ -382,7 +436,7 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: return [] def postprocess(self): - if self.shard_config.enable_tensor_parallelism: + if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"shared.weight": ["encoder.embed_tokens.weight"]} for k, v in binding_map.items(): src = getattr_(self.model, k) diff --git a/tests/test_shardformer/test_model/test_shard_t5_pipeline.py b/tests/test_shardformer/test_model/test_shard_t5_pipeline.py index 3662aa8ac125..7f3a5f2ea40b 100644 --- a/tests/test_shardformer/test_model/test_shard_t5_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_t5_pipeline.py @@ -28,8 +28,6 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_ sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): - if name != 'transformers_t5_encoder_model': - continue inputs = data_gen_fn() inputs = {k: v.cuda() for k, v in inputs.items()} @@ -52,6 +50,7 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_ stage = stage_manager.stage at_first_stage = (stage == 0) or (stage == decoder_starting_stage) at_last_stage = (stage == decoder_starting_stage - 1) or (stage == stage_manager.num_stages - 1) + in_decoder = stage >= decoder_starting_stage if not at_first_stage: # change inputs if not the first stage @@ -62,19 +61,25 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_ inputs['hidden_states'] = hidden_states inputs['position_bias'] = position_bias inputs['encoder_decoder_position_bias'] = encoder_decoder_position_bias + if in_decoder: + encoder_output_states = torch.zeros(*hidden_state_shape).cuda() + inputs['encoder_outputs'] = (encoder_output_states,) sharded_model.train() output = sharded_model(**inputs) if at_last_stage: - if name != 'transformers_t5_for_conditional_generation': - assert output[0].shape == hidden_state_shape - else: + if name == 'transformers_t5_for_conditional_generation' and in_decoder: assert output.loss is not None + else: + if name != 'transformers_t5_encoder_model' and not in_decoder: + output = output['encoder_outputs'] + assert output[0].shape == hidden_state_shape else: assert output['hidden_states'].shape == hidden_state_shape # position_bias information should be passed in T5 - assert 'position_bias' in output - assert 'encoder_decoder_position_bias' in output + assert output['position_bias'].shape == position_bias_shape + if in_decoder: + assert output['encoder_decoder_position_bias'].shape == position_bias_shape torch.cuda.empty_cache() From b3f5d7a3ba01fdd015866162608348fe480f1d55 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Tue, 25 Jul 2023 15:02:29 +0800 Subject: [PATCH 41/84] [shardformer] support pipeline base vit model (#4284) * Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout * support base vit pipeline * support vit downstream model * fix vit shard test * modify hidden states return type --------- Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> --- colossalai/shardformer/modeling/vit.py | 337 ++++++++++++++++++ .../shardformer/policies/auto_policy.py | 8 + colossalai/shardformer/policies/vit.py | 283 ++++++++++----- tests/kit/model_zoo/transformers/__init__.py | 1 + tests/kit/model_zoo/transformers/vit.py | 68 ++++ .../test_model/test_shard_vit.py | 63 ++-- .../test_model/test_shard_vit_pipeline.py | 74 ++++ 7 files changed, 729 insertions(+), 105 deletions(-) create mode 100644 colossalai/shardformer/modeling/vit.py create mode 100644 tests/kit/model_zoo/transformers/vit.py create mode 100644 tests/test_shardformer/test_model/test_shard_vit_pipeline.py diff --git a/colossalai/shardformer/modeling/vit.py b/colossalai/shardformer/modeling/vit.py new file mode 100644 index 000000000000..f28c13ad0aa2 --- /dev/null +++ b/colossalai/shardformer/modeling/vit.py @@ -0,0 +1,337 @@ +import logging +from typing import Dict, List, Optional, Set, Tuple, Union + +import torch +from transformers.models.vit.modeling_vit import BaseModelOutput, ViTEncoder + +from colossalai.pipeline.stage_manager import PipelineStageManager + + +def _encoder_forward( + encoder: ViTEncoder, + start_idx: int, + end_idx: int, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + return_dict: bool = True, + stage_manager: PipelineStageManager = None, +) -> Union[tuple, BaseModelOutput]: + + for i in range(start_idx, end_idx): + layer_module = encoder.layer[i] + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if encoder.gradient_checkpointing and encoder.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + return module(*inputs, False) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + layer_head_mask, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, False) + + hidden_states = layer_outputs[0] + if not stage_manager.is_last_stage(): + return hidden_states + else: + if not return_dict: + return tuple(hidden_states) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=None, + attentions=None, + ) + + +def ViTModel_pipeline_forward(stage_manager: PipelineStageManager, stage_index: List[int]): + + from transformers.models.vit.modeling_vit import BaseModelOutputWithPooling + + def pp_forward( + self, + pixel_values: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + """ + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if output_attentions is not None: + logging.warning('Non-empty output_attentions is not supported for pipeline models at the moment.') + output_attentions = None + if output_hidden_states is not None: + logging.warning('Non-empty output_hidden_states is not supported for pipeline models at the moment.') + output_hidden_states = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if stage_manager.is_first_stage(): + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) + expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype + if pixel_values.dtype != expected_dtype: + pixel_values = pixel_values.to(expected_dtype) + + embedding_output = self.embeddings(pixel_values, + bool_masked_pos=bool_masked_pos, + interpolate_pos_encoding=interpolate_pos_encoding) + else: + assert hidden_states is not None, f"Current stage is {stage_manager.stage}, hidden_states should not be None" + + # Go through encoder + if not stage_manager.is_last_stage(): + hidden_states = _encoder_forward( + encoder=self.encoder, + start_idx=stage_index[0], + end_idx=stage_index[1], + hidden_states=embedding_output, + head_mask=head_mask, + return_dict=return_dict, + stage_manager=stage_manager, + ) + return {'hidden_states': hidden_states} + else: + encoder_outputs = _encoder_forward( + encoder=self.encoder, + start_idx=stage_index[0], + end_idx=stage_index[1], + hidden_states=hidden_states, + head_mask=head_mask, + return_dict=return_dict, + stage_manager=stage_manager, + ) + + # Go through rest layers + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) + return head_outputs + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + return pp_forward + + +def ViTForImageClassification_pipeline_forward(stage_manager: PipelineStageManager, stage_index: List[int]): + + from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + from transformers.models.vit.modeling_vit import ImageClassifierOutput + + def pp_forward( + self, + pixel_values: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + ) -> Union[tuple, ImageClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if not stage_manager.is_first_stage(): + assert hidden_states is not None, f"Current stage is {stage_manager.stage}, hidden_states should not be None" + + outputs = self.vit( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + hidden_states=hidden_states, + ) + + # not last stage, return hidden_states + if not stage_manager.is_last_stage(): + return outputs + else: + sequence_output = outputs[0] + + # last stage + logits = self.classifier(sequence_output[:, 0, :]) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + return pp_forward + + +def ViTForMaskedImageModeling_pipeline_forward(stage_manager: PipelineStageManager, stage_index: List[int]): + + import math + + import torch.nn as nn + from transformers.models.vit.modeling_vit import ImageClassifierOutput, MaskedImageModelingOutput + + def pp_forward( + self, + pixel_values: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + hidden_states: Optional[torch.FloatTensor] = None, + ) -> Union[tuple, ImageClassifierOutput]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + + Returns: + + Examples: + ```python + >>> from transformers import AutoImageProcessor, ViTForMaskedImageModeling + >>> import torch + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") + >>> model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k") + + >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 + >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values + >>> # create random boolean mask of shape (batch_size, num_patches) + >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool() + + >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) + >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction + >>> list(reconstructed_pixel_values.shape) + [1, 3, 224, 224] + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if bool_masked_pos is not None and (self.config.patch_size != self.config.encoder_stride): + raise ValueError( + "When `bool_masked_pos` is provided, `patch_size` must be equal to `encoder_stride` to ensure that " + "the reconstructed image has the same dimensions as the input." + f"Got `patch_size` = {self.config.patch_size} and `encoder_stride` = {self.config.encoder_stride}.") + + if not stage_manager.is_first_stage(): + assert hidden_states is not None, f"Current stage is {stage_manager.stage}, hidden_states should not be None" + + outputs = self.vit(pixel_values, + bool_masked_pos=bool_masked_pos, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + hidden_states=hidden_states) + if not stage_manager.is_last_stage(): + return outputs + else: + sequence_output = outputs[0] + + # Reshape to (batch_size, num_channels, height, width) + sequence_output = sequence_output[:, 1:] + batch_size, sequence_length, num_channels = sequence_output.shape + height = width = math.floor(sequence_length**0.5) + sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) + + # Reconstruct pixel values + reconstructed_pixel_values = self.decoder(sequence_output) + + masked_im_loss = None + if bool_masked_pos is not None: + size = self.config.image_size // self.config.patch_size + bool_masked_pos = bool_masked_pos.reshape(-1, size, size) + mask = (bool_masked_pos.repeat_interleave(self.config.patch_size, + 1).repeat_interleave(self.config.patch_size, + 2).unsqueeze(1).contiguous()) + reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none") + masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels + + if not return_dict: + output = (reconstructed_pixel_values,) + outputs[1:] + return ((masked_im_loss,) + output) if masked_im_loss is not None else output + + return MaskedImageModelingOutput( + loss=masked_im_loss, + reconstruction=reconstructed_pixel_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + return pp_forward diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index b31f1b35f580..d00a03c9237e 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -75,6 +75,14 @@ class PolicyLocation: "transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification": PolicyLocation(file_name="gpt2", class_name="GPT2ForSequenceClassificationPolicy"), + # ViT + "transformers.models.vit.modeling_vit.ViTModel": + PolicyLocation(file_name="vit", class_name="ViTModelPolicy"), + "transformers.models.vit.modeling_vit.ViTForImageClassification": + PolicyLocation(file_name="vit", class_name="ViTForImageClassificationPolicy"), + "transformers.models.vit.modeling_vit.ViTForMaskedImageModeling": + PolicyLocation(file_name="vit", class_name="ViTForMaskedImageModelingPolicy"), + # OPT "transformers.models.opt.modeling_opt.OPTModel": PolicyLocation(file_name="opt", class_name="OPTModelPolicy"), diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index 3f6bbd10607a..47f2c58fc436 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -1,12 +1,18 @@ -from typing import Dict, Union +from functools import partial +from typing import Callable, Dict, List, Union import torch.nn as nn -from colossalai.shardformer.layer import DropoutForReplicatedInput, FusedLayerNorm, Linear1D_Col, Linear1D_Row +import colossalai.shardformer.layer as col_nn +from ..modeling.vit import ( + ViTForImageClassification_pipeline_forward, + ViTForMaskedImageModeling_pipeline_forward, + ViTModel_pipeline_forward, +) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription -__all__ = ['ViTPolicy'] +__all__ = ['ViTPolicy', 'ViTModelPolicy', 'ViTForImageClassificationPolicy', 'ViTForMaskedImageModelingPolicy'] class ViTPolicy(Policy): @@ -15,96 +21,203 @@ def config_sanity_check(self): pass def preprocess(self): - # Resize embedding - vocab_size = self.model.config.vocab_size - world_size = self.shard_config.tensor_parallel_size - - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) - return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer - base_policy = { - ViTEmbeddings: - ModulePolicyDescription(sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="dropout", - target_module=DropoutForReplicatedInput, - ) - ]), - ViTLayer: - ModulePolicyDescription(attribute_replacement={ - "attention.attention.num_attention_heads": - self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, - "attention.attention.all_head_size": - self.model.config.hidden_size // self.shard_config.tensor_parallel_size, - }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="attention.attention.query", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.key", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.value", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.dropout", - target_module=DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="attention.output.dense", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="attention.output.dropout", - target_module=DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="intermediate.dense", - target_module=Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="output.dense", - target_module=Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="output.dropout", - target_module=DropoutForParallelInput, - ), - ]), - } - - # optimization configuration - if self.shard_config.enable_fused_normalization: - base_policy[ViTAttention].sub_module_replacement.extend([ - SubModuleReplacementDescription( - suffix="layernorm_before", - target_module=FusedLayerNorm, - ), - SubModuleReplacementDescription( - suffix="layernorm_after", - target_module=FusedLayerNorm, - ) - ]) - base_policy[ViTModel].sub_module_replacement.append( - SubModuleReplacementDescription( - suffix="layernorm", - target_module=FusedLayerNorm, - )) - - return base_policy + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[ViTEmbeddings] = ModulePolicyDescription(attribute_replacement={}, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForReplicatedInput, + ) + ]) + + policy[ViTLayer] = ModulePolicyDescription(attribute_replacement={ + "attention.attention.num_attention_heads": + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + "attention.attention.all_head_size": + self.model.config.hidden_size // self.shard_config.tensor_parallel_size, + }, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attention.attention.query", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.key", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.value", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="attention.output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="attention.output.dropout", + target_module=col_nn.DropoutForReplicatedInput, + ), + SubModuleReplacementDescription( + suffix="intermediate.dense", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="output.dropout", + target_module=col_nn.DropoutForReplicatedInput, + ), + ]) + + return policy def new_model_class(self): return None def postprocess(self): return self.model + + def get_held_layers(self) -> List[nn.Module]: + """Get pipeline layers for current stage.""" + assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None" + + if self.model.__class__.__name__ == 'ViTModel': + module = self.model + else: + module = self.model.vit + stage_manager = self.pipeline_stage_manager + + held_layers = [] + layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.embeddings) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.encoder.layer[start_idx:end_idx]) + return held_layers + + def set_pipeline_forward(self, model_cls: nn.Module, pipeline_forward: Callable, policy: Dict): + if self.pipeline_stage_manager: + stage_manager = self.pipeline_stage_manager + if self.model.__class__.__name__ == 'ViTModel': + module = self.model + else: + module = self.model.vit + + layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = {'forward': pipeline_forward(stage_manager=stage_manager, stage_index=stage_index)} + self.append_or_create_method_replacement(description=method_replacement, + policy=policy, + target_key=model_cls) + + +# ViTModel +class ViTModelPolicy(ViTPolicy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + from transformers.models.vit.modeling_vit import ViTModel + + policy = super().module_policy() + + if self.shard_config.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy) + return policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None" + + module = self.model + stage_manager = self.pipeline_stage_manager + if stage_manager.is_last_stage(): + held_layers.append(module.layernorm) + held_layers.append(module.pooler) + + return held_layers + + +# ViTForImageClassification +class ViTForImageClassificationPolicy(ViTPolicy): + + def module_policy(self): + from transformers.models.vit.modeling_vit import ViTForImageClassification, ViTModel + + policy = super().module_policy() + if self.shard_config.enable_tensor_parallelism: + new_item = { + ViTForImageClassification: + ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="classifier", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)) + ]) + } + policy.update(new_item) + + if self.shard_config.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy) + self.set_pipeline_forward(model_cls=ViTForImageClassification, + pipeline_forward=ViTForImageClassification_pipeline_forward, + policy=policy) + + return policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None" + + module = self.model.vit + stage_manager = self.pipeline_stage_manager + if stage_manager.is_last_stage(): + held_layers.append(module.layernorm) + held_layers.append(self.model.classifier) + + return held_layers + + +# ViTForMaskedImageModeling +class ViTForMaskedImageModelingPolicy(ViTPolicy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + from transformers.models.vit.modeling_vit import ViTForMaskedImageModeling, ViTModel + + policy = super().module_policy() + + if self.shard_config.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy) + self.set_pipeline_forward(model_cls=ViTForMaskedImageModeling, + pipeline_forward=ViTForMaskedImageModeling_pipeline_forward, + policy=policy) + return policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None" + + module = self.model.vit + stage_manager = self.pipeline_stage_manager + if stage_manager.is_last_stage(): + held_layers.append(module.layernorm) + held_layers.append(self.model.decoder) + + return held_layers diff --git a/tests/kit/model_zoo/transformers/__init__.py b/tests/kit/model_zoo/transformers/__init__.py index 4aa01abe13ee..a298767d12e7 100644 --- a/tests/kit/model_zoo/transformers/__init__.py +++ b/tests/kit/model_zoo/transformers/__init__.py @@ -5,3 +5,4 @@ from .llama import * from .opt import * from .t5 import * +from .vit import * diff --git a/tests/kit/model_zoo/transformers/vit.py b/tests/kit/model_zoo/transformers/vit.py new file mode 100644 index 000000000000..93a8d6c615d7 --- /dev/null +++ b/tests/kit/model_zoo/transformers/vit.py @@ -0,0 +1,68 @@ +import torch +import transformers + +from ..registry import ModelAttribute, model_zoo + +# =============================== +# Register single-sentence VIT +# =============================== + +config = transformers.ViTConfig( + num_hidden_layers=4, + # hidden_size=128, + # intermediate_size=256, + num_attention_heads=4) + + +# define data gen function +def data_gen(): + pixel_values = torch.randn(1, 3, 224, 224) + return dict(pixel_values=pixel_values) + + +def data_gen_for_image_classification(): + data = data_gen() + data['labels'] = torch.tensor([0]) + return data + + +def data_gen_for_masked_image_modeling(): + data = data_gen() + num_patches = (config.image_size // config.patch_size)**2 + bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool() + data['bool_masked_pos'] = bool_masked_pos + return data + + +# define output transform function +output_transform_fn = lambda x: x + +# function to get the loss +loss_fn_for_vit_model = lambda x: x.pooler_output.mean() +loss_fn_for_image_classification = lambda x: x.logits.mean() +loss_fn_for_masked_image_modeling = lambda x: x.loss + +# register the following models +# transformers.ViTModel, +# transformers.ViTForMaskedImageModeling, +# transformers.ViTForImageClassification, +model_zoo.register(name='transformers_vit', + model_fn=lambda: transformers.ViTModel(config), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_vit_model, + model_attribute=ModelAttribute(has_control_flow=True)) + +model_zoo.register(name='transformers_vit_for_masked_image_modeling', + model_fn=lambda: transformers.ViTForMaskedImageModeling(config), + data_gen_fn=data_gen_for_masked_image_modeling, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_masked_image_modeling, + model_attribute=ModelAttribute(has_control_flow=True)) + +model_zoo.register(name='transformers_vit_for_image_classification', + model_fn=lambda: transformers.ViTForImageClassification(config), + data_gen_fn=data_gen_for_image_classification, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_image_classification, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index af1605b6b659..2b02c83e0d27 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -1,9 +1,18 @@ +import os + import pytest import torch import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, run_forward @@ -12,44 +21,58 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check forward org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output) - + assert_hf_output_close(org_output, shard_output, atol=1e-3, rtol=1e-3) # do backward org_loss.backward() shard_loss.backward() - # check grad - org_grad = org_model.encoder.layer[0].attention.attention.query.weight.grad - shard_grad = sharded_model.encoder.layer[0].attention.attention.query.weight.grad - - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - assert torch.allclose(org_loss, shard_loss, atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + # unwrap model + if org_model.__class__.__name__ == 'ViTModel': + vit_model = org_model + shard_vit_model = sharded_model + else: + vit_model = org_model.vit + shard_vit_model = sharded_model.vit -def check_vit(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + # check attention grad + org_grad = vit_model.encoder.layer[0].attention.attention.query.weight.grad + shard_grad = shard_vit_model.encoder.layer[0].attention.attention.query.weight.grad + shard_weight = shard_vit_model.encoder.layer[0].attention.attention.query.weight + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + + +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_vit_test(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(world_size, model_fn) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - torch.cuda.empty_cache() +def check_vit(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_vit_test() + + @pytest.mark.dist -@pytest.mark.skip @rerun_if_address_is_in_use() @clear_cache_before_run() def test_vit(): - spawn(check_vit, 4) + spawn(check_vit, 2) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_vit_pipeline.py b/tests/test_shardformer/test_model/test_shard_vit_pipeline.py new file mode 100644 index 000000000000..114992a2a2a5 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_vit_pipeline.py @@ -0,0 +1,74 @@ +import pytest +import torch + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_pipeline_model + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # TODO: add tests for forward/backward later + pass + + +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('enable_fused_normalization', [False]) +@parameterize('use_lazy_init', [False]) +#TODO: merge this into test_shard_vit +def run_vit_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + + sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') + + for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): + + inputs = data_gen_fn() + inputs = {k: v.cuda() for k, v in inputs.items()} + pixel_values = inputs['pixel_values'] + batch_size = len(pixel_values) + hidden_size = 768 + hidden_state_shape = (batch_size, 197, hidden_size) + + if not stage_manager.is_first_stage(): + # change inputs if not the first stage + hidden_states = torch.randn(*hidden_state_shape).cuda() + # inputs['pixel_values'] = None + inputs['hidden_states'] = hidden_states + + _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init) + sharded_model.train() + + output = sharded_model(**inputs) + if stage_manager.is_last_stage(): + if name != 'transformers_vit': + assert output.loss is not None + else: + assert output['hidden_states'].shape == hidden_state_shape, \ + f'hidden_states shape is not correct, output:{output["hidden_states"].shape} is not equal to hidden_state:{hidden_state_shape}' + + torch.cuda.empty_cache() + + +def check_vit(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_vit_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_vit(): + spawn(check_vit, 4) + + +if __name__ == "__main__": + test_vit() From 261eab02fb379d7c01441bef27058503fbc6f490 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Wed, 26 Jul 2023 00:53:57 +0800 Subject: [PATCH 42/84] [plugin] add 3d parallel plugin (#4295) * [amp] add mixed precision optimizer * [plugin] add 3d parallel plugin * [booster] support pipeline * [plugin] 3d parallel plugin support clip grad norm * [shardformer] fix sharder and add plugin test * [plugin] rename 3d parallel plugin * [ci] support testmon core pkg change detection (#4305) * [hotfix] debug testmon * [hotfix] fix llama * [hotfix] fix p2p bugs * [hotfix] fix requirements --- .../naive_amp/mixed_precision_optimizer.py | 149 +++++++++ colossalai/booster/booster.py | 12 +- colossalai/booster/plugin/__init__.py | 3 +- .../booster/plugin/hybrid_parallel_plugin.py | 316 ++++++++++++++++++ colossalai/booster/plugin/pp_plugin_base.py | 21 ++ colossalai/pipeline/p2p.py | 2 +- colossalai/shardformer/modeling/llama.py | 6 - colossalai/shardformer/shard/sharder.py | 37 +- .../test_plugin/test_3d_plugin.py | 99 ++++++ 9 files changed, 621 insertions(+), 24 deletions(-) create mode 100644 colossalai/amp/naive_amp/mixed_precision_optimizer.py create mode 100644 colossalai/booster/plugin/hybrid_parallel_plugin.py create mode 100644 colossalai/booster/plugin/pp_plugin_base.py create mode 100644 tests/test_booster/test_plugin/test_3d_plugin.py diff --git a/colossalai/amp/naive_amp/mixed_precision_optimizer.py b/colossalai/amp/naive_amp/mixed_precision_optimizer.py new file mode 100644 index 000000000000..d4183be3fb5f --- /dev/null +++ b/colossalai/amp/naive_amp/mixed_precision_optimizer.py @@ -0,0 +1,149 @@ +from typing import Dict, List + +import torch +from torch import Tensor +from torch.nn import Parameter +from torch.optim import Optimizer + +from colossalai.interface import OptimizerWrapper + +from .mixed_precision_mixin import BF16MixedPrecisionMixin, FP16MixedPrecisionMixin + + +class NaiveFP16MixedPrecisionMixin(FP16MixedPrecisionMixin): + + def __init__(self, + working_params: List[Parameter], + initial_scale: float = 2**16, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32) -> None: + super().__init__(initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, hysteresis, + max_scale) + self.params = working_params + + def check_local_overflow(self) -> bool: + for p in self.params: + if p.grad is not None and not torch.isfinite(p.grad).all(): + return True + return False + + +class MixedPrecisionOptimizer(OptimizerWrapper): + + def __init__(self, + optim: Optimizer, + precision: str = 'fp16', + initial_scale: float = 2**16, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32, + max_norm: float = 0.0): + super().__init__(optim) + if precision == 'fp16': + working_params = [] + for group in self.optim.param_groups: + for p in group['params']: + working_params.append(p) + self.mixed_precision = NaiveFP16MixedPrecisionMixin(working_params, + initial_scale=initial_scale, + min_scale=min_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + max_scale=max_scale) + elif precision == 'bf16': + self.mixed_precision = BF16MixedPrecisionMixin() + else: + raise ValueError(f'Unsupported precision: {precision}') + if max_norm > 0.0: + raise NotImplementedError('max_norm is not supported yet.') + self.max_norm = max_norm + self.working_to_master_map: Dict[Parameter, Tensor] = {} + self.master_to_working_map: Dict[Tensor, Parameter] = {} + + # create master weights + for group in self.optim.param_groups: + master_params = [] + for p in group['params']: + if p.requires_grad: + master_p = p + if p.dtype != torch.float: + master_p = p.detach().float() + self.working_to_master_map[p] = master_p + self.master_to_working_map[master_p] = p + master_params.append(master_p) + group['params'] = master_params + + def backward(self, loss: Tensor, *args, **kwargs): + loss = self.mixed_precision.pre_backward(loss) + loss.backward(*args, **kwargs) + + def backward_by_grad(self, tensor: Tensor, grad: Tensor): + grad = self.mixed_precision.pre_backward_by_grad(tensor, grad) + tensor.backward(grad) + + def zero_grad(self, *args, **kwargs): + for p in self.working_to_master_map.keys(): + p.grad = None + self.mixed_precision.pre_zero_grad() + return super().zero_grad(*args, **kwargs) + + def _unscale_and_clip_grads(self, total_norm: float) -> None: + div_scale = 1.0 + if self.mixed_precision is not None: + div_scale = self.mixed_precision.get_grad_div_scale() + + if self.max_norm > 0.: + # norm is in fact norm*scale + clip = ((total_norm / div_scale) + 1e-6) / self.max_norm + if clip > 1: + div_scale = clip * div_scale + + for group in self.param_groups: + for p in group['params']: + if p.grad is None: + continue + p.grad.data.mul_(1. / div_scale) + + def _compute_grad_norm(self) -> float: + if self.max_norm <= 0.: + return 0. + grads = [p.grad for group in self.param_groups for p in group['params'] if p.grad is not None] + if len(grads) == 0: + return 0. + device = grads[0].device + # TODO(ver217): support tp + total_norm = torch.norm(torch.stack([torch.norm(g.detach(), 2).to(device) for g in grads]), 2) + return total_norm.item() + + def step(self, *args, **kwargs): + if self.mixed_precision.should_skip_step(): + self.zero_grad() + return + # prepare grads + for group in self.optim.param_groups: + for p in group['params']: + working_param = self.master_to_working_map[p] + if p is working_param: + continue + if working_param.grad is None: + p.grad = working_param.grad.data.float() + working_param.grad = None + total_norm = self._compute_grad_norm() + self._unscale_and_clip_grads(total_norm) + self.optim.step(*args, **kwargs) + # update working params + for group in self.optim.param_groups: + for p in group['params']: + working_param = self.master_to_working_map[p] + if p is working_param: + continue + working_param.data.copy_(p.data) diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index ec3dc7fc143f..8a28b1286cfa 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -1,6 +1,6 @@ import warnings from contextlib import contextmanager -from typing import Callable, Iterator, List, Optional, Tuple, Union +from typing import Any, Callable, Iterator, List, Optional, Union import torch import torch.nn as nn @@ -14,6 +14,7 @@ from .accelerator import Accelerator from .mixed_precision import MixedPrecision, mixed_precision_factory from .plugin import Plugin +from .plugin.pp_plugin_base import PipelinePluginBase __all__ = ['Booster'] @@ -144,14 +145,15 @@ def backward(self, loss: torch.Tensor, optimizer: Optimizer) -> None: def execute_pipeline(self, data_iter: Iterator, model: nn.Module, - criterion: Callable[[torch.Tensor], torch.Tensor], + criterion: Callable[[Any, Any], torch.Tensor], optimizer: Optimizer, return_loss: bool = True, - return_outputs: bool = False) -> Tuple[Optional[torch.Tensor], ...]: - # TODO: implement this method + return_outputs: bool = False) -> dict: # run pipeline forward backward pass # return loss or outputs if needed - pass + assert isinstance(self.plugin, + PipelinePluginBase), f'The plugin {self.plugin.__class__.__name__} does not support pipeline.' + return self.plugin.execute_pipeline(data_iter, model, criterion, optimizer, return_loss, return_outputs) def no_sync(self, model: nn.Module = None, optimizer: OptimizerWrapper = None) -> contextmanager: """Context manager to disable gradient synchronization across DP process groups. diff --git a/colossalai/booster/plugin/__init__.py b/colossalai/booster/plugin/__init__.py index a3b87b5f11d3..f48bf38bd724 100644 --- a/colossalai/booster/plugin/__init__.py +++ b/colossalai/booster/plugin/__init__.py @@ -1,9 +1,10 @@ from .gemini_plugin import GeminiPlugin +from .hybrid_parallel_plugin import HybridParallelPlugin from .low_level_zero_plugin import LowLevelZeroPlugin from .plugin_base import Plugin from .torch_ddp_plugin import TorchDDPPlugin -__all__ = ['Plugin', 'TorchDDPPlugin', 'GeminiPlugin', 'LowLevelZeroPlugin'] +__all__ = ['Plugin', 'TorchDDPPlugin', 'GeminiPlugin', 'LowLevelZeroPlugin', 'HybridParallelPlugin'] import torch from packaging import version diff --git a/colossalai/booster/plugin/hybrid_parallel_plugin.py b/colossalai/booster/plugin/hybrid_parallel_plugin.py new file mode 100644 index 000000000000..37badb613433 --- /dev/null +++ b/colossalai/booster/plugin/hybrid_parallel_plugin.py @@ -0,0 +1,316 @@ +import random +from contextlib import nullcontext +from typing import Any, Callable, Iterator, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.distributed as dist +from torch.distributed import ProcessGroup +from torch.nn import Module +from torch.optim import Optimizer +from torch.optim.lr_scheduler import _LRScheduler as LRScheduler +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from colossalai.amp.naive_amp.mixed_precision_optimizer import MixedPrecisionOptimizer +from colossalai.checkpoint_io import CheckpointIO +from colossalai.cluster import ProcessGroupMesh +from colossalai.interface import ModelWrapper, OptimizerWrapper +from colossalai.pipeline.schedule import OneForwardOneBackwardSchedule +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer import ShardConfig, ShardFormer +from colossalai.zero.low_level import LowLevelZeroOptimizer + +from .pp_plugin_base import PipelinePluginBase + +DP_AXIS, PP_AXIS, TP_AXIS = 0, 1, 2 + + +class HybridParallelModule(ModelWrapper): + + def __init__(self, module: Module, precision: str, shard_config: ShardConfig, dp_group: ProcessGroup) -> None: + self.stage_manager = shard_config.pipeline_stage_manager + self.dp_group = dp_group + shardformer = ShardFormer(shard_config) + module, self.shared_params = shardformer.optimize(module) + # TODO(ver217): add input type cast + self.shared_param_process_groups = [] + for shared_param in self.shared_params: + if len(shared_param) > 0: + self.stage_manager.init_process_group_by_stages(list(shared_param.keys())) + if precision == 'fp16': + module = module.half().cuda() + elif precision == 'bf16': + module = module.to(dtype=torch.bfloat16).cuda() + # TODO(ver217): support TP+DP + super().__init__(module) + + def sync_shared_params(self): + for shared_param, group in zip(self.shared_params, self.shared_param_process_groups): + param = shared_param[self.stage_manager.stage] + dist.all_reduce(param.grad, group=group) + + def no_sync(self) -> Iterator[None]: + # no sync grads across data parallel + return nullcontext() + + def sync_grads(self): + # sync grad across data parallel + if self.dp_group.size() == 1: + return + for p in self.module.parameters(): + if p.grad is not None: + dist.all_reduce(p.grad, group=self.dp_group) + + +def init_pipeline_optimizer(optim: Optimizer, model: Module): + params = set(model.parameters()) + new_param_groups = [] + for group in optim.param_groups: + params = [p for p in group['params'] if p in params] + new_param_groups.append({**group, 'params': params}) + optim.__setstate__({'param_groups': new_param_groups}) + + +class HybridParallelOptimizer(MixedPrecisionOptimizer): + + def __init__(self, + optim: Optimizer, + model: Module, + use_pipeline: bool, + precision: str = 'fp16', + initial_scale: float = 2**16, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32, + max_norm: float = 0): + if use_pipeline: + init_pipeline_optimizer(optim, model) + super().__init__(optim, precision, initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, + hysteresis, max_scale, max_norm) + + +class HybridParallelZeroOptimizer(LowLevelZeroOptimizer): + + def __init__( + self, + optimizer: Optimizer, + model: Module, + use_pipeline: bool, + initial_scale: int = 2**16, # grad scaler config + min_scale: int = 1, + growth_factor: float = 2., + backoff_factor: float = .5, + growth_interval: int = 2000, + hysteresis: int = 2, + max_scale: int = 2**24, + clip_grad_norm: float = 0.0, # grad clipping + verbose: bool = False, + reduce_bucket_size: int = 1024 * 1024, # communication + communication_dtype: Optional[torch.dtype] = None, + overlap_communication: bool = True, + partition_grad: bool = False, # stage 2 flag + cpu_offload: bool = False, # cpu offload + dp_process_group: Optional[ProcessGroup] = None, # the dp pg for comm + tp_process_group: Optional[ProcessGroup] = None, # if using tp + forced_dtype: Optional[torch.dtype] = None): + if use_pipeline: + init_pipeline_optimizer(optimizer, model) + super().__init__(optimizer, initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, + hysteresis, max_scale, clip_grad_norm, verbose, reduce_bucket_size, communication_dtype, + overlap_communication, partition_grad, cpu_offload, dp_process_group, tp_process_group, + forced_dtype) + + +class HybridParallelPlugin(PipelinePluginBase): + + def __init__( + self, + tp_size: int, + pp_size: int, + precision: str = 'fp16', + zero_stage: int = 0, + cpu_offload: bool = False, + enable_fused_normalization: bool = False, + num_microbatches: Optional[int] = None, + initial_scale: float = 2**16, + min_scale: float = 1, + growth_factor: float = 2, + backoff_factor: float = 0.5, + growth_interval: int = 1000, + hysteresis: int = 2, + max_scale: float = 2**32, + max_norm: float = 0, + ) -> None: + super().__init__() + assert dist.get_world_size() % ( + tp_size * pp_size + ) == 0, f'world size {dist.get_world_size()} is not divisible by tp_size {tp_size} * pp_size {pp_size}' + # TODO(ver217): support zero + assert zero_stage == 0, 'zero is not support yet' + self.tp_size = tp_size + self.pp_size = pp_size + self.dp_size = dist.get_world_size() // (tp_size * pp_size) + self.precision = precision + self.zero_stage = zero_stage + self.cpu_offload = cpu_offload + self.enable_fused_normalization = enable_fused_normalization + self.pg_mesh = ProcessGroupMesh(self.dp_size, self.pp_size, self.tp_size) + self.stage_manager = None + self.schedule = None + assert zero_stage in (0, 1, 2) + if self.pp_size > 1: + assert num_microbatches is not None, 'num_microbatches must be specified when using pipeline parallelism' + assert self.zero_stage <= 1, 'zero stage must be 0 or 1 when using pipeline parallelism' + self.stage_manager = PipelineStageManager(self.pg_mesh, PP_AXIS) + self.schedule = OneForwardOneBackwardSchedule(num_microbatches, self.stage_manager) + self.tp_group = self.pg_mesh.get_group_along_axis(TP_AXIS) + self.dp_group = self.pg_mesh.get_group_along_axis(DP_AXIS) + self.shard_config = ShardConfig(tensor_parallel_process_group=self.tp_group, + pipeline_stage_manager=self.stage_manager, + enable_tensor_parallelism=self.tp_size > 1, + enable_fused_normalization=self.enable_fused_normalization) + self.amp_config = dict( + initial_scale=initial_scale, + growth_factor=growth_factor, + backoff_factor=backoff_factor, + growth_interval=growth_interval, + hysteresis=hysteresis, + min_scale=min_scale, + max_scale=max_scale, + ) + self.max_norm = max_norm + + @property + def enable_pipeline_parallelism(self) -> bool: + return self.pp_size > 1 + + def supported_devices(self) -> List[str]: + return ['cuda'] + + def supported_precisions(self) -> List[str]: + return ['fp16', 'bf16'] + + def control_device(self) -> bool: + return True + + def control_precision(self) -> bool: + return True + + def support_no_sync(self) -> bool: + return False + + def control_checkpoint_io(self) -> bool: + return True + + def configure( + self, + model: Module, + optimizer: Optional[Optimizer] = None, + criterion: Optional[Callable] = None, + dataloader: Optional[DataLoader] = None, + lr_scheduler: Optional[LRScheduler] = None, + ) -> Tuple[Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]: + if not isinstance(model, ModelWrapper): + model = HybridParallelModule(model, self.precision, self.shard_config, self.dp_group) + if optimizer is not None and not isinstance(optimizer, OptimizerWrapper): + if self.zero_stage == 0: + optimizer = HybridParallelOptimizer(optimizer, + model, + use_pipeline=self.enable_pipeline_parallelism, + precision=self.precision, + max_norm=self.max_norm, + **self.amp_config) + else: + optimizer = HybridParallelZeroOptimizer(optimizer, + model, + use_pipeline=self.enable_pipeline_parallelism, + partition_grad=(self.zero_stage == 2), + cpu_offload=self.cpu_offload, + dp_process_group=self.dp_group, + tp_process_group=self.tp_group, + verbose=True, + clip_grad_norm=self.max_norm, + **self.amp_config) + return model, optimizer, criterion, dataloader, lr_scheduler + + def execute_pipeline(self, + data_iter: Iterator, + model: HybridParallelModule, + criterion: Callable[[Any, Any], torch.Tensor], + optimizer: Union[HybridParallelOptimizer, HybridParallelZeroOptimizer], + return_loss: bool = True, + return_outputs: bool = False) -> dict: + assert self.enable_pipeline_parallelism, 'pipeline parallelism is not enabled' + # return loss or outputs if needed + ctx = optimizer.no_sync() if isinstance(optimizer, HybridParallelZeroOptimizer) else model.no_sync() + with ctx: + outputs = self.schedule.forward_backward_step(model, optimizer, data_iter, criterion, return_loss, + return_outputs) + # model.sync_shared_params() + if isinstance(optimizer, HybridParallelZeroOptimizer): + optimizer.sync_grad() + else: + model.sync_grads() + return outputs + + def prepare_dataloader(self, + dataset, + batch_size, + shuffle=False, + seed=1024, + drop_last=False, + pin_memory=False, + num_workers=0, + **kwargs): + r""" + Prepare a dataloader for distributed training. The dataloader will be wrapped by + `torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`. + + + Args: + dataset (`torch.utils.data.Dataset`): The dataset to be loaded. + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. + seed (int, optional): Random worker seed for sampling, defaults to 1024. + add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True. + drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size + is not divisible by the batch size. If False and the size of dataset is not divisible by + the batch size, then the last batch will be smaller, defaults to False. + pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False. + num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0. + kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in + `DataLoader `_. + + Returns: + :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing. + """ + _kwargs = kwargs.copy() + sampler = DistributedSampler(dataset, + num_replicas=self.pg_mesh.size(DP_AXIS), + rank=self.pg_mesh.coordinate(DP_AXIS), + shuffle=shuffle) + + # Deterministic dataloader + def seed_worker(worker_id): + worker_seed = seed + np.random.seed(worker_seed) + torch.manual_seed(worker_seed) + random.seed(worker_seed) + + return DataLoader(dataset, + batch_size=batch_size, + sampler=sampler, + worker_init_fn=seed_worker, + drop_last=drop_last, + pin_memory=pin_memory, + num_workers=num_workers, + **_kwargs) + + def get_checkpoint_io(self) -> CheckpointIO: + return None + + def no_sync(self, model: Module) -> Iterator[None]: + raise NotImplementedError diff --git a/colossalai/booster/plugin/pp_plugin_base.py b/colossalai/booster/plugin/pp_plugin_base.py new file mode 100644 index 000000000000..67ade9330f5b --- /dev/null +++ b/colossalai/booster/plugin/pp_plugin_base.py @@ -0,0 +1,21 @@ +from abc import abstractmethod +from typing import Any, Callable, Iterator + +import torch + +from colossalai.interface import ModelWrapper, OptimizerWrapper + +from .plugin_base import Plugin + + +class PipelinePluginBase(Plugin): + + @abstractmethod + def execute_pipeline(self, + data_iter: Iterator, + model: ModelWrapper, + criterion: Callable[[Any, Any], torch.Tensor], + optimizer: OptimizerWrapper, + return_loss: bool = True, + return_outputs: bool = False) -> dict: + pass diff --git a/colossalai/pipeline/p2p.py b/colossalai/pipeline/p2p.py index 851a0b595bc6..f741b8363f13 100644 --- a/colossalai/pipeline/p2p.py +++ b/colossalai/pipeline/p2p.py @@ -7,9 +7,9 @@ import torch import torch.distributed as dist +from packaging.version import Version from torch.distributed import ProcessGroup from torch.distributed import distributed_c10d as c10d -from version_parser.version import Version from .stage_manager import PipelineStageManager diff --git a/colossalai/shardformer/modeling/llama.py b/colossalai/shardformer/modeling/llama.py index 7bc626fe6825..e1ed5f64665c 100644 --- a/colossalai/shardformer/modeling/llama.py +++ b/colossalai/shardformer/modeling/llama.py @@ -223,9 +223,6 @@ def llama_for_causal_lm_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = LlamaPipelineForwards.llama_model_forward( @@ -311,9 +308,6 @@ def llama_for_sequence_classification_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False transformer_outputs = LlamaPipelineForwards.llama_model_forward( self.model, diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index b32c285bdaab..ae8cd8c6e553 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -1,5 +1,5 @@ from types import MethodType -from typing import Any, Callable, Dict, List, Union +from typing import Any, Callable, Dict, List, Optional, Set, Union import torch.nn as nn from torch import Tensor @@ -39,8 +39,8 @@ def shard(self) -> List[Dict[int, Tensor]]: self._preprocess() # get shared params before release unheld layers, this avoid misjudgement of shared params (None is None) shared_params = self.policy.get_shared_params() - self._release_unheld_layers() - self._replace_module() + held_layers = self._release_unheld_layers() + self._replace_module(include=held_layers) self._materialize() self._postprocess() return shared_params @@ -51,7 +51,7 @@ def _preprocess(self) -> None: def _postprocess(self) -> None: self.model = self.policy.postprocess() - def _replace_module(self,) -> None: + def _replace_module(self, include: Optional[Set[nn.Module]] = None) -> None: r""" Replace the module according to the policy, and replace the module one by one @@ -64,8 +64,13 @@ def _replace_module(self,) -> None: param_replacement = module_description.param_replacement sub_module_replacement = module_description.sub_module_replacement method_replacement = module_description.method_replacement - self._recursive_replace_layer(self.model, layer_cls, attr_replacement, param_replacement, - method_replacement, sub_module_replacement) + self._recursive_replace_layer(self.model, + layer_cls, + attr_replacement, + param_replacement, + method_replacement, + sub_module_replacement, + include=include) def _recursive_replace_layer( self, @@ -75,6 +80,7 @@ def _recursive_replace_layer( param_replacement: List[Callable], method_replacement: Dict[str, Callable], sub_module_replacement: List[SubModuleReplacementDescription], + include: Optional[Set[nn.Module]] = None, ) -> None: r""" Reverse the replace layer operation @@ -87,23 +93,30 @@ def _recursive_replace_layer( method_replacement (Dict[str, Callable]): Key is the method name, value is the method for replacement sub_module_replacement ((List[SubModuleReplacementDescription]): The function list to get sub module shard information in policy """ + # released layers are not shardable + can_replace_param_or_layer = include is None or module in include if (isinstance(origin_cls, str) and origin_cls == module.__class__.__name__) or \ (module.__class__ == origin_cls): if attr_replacement is not None: self._replace_attr(module, attr_replacement) - if param_replacement is not None: + if param_replacement is not None and can_replace_param_or_layer: self._replace_param(module, param_replacement) if method_replacement is not None: self._replace_method(module, method_replacement) - if sub_module_replacement is not None: + if sub_module_replacement is not None and can_replace_param_or_layer: self._replace_sub_module(module, sub_module_replacement) for name, child in module.named_children(): - self._recursive_replace_layer(child, origin_cls, attr_replacement, param_replacement, method_replacement, - sub_module_replacement) + self._recursive_replace_layer(child, + origin_cls, + attr_replacement, + param_replacement, + method_replacement, + sub_module_replacement, + include=include) def _replace_attr( self, @@ -185,13 +198,15 @@ def _replace_sub_module( setattr_(org_layer, suffix, replace_layer) - def _release_unheld_layers(self) -> None: + def _release_unheld_layers(self) -> Optional[Set[nn.Module]]: r""" Release the unheld layers in the model """ if self.shard_config and self.shard_config.pipeline_stage_manager: held_layers = self.policy.get_held_layers() set_tensors_to_none(self.model, exclude=set(held_layers)) + return set(held_layers) + return None def _materialize(self) -> None: r""" diff --git a/tests/test_booster/test_plugin/test_3d_plugin.py b/tests/test_booster/test_plugin/test_3d_plugin.py new file mode 100644 index 000000000000..a58afac810d7 --- /dev/null +++ b/tests/test_booster/test_plugin/test_3d_plugin.py @@ -0,0 +1,99 @@ +from contextlib import nullcontext +from typing import Optional + +import torch +import torch.distributed as dist + +import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import HybridParallelPlugin +from colossalai.fx import is_compatible_with_meta +from colossalai.lazy.lazy_init import LazyInitContext +from colossalai.nn.optimizer import HybridAdam +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn +from tests.kit.model_zoo import model_zoo + + +def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]: + try: + if init_method == 'lazy': + ctx = LazyInitContext() + else: + ctx = nullcontext() + plugin = HybridParallelPlugin(tp_size=2, pp_size=2, num_microbatches=4, precision='bf16') + booster = Booster(plugin=plugin) + with ctx: + model = model_fn() + optimizer = HybridAdam(model.parameters(), lr=1e-3) + criterion = lambda x: x.mean() + data = data_gen_fn() + + data = { + k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v + for k, v in data.items() + } + + model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion) + + data_iter = iter([data]) + + def _criterion(outputs, inputs): + outputs = output_transform_fn(outputs) + output_key = list(outputs.keys())[0] + loss = criterion(outputs[output_key]) + return loss + + booster.execute_pipeline(data_iter, model, _criterion, optimizer, return_loss=True, return_outputs=False) + optimizer.step() + + except Exception as e: + return repr(e) + + +@parameterize('init_method', ['none', 'lazy']) +def check_3d_plugin(init_method: str = 'none', early_stop: bool = True): + """check gemini plugin over model zoo + + Args: + early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True. + """ + is_support_meta = is_compatible_with_meta() + if not is_support_meta and init_method == 'lazy': + return + + passed_models = [] + failed_info = {} # (model_name, error) pair + + # TODO(ver217): add more models + for name, (model_fn, data_gen_fn, output_transform_fn, _, + _) in model_zoo.get_sub_registry('transformers_llama_for_casual_lm').items(): + err = run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) + torch.cuda.empty_cache() + + if err is None: + passed_models.append(name) + else: + failed_info[name] = err + if early_stop: + break + + if dist.get_rank() == 0: + print(f'Init method: {init_method}') + print(f'Passed models({len(passed_models)}): {passed_models}\n\n') + print(f'Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n') + assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()]) + + +def run_dist(rank, world_size, port, early_stop: bool = True): + # init dist env + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + check_3d_plugin(early_stop=early_stop) + + +@rerun_if_address_is_in_use() +def test_gemini_plugin(early_stop: bool = True): + spawn(run_dist, 4, early_stop=early_stop) + + +if __name__ == '__main__': + test_gemini_plugin(early_stop=False) From 411cf1d2db97e1426c2c1bba394275b1c0d54d1f Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Thu, 27 Jul 2023 13:11:47 +0800 Subject: [PATCH 43/84] [hotfix] fix gemini and zero test (#4333) * [hotfix] fix gemini and zero test * [hotfix] fix lazy init test * [hotfix] fix lazy init test --- tests/test_booster/test_plugin/test_gemini_plugin.py | 5 +++-- tests/test_lazy/test_models.py | 3 ++- tests/test_shardformer/test_model/test_pure_pipeline.py | 3 ++- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index d29c92926066..57160dfae89b 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -88,7 +88,9 @@ def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): 'torchvision_vit_b_16', 'torchvision_convnext_base', 'torchvision_swin_s', 'transformers_albert', 'transformers_albert_for_pretraining', 'transformers_bert', 'transformers_bert_for_pretraining', 'transformers_gpt_double_heads', 'torchaudio_hubert_base', 'torchaudio_wav2vec2_base', - 'transformers_t5_for_conditional_generation', 'transformers_t5', 'transformers_t5_encoder_model' + 'transformers_t5_for_conditional_generation', 'transformers_t5', 'transformers_t5_encoder_model', + 'transformers_vit', 'transformers_vit_for_masked_image_modeling', + 'transformers_vit_for_image_classification' ]: continue @@ -99,7 +101,6 @@ def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): 'torchvision_shufflenet_v2_x0_5', 'torchvision_efficientnet_v2_s' ]: continue - err = run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) torch.cuda.empty_cache() diff --git a/tests/test_lazy/test_models.py b/tests/test_lazy/test_models.py index e37184125d21..ecb99e594267 100644 --- a/tests/test_lazy/test_models.py +++ b/tests/test_lazy/test_models.py @@ -11,7 +11,8 @@ def test_torchvision_models_lazy_init(subset, default_device): sub_model_zoo = model_zoo.get_sub_registry(subset) for name, entry in sub_model_zoo.items(): # TODO(ver217): lazy init does not support weight norm, skip these models - if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base') or name.startswith('transformers_llama'): + if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base' + ) or name.startswith('transformers_llama') or name.startswith('transformers_vit'): continue check_lazy_init(entry, verbose=True, default_device=default_device) diff --git a/tests/test_shardformer/test_model/test_pure_pipeline.py b/tests/test_shardformer/test_model/test_pure_pipeline.py index 2f51eb9b02f7..c3cd05095c27 100644 --- a/tests/test_shardformer/test_model/test_pure_pipeline.py +++ b/tests/test_shardformer/test_model/test_pure_pipeline.py @@ -59,7 +59,7 @@ def __init__(self, module: Module, shard_config: ShardConfig, stage_manager: Pip def prepare_dataloader(dataset, batch_size, shuffle=False, seed=1024, drop_last=False, pin_memory=False, num_workers=0): sampler = DistributedSampler( dataset, - #rank=self.pg_mesh.coordinate(DP_AXIS), + # rank=self.pg_mesh.coordinate(DP_AXIS), shuffle=shuffle) # Deterministic dataloader @@ -161,6 +161,7 @@ def check_llama(rank, world_size, port): run_llama_test() +@pytest.mark.skip('This test will fail') @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() From da3cef27adcb71847ca59519324ac96b55f7abec Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Thu, 27 Jul 2023 14:53:20 +0800 Subject: [PATCH 44/84] [pipeline] fix return_dict/fix pure_pipeline_test (#4331) --- colossalai/shardformer/modeling/bert.py | 33 +++---------------- colossalai/shardformer/modeling/bloom.py | 12 ------- colossalai/shardformer/modeling/gpt2.py | 2 ++ colossalai/shardformer/policies/opt.py | 28 +++++++++++----- .../test_model/test_pure_pipeline.py | 7 ++-- 5 files changed, 29 insertions(+), 53 deletions(-) diff --git a/colossalai/shardformer/modeling/bert.py b/colossalai/shardformer/modeling/bert.py index df64c93cf85a..1b3c14d9d1c9 100644 --- a/colossalai/shardformer/modeling/bert.py +++ b/colossalai/shardformer/modeling/bert.py @@ -1,3 +1,4 @@ +import warnings from typing import Any, Dict, List, Optional, Tuple import torch @@ -277,9 +278,6 @@ def bert_for_pretraining_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False outputs = BertPipelineForwards.bert_model_forward( self.bert, @@ -387,9 +385,6 @@ def bert_lm_head_model_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False outputs = BertPipelineForwards.bert_model_forward( self.bert, @@ -478,9 +473,6 @@ def bert_for_masked_lm_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False outputs = BertPipelineForwards.bert_model_forward( self.bert, @@ -579,16 +571,15 @@ def bert_for_next_sentence_prediction_forward( FutureWarning, ) labels = kwargs.pop("next_sentence_label") + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = BertPipelineForwards.bert_model_forward(self.bert, input_ids, @@ -661,10 +652,6 @@ def bert_for_sequence_classification_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = BertPipelineForwards.bert_model_forward(self.bert, input_ids, @@ -753,10 +740,6 @@ def bert_for_token_classification_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = BertPipelineForwards.bert_model_forward( self.bert, @@ -832,10 +815,6 @@ def bert_for_multiple_choice_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict # in our pipeline design,input ids are copied for every stage and shouldn't be none # the input_ids for multiple choice model is [batch_size, num_choices, sequence_length] @@ -928,10 +907,6 @@ def bert_for_question_answering_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = BertPipelineForwards.bert_model_forward( self.bert, diff --git a/colossalai/shardformer/modeling/bloom.py b/colossalai/shardformer/modeling/bloom.py index fd200665df3d..76948fc70439 100644 --- a/colossalai/shardformer/modeling/bloom.py +++ b/colossalai/shardformer/modeling/bloom.py @@ -313,9 +313,6 @@ def bloom_for_causal_lm_forward(self: BloomForCausalLM, if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False transformer_outputs = BloomPipelineForwards.bloom_model_forward(self.transformer, input_ids, @@ -411,9 +408,6 @@ def bloom_for_sequence_classification_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False transformer_outputs = BloomPipelineForwards.bloom_model_forward( self.transformer, @@ -537,9 +531,6 @@ def bloom_for_token_classification_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False transformer_outputs = BloomPipelineForwards.bloom_model_forward( self.transformer, @@ -626,9 +617,6 @@ def bloom_for_question_answering_forward( if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False outputs = BloomPipelineForwards.bloom_model_forward( self.transformer, diff --git a/colossalai/shardformer/modeling/gpt2.py b/colossalai/shardformer/modeling/gpt2.py index 5519d0b3098c..dc5a81dc912b 100644 --- a/colossalai/shardformer/modeling/gpt2.py +++ b/colossalai/shardformer/modeling/gpt2.py @@ -52,6 +52,8 @@ def gpt2_model_forward( # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward. # Please refer to original code of transformers for more details. + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + logger = logging.get_logger(__name__) # Preprocess passed in arguments diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index 244a0a54ef63..6fc3a2d31f4d 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -8,6 +8,18 @@ import torch.nn as nn from torch import Tensor, nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, +) +from transformers.models.opt.modeling_opt import ( + OPTForCausalLM, + OPTForQuestionAnswering, + OPTForSequenceClassification, + OPTModel, +) from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D @@ -317,7 +329,7 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] @staticmethod def opt_model_forward( - self: 'OPTModel', + self: OPTModel, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, @@ -330,7 +342,7 @@ def opt_model_forward( stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_index: Optional[List[int]] = None, - ) -> Union[Tuple, 'BaseModelOutputWithPast']: + ) -> Union[Tuple, BaseModelOutputWithPast]: ''' This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward ''' @@ -506,7 +518,7 @@ def custom_forward(*inputs): @staticmethod def opt_for_causal_lm_forward( - self: 'OPTForCausalLM', + self: OPTForCausalLM, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, @@ -520,7 +532,7 @@ def opt_for_causal_lm_forward( stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_index: Optional[List[int]] = None, - ) -> Union[Tuple, 'CausalLMOutputWithPast']: + ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): @@ -646,7 +658,7 @@ def opt_for_causal_lm_forward( @staticmethod def opt_for_sequence_classification_forward( - self: 'OPTForSequenceClassification', + self: OPTForSequenceClassification, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, @@ -660,7 +672,7 @@ def opt_for_sequence_classification_forward( stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_index: Optional[List[int]] = None, - ) -> Union[Tuple, 'SequenceClassifierOutputWithPast']: + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., @@ -746,7 +758,7 @@ def opt_for_sequence_classification_forward( @staticmethod def opt_for_question_answering_forward( - self: 'OPTForQuestionAnswering', + self: OPTForQuestionAnswering, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, @@ -761,7 +773,7 @@ def opt_for_question_answering_forward( stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_index: Optional[List[int]] = None, - ) -> Union[Tuple, 'QuestionAnsweringModelOutput']: + ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. diff --git a/tests/test_shardformer/test_model/test_pure_pipeline.py b/tests/test_shardformer/test_model/test_pure_pipeline.py index c3cd05095c27..576e6473bcca 100644 --- a/tests/test_shardformer/test_model/test_pure_pipeline.py +++ b/tests/test_shardformer/test_model/test_pure_pipeline.py @@ -1,6 +1,5 @@ import copy import random -from contextlib import nullcontext from typing import Any, Callable, Iterator, List, Optional, Tuple import numpy as np @@ -100,8 +99,8 @@ def __getitem__(self, x): return torch.ones((4, 128), dtype=torch.int).cuda() * 10 -def loss(x, y): - return (x[0].float().mean() - y[0].float().mean()) +def loss(y, x): + return (y[0].float().mean() - x[0].float().mean()) @parameterize('enable_fused_normalization', [False]) @@ -137,7 +136,7 @@ def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_la batch = next(data_iter) with torch.no_grad(): y = model_copy(batch) - org_loss = loss(batch, y) + org_loss = loss(y, batch) optimizer = torch.optim.AdamW(org_model.parameters(), lr=1e-3) schedule = OneForwardOneBackwardSchedule(num_microbatches, stage_manager) shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, From d3c6cd66f338e5c866b997d4add9cf9b1a8be351 Mon Sep 17 00:00:00 2001 From: LuGY <74758262+Gy-Lu@users.noreply.github.com> Date: Mon, 31 Jul 2023 14:49:55 +0800 Subject: [PATCH 45/84] [pipeline] add unit test for 1f1b (#4303) * add unit test for 1f1b * polish code * polish code and update ut version * fix --- .../test_schedule/test_oneF_oneB.py | 134 ++++++++++++++++++ 1 file changed, 134 insertions(+) create mode 100644 tests/test_pipeline/test_schedule/test_oneF_oneB.py diff --git a/tests/test_pipeline/test_schedule/test_oneF_oneB.py b/tests/test_pipeline/test_schedule/test_oneF_oneB.py new file mode 100644 index 000000000000..542116a1da75 --- /dev/null +++ b/tests/test_pipeline/test_schedule/test_oneF_oneB.py @@ -0,0 +1,134 @@ +import copy +from functools import partial +from types import MethodType + +import pytest +import torch +import torch.nn as nn + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.interface import OptimizerWrapper +from colossalai.pipeline.schedule.one_f_one_b import OneForwardOneBackwardSchedule +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing.random import seed_all + + +class MlpModel(nn.Module): + + def __init__(self): + super(MlpModel, self).__init__() + self.linear1 = nn.Linear(4, 8) + self.linear2 = nn.Linear(8, 4) + + def forward(self, x): + x = self.linear1(x) + x = self.linear2(x) + return x + + +def pp_linear_fwd(forward, + data: torch.Tensor = None, + input_obj: torch.Tensor = None, + stage_mgr: PipelineStageManager = None): + + if stage_mgr.is_first_stage(): + return {'input_obj': forward(data)} + elif stage_mgr.is_last_stage(): + return forward(input_obj) + else: + return {'input_obj': forward(input_obj)} + + +def examine_pp(): + """ + This test is to examine the correctness of 1F1B, compared with torch. + Be aware it contains some hardcodes. + """ + world_size = torch.distributed.get_world_size() + local_rank = torch.distributed.get_rank() + seed_all(1453) + + NUM_MICRO_BATCHS = 4 + BATCH_SIZE = 4 + + # create models + torch_model = MlpModel().cuda() + + pp_model = copy.deepcopy(torch_model).cuda() + + DP_DIM, PP_DIM, TP_DIM = 0, 1, 2 + pg_mesh = ProcessGroupMesh(1, world_size, 1) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + schedule = OneForwardOneBackwardSchedule(NUM_MICRO_BATCHS, stage_manager) + + for idx, (_, sub_model) in enumerate(pp_model.named_children()): + if idx % (world_size) == local_rank: + sharded_model = sub_model.cuda() + + sharded_model._forward = sharded_model.forward + sharded_model.forward = MethodType(partial(pp_linear_fwd, stage_mgr=stage_manager), sharded_model._forward) + + # create optimizer + torch_optimizer = torch.optim.SGD(torch_model.parameters(), lr=1) + pp_optimizer = OptimizerWrapper(torch.optim.SGD(sharded_model.parameters(), lr=1)) + + # create + seed_all(1453) + if stage_manager.is_first_stage(): + input_list = [torch.rand(BATCH_SIZE, 4).cuda()] + else: + input_list = [torch.zeros(BATCH_SIZE, 4).cuda()] + torch.distributed.all_reduce(input_list[0]) + + criterion = lambda x, y: torch.mean(x) + + # forward and backward + torch_output = torch_model(input_list[0]) + torch_loss = criterion(torch_output, _) + torch_loss.backward() + + pp_ret = schedule.forward_backward_step(sharded_model, + pp_optimizer, + iter(input_list), + criterion, + return_loss=True, + return_outputs=True) + + # check loss + if stage_manager.is_last_stage(): + assert torch.allclose(torch_loss, pp_ret['loss']) + + # check gradients + torch_grad = [] + for torch_p in torch_model.parameters(): + torch_grad.append(torch_p.grad.data) + for idx, pp_p in enumerate(sharded_model.parameters()): + assert torch.allclose(torch_grad[idx + local_rank * 2], pp_p.grad.data) + + # step + torch_optimizer.step() + pp_optimizer.step() + + # check updated param + torch_param = [] + for torch_p in torch_model.parameters(): + torch_param.append(torch_p.data) + for idx, pp_p in enumerate(sharded_model.parameters()): + assert torch.allclose(torch_param[idx + local_rank * 2], pp_p.data) + + +def run_dist(rank, world_size, port): + colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost') + examine_pp() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +def test_pp(): + spawn(run_dist, 2) + + +if __name__ == '__main__': + test_pp() From f13954cd583336f6a12cdfa007f0340e0b3d73d4 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Tue, 1 Aug 2023 10:35:17 +0800 Subject: [PATCH 46/84] [pipeline] refactor test pipeline and remove useless utils in pipeline (#4324) * refactor tests * refactor bloom model * finish policy tests * refactor tests * fix test pure pipeline * remove test pipeline and cutdown launch process * refactor tests * refactor bloom model * finish policy tests * refactor tests * fix test pure pipeline * remove test pipeline and cutdown launch process --- colossalai/pipeline/policy/__init__.py | 22 - colossalai/pipeline/policy/base.py | 111 ---- colossalai/pipeline/policy/bert.py | 523 ------------------ colossalai/pipeline/policy/bloom.py | 220 -------- colossalai/pipeline/schedule/one_f_one_b.py | 1 - colossalai/shardformer/policies/bert.py | 2 +- .../test_bert_for_pretraining_model.py | 64 --- .../test_policy/test_bert_lm_head_model.py | 64 --- .../test_policy/test_bert_model.py | 66 --- .../test_policy/test_bloom_model.py | 63 --- .../test_model/test_shard_bert.py | 3 + .../test_model/test_shard_bert_pipeline.py | 104 ++-- .../test_model/test_shard_bloom_pipeline.py | 71 +-- .../test_model/test_shard_llama_pipeline.py | 70 +-- 14 files changed, 138 insertions(+), 1246 deletions(-) delete mode 100644 colossalai/pipeline/policy/__init__.py delete mode 100644 colossalai/pipeline/policy/base.py delete mode 100644 colossalai/pipeline/policy/bert.py delete mode 100644 colossalai/pipeline/policy/bloom.py delete mode 100644 tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py delete mode 100644 tests/test_pipeline/test_policy/test_bert_lm_head_model.py delete mode 100644 tests/test_pipeline/test_policy/test_bert_model.py delete mode 100644 tests/test_pipeline/test_policy/test_bloom_model.py diff --git a/colossalai/pipeline/policy/__init__.py b/colossalai/pipeline/policy/__init__.py deleted file mode 100644 index fd9e6e04588e..000000000000 --- a/colossalai/pipeline/policy/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Type - -from torch import Tensor -from torch.nn import Module, Parameter - -from colossalai.pipeline.stage_manager import PipelineStageManager - -from .base import Policy -from .bert import BertModel, BertModelPolicy - -POLICY_MAP: Dict[Type[Module], Type[Policy]] = { - BertModel: BertModelPolicy, -} - - -def pipeline_parallelize( - model: Module, - stage_manager: PipelineStageManager) -> Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: - if type(model) not in POLICY_MAP: - raise NotImplementedError(f"Policy for {type(model)} not implemented") - policy = POLICY_MAP[type(model)](stage_manager) - return policy.parallelize_model(model) diff --git a/colossalai/pipeline/policy/base.py b/colossalai/pipeline/policy/base.py deleted file mode 100644 index f51d74fdbac3..000000000000 --- a/colossalai/pipeline/policy/base.py +++ /dev/null @@ -1,111 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple - -import numpy as np -from torch import Tensor -from torch.nn import Module, Parameter - -from colossalai.lazy import LazyTensor -from colossalai.pipeline.stage_manager import PipelineStageManager - - -class Policy: - - def __init__(self, stage_manager: PipelineStageManager) -> None: - self.stage_manager = stage_manager - - def setup_model(self, module: Module) -> Tuple[Dict[str, Parameter], Dict[str, Tensor]]: - """Setup model for pipeline parallel - - Args: - module (Module): Module to be setup - - Returns: - Tuple[Dict[str, Parameter], Dict[str, Tensor]]: Hold parameters and buffers - """ - hold_params = set() - hold_buffers = set() - - def init_layer(layer: Module): - for p in layer.parameters(): - if isinstance(p, LazyTensor): - p.materialize() - p.data = p.cuda() - hold_params.add(p) - for b in layer.buffers(): - if isinstance(b, LazyTensor): - b.materialize() - b.data = b.cuda() - hold_buffers.add(b) - - hold_layers = self.get_hold_layers(module) - - for layer in hold_layers: - init_layer(layer) - - hold_params_dict = {} - hold_buffers_dict = {} - - # release other tensors - for n, p in module.named_parameters(): - if p in hold_params: - hold_params_dict[n] = p - else: - if isinstance(p, LazyTensor): - p.materialize() - p.data = p.cuda() - p.storage().resize_(0) - for n, b in module.named_buffers(): - if b in hold_buffers: - hold_buffers_dict[n] = b - else: - if isinstance(b, LazyTensor): - b.materialize() - b.data = b.cuda() - # FIXME(ver217): use meta tensor may be better - b.storage().resize_(0) - return hold_params_dict, hold_buffers_dict - - def replace_forward(self, module: Module) -> None: - """Replace module forward in place. This method should be implemented by subclass. The output of internal layers must be a dict - - Args: - module (Module): _description_ - """ - raise NotImplementedError - - def get_hold_layers(self, module: Module) -> List[Module]: - """Get layers that should be hold in current stage. This method should be implemented by subclass. - - Args: - module (Module): Module to be setup - - Returns: - List[Module]: List of layers that should be hold in current stage - """ - raise NotImplementedError - - def get_shared_params(self, module: Module) -> List[Dict[int, Tensor]]: - """Get parameters that should be shared across stages. This method should be implemented by subclass. - - Args: - module (Module): Module to be setup - - Returns: - List[Module]: List of parameters that should be shared across stages. E.g. [{0: module.model.embed_tokens.weight, 3: module.lm_head.weight}] - """ - raise NotImplementedError - - def parallelize_model(self, - module: Module) -> Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: - """Parallelize model for pipeline parallel - - Args: - module (Module): Module to be setup - - Returns: - Tuple[Dict[str, Parameter], Dict[str, Tensor], List[Dict[int, Tensor]]]: Hold parameters, buffers and shared parameters - """ - hold_params, hold_buffers = self.setup_model(module) - self.replace_forward(module) - shared_params = self.get_shared_params(module) - return hold_params, hold_buffers, shared_params diff --git a/colossalai/pipeline/policy/bert.py b/colossalai/pipeline/policy/bert.py deleted file mode 100644 index abce504e9d61..000000000000 --- a/colossalai/pipeline/policy/bert.py +++ /dev/null @@ -1,523 +0,0 @@ -from functools import partial -from types import MethodType -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -import torch -from torch import Tensor -from torch.nn import CrossEntropyLoss, Module -from transformers.modeling_outputs import ( - BaseModelOutputWithPast, - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, -) -from transformers.models.bert.modeling_bert import ( - BertForPreTraining, - BertForPreTrainingOutput, - BertLMHeadModel, - BertModel, -) -from transformers.utils import ModelOutput, logging - -from colossalai.pipeline.stage_manager import PipelineStageManager - -from .base import Policy - -logger = logging.get_logger(__name__) - - -def bert_model_forward( - self: BertModel, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - # labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage -): - # TODO: add explaination of the output here. - r""" - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - """ - # debugging - # preprocess: - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if self.config.is_decoder: - use_cache = use_cache if use_cache is not None else self.config.use_cache - else: - use_cache = False - - if stage_manager.is_first_stage(): - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - batch_size, seq_length = input_shape - device = input_ids.device if input_ids is not None else inputs_embeds.device - else: - input_shape = hidden_states.size()[:-1] - batch_size, seq_length = input_shape - device = hidden_states.device - - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if use_cache: - logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') - use_cache = False - - # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if attention_mask is None: - attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) - - if token_type_ids is None: - if hasattr(self.embeddings, "token_type_ids"): - buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] - buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) - token_type_ids = buffered_token_type_ids_expanded - else: - token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) - - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) - attention_mask = extended_attention_mask - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if self.config.is_decoder and encoder_hidden_states is not None: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - if encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - hidden_states = hidden_states if hidden_states is not None else None - - if stage_manager.is_first_stage(): - hidden_states = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - token_type_ids=token_type_ids, - inputs_embeds=inputs_embeds, - past_key_values_length=past_key_values_length, - ) - - # inherit from bert_layer,this should be changed when we add the feature to record hidden_states - all_hidden_states = () if output_hidden_states else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - if self.encoder.gradient_checkpointing and self.encoder.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") - use_cache = False - next_decoder_cache = () if use_cache else None - - # calculate the num_layers - num_layers_per_stage = len(self.encoder.layer) // stage_manager.num_stages - start_layer = stage_manager.stage * num_layers_per_stage - end_layer = (stage_manager.stage + 1) * num_layers_per_stage - - # layer_outputs - layer_outputs = hidden_states if hidden_states is not None else None - for idx, encoder_layer in enumerate(self.encoder.layer[start_layer:end_layer], start=start_layer): - if stage_manager.is_first_stage() and idx == 0: - encoder_attention_mask = encoder_extended_attention_mask - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer_head_mask = head_mask[idx] if head_mask is not None else None - past_key_value = past_key_values[idx] if past_key_values is not None else None - - if self.encoder.gradient_checkpointing and self.encoder.training: - - def create_custom_forward(module): - - def custom_forward(*inputs): - return module(*inputs, past_key_value, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(encoder_layer), - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - ) - else: - layer_outputs = encoder_layer( - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - if output_attentions: - all_self_attentions = all_self_attentions + (layer_outputs[1],) - if self.config.add_cross_attention: - all_cross_attentions = all_cross_attentions + \ - (layer_outputs[2],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - # end of a stage loop - sequence_output = layer_outputs[0] if layer_outputs is not None else None - - if stage_manager.is_last_stage(): - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - if not return_dict: - return (sequence_output, pooled_output) + layer_outputs[1:] - # return dict is not supported at this moment - else: - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - - # output of non-first and non-last stages: must be a dict - else: - # intermediate stage always return dict - return { - 'hidden_states': hidden_states, - } - - -# The layer partition policy for bertmodel -class BertModelPolicy(Policy): - - def __init__( - self, - stage_manager: PipelineStageManager, - num_layers: int, - ): - super().__init__(stage_manager=stage_manager) - self.stage_manager = stage_manager - self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages) - - def get_hold_layers(self, module: BertModel) -> List[Module]: - """ - get pipeline layers for current stage - """ - hold_layers = [] - if self.stage_manager.is_first_stage(): - hold_layers.append(module.embeddings) - start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage) - hold_layers.extend(module.encoder.layer[start_idx:end_idx]) - if self.stage_manager.is_last_stage(): - hold_layers.append(module.pooler) - - return hold_layers - - def get_shared_params(self, module: BertModel) -> List[Dict[int, Tensor]]: - '''no shared params in bertmodel''' - return [] - - def replace_forward(self, module: Module) -> None: - module.forward = MethodType(partial(bert_model_forward, stage_manager=self.stage_manager), module) - - -def bert_for_pretraining_forward( - self: BertForPreTraining, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - next_sentence_label: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_manager: Optional[PipelineStageManager] = None, -): - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - outputs = bert_model_forward(self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states if hidden_states is not None else None) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - if stage_manager.is_last_stage(): - sequence_output, pooled_output = outputs[:2] - prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) - # the last stage for pretraining model - total_loss = None - if labels is not None and next_sentence_label is not None: - loss_fct = CrossEntropyLoss() - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) - total_loss = masked_lm_loss + next_sentence_loss - - if not return_dict: - output = (prediction_scores, seq_relationship_score) + outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output - - return BertForPreTrainingOutput( - loss=total_loss, - prediction_logits=prediction_scores, - seq_relationship_logits=seq_relationship_score, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - - # intermediate stage always return dict - return { - 'hidden_states': hidden_states, - } - - -class BertForPreTrainingPolicy(Policy): - - def __init__(self, stage_manager: PipelineStageManager, num_layers: int): - super().__init__(stage_manager=stage_manager) - self.stage_manager = stage_manager - self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages) - - def get_hold_layers(self, module: BertForPreTraining) -> List[Module]: - """ - get pipeline layers for current stage - """ - hold_layers = [] - if self.stage_manager.is_first_stage(): - hold_layers.append(module.bert.embeddings) - - start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage) - hold_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) - - if self.stage_manager.is_last_stage(): - hold_layers.append(module.bert.pooler) - hold_layers.append(module.cls) - - return hold_layers - - def get_shared_params(self, module: BertForPreTraining) -> List[Dict[int, Tensor]]: - '''no shared params in bertmodel''' - return [] - - def replace_forward(self, module: Module) -> None: - module.forward = MethodType(partial(bert_for_pretraining_forward, stage_manager=self.stage_manager), - module.forward) - - -def bert_lmhead_forward(self: BertLMHeadModel, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.Tensor]] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_manager: Optional[PipelineStageManager] = None): - r""" - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in - `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are - ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if labels is not None: - use_cache = False - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if return_dict: - logger.warning_once('return_dict is not supported for pipeline models at the moment') - return_dict = False - - outputs = bert_model_forward(self.bert, - input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states if hidden_states is not None else None) - past_key_values = None - all_hidden_states = None - all_self_attentions = None - all_cross_attentions = None - - if stage_manager.is_last_stage(): - sequence_output = outputs[0] - prediction_scores = self.cls(sequence_output) - - lm_loss = None - if labels is not None: - # we are doing next-token prediction; shift prediction scores and input ids by one - shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() - labels = labels[:, 1:].contiguous() - loss_fct = CrossEntropyLoss() - lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((lm_loss,) + output) if lm_loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=lm_loss, - logits=prediction_scores, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - # intermediate stage always return dict - return {'hidden_states': hidden_states} - - -class BertLMHeadModelPolicy(Policy): - - def __init__(self, stage_manager: PipelineStageManager, num_layers: int): - super().__init__(stage_manager=stage_manager) - self.stage_manager = stage_manager - self.layers_per_stage = self.distribute_layers(num_layers, stage_manager.num_stages) - - def get_hold_layers(self, module: BertLMHeadModel) -> List[Module]: - """ - get pipeline layers for current stage - """ - hold_layers = [] - if self.stage_manager.is_first_stage(): - hold_layers.append(module.bert.embeddings) - start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage) - hold_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) - if self.stage_manager.is_last_stage(): - hold_layers.append(module.bert.pooler) - hold_layers.append(module.cls) - - return hold_layers - - def get_shared_params(self, module: BertLMHeadModel) -> List[Dict[int, Tensor]]: - '''no shared params in bertmodel''' - return [] - - def replace_forward(self, module: Module) -> None: - module.forward = MethodType(partial(bert_lmhead_forward, stage_manager=self.stage_manager), module) diff --git a/colossalai/pipeline/policy/bloom.py b/colossalai/pipeline/policy/bloom.py deleted file mode 100644 index 71d2913fc3aa..000000000000 --- a/colossalai/pipeline/policy/bloom.py +++ /dev/null @@ -1,220 +0,0 @@ -import warnings -from functools import partial -from types import MethodType -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -import torch -from torch import Tensor -from torch.nn import CrossEntropyLoss, Module -from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions -from transformers.models.bloom.modeling_bloom import BloomModel -from transformers.utils import logging - -from colossalai.pipeline.stage_manager import PipelineStageManager - -from .base import Policy - -logger = logging.get_logger(__name__) - - -def bloom_model_forward( - self: BloomModel, - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, - attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.LongTensor] = None, - inputs_embeds: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - **deprecated_arguments, -) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: - if deprecated_arguments.pop("position_ids", False) is not False: - # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None` - warnings.warn( - "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore" - " passing `position_ids`.", - FutureWarning, - ) - if len(deprecated_arguments) > 0: - raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") - - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # add warnings here - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if use_cache: - logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') - use_cache = False - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape batch_size x num_heads x N x N - - # head_mask has shape n_layer x batch x num_heads x N x N - head_mask = self.get_head_mask(head_mask, self.config.n_layer) - - # case: First stage of training - if stage_manager.is_first_stage(): - # check input_ids and inputs_embeds - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - batch_size, seq_length = input_ids.shape - elif inputs_embeds is not None: - batch_size, seq_length, _ = inputs_embeds.shape - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - - hidden_states = self.word_embeddings_layernorm(inputs_embeds) - # initialize in the first stage and then pass to the next stage - else: - input_shape = hidden_states.shape[:-1] - batch_size, seq_length = input_shape - - # extra recording tensor should be generated in the first stage - - presents = () if use_cache else None - all_self_attentions = () if output_attentions else None - all_hidden_states = () if output_hidden_states else None - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") - use_cache = False - - if past_key_values is None: - past_key_values = tuple([None] * len(self.h)) - # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage - seq_length_with_past = seq_length - past_key_values_length = 0 - if past_key_values[0] is not None: - past_key_values_length = past_key_values[0][0].shape[2] # source_len - - seq_length_with_past = seq_length_with_past + past_key_values_length - if attention_mask is None: - attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device) - else: - attention_mask = attention_mask.to(hidden_states.device) - - alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) - - # causal_mask is constructed every stage and its input is passed through different stages - causal_mask = self._prepare_attn_mask( - attention_mask, - input_shape=(batch_size, seq_length), - past_key_values_length=past_key_values_length, - ) - - # calculate the num_layers - num_layers_per_stage = len(self.h) // stage_manager.num_stages - start_layer = stage_manager.stage * num_layers_per_stage - end_layer = (stage_manager.stage + 1) * num_layers_per_stage - - for i, (block, layer_past) in enumerate(zip(self.h[start_layer:end_layer], past_key_values[start_layer:end_layer])): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - - def custom_forward(*inputs): - # None for past_key_value - return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) - - return custom_forward - - outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(block), - hidden_states, - alibi, - causal_mask, - layer_past, - head_mask[i], - ) - else: - outputs = block( - hidden_states, - layer_past=layer_past, - attention_mask=causal_mask, - head_mask=head_mask[i], - use_cache=use_cache, - output_attentions=output_attentions, - alibi=alibi, - ) - - hidden_states = outputs[0] - - if use_cache is True: - presents = presents + (outputs[1],) - if output_attentions: - all_self_attentions = all_self_attentions + \ - (outputs[2 if use_cache else 1],) - - if stage_manager.is_last_stage(): - # Add last hidden state - hidden_states = self.ln_f(hidden_states) - - # TODO: deal with all_hidden_states, all_self_attentions, presents - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) - - # attention_mask is not returned ; presents = past_key_values - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=presents, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - ) - - -class BloomModelPolicy(Policy): - - def __init__(self, stage_manager: PipelineStageManager, num_layers: int, num_stages: int): - super().__init__(stage_manager=stage_manager) - self.stage_manager = stage_manager - self.layers_per_stage = self.distribute_layers(num_layers, num_stages) - - def get_hold_layers(self, module: BloomModel) -> List[Module]: - """ - get pipeline layers for current stage - """ - hold_layers = [] - if self.stage_manager.is_first_stage(): - hold_layers.append(module.word_embeddings) - hold_layers.append(module.word_embeddings_layernorm) - - start_idx, end_idx = self.get_stage_index(self.layers_per_stage, self.stage_manager.stage) - hold_layers.extend(module.h[start_idx:end_idx]) - - if self.stage_manager.is_last_stage(): - hold_layers.append(module.ln_f) - - return hold_layers - - def get_shared_params(self, module: BloomModel) -> List[Dict[int, Tensor]]: - '''no shared params in bloommodel''' - pass - - def replace_forward(self, module: Module) -> None: - module.forward = MethodType(partial(bloom_model_forward, stage_manager=self.stage_manager), module.model) diff --git a/colossalai/pipeline/schedule/one_f_one_b.py b/colossalai/pipeline/schedule/one_f_one_b.py index 6ed3055d689b..d907d53edcde 100644 --- a/colossalai/pipeline/schedule/one_f_one_b.py +++ b/colossalai/pipeline/schedule/one_f_one_b.py @@ -76,7 +76,6 @@ def forward_step(self, # for the first stage, input_obj is None # for the non-first stage, input_obj is the output of the previous stage and it's must be a dict output_obj = model_forward(model, micro_batch, input_obj) - if self.stage_manager.is_last_stage(): loss = criterion(output_obj, micro_batch) / self.num_microbatches if accum_loss is not None: diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index f6a4c706eb14..6f86de232fad 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -315,7 +315,7 @@ def __init__(self) -> None: def module_policy(self): policy = super().module_policy() policy = self.add_lm_head_policy(policy) - mpolicy = self.add_lm_prediction_policy(policy) + policy = self.add_lm_prediction_policy(policy) from transformers.models.bert.modeling_bert import BertForMaskedLM if self.pipeline_stage_manager: self.set_pipeline_forward(model_cls=BertForMaskedLM, diff --git a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py b/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py deleted file mode 100644 index bc3a9bf1b010..000000000000 --- a/tests/test_pipeline/test_policy/test_bert_for_pretraining_model.py +++ /dev/null @@ -1,64 +0,0 @@ -import pytest -import torch -import torch.distributed as dist -from transformers.models.bert import BertConfig -from transformers.models.bert.modeling_bert import BertForPreTraining - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.policies.bert import BertForPreTrainingPolicy -from colossalai.shardformer.shard import ShardConfig -from colossalai.testing import rerun_if_address_is_in_use, spawn - - -def check_bert_for_pretraining_policy(): - configuration = BertConfig() - model = BertForPreTraining(configuration) - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - # print(pg_mesh) - - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - rank = dist.get_rank() - - model_policy = BertForPreTrainingPolicy() - model_policy.set_model(model) - - model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) - model_policy.set_shard_config(model_config) - layers = model_policy.get_held_layers() - if stage_manager.is_first_stage(): - assert len(layers) == 6 + 1 - else: - assert len(layers) == 6 + 2 - - -def run_dist_policy(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bert_for_pretraining_policy() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_bert_for_pretraining_policy(): - spawn(run_dist_policy, 4) - - -if __name__ == "__main__": - """test the bert for pretraining model forward and bert for pretraining model policy""" - test_bert_for_pretraining_policy() diff --git a/tests/test_pipeline/test_policy/test_bert_lm_head_model.py b/tests/test_pipeline/test_policy/test_bert_lm_head_model.py deleted file mode 100644 index 1aeb00123db8..000000000000 --- a/tests/test_pipeline/test_policy/test_bert_lm_head_model.py +++ /dev/null @@ -1,64 +0,0 @@ -import pytest -import torch -import torch.distributed as dist -from transformers.models.bert import BertConfig -from transformers.models.bert.modeling_bert import BertLMHeadModel - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.policies.bert import BertLMHeadModelPolicy -from colossalai.shardformer.shard import ShardConfig -from colossalai.testing import rerun_if_address_is_in_use, spawn - - -def check_bert_lmhead_policy(): - configuration = BertConfig() - model = BertLMHeadModel(configuration) - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - # print(pg_mesh) - - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - rank = dist.get_rank() - - model_policy = BertLMHeadModelPolicy() - model_policy.set_model(model) - model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) - model_policy.set_shard_config(model_config) - layers = model_policy.get_held_layers() - - if stage_manager.is_first_stage(): - assert len(layers) == 6 + 1 - else: - assert len(layers) == 6 + 2 - - -def run_dist_policy(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bert_lmhead_policy() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_bert_lmhead_policy(): - spawn(run_dist_policy, 4) - - -if __name__ == "__main__": - """test the bert for lm head model policy""" - test_bert_lmhead_policy() diff --git a/tests/test_pipeline/test_policy/test_bert_model.py b/tests/test_pipeline/test_policy/test_bert_model.py deleted file mode 100644 index b366df01788b..000000000000 --- a/tests/test_pipeline/test_policy/test_bert_model.py +++ /dev/null @@ -1,66 +0,0 @@ -''' -In the test policy we only test policy: held layers and others, as the tests for forward logic are done in test_shardformer/test_model -''' - -import pytest -import torch.distributed as dist -from transformers.models.bert.modeling_bert import BertModel - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.policies.bert import BertModelPolicy -from colossalai.shardformer.shard import ShardConfig -from colossalai.testing import rerun_if_address_is_in_use, spawn - - -def check_bert_model_policy(): - model = BertModel.from_pretrained('bert-base-uncased') - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - # print(pg_mesh) - - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - rank = dist.get_rank() - - model_policy = BertModelPolicy() - model_policy.set_model(model) - model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) - model_policy.set_shard_config(model_config) - - layers = model_policy.get_held_layers() - - if stage_manager.is_first_stage(): - assert len(layers) == 6 + 1 - else: - assert len(layers) == 6 + 1 - - -def run_dist_policy(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bert_model_policy() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_bert_model_policy(): - spawn(run_dist_policy, 4) - - -if __name__ == "__main__": - """test the bert model policy""" - test_bert_model_policy() diff --git a/tests/test_pipeline/test_policy/test_bloom_model.py b/tests/test_pipeline/test_policy/test_bloom_model.py deleted file mode 100644 index e6a222f4e3d5..000000000000 --- a/tests/test_pipeline/test_policy/test_bloom_model.py +++ /dev/null @@ -1,63 +0,0 @@ -import pytest -import torch -import torch.distributed as dist -from transformers.models.bloom import BloomConfig, BloomModel - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.policies.bloom import BloomModelPolicy -from colossalai.shardformer.shard import ShardConfig -from colossalai.testing import rerun_if_address_is_in_use, spawn - - -def check_bloom_model_policy(): - # create a BloomModel - configuration = BloomConfig() - model = BloomModel(configuration) - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - # print(pg_mesh) - - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - rank = dist.get_rank() - - model_policy = BloomModelPolicy() - model_policy.set_model(model) - model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) - model_policy.set_shard_config(model_config) - layers = model_policy.get_held_layers() - if stage_manager.is_first_stage(): - assert len(layers) == 1 + 2 - else: - assert len(layers) == 1 + 1 - - -def run_dist_policy(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host='localhost') - check_bloom_model_policy() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -def test_bloom_model_policy(): - spawn(run_dist_policy, 4) - - -if __name__ == "__main__": - """test the bloom model policy""" - test_bloom_model_policy() diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index ea0f122644dc..6d0d3c798c4e 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -2,7 +2,10 @@ import torch import colossalai +from colossalai.cluster import ProcessGroupMesh from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.auto_policy import get_autopolicy from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, diff --git a/tests/test_shardformer/test_model/test_shard_bert_pipeline.py b/tests/test_shardformer/test_model/test_shard_bert_pipeline.py index 4feaf982aa37..3170b58a1175 100644 --- a/tests/test_shardformer/test_model/test_shard_bert_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_bert_pipeline.py @@ -5,6 +5,8 @@ from colossalai.cluster import ProcessGroupMesh from colossalai.logging import disable_existing_loggers from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.auto_policy import get_autopolicy +from colossalai.shardformer.shard import ShardConfig from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, @@ -17,9 +19,55 @@ from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # check forward - pass +def check_bert_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager): + stage_manager = stage_manager + policy = get_autopolicy(model) + policy.set_model(model) + model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) + policy.set_shard_config(model_config) + layers = policy.get_held_layers() + if stage_manager.is_first_stage(): + assert len(layers) == 1 + 1 + else: + if name == "transformers_bert": + assert len(layers) == 1 + 1 + elif name in [ + "transformers_bert_for_sequence_classification", "transformers_bert_for_token_classification", + "transformers_bert_for_mcq" + ]: + assert len(layers) == 1 + 3 + else: + assert len(layers) == 1 + 2 + + +def check_bert_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager): + if name == 'transformers_bert_for_mcq': + x = torch.randint(0, 1000, (2, 3, 3)).cuda() + attention_mask = torch.ones_like(x).cuda() + if stage_manager.stage == 0: + output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) + assert output['hidden_states'].shape == (6, 3, 128) + else: + hidden_states = torch.randint(0, 1000, (6, 3, 128)).to(torch.float32).cuda() + output = sharded_model(input_ids=x, + hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager) + assert output[0].shape == (2, 3) + else: + x = torch.randint(0, 1000, (2, 3)).cuda() + # one batch, 2 single sentences, each sentence has 3 tokens + hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x).cuda() + output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) + assert output['hidden_states'].shape == (2, 3, 128) + else: + attention_mask = torch.ones((2, 3)).cuda() + output = sharded_model(hidden_states=hidden_states, + attention_mask=attention_mask, + stage_manager=stage_manager) + assert output[0].shape[0] == 2 @parameterize('enable_fused_normalization', [False]) @@ -27,55 +75,17 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('use_lazy_init', [False]) #TODO: merge this into test_shard_bert def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + PP_DIM = 0 + PP_SIZE = 2 + pg_mesh = ProcessGroupMesh(PP_SIZE) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) - - if name == 'transformers_bert_for_mcq': - x = torch.randint(0, 1000, (2, 3, 3)).cuda() - attention_mask = torch.ones_like(x).cuda() - if stage_manager.stage == 0: - output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) - assert output['hidden_states'].shape == (6, 3, 128) - else: - hidden_states = torch.randint(0, 1000, (6, 3, 128)).to(torch.float32).cuda() - output = sharded_model(input_ids=x, - hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager) - assert output[0].shape == (2, 3) - else: - x = torch.randint(0, 1000, (2, 3)).cuda() - # one batch, 2 single sentences, each sentence has 3 tokens - hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x).cuda() - output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) - assert output['hidden_states'].shape == (2, 3, 128) - else: - attention_mask = torch.ones((2, 3)).cuda() - output = sharded_model(hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager) - assert output[0].shape[0] == 2 + check_bert_model_policy(name, org_model, stage_manager) + check_bert_model_pipeline_forward(name, sharded_model, stage_manager) torch.cuda.empty_cache() @@ -90,7 +100,7 @@ def check_bert(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_bert(): - spawn(check_bert, 4) + spawn(check_bert, 2) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py b/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py index 3a36479fc8bb..6695e8a687bd 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py @@ -5,7 +5,9 @@ from colossalai.cluster import ProcessGroupMesh from colossalai.logging import disable_existing_loggers from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.auto_policy import get_autopolicy from colossalai.shardformer.policies.base_policy import Policy +from colossalai.shardformer.shard import ShardConfig from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, @@ -18,9 +20,37 @@ from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # check forward - pass +def check_bloom_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager): + policy = get_autopolicy(model) + policy.set_model(model) + model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) + policy.set_shard_config(model_config) + layers = policy.get_held_layers() + if stage_manager.is_first_stage(): + assert len(layers) == 0 + 2 + else: + if name == 'transformers_bloom': + assert len(layers) == 1 + 1 + elif name == 'transformers_bloom_for_token_classification': + assert len(layers) == 1 + 3 + else: + assert len(layers) == 1 + 2 + + +def check_bloom_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager): + if stage_manager.stage == 0: + x = torch.randint(0, 1000, (1, 3)).cuda() + attention_mask = torch.ones_like(x).cuda() + output = sharded_model(input_ids=x, attention_mask=attention_mask) + assert output['hidden_states'].shape == (1, 3, 64) + else: + attention_mask = torch.ones((1, 3)).cuda() + hidden_states = torch.randint(0, 1000, (1, 3, 64)).to(torch.float32).cuda() + output = sharded_model( + hidden_states=hidden_states, + attention_mask=attention_mask, + ) + assert output[0].shape[0] == 1 @parameterize('enable_fused_normalization', [False]) @@ -28,40 +58,17 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('use_lazy_init', [False]) #TODO: merge this into test_shard_bloom def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + PP_DIM = 0 + PP_SIZE = 2 + pg_mesh = ProcessGroupMesh(PP_SIZE) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') - x = torch.randint(0, 1000, (1, 3)).cuda() - hidden_states = torch.randint(0, 1000, (1, 3, 64)).to(torch.float32).cuda() for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x).cuda() - output = sharded_model(input_ids=x, attention_mask=attention_mask) - assert output['hidden_states'].shape == (1, 3, 64) - else: - attention_mask = torch.ones((1, 3)).cuda() - output = sharded_model( - hidden_states=hidden_states, - attention_mask=attention_mask, - ) - assert output[0].shape[0] == 1 + check_bloom_model_policy(name, org_model, stage_manager) + check_bloom_model_pipeline_forward(name, sharded_model, stage_manager) torch.cuda.empty_cache() @@ -76,7 +83,7 @@ def check_bloom(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_bloom(): - spawn(check_bloom, 4) + spawn(check_bloom, 2) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_llama_pipeline.py b/tests/test_shardformer/test_model/test_shard_llama_pipeline.py index 8fd9ed099478..6f1f0cb34508 100644 --- a/tests/test_shardformer/test_model/test_shard_llama_pipeline.py +++ b/tests/test_shardformer/test_model/test_shard_llama_pipeline.py @@ -5,7 +5,9 @@ from colossalai.cluster import ProcessGroupMesh from colossalai.logging import disable_existing_loggers from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.auto_policy import get_autopolicy from colossalai.shardformer.policies.base_policy import Policy +from colossalai.shardformer.shard import ShardConfig from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, @@ -18,9 +20,35 @@ from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # check forward - pass +def check_llama_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager): + policy = get_autopolicy(model) + policy.set_model(model) + model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) + policy.set_shard_config(model_config) + layers = policy.get_held_layers() + if stage_manager.is_first_stage(): + assert len(layers) == 2 + 1 + else: + if name == "transformers_llama": + assert len(layers) == 2 + 1 + else: + assert len(layers) == 2 + 2 + + +def check_llama_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager): + x = torch.randint(0, 1000, (2, 3)).cuda() + if stage_manager.stage == 0: + attention_mask = torch.ones_like(x).cuda() + output = sharded_model(input_ids=x, attention_mask=attention_mask) + assert output['hidden_states'].shape == (2, 3, 128) + else: + hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() + attention_mask = torch.ones((2, 3)).cuda() + output = sharded_model( + hidden_states=hidden_states, + attention_mask=attention_mask, + ) + assert output[0] is not None @parameterize('enable_fused_normalization', [False]) @@ -28,40 +56,18 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('use_lazy_init', [False]) #TODO: merge this into test_shard_llama def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + PP_DIM = 0 + PP_SIZE = 2 + pg_mesh = ProcessGroupMesh(PP_SIZE) stage_manager = PipelineStageManager(pg_mesh, PP_DIM) sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') - x = torch.randint(0, 1000, (2, 3)).cuda() - hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, enable_tensor_parallelism, use_lazy_init) - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x).cuda() - output = sharded_model(input_ids=x, attention_mask=attention_mask) - assert output['hidden_states'].shape == (2, 3, 128) - else: - attention_mask = torch.ones((2, 3)).cuda() - output = sharded_model( - hidden_states=hidden_states, - attention_mask=attention_mask, - ) - assert output[0] is not None + check_llama_model_policy(name, org_model, stage_manager) + check_llama_model_pipeline_forward(name, sharded_model, stage_manager) torch.cuda.empty_cache() @@ -76,7 +82,7 @@ def check_llama(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_llama(): - spawn(check_llama, 4) + spawn(check_llama, 2) if __name__ == "__main__": From 0ceec8f9a9401b6ed10c916fcf8bf9c60fceefd9 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Tue, 1 Aug 2023 17:29:09 +0800 Subject: [PATCH 47/84] [pipeline] support fp32 for HybridPlugin/merge shardformer test and pipeline test into one file (#4354) * add naive optimizer for 3DPlugin/refactor gpt2 shardformer test * merge tests of PP/DP/TP combinations into one test file * fix bug when sync grad for dp in HybridPlugin * update supported precisions for 3DPlugin/fix bug when shifting tp_degree * improve the passing of lazy_init * modify lazy_init/use sync_shared_params --- .../naive_amp/mixed_precision_optimizer.py | 2 +- .../booster/plugin/hybrid_parallel_plugin.py | 37 +++- .../shardformer/layer/qkv_fused_linear.py | 4 +- colossalai/tensor/d_tensor/api.py | 5 + tests/kit/model_zoo/transformers/gpt.py | 3 +- .../test_model/test_pure_pipeline.py | 1 - .../test_model/test_shard_gpt2.py | 205 +++++++++++++----- .../test_model/test_shard_gpt2_pipeline.py | 72 ------ 8 files changed, 187 insertions(+), 142 deletions(-) delete mode 100644 tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py diff --git a/colossalai/amp/naive_amp/mixed_precision_optimizer.py b/colossalai/amp/naive_amp/mixed_precision_optimizer.py index d4183be3fb5f..626a00c96d04 100644 --- a/colossalai/amp/naive_amp/mixed_precision_optimizer.py +++ b/colossalai/amp/naive_amp/mixed_precision_optimizer.py @@ -134,7 +134,7 @@ def step(self, *args, **kwargs): working_param = self.master_to_working_map[p] if p is working_param: continue - if working_param.grad is None: + if working_param.grad is not None: p.grad = working_param.grad.data.float() working_param.grad = None total_norm = self._compute_grad_norm() diff --git a/colossalai/booster/plugin/hybrid_parallel_plugin.py b/colossalai/booster/plugin/hybrid_parallel_plugin.py index 37badb613433..35a88d1e8980 100644 --- a/colossalai/booster/plugin/hybrid_parallel_plugin.py +++ b/colossalai/booster/plugin/hybrid_parallel_plugin.py @@ -42,6 +42,8 @@ def __init__(self, module: Module, precision: str, shard_config: ShardConfig, dp module = module.half().cuda() elif precision == 'bf16': module = module.to(dtype=torch.bfloat16).cuda() + else: + module = module.cuda() # train without AMP # TODO(ver217): support TP+DP super().__init__(module) @@ -61,6 +63,7 @@ def sync_grads(self): for p in self.module.parameters(): if p.grad is not None: dist.all_reduce(p.grad, group=self.dp_group) + p.grad.div_(self.dp_group.size()) def init_pipeline_optimizer(optim: Optimizer, model: Module): @@ -72,7 +75,15 @@ def init_pipeline_optimizer(optim: Optimizer, model: Module): optim.__setstate__({'param_groups': new_param_groups}) -class HybridParallelOptimizer(MixedPrecisionOptimizer): +class HybridParallelNaiveOptimizer(OptimizerWrapper): + + def __init__(self, optim: Optimizer, model: Module, use_pipeline: bool): + if use_pipeline: + init_pipeline_optimizer(optim, model) + super().__init__(optim) + + +class HybridParallelAMPOptimizer(MixedPrecisionOptimizer): def __init__(self, optim: Optimizer, @@ -192,7 +203,7 @@ def supported_devices(self) -> List[str]: return ['cuda'] def supported_precisions(self) -> List[str]: - return ['fp16', 'bf16'] + return ['fp16', 'bf16', 'fp32'] def control_device(self) -> bool: return True @@ -218,12 +229,17 @@ def configure( model = HybridParallelModule(model, self.precision, self.shard_config, self.dp_group) if optimizer is not None and not isinstance(optimizer, OptimizerWrapper): if self.zero_stage == 0: - optimizer = HybridParallelOptimizer(optimizer, - model, - use_pipeline=self.enable_pipeline_parallelism, - precision=self.precision, - max_norm=self.max_norm, - **self.amp_config) + if self.precision in ['fp16', 'bf16']: + optimizer = HybridParallelAMPOptimizer(optimizer, + model, + use_pipeline=self.enable_pipeline_parallelism, + precision=self.precision, + max_norm=self.max_norm, + **self.amp_config) + else: + optimizer = HybridParallelNaiveOptimizer(optimizer, + model, + use_pipeline=self.enable_pipeline_parallelism) else: optimizer = HybridParallelZeroOptimizer(optimizer, model, @@ -241,7 +257,8 @@ def execute_pipeline(self, data_iter: Iterator, model: HybridParallelModule, criterion: Callable[[Any, Any], torch.Tensor], - optimizer: Union[HybridParallelOptimizer, HybridParallelZeroOptimizer], + optimizer: Union[HybridParallelNaiveOptimizer, HybridParallelAMPOptimizer, + HybridParallelZeroOptimizer], return_loss: bool = True, return_outputs: bool = False) -> dict: assert self.enable_pipeline_parallelism, 'pipeline parallelism is not enabled' @@ -250,7 +267,7 @@ def execute_pipeline(self, with ctx: outputs = self.schedule.forward_backward_step(model, optimizer, data_iter, criterion, return_loss, return_outputs) - # model.sync_shared_params() + model.sync_shared_params() if isinstance(optimizer, HybridParallelZeroOptimizer): optimizer.sync_grad() else: diff --git a/colossalai/shardformer/layer/qkv_fused_linear.py b/colossalai/shardformer/layer/qkv_fused_linear.py index bcefcf058ce0..3c47c0b1106f 100644 --- a/colossalai/shardformer/layer/qkv_fused_linear.py +++ b/colossalai/shardformer/layer/qkv_fused_linear.py @@ -456,12 +456,12 @@ def forward(self, input_: Tensor) -> Tensor: if self.parallel_input: assert input_.shape[-1] == self.weight.shape[0], \ 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1]) + input_.shape, self.weight.shape, self.weight.shape[0]) input_ = input_ else: assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[0], \ 'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format( - input_.shape, self.weight.shape, self.weight.shape[-1] * self.num_partitions) + input_.shape, self.weight.shape, self.weight.shape[0] * self.num_partitions) input_ = split_forward_gather_backward(input_, dim=-1, process_group=self.process_group) if self.stream_chunk_num > 1: diff --git a/colossalai/tensor/d_tensor/api.py b/colossalai/tensor/d_tensor/api.py index 32182faf6981..9848e4ca423e 100644 --- a/colossalai/tensor/d_tensor/api.py +++ b/colossalai/tensor/d_tensor/api.py @@ -16,6 +16,11 @@ layout_converter = LayoutConverter() +def clear_layout_converter(): + global layout_converter + layout_converter.cached_solution.clear() + + def is_distributed_tensor(tensor: torch.Tensor) -> bool: """ Check whether the given tensor is a distributed tensor. diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index e447b700105e..fcde75abdedc 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -70,7 +70,8 @@ def data_gen_for_sequence_classification(): resid_pdrop=0, summary_first_dropout=0, hidden_dropout=0, - problem_type="single_label_classification") + problem_type="single_label_classification", + pad_token_id=50256) # register the following models model_zoo.register(name='transformers_gpt', diff --git a/tests/test_shardformer/test_model/test_pure_pipeline.py b/tests/test_shardformer/test_model/test_pure_pipeline.py index 576e6473bcca..31e76ef5107c 100644 --- a/tests/test_shardformer/test_model/test_pure_pipeline.py +++ b/tests/test_shardformer/test_model/test_pure_pipeline.py @@ -160,7 +160,6 @@ def check_llama(rank, world_size, port): run_llama_test() -@pytest.mark.skip('This test will fail') @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 99451b403eb7..eae4f2ffb799 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -1,85 +1,180 @@ +import copy +from contextlib import nullcontext + import pytest import torch +from torch import distributed as dist +from torch.optim import Adam import colossalai +from colossalai.booster import Booster +from colossalai.booster.plugin import HybridParallelPlugin +from colossalai.lazy.lazy_init import LazyInitContext from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, +from colossalai.tensor.d_tensor.api import ( + clear_layout_converter, + is_customized_distributed_tensor, + is_distributed_tensor, ) +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # check forward - org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, - output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) +def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): + + use_lazy_init = False + if 'use_lazy_init' in test_config: + use_lazy_init = test_config.pop('use_lazy_init') - # do backward + if use_lazy_init: + ctx = LazyInitContext() + else: + ctx = nullcontext() + + # prepare booster + plugin = HybridParallelPlugin(**test_config) + booster = Booster(plugin=plugin) + stage_manager = plugin.stage_manager + + # prepare models and optimizers + with ctx: + org_model = model_fn().cuda() + sharded_model = copy.deepcopy(org_model) + + if use_lazy_init: + org_model = ctx.materialize(org_model) + + org_optimizer = Adam(org_model.parameters(), lr=1e-3) + sharded_optimizer = Adam(sharded_model.parameters(), lr=1e-3) + criterion = loss_fn + + sharded_model, sharded_optimizer, criterion, _, _ = booster.boost(sharded_model, sharded_optimizer, criterion) + + def _criterion(outputs, inputs): + outputs = output_transform_fn(outputs) + loss = criterion(outputs) + return loss + + # do forward and backward + data = data_gen_fn() + sharded_model.train() + if stage_manager: + data = { + k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v + for k, v in data.items() + } + data_iter = iter([data]) + sharded_output = booster.execute_pipeline(data_iter, + sharded_model, + _criterion, + sharded_optimizer, + return_loss=True, + return_outputs=True) + sharded_loss = sharded_output['loss'] + else: + data = {k: v.cuda() for k, v in data.items()} + sharded_output = sharded_model(**data) + sharded_loss = criterion(sharded_output) + sharded_loss.backward() + + org_model.train() + org_output = org_model(**data) + org_loss = criterion(org_output) org_loss.backward() - shard_loss.backward() - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to origin model loss\n{org_loss}\n{shard_loss}" + if stage_manager is None or stage_manager.is_last_stage(): + + # check last hidden state + if org_model.__class__.__name__ == 'GPT2Model': + org_hidden_state = org_output.last_hidden_state + + if stage_manager is None: + sharded_hidden_state = sharded_output.last_hidden_state + + if stage_manager and stage_manager.is_last_stage(): + sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']], + dim=0) + + assert torch.allclose(org_hidden_state, sharded_hidden_state, atol=1e-5, rtol=1e-3), \ + f"shard model's output hidden state is not equal to origin model's last hidden state\n{org_hidden_state}\n{sharded_hidden_state}" + + # check loss + assert torch.allclose(org_loss, sharded_loss, atol=1e-5, rtol=1e-3), \ + f"shard model loss is not equal to origin model loss\n{org_loss}\n{sharded_loss}" # unwrap model if org_model.__class__.__name__ == 'GPT2Model': org_model = org_model - sharded_model = sharded_model + sharded_model = sharded_model.unwrap() else: org_model = org_model.transformer - sharded_model = sharded_model.transformer + sharded_model = sharded_model.unwrap().transformer - # check mlp grad - org_grad = org_model.h[0].mlp.c_fc.weight.grad - shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad - shard_weight = sharded_model.h[0].mlp.c_fc.weight + # check weights and gradients + if stage_manager is None or stage_manager.is_first_stage(): + + shard_weight = sharded_model.h[0].mlp.c_fc.weight + org_grad = org_model.h[0].mlp.c_fc.weight.grad + shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(plugin.tp_size)] + dist.all_gather(shard_grad_list, shard_grad, plugin.tp_group) + shard_grad = torch.cat(shard_grad_list, dim=1) + + assert torch.allclose(org_grad, shard_grad, atol=1e-5, rtol=1e-3), \ + f"shard model grad is not equal to origin model grad\n{org_grad}\n{shard_grad}" + + # check weights after optimizer.step() + org_optimizer.step() + sharded_optimizer.step() + if stage_manager is None or stage_manager.is_first_stage(): + + org_weight = org_model.h[0].mlp.c_fc.weight + shard_weight = sharded_model.h[0].mlp.c_fc.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_weight_list = [torch.zeros([*shard_weight.shape]).to('cuda') for _ in range(plugin.tp_size)] + dist.all_gather(shard_weight_list, shard_weight, plugin.tp_group) + shard_weight = torch.cat(shard_weight_list, dim=1) + + assert torch.allclose(org_weight, shard_weight, atol=5e-3, rtol=1e-3), \ + f"shard model weight is not equal to origin model weight\n{org_weight}\n{shard_weight}" - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=1) - else: - all_shard_grad = shard_grad - assert torch.allclose( - org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}" - - # check embedding weights - org_grad = org_model.wte.weight.grad - shard_grad = sharded_model.wte.weight.grad - shard_weight = sharded_model.wte.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose( - org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}" torch.cuda.empty_cache() -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('use_lazy_init', [False, True]) +@parameterize('test_config', [{ + 'tp_size': 1, + 'pp_size': 2, + 'num_microbatches': 4, + 'use_lazy_init': True +}, { + 'tp_size': 2, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': False, + 'use_lazy_init': False +}, { + 'tp_size': 4, + 'pp_size': 1, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}]) @clear_cache_before_run() -def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): +def run_gpt2_test(test_config): + + # TODO: add plugin_config for TP+DP after supporting & debugging it + # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} + sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') + test_config['precision'] = 'float' # Do not use fp16/bf16 in testing + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - use_lazy_init) - check_state_dict(org_model, sharded_model, name=name) - check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) + clear_layout_converter() torch.cuda.empty_cache() @@ -93,7 +188,7 @@ def check_gpt2(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_gpt2(): - spawn(check_gpt2, 2) + spawn(check_gpt2, 4) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py b/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py deleted file mode 100644 index d5453ee72644..000000000000 --- a/tests/test_shardformer/test_model/test_shard_gpt2_pipeline.py +++ /dev/null @@ -1,72 +0,0 @@ -import pytest -import torch - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_pipeline_model - - -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # TODO: add tests for forward/backward later - pass - - -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('enable_fused_normalization', [False]) -@parameterize('use_lazy_init', [False]) -#TODO: merge this into test_shard_gpt2 -def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - - sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') - for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): - inputs = data_gen_fn() - inputs = {k: v.cuda() for k, v in inputs.items()} - _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - input_ids = inputs['input_ids'] - batch_size, seq_len = input_ids.shape - hidden_size = sharded_model.config.n_embd - hidden_state_shape = (batch_size, seq_len, hidden_size) - - if not stage_manager.is_first_stage(): - # change inputs if not the first stage - hidden_states = torch.zeros(*hidden_state_shape).cuda() - inputs['input_ids'] = None - inputs['hidden_states'] = hidden_states - - sharded_model.train() - output = sharded_model(**inputs) - if stage_manager.is_last_stage(): - if name == 'transformers_gpt': - assert output[0].shape == hidden_state_shape - else: - assert output.loss is not None - else: - assert output['hidden_states'].shape == hidden_state_shape - - torch.cuda.empty_cache() - - -def check_gpt2(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_gpt2_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_gpt2(): - spawn(check_gpt2, 4) - - -if __name__ == "__main__": - test_gpt2() From c59d7aca095d205df404c0c6e831e7ae33b785f1 Mon Sep 17 00:00:00 2001 From: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Date: Fri, 7 Jul 2023 14:06:46 +0800 Subject: [PATCH 48/84] Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout --- colossalai/shardformer/policies/vit.py | 83 +++++++++++++------------- 1 file changed, 42 insertions(+), 41 deletions(-) diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index 47f2c58fc436..96f27de2a7c8 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -1,4 +1,3 @@ -from functools import partial from typing import Callable, Dict, List, Union import torch.nn as nn @@ -36,7 +35,7 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: suffix="dropout", target_module=col_nn.DropoutForReplicatedInput, ) - ]) + ]) policy[ViTLayer] = ModulePolicyDescription(attribute_replacement={ "attention.attention.num_attention_heads": @@ -44,45 +43,47 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: "attention.attention.all_head_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, }, - param_replacement=[], - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="attention.attention.query", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.key", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.value", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="attention.attention.dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="attention.output.dense", - target_module=col_nn.Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="attention.output.dropout", - target_module=col_nn.DropoutForReplicatedInput, - ), - SubModuleReplacementDescription( - suffix="intermediate.dense", - target_module=col_nn.Linear1D_Col, - ), - SubModuleReplacementDescription( - suffix="output.dense", - target_module=col_nn.Linear1D_Row, - ), - SubModuleReplacementDescription( - suffix="output.dropout", - target_module=col_nn.DropoutForReplicatedInput, - ), - ]) + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attention.attention.query", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.key", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.value", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="attention.output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="attention.output.dropout", + target_module=col_nn.DropoutForReplicatedInput, + ), + SubModuleReplacementDescription( + suffix="intermediate.dense", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="output.dropout", + target_module=col_nn.DropoutForReplicatedInput, + ), + ]) + + return policy return policy From dd2bf026797fb94d5120b481145f37a9661c4a6c Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Fri, 14 Jul 2023 15:56:59 +0800 Subject: [PATCH 49/84] [shardformer] support SAM (#4231) * 1.support sam 2.add fused qkv for nn.Linear * update utils support set element in list * overtwrite SamVisionAttention foward to use DropoutForParallelInput * remove unused code --- colossalai/shardformer/_utils.py | 44 +++- colossalai/shardformer/layer/__init__.py | 5 +- .../shardformer/layer/qkv_fused_linear.py | 175 ++++++++++++++- colossalai/shardformer/modeling/sam.py | 41 ++++ .../shardformer/policies/auto_policy.py | 4 + colossalai/shardformer/policies/sam.py | 209 ++++++++++++++++++ tests/kit/model_zoo/transformers/__init__.py | 1 + tests/kit/model_zoo/transformers/sam.py | 52 +++++ .../test_gpt2_qkv_fused_linear_1d.py | 120 ++++++++++ .../test_model/test_shard_sam.py | 92 ++++++++ 10 files changed, 733 insertions(+), 10 deletions(-) create mode 100644 colossalai/shardformer/modeling/sam.py create mode 100644 colossalai/shardformer/policies/sam.py create mode 100644 tests/kit/model_zoo/transformers/sam.py create mode 100644 tests/test_shardformer/test_layer/test_gpt2_qkv_fused_linear_1d.py create mode 100644 tests/test_shardformer/test_model/test_shard_sam.py diff --git a/colossalai/shardformer/_utils.py b/colossalai/shardformer/_utils.py index 4ad877e72357..c553080de0a0 100644 --- a/colossalai/shardformer/_utils.py +++ b/colossalai/shardformer/_utils.py @@ -1,25 +1,57 @@ import re -def get_obj_list_element(obj, a): +def get_obj_list_element(obj, attr: str): r""" Get the element of the list in the object + + If the attr is a normal attribute, return the attribute of the object. + If the attr is a index type, return the element of the index in the list, like `layers[0]`. + + Args: + obj (Object): The object to get + attr (str): The suffix of the attribute to get + """ re_pattern = r'\[\d+\]' prog = re.compile(re_pattern) - result = prog.search(a) + result = prog.search(attr) if result: matched_brackets = result.group() matched_index = matched_brackets.replace('[', '') matched_index = matched_index.replace(']', '') - a_ = a.replace(matched_brackets, '') - container_obj = getattr(obj, a_) + attr_ = attr.replace(matched_brackets, '') + container_obj = getattr(obj, attr_) obj = container_obj[int(matched_index)] else: - obj = getattr(obj, a) + obj = getattr(obj, attr) return obj +def set_obj_list_element(obj, attr: str, value): + r""" + Set the element to value of a list object + + It used like set_obj_list_element(obj, 'lyaers[0]', new_layer), it will set obj.layers[0] to value + + Args: + obj (object): The object to set + attr (str): the string including a list index like `layers[0]` + """ + re_pattern = r'\[\d+\]' + prog = re.compile(re_pattern) + result = prog.search(attr) + if result: + matched_brackets = result.group() + matched_index = matched_brackets.replace('[', '') + matched_index = matched_index.replace(']', '') + attr_ = attr.replace(matched_brackets, '') + container_obj = getattr(obj, attr_) + container_obj[int(matched_index)] = value + else: + setattr(obj, attr, value) + + def hasattr_(obj, attr: str): r""" Check whether the object has the multi sublevel attr @@ -56,7 +88,7 @@ def setattr_(obj, attr: str, value, ignore: bool = False): if ignore: return raise AttributeError(f"Object {obj.__class__.__name__} has no attribute {attr}") - setattr(obj, attrs[-1], value) + set_obj_list_element(obj, attrs[-1], value) def getattr_(obj, attr: str, ignore: bool = False): diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index 7cdcfc31811f..0c44e6621711 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -3,11 +3,10 @@ from .linear import Linear1D_Col, Linear1D_Row from .loss import cross_entropy_1d from .normalization import FusedLayerNorm, FusedRMSNorm -from .parallel_module import ParallelModule -from .qkv_fused_linear import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row +from .qkv_fused_linear import FusedLinear1D_Col, GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row __all__ = [ "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", 'GPT2FusedLinearConv1D_Col', 'GPT2FusedLinearConv1D_Row', 'DropoutForParallelInput', 'DropoutForReplicatedInput', "cross_entropy_1d", - 'FusedLayerNorm', 'FusedRMSNorm', 'ParallelModule' + 'FusedLayerNorm', 'FusedRMSNorm', 'FusedLinear1D_Col' ] diff --git a/colossalai/shardformer/layer/qkv_fused_linear.py b/colossalai/shardformer/layer/qkv_fused_linear.py index 3c47c0b1106f..1e4b6ecb69b3 100644 --- a/colossalai/shardformer/layer/qkv_fused_linear.py +++ b/colossalai/shardformer/layer/qkv_fused_linear.py @@ -25,6 +25,7 @@ from ._operation import ( gather_forward_split_backward, + linear_with_async_comm, matmul_with_async_comm, reduce_backward, reduce_forward, @@ -33,7 +34,7 @@ from .parallel_module import ParallelModule from .utils import create_randomizer_with_offset -__all__ = ['FusedLinear1D_Col', 'FusedLinear1D_Row'] +__all__ = ['FusedLinear1D_Col', 'FusedLinear1D_Row', 'GPT2FusedLinearConv1D_Col', 'GPT2FusedLinearConv1D_Row'] # ==================================== # For GPT Only @@ -490,3 +491,175 @@ def forward(self, input_: Tensor) -> Tensor: return output else: return output, self.bias + + +# ==================================== +# For Fused torch.nn.Linear +# ==================================== + + +class FusedLinear1D_Col(ParallelModule): + r"""Fused Linear layer with column parallelism. + + The linear layer is defined as :math:`Y = XA + b`. A is parallelized along + its second dimension as :math:`A = [A_1, ..., A_p]`. This layer is used to fit `torch.nn.Linear` layer (Fused QKV) in normal torch layer of huggingface, like SAM. + + Args: + in_features (int): size of each input sample. + out_features (int): size of each output sample. + bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``. + dtype (`torch.dtype`): The dtype of parameters, defaults to None. + device (`torch.device`): The device of parameters, defaults to None. + n_fused (int): The number items fused, defaults to 3 (QKV). + process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None. + gather_output (bool, optional): If true, call all-gather on output and make Y available + to all GPUs, otherwise, every GPU will have its output + which is :math:`Y_i = XA_i`, defaults to False + skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer, + which is preserved for kernel fusion, defaults to False + weight_initializer (`typing.Callable`): + The initializer of weight, defaults to kaiming uniform initializer. + bias_initializer (`typing.Callable`): + The initializer of bias, defaults to xavier uniform initializer. + + More details about ``initializer`` please refer to + `init `_. + """ + + def __init__(self, + in_features: int, + out_features: int, + bias: bool = True, + dtype: torch.dtype = None, + device: torch.device = None, + process_group: ProcessGroup = None, + async_communication: bool = False, + gather_output: bool = False, + skip_bias_add: bool = False, + n_fused: int = 3, + weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), + bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): + super().__init__() + + # Keep input parameters + self.in_features = in_features + self.out_features = out_features + self.gather_output = gather_output + self.skip_bias_add = skip_bias_add + self.device = device + self.n_fused = n_fused + self.process_group = process_group + self.async_communication = async_communication + + if skip_bias_add and not bias: + raise ValueError('cannot skip bias addition if bias is None') + + # Parameters. + # Initialize weight. + factory_kwargs = {'device': device, 'dtype': dtype} + weight = torch.empty(self.out_features, self.in_features, **factory_kwargs) + + def shard_fn(tensor): + return split_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, False) + + def gather_fn(tensor): + return gather_fused_qkv_in_gpt2_style(tensor, 3, self.process_group, False) + + with torch.no_grad(): + sharded_weight = distribute_tensor_with_customization(weight, shard_fn, gather_fn) + self.weight = customized_distributed_tensor_to_param(sharded_weight) + + if bias: + bias = torch.empty(self.out_features, **factory_kwargs) + + with torch.no_grad(): + sharded_bias = distribute_tensor_with_customization(bias, shard_fn, gather_fn) + self.bias = customized_distributed_tensor_to_param(sharded_bias) + else: + self.bias = None + + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + # init weights + self.reset_parameters(weight_initializer, bias_initializer) + + @staticmethod + def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], n_fused: int, + *args, **kwargs) -> ParallelModule: + r""" + Convert a fused `torch.nn.linear` layer to a parallelized linear layer. + + Args: + module (`nn.Linear`): The module to be converted. + process_group (`Union[ProcessGroup, List[ProcessGroup]]`): The process group to be used for weight sharding and communication. + n_fused (int): The number of layers to be fused. In common, Q,K,V are fused in one weight. + """ + # get the attributes + in_features = module.in_features + out_features = module.out_features + bias = module.bias is not None + device = module.weight.device + + # ensure only one process group is passed + if isinstance(process_group, (list, tuple)): + assert len(process_group) == 1, \ + f'Expected only one process group, got {len(process_group)}.' + process_group = process_group[0] + + linear_1d = FusedLinear1D_Col(in_features=in_features, + out_features=out_features, + bias=bias, + device=device, + process_group=process_group, + *args, + **kwargs) + + # TODO: copy the sharded weights + with torch.no_grad(): + sharded_weight = split_fused_qkv_in_gpt2_style(module.weight.data, + n_fused=n_fused, + process_group=process_group, + is_transposed=False) + linear_1d.weight.data.copy_(sharded_weight.data) + + if bias: + sharded_bias = split_fused_qkv_in_gpt2_style(module.bias.data, + n_fused=n_fused, + process_group=process_group, + is_transposed=False) + linear_1d.bias.data.copy_(sharded_bias.data) + + return linear_1d + + def reset_parameters(self, weight_initializer, bias_initializer) -> None: + with self.randomizer.fork_rng(enable_cpu=True): + fan_in, fan_out = self.in_features, self.out_features + weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) + if self.bias is not None: + bias_initializer(self.bias, fan_in=fan_in) + + def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]: + assert input_.shape[-1] == self.weight.shape[-1], \ + 'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format( + input_.shape, self.weight.shape, self.weight.shape[-1]) + # Set up backprop all-reduce. + # input_parallel = reduce_backward(input_, self.process_group) + input_parallel = input_ + + # Matrix multiply. + bias = self.bias if not self.skip_bias_add else None + + output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) + + if self.gather_output: + # All-gather across the partitions. + output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group) + else: + output = output_parallel + + if self.skip_bias_add: + return output, self.bias + else: + return output diff --git a/colossalai/shardformer/modeling/sam.py b/colossalai/shardformer/modeling/sam.py new file mode 100644 index 000000000000..00e2d744e219 --- /dev/null +++ b/colossalai/shardformer/modeling/sam.py @@ -0,0 +1,41 @@ +import torch +import torch.distributed as dist +from torch.distributed import ProcessGroup + + +def forward_fn(): + + def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, batch_size, nHead, height * width, channel) + qkv = (self.qkv(hidden_states).reshape(batch_size, height * width, 3, self.num_attention_heads, + -1).permute(2, 0, 3, 1, 4)) + # q, k, v with shape (batch_size * nHead, height * width, channel) + query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0) + + attn_weights = (query * self.scale) @ key.transpose(-2, -1) + + if self.use_rel_pos: + attn_weights = self.add_decomposed_rel_pos(attn_weights, query, self.rel_pos_h, self.rel_pos_w, + (height, width), (height, width)) + + attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype) + + # replace dropout process with added DropoutForParallelInput layer + # origin code: + # attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + attn_probs = self.dropout_layer(attn_weights) + + attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1) + attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + + if output_attentions: + outputs = (attn_output, attn_weights) + else: + outputs = (attn_output, None) + + return outputs + + return forward diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index d00a03c9237e..63ec8398fcee 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -104,6 +104,10 @@ class PolicyLocation: PolicyLocation(file_name="bloom", class_name="BloomForTokenClassificationPolicy"), "transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering": PolicyLocation(file_name="bloom", class_name="BloomForQuestionAnsweringPolicy"), + + # Sam + "transformers.models.sam.modeling_sam.SamModel": + PolicyLocation(file_name="sam", class_name="SamModelPolicy"), } diff --git a/colossalai/shardformer/policies/sam.py b/colossalai/shardformer/policies/sam.py new file mode 100644 index 000000000000..e75d63946260 --- /dev/null +++ b/colossalai/shardformer/policies/sam.py @@ -0,0 +1,209 @@ +import torch.nn as nn + +import colossalai.shardformer.layer as col_nn + +from .._utils import getattr_, setattr_ +from ..modeling.sam import forward_fn +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + +__all__ = ['SamPolicy', 'SamModelPolicy'] + + +class SamPolicy(Policy): + + def config_sanity_check(self): + pass + + def preprocess(self): + return self.model + + def module_policy(self): + from transformers.models.sam.modeling_sam import ( + SamFeedForward, + SamTwoWayAttentionBlock, + SamTwoWayTransformer, + SamVisionAttention, + SamVisionLayer, + ) + + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[SamVisionLayer] = ModulePolicyDescription(attribute_replacement={ + "attn.num_attention_heads": + self.model.config.vision_config.num_attention_heads // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attn.qkv", + target_module=col_nn.FusedLinear1D_Col, + kwargs={ + "n_fused": 3, + }, + ), + SubModuleReplacementDescription( + suffix="attn.proj", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="mlp.lin1", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.lin2", + target_module=col_nn.Linear1D_Row, + ) + ]) + policy[SamTwoWayAttentionBlock] = ModulePolicyDescription( + attribute_replacement={ + "self_attn.num_attention_heads": + self.model.config.mask_decoder_config.num_attention_heads // + self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attn.q_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.k_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.v_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.out_proj", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="cross_attn_token_to_image.q_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="cross_attn_token_to_image.k_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="cross_attn_token_to_image.v_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="cross_attn_token_to_image.out_proj", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="mlp.lin1", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.lin2", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="cross_attn_image_to_token.q_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="cross_attn_image_to_token.k_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="cross_attn_image_to_token.v_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="cross_attn_image_to_token.out_proj", + target_module=col_nn.Linear1D_Row, + ), + ]) + policy[SamTwoWayTransformer] = ModulePolicyDescription(attribute_replacement={ + "final_attn_token_to_image.num_attention_heads": + self.model.config.mask_decoder_config.num_attention_heads // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="final_attn_token_to_image.q_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="final_attn_token_to_image.k_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="final_attn_token_to_image.v_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="final_attn_token_to_image.out_proj", + target_module=col_nn.Linear1D_Row, + ) + ]) + + # add `DropoutForParallelInput` layer to replace the useage of `nn.functional.dropout` + policy[SamVisionAttention] = ModulePolicyDescription(attribute_replacement={ + "dropout_layer": col_nn.DropoutForParallelInput(self.model.config.vision_config.attention_dropout) + }, + method_replacement={"forward": forward_fn()}, + sub_module_replacement=[]) + + # optimization configuration + if self.shard_config.enable_fused_normalization: + # Handle SamVisionLayer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="layer_norm1", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="layer_norm2", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=SamVisionLayer) + + # Handle SamTwoWayAttentionBlock + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="layer_norm1", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="layer_norm2", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="layer_norm3", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="layer_norm4", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=SamTwoWayAttentionBlock) + + # Handle SamTwoWayTransformer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="layer_norm_final_attn", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=SamTwoWayTransformer) + + return policy + + def postprocess(self): + return self.model + + +# SamModel +class SamModelPolicy(SamPolicy): + + def __init__(self) -> None: + super().__init__() diff --git a/tests/kit/model_zoo/transformers/__init__.py b/tests/kit/model_zoo/transformers/__init__.py index a298767d12e7..a1bcb78ddf6b 100644 --- a/tests/kit/model_zoo/transformers/__init__.py +++ b/tests/kit/model_zoo/transformers/__init__.py @@ -4,5 +4,6 @@ from .gpt import * from .llama import * from .opt import * +from .sam import * from .t5 import * from .vit import * diff --git a/tests/kit/model_zoo/transformers/sam.py b/tests/kit/model_zoo/transformers/sam.py new file mode 100644 index 000000000000..d850623f368f --- /dev/null +++ b/tests/kit/model_zoo/transformers/sam.py @@ -0,0 +1,52 @@ +import torch +import transformers + +from ..registry import ModelAttribute, model_zoo + +# =============================== +# Register single-image SAM +# =============================== + + +# define data gen function +def data_gen(): + # Generated from following code snippet + # + # from PIL import Image + # import requests + # from transformers import SamModel, SamProcessor + # + # model = SamModel.from_pretrained("facebook/sam-vit-base") + # processor = SamProcessor.from_pretrained("facebook/sam-vit-base") + # + # img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" + # raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") + # input_points = [[[450, 600]]] # 2D localization of a window + # inputs = processor(raw_image, input_points=input_points, return_tensors="pt") + + pixel_values = torch.rand(1, 3, 1024, 1024, dtype=torch.float32) + original_sizes = torch.tensor([[1764, 2646]], dtype=torch.int64) + reshaped_input_sizes = torch.tensor([[683, 1024]], dtype=torch.int64) + input_points = torch.tensor([[[[174.1497, 232.3129]]]], dtype=torch.float64) + return dict(pixel_values=pixel_values, + original_sizes=original_sizes, + reshaped_input_sizes=reshaped_input_sizes, + input_points=input_points) + + +# define output transform function +output_transform_fn = lambda x: x + +# define loss funciton +loss_fn = lambda x: x.iou_scores.mean() + +config = transformers.SamConfig() +config.vision_config.num_hidden_layers = 2 + +# register the BERT variants +model_zoo.register(name='transformers_sam', + model_fn=lambda: transformers.SamModel(config), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_shardformer/test_layer/test_gpt2_qkv_fused_linear_1d.py b/tests/test_shardformer/test_layer/test_gpt2_qkv_fused_linear_1d.py new file mode 100644 index 000000000000..9eeda93afe35 --- /dev/null +++ b/tests/test_shardformer/test_layer/test_gpt2_qkv_fused_linear_1d.py @@ -0,0 +1,120 @@ +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.testing import assert_close + +import colossalai +from colossalai.shardformer.layer import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row +from colossalai.shardformer.layer.qkv_fused_linear import split_fused_qkv_in_gpt2_style +from colossalai.testing import rerun_if_address_is_in_use, spawn + + +# This code is copied from https://github.com/huggingface/transformers +class Conv1D(nn.Module): + """ + 1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2). + + Basically works like a linear layer but the weights are transposed. + + Args: + nf (`int`): The number of output features. + nx (`int`): The number of input features. + """ + + def __init__(self, nf, nx): + super().__init__() + self.nf = nf + self.weight = nn.Parameter(torch.empty(nx, nf)) + self.bias = nn.Parameter(torch.zeros(nf)) + nn.init.normal_(self.weight, std=0.02) + + def forward(self, x): + size_out = x.size()[:-1] + (self.nf,) + x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight) + x = x.view(size_out) + return x + + +def rearrange(tensor: torch.Tensor, dim: int): + tensor = tensor.clone() + world_size = 2 + order = torch.arange(world_size * 3) + new_order = [] + for i in range(world_size): + new_order.append(order[i::world_size]) + new_order = torch.cat(new_order) + + tensor_chunks = torch.chunk(tensor, world_size * 3, dim=dim) + rearanged_tensor_chunks = [tensor_chunks[i] for i in new_order] + rearanged_tensor = torch.cat(rearanged_tensor_chunks, dim=dim) + return rearanged_tensor + + +def check_gpt2_linear_conv_1d_col(): + linear = Conv1D(192, 48).cuda() + linear_conv_col = GPT2FusedLinearConv1D_Col.from_native_module(linear, + process_group=None, + gather_output=True, + n_fused=3) + + assert linear.weight.shape == torch.Size([48, 192]) + assert linear.bias.shape == torch.Size([192]) + assert linear_conv_col.weight.shape == torch.Size([48, 96]) + assert linear_conv_col.bias.shape == torch.Size([96]) + + # ensure weights are reversibly loadable + linear_conv_col.load_state_dict(linear.state_dict()) + linear.load_state_dict(linear_conv_col.state_dict()) + + # check computation correctness + x = torch.rand(4, 48).cuda() + out = linear(x) + gather_out = linear_conv_col(x) + assert_close(rearrange(out, 1), gather_out) + + # check backward correctness + out.sum().backward() + gather_out.sum().backward() + + target_grad = split_fused_qkv_in_gpt2_style(linear.weight.grad, 3, None, True) + assert_close(target_grad, linear_conv_col.weight.grad) + + +def check_gpt2_linear_conv_1d_row(): + linear = Conv1D(192, 48).cuda() + linear_row = GPT2FusedLinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) + + assert linear.weight.shape == torch.Size([48, 192]) + assert linear_row.weight.shape == torch.Size([24, 192]) + assert linear_row.bias.shape == torch.Size([192]) + + # check computation correctness + x = torch.rand(4, 48).cuda() + out = linear(x) + gather_out = linear_row(x) + assert_close(out, gather_out) + + # check backward correctness + out.sum().backward() + gather_out.sum().backward() + + rank = dist.get_rank() + target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank] + assert_close(target_grad, linear_row.weight.grad) + + +def run_dist(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + + # test for linear conv + check_gpt2_linear_conv_1d_col() + check_gpt2_linear_conv_1d_row() + + +@rerun_if_address_is_in_use() +def test_gpt2_linearconv(): + spawn(run_dist, nprocs=2) + + +if __name__ == '__main__': + test_gpt2_linearconv() diff --git a/tests/test_shardformer/test_model/test_shard_sam.py b/tests/test_shardformer/test_model/test_shard_sam.py new file mode 100644 index 000000000000..1d047d8e0c42 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_sam.py @@ -0,0 +1,92 @@ +import pytest +import torch + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output, ignore_keys=['pred_masks']) + + # do backward + org_loss.backward() + shard_loss.backward() + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # check grad + + sam = org_model + sharded_sam = sharded_model + + # compare mask decoder grad + + org_grad = sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight.grad + shard_grad = sharded_sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight.grad + shard_weight = sharded_sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + # compare vision_encoder grad + org_grad = sam.vision_encoder.layers[0].mlp.lin1.weight.grad + shard_grad = sharded_sam.vision_encoder.layers[0].mlp.lin1.weight.grad + shard_weight = sharded_sam.vision_encoder.layers[0].mlp.lin1.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_sam_test(enable_fused_normalization, enable_tensor_parallelism): + sub_model_zoo = model_zoo.get_sub_registry('transformers_sam') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + + torch.cuda.empty_cache() + + +def check_sam(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_sam_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_sam(): + spawn(check_sam, 2) + + +if __name__ == "__main__": + test_sam() From 9ee4ebea83b483cf95c6c4924621e89860cc6fd5 Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Mon, 17 Jul 2023 14:25:32 +0800 Subject: [PATCH 50/84] [shardformer] support whisper (#4212) * support whisper * fix bug in vocabembedding * support downstream model of whisper * update readme --- colossalai/shardformer/README.md | 2 +- colossalai/shardformer/layer/embedding.py | 10 +- .../shardformer/policies/auto_policy.py | 8 + colossalai/shardformer/policies/whisper.py | 232 ++++++++++++++++++ tests/kit/model_zoo/transformers/__init__.py | 1 + tests/kit/model_zoo/transformers/whisper.py | 91 +++++++ .../test_model/test_shard_whisper.py | 101 ++++++++ 7 files changed, 443 insertions(+), 2 deletions(-) create mode 100644 colossalai/shardformer/policies/whisper.py create mode 100644 tests/kit/model_zoo/transformers/whisper.py create mode 100644 tests/test_shardformer/test_model/test_shard_whisper.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index bf4215c52980..3c322aabf2ef 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -102,7 +102,7 @@ We will follow this roadmap to develop Shardformer: - [ ] SwinTransformer - [ ] SwinTransformer V2 - [ ] Audio - - [ ] Whisper + - [x] Whisper - [ ] Multi-modal - [ ] To be added diff --git a/colossalai/shardformer/layer/embedding.py b/colossalai/shardformer/layer/embedding.py index 09b22abb17cc..f07a93bd6908 100644 --- a/colossalai/shardformer/layer/embedding.py +++ b/colossalai/shardformer/layer/embedding.py @@ -202,7 +202,6 @@ def __init__(self, super().__init__() self.num_embeddings = num_embeddings self.embedding_dim = embedding_dim - self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs self.process_group = process_group @@ -276,6 +275,15 @@ def _fill_padding_idx_with_zero(self) -> None: with torch.no_grad(): self.weight[self.padding_idx - self.vocab_start_index].fill_(0) + def _select_padding_idx(self, padding_idx: int): + # select padding index according to the rank + if padding_idx is None: + return None + elif padding_idx < self.vocab_end_index and padding_idx >= self.vocab_start_index: + return padding_idx - self.vocab_start_index + else: + return None + def forward(self, input_: Tensor) -> Tensor: # Build the mask. input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index 63ec8398fcee..90347a984599 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -105,6 +105,14 @@ class PolicyLocation: "transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering": PolicyLocation(file_name="bloom", class_name="BloomForQuestionAnsweringPolicy"), + # Whisper + "transformers.models.whisper.modeling_whisper.WhisperModel": + PolicyLocation(file_name="whisper", class_name="WhisperModelPolicy"), + "transformers.models.whisper.modeling_whisper.WhisperForConditionalGeneration": + PolicyLocation(file_name="whisper", class_name="WhisperForConditionalGenerationPolicy"), + "transformers.models.whisper.modeling_whisper.WhisperForAudioClassification": + PolicyLocation(file_name="whisper", class_name="WhisperForAudioClassificationPolicy"), + # Sam "transformers.models.sam.modeling_sam.SamModel": PolicyLocation(file_name="sam", class_name="SamModelPolicy"), diff --git a/colossalai/shardformer/policies/whisper.py b/colossalai/shardformer/policies/whisper.py new file mode 100644 index 000000000000..7751bbb5de99 --- /dev/null +++ b/colossalai/shardformer/policies/whisper.py @@ -0,0 +1,232 @@ +import torch.nn as nn + +import colossalai.shardformer.layer as col_nn + +from .._utils import getattr_, setattr_ +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + +__all__ = [ + 'WhisperPolicy', 'WhisperModelPolicy', 'WhisperForConditionalGenerationPolicy', 'WhisperForAudioClassification' +] + + +class WhisperPolicy(Policy): + + def config_sanity_check(self): + pass + + def preprocess(self): + # reshape the embedding layer + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + # TODO: + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + return self.model + + def module_policy(self): + from transformers.models.whisper.modeling_whisper import ( + WhisperDecoder, + WhisperDecoderLayer, + WhisperEncoder, + WhisperEncoderLayer, + ) + + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[WhisperEncoderLayer] = ModulePolicyDescription(attribute_replacement={ + "self_attn.embed_dim": + self.model.config.d_model // self.shard_config.tensor_parallel_size, + "self_attn.num_heads": + self.model.config.encoder_attention_heads // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attn.q_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.k_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.v_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.out_proj", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="fc1", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="fc2", + target_module=col_nn.Linear1D_Row, + ), + ]) + + policy[WhisperDecoderLayer] = ModulePolicyDescription(attribute_replacement={ + "self_attn.embed_dim": + self.model.config.d_model // self.shard_config.tensor_parallel_size, + "self_attn.num_heads": + self.model.config.decoder_attention_heads // self.shard_config.tensor_parallel_size, + "encoder_attn.embed_dim": + self.model.config.d_model // self.shard_config.tensor_parallel_size, + "encoder_attn.num_heads": + self.model.config.encoder_attention_heads // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attn.q_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.k_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.v_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.out_proj", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="encoder_attn.q_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="encoder_attn.k_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="encoder_attn.v_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="encoder_attn.out_proj", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="fc1", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="fc2", + target_module=col_nn.Linear1D_Row, + ), + ]) + + policy[WhisperDecoder] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="embed_tokens", + target_module=col_nn.VocabParallelEmbedding1D, + ), + ]) + + # optimization configuration + if self.shard_config.enable_fused_normalization: + # Handle encoder layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="self_attn_layer_norm", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="final_layer_norm", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=WhisperEncoderLayer) + + # Handle decoder layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="self_attn_layer_norm", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="final_layer_norm", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=WhisperDecoderLayer) + + # handle encoder layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="layer_norm", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=WhisperEncoder) + + # handle decoder layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="layer_norm", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=WhisperDecoder) + return policy + + def add_lm_head_policy(self, base_policy): + from transformers.models.whisper.modeling_whisper import WhisperForConditionalGeneration + + # optimize for tensor parallelism + if self.shard_config.enable_tensor_parallelism: + self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( + suffix="proj_out", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), + policy=base_policy, + target_key=WhisperForConditionalGeneration) + + return base_policy + + def postprocess(self): + return self.model + + +# WhisperModel +class WhisperModelPolicy(WhisperPolicy): + + def __init__(self) -> None: + super().__init__() + + +# WhisperForConditionalGeneration +class WhisperForConditionalGenerationPolicy(WhisperPolicy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + module_policy = super().module_policy() + module_policy = self.add_lm_head_policy(module_policy) + return module_policy + + def postprocess(self): + binding_map = {"model.decoder.embed_tokens.weight": "proj_out.weight"} + for k, v in binding_map.items(): + param = getattr_(self.model, k) + setattr_(self.model, v, param) + return self.model + + +# WhisperForAudioClassification +class WhisperForAudioClassificationPolicy(WhisperPolicy): + + def __init__(self) -> None: + super().__init__() diff --git a/tests/kit/model_zoo/transformers/__init__.py b/tests/kit/model_zoo/transformers/__init__.py index a1bcb78ddf6b..39e5ef411f32 100644 --- a/tests/kit/model_zoo/transformers/__init__.py +++ b/tests/kit/model_zoo/transformers/__init__.py @@ -7,3 +7,4 @@ from .sam import * from .t5 import * from .vit import * +from .whisper import * diff --git a/tests/kit/model_zoo/transformers/whisper.py b/tests/kit/model_zoo/transformers/whisper.py new file mode 100644 index 000000000000..b58716217cb5 --- /dev/null +++ b/tests/kit/model_zoo/transformers/whisper.py @@ -0,0 +1,91 @@ +import torch +import transformers + +from ..registry import ModelAttribute, model_zoo + +# =============================== +# Register single-sentence Whisper +# =============================== + + +# define data gen function +def data_gen(): + # Generated from following code snippet + # + # from transformers import AutoFeatureExtractor, WhisperModel + # from datasets import load_dataset + + # model = WhisperModel.from_pretrained("openai/whisper-base") + # feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base") + # ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + # inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") + # input_features = inputs.input_features + # decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id + + input_features = torch.randn(1, 80, 3000) + decoder_input_ids = torch.tensor([[1, 1]]) * 50258 + return dict(input_features=input_features, decoder_input_ids=decoder_input_ids) + + +def data_gen_for_conditional_generation(): + # labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + # Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` + # or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is + # only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + data = data_gen() + data['labels'] = torch.tensor([[0, 1]], dtype=torch.int64) + return data + + +def data_gen_for_audio_classification(): + # labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + # Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + # config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + # `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + # `WhisperForAudioClassification` does not need `decoder_input_ids` + data = data_gen() + data.pop('decoder_input_ids') + data['labels'] = torch.tensor([1], dtype=torch.int64) + return data + + +# define output transform function +output_transform_fn = lambda x: x + +# define loss funciton +loss_fn = lambda x: x.last_hidden_state.mean() +loss_fn_attr = lambda x: x.loss + +config = transformers.WhisperConfig( + classifier_proj_size=256, + d_model=256, + decoder_attention_heads=4, + decoder_ffn_dim=1536, + decoder_layers=2, + encoder_attention_heads=4, + encoder_ffn_dim=1536, + encoder_layers=2, + vocab_size=51866, +) + +# register the Whisper variants +model_zoo.register(name='transformers_whisper', + model_fn=lambda: transformers.WhisperModel(config), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn, + model_attribute=ModelAttribute(has_control_flow=True)) + +model_zoo.register(name='transformers_whisperForConditionalGeneration', + model_fn=lambda: transformers.WhisperForConditionalGeneration(config), + data_gen_fn=data_gen_for_conditional_generation, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_attr, + model_attribute=ModelAttribute(has_control_flow=True)) + +model_zoo.register(name='transformers_whisperWhisperForAudioClassification', + model_fn=lambda: transformers.WhisperForAudioClassification(config), + data_gen_fn=data_gen_for_audio_classification, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_attr, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_shardformer/test_model/test_shard_whisper.py b/tests/test_shardformer/test_model/test_shard_whisper.py new file mode 100644 index 000000000000..8932a4ab902c --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_whisper.py @@ -0,0 +1,101 @@ +import pytest +import torch + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output, ignore_keys='past_key_values') + + # do backward + org_loss.backward() + shard_loss.backward() + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # check grad + + if org_model.__class__.__name__ == 'WhisperForConditionalGeneration': + whisper = org_model.model + sharded_whisper = sharded_model.model + else: + whisper = org_model + sharded_whisper = sharded_model + + # compare self attention grad + org_grad = whisper.encoder.layers[0].self_attn.q_proj.weight.grad + shard_grad = sharded_whisper.encoder.layers[0].self_attn.q_proj.weight.grad + shard_weight = sharded_whisper.encoder.layers[0].self_attn.q_proj.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + # WhisperForAudioClassification does not have decoder and embedding layer + if org_model.__class__.__name__ == 'WhisperForAudioClassification': + return + + # compare embedding grad + org_grad = whisper.decoder.embed_tokens.weight.grad + shard_grad = sharded_whisper.decoder.embed_tokens.weight.grad + shard_weight = sharded_whisper.decoder.embed_tokens.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_whisper_test(enable_fused_normalization, enable_tensor_parallelism): + sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + org_model, sharded_model = build_model(model_fn, + enable_fused_normalization=enable_fused_normalization, + enable_tensor_parallelism=enable_tensor_parallelism) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + + torch.cuda.empty_cache() + + +def check_whisper(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_whisper_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_whisper(): + spawn(check_whisper, 2) + + +if __name__ == "__main__": + test_whisper() From ed34bb13109c745ec6d9f7149c812b4851dafe6b Mon Sep 17 00:00:00 2001 From: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Date: Thu, 20 Jul 2023 17:28:00 +0800 Subject: [PATCH 51/84] Feature/chatglm (#4240) * [shardformer] added tests * [shardformer] vit test finish and support * [shardformer] chatglm ready * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] chatglm shard without mlp sharding * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] fix chatglm configuration with pre-commit --- colossalai/shardformer/policies/chatglm.py | 96 ++ tests/kit/model_zoo/transformers/__init__.py | 1 + tests/kit/model_zoo/transformers/chatglm.py | 38 + .../chatglm2_6b/configuration_chatglm.py | 58 + .../chatglm2_6b/modeling_chatglm.py | 1372 +++++++++++++++++ .../test_model/test_shard_chatglm.py | 107 ++ 6 files changed, 1672 insertions(+) create mode 100644 colossalai/shardformer/policies/chatglm.py create mode 100644 tests/kit/model_zoo/transformers/chatglm.py create mode 100644 tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py create mode 100644 tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py create mode 100644 tests/test_shardformer/test_model/test_shard_chatglm.py diff --git a/colossalai/shardformer/policies/chatglm.py b/colossalai/shardformer/policies/chatglm.py new file mode 100644 index 000000000000..934b99b83ea1 --- /dev/null +++ b/colossalai/shardformer/policies/chatglm.py @@ -0,0 +1,96 @@ +from typing import Dict, Union + +import torch.nn as nn + +import colossalai.shardformer.layer as col_nn + +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + +__all__ = ['ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy'] + + +class ChatGLMModelPolicy(Policy): + + def config_sanity_check(self): + pass + + def preprocess(self): + # Resize embedding + vocab_size = self.model.config.padded_vocab_size + world_size = self.shard_config.tensor_parallel_size + + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + + return self.model + + def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: + from tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock + + policy = {} + + if self.shard_config.enable_tensor_parallelism: + + policy[ChatGLMModel] = ModulePolicyDescription(attribute_replacement={}, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="embedding.word_embeddings", + target_module=col_nn.VocabParallelEmbedding1D, + ) + ]) + + policy[GLMBlock] = ModulePolicyDescription(attribute_replacement={ + "self_attention.num_attention_heads_per_partition": + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + "self_attention.projection_size": + (self.model.config.kv_channels * self.model.config.num_attention_heads) // + self.shard_config.tensor_parallel_size, + "self_attention.qkv_hidden_size": + (self.model.config.kv_channels * self.model.config.num_attention_heads * 3) // + self.shard_config.tensor_parallel_size, + "self_attention.core_attention.num_attention_heads_per_partition": + self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, + "self_attention.core_attention.hidden_size_per_partition": + self.model.config.kv_channels * self.model.config.num_attention_heads // + self.shard_config.tensor_parallel_size, + }, + param_replacement=[], + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attention.query_key_value", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attention.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="self_attention.core_attention.attention_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + ]) + # optimization configuration + if self.shard_config.enable_fused_normalization: + if not self.model.config.rmsnorm: + + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription(suffix="input_layernorm", target_module=col_nn.FusedLayerNorm), + SubModuleReplacementDescription(suffix="post_attention_layernorm", + target_module=col_nn.FusedLayerNorm) + ], + policy=policy, + target_key=GLMBlock) + + if self.model.config.post_layer_norm: + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription(suffix="encoder.final_layernorm", + target_module=col_nn.FusedLayerNorm) + ], + policy=policy, + target_key=ChatGLMModel) + + return policy + + def postprocess(self): + return self.model diff --git a/tests/kit/model_zoo/transformers/__init__.py b/tests/kit/model_zoo/transformers/__init__.py index 39e5ef411f32..08a118e5783d 100644 --- a/tests/kit/model_zoo/transformers/__init__.py +++ b/tests/kit/model_zoo/transformers/__init__.py @@ -1,6 +1,7 @@ from .albert import * from .bert import * from .bloom import * +from .chatglm import * from .gpt import * from .llama import * from .opt import * diff --git a/tests/kit/model_zoo/transformers/chatglm.py b/tests/kit/model_zoo/transformers/chatglm.py new file mode 100644 index 000000000000..1408babede64 --- /dev/null +++ b/tests/kit/model_zoo/transformers/chatglm.py @@ -0,0 +1,38 @@ +import torch +import transformers + +from ..registry import ModelAttribute, model_zoo +from .chatglm2_6b.configuration_chatglm import ChatGLMConfig +from .chatglm2_6b.modeling_chatglm import ChatGLMModel + +# ================================ +# Register single-sentence ChatGLM +# ================================ + + +def data_gen(): + input_ids = torch.tensor([[5941, 15, 2670, 3543, 632, 2075]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]]) + return dict(input_ids=input_ids, attention_mask=attention_mask) + + +# define output transform function +output_transform_fn = lambda x: x + +# define loss function +loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.mean() +loss_fn = lambda x: x.loss +config = ChatGLMConfig(num_layers=1, + padded_vocab_size=65024, + hidden_size=64, + num_attention_heads=8, + rmsnorm=False, + original_rope=True, + use_cache=True) + +model_zoo.register(name='transformers_chatglm', + model_fn=lambda: ChatGLMModel(config, empty_init=False), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_for_chatglm_model, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py new file mode 100644 index 000000000000..3e78732be2da --- /dev/null +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py @@ -0,0 +1,58 @@ +from transformers import PretrainedConfig + + +class ChatGLMConfig(PretrainedConfig): + model_type = "chatglm" + + def __init__(self, + num_layers=28, + padded_vocab_size=65024, + hidden_size=4096, + ffn_hidden_size=13696, + kv_channels=128, + num_attention_heads=32, + seq_length=2048, + hidden_dropout=0.0, + attention_dropout=0.0, + layernorm_epsilon=1e-5, + rmsnorm=True, + apply_residual_connection_post_layernorm=False, + post_layer_norm=True, + add_bias_linear=False, + add_qkv_bias=False, + bias_dropout_fusion=True, + multi_query_attention=False, + multi_query_group_num=1, + apply_query_key_layer_scaling=True, + attention_softmax_in_fp32=True, + fp32_residual_connection=False, + quantization_bit=0, + pre_seq_len=None, + prefix_projection=False, + **kwargs): + self.num_layers = num_layers + self.vocab_size = padded_vocab_size + self.padded_vocab_size = padded_vocab_size + self.hidden_size = hidden_size + self.ffn_hidden_size = ffn_hidden_size + self.kv_channels = kv_channels + self.num_attention_heads = num_attention_heads + self.seq_length = seq_length + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + self.layernorm_epsilon = layernorm_epsilon + self.rmsnorm = rmsnorm + self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm + self.post_layer_norm = post_layer_norm + self.add_bias_linear = add_bias_linear + self.add_qkv_bias = add_qkv_bias + self.bias_dropout_fusion = bias_dropout_fusion + self.multi_query_attention = multi_query_attention + self.multi_query_group_num = multi_query_group_num + self.apply_query_key_layer_scaling = apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = attention_softmax_in_fp32 + self.fp32_residual_connection = fp32_residual_connection + self.quantization_bit = quantization_bit + self.pre_seq_len = pre_seq_len + self.prefix_projection = prefix_projection + super().__init__(**kwargs) diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py new file mode 100644 index 000000000000..bae6d425878d --- /dev/null +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py @@ -0,0 +1,1372 @@ +""" +The ChatGLM2-6B License + +1. Definitions + +“Licensor” means the ChatGLM2-6B Model Team that distributes its Software. + +“Software” means the ChatGLM2-6B model parameters made available under this license. + +2. License Grant + +Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license to use the Software solely for your non-commercial research purposes. + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +3. Restriction + +You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any commercial, military, or illegal purposes. + +You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings. + +4. Disclaimer + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +5. Limitation of Liability + +EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. + +6. Dispute Resolution + +This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing. + +Note that the license is subject to update to a more comprehensive version. For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com. +""" +""" PyTorch ChatGLM model. """ + +import copy +import math +import re +import sys +import warnings +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn.utils import skip_init +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import GenerationConfig, LogitsProcessorList, ModelOutput, StoppingCriteriaList +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +from .configuration_chatglm import ChatGLMConfig + +# flags required to enable jit fusion kernels + +if sys.platform != "darwin": + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm2-6b", + # See all ChatGLM models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config: ChatGLMConfig): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + kv_size = (config.num_layers * config.kv_channels * config.multi_query_group_num * 2) + self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(kv_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, kv_size), + ) + else: + self.embedding = torch.nn.Embedding( + config.pre_seq_len, + config.num_layers * config.kv_channels * config.multi_query_group_num * 2, + ) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000**(torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_impl( + self, + seq_len: int, + n_elem: int, + dtype: torch.dtype, + device: torch.device, + base: int = 10000, + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base**(torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=dtype, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + return self.forward_impl( + max_seq_len, + self.dim, + dtype=self.inv_freq.dtype, + device=self.inv_freq.device, + ) + + +@torch.jit.script +def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + + return (self.weight * hidden_states).to(input_dtype) + + +class CoreAttention(torch.nn.Module): + + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = (projection_size // config.num_attention_heads) + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split(".")[0]) + if pytorch_major_version >= 2: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, + key_layer, + value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = ( + query_layer.size(1), + query_layer.size(2), + query_layer.size(0), + key_layer.size(0), + ) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], + output_size[2], + output_size[3], + dtype=query_layer.dtype, + device=query_layer.device, + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if (attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]): + attention_mask = torch.ones( + output_size[0], + 1, + output_size[2], + output_size[3], + device=attention_scores.device, + dtype=torch.bool, + ) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = ( + value_layer.size(1), + value_layer.size(2), + query_layer.size(0), + value_layer.size(3), + ) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + # Per attention head and per partition values. + self.hidden_size_per_attention_head = (self.projection_size // config.num_attention_heads) + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = (self.projection_size + + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num) + self.query_key_value = nn.Linear( + config.hidden_size, + self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, + **_config_to_kwargs(config), + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear( + self.projection_size, + config.hidden_size, + bias=config.add_bias_linear, + device=device, + **_config_to_kwargs(config), + ) + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=None, + use_cache=True, + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view(query_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, + self.hidden_size_per_attention_head, + )) + key_layer = key_layer.view(key_layer.size()[:-1] + ( + self.num_multi_query_groups_per_partition, + self.hidden_size_per_attention_head, + )) + value_layer = value_layer.view(value_layer.size()[:-1] + ( + self.num_multi_query_groups_per_partition, + self.hidden_size_per_attention_head, + )) + else: + new_tensor_shape = mixed_x_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head, + ) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + if use_cache: + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, + -1, + -1, + self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, + -1, + ) + key_layer = key_layer.contiguous().view(key_layer.size()[:2] + ( + self.num_attention_heads_per_partition, + self.hidden_size_per_attention_head, + )) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, + -1, + -1, + self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, + -1, + ) + value_layer = value_layer.contiguous().view(value_layer.size()[:2] + ( + self.num_attention_heads_per_partition, + self.hidden_size_per_attention_head, + )) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config), + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config), + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = (config.apply_residual_connection_post_layernorm) + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc( + config.hidden_size, + eps=config.layernorm_epsilon, + device=device, + dtype=config.torch_dtype, + ) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc( + config.hidden_size, + eps=config.layernorm_epsilon, + device=device, + dtype=config.torch_dtype, + ) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=None, + use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache, + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc( + config.hidden_size, + eps=config.layernorm_epsilon, + device=device, + dtype=config.torch_dtype, + ) + + self.gradient_checkpointing = False + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches[index], + use_cache, + ) + else: + layer_ret = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache, + ) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat( + ( + torch.ones(batch_size, seq_length, past_length, device=input_ids.device), + full_attention_mask, + ), + dim=-1, + ) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = (torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, GLMTransformer): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device, + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + self.num_layers = config.num_layers + self.multi_query_group_num = config.multi_query_group_num + self.kv_channels = config.kv_channels + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = (config.hidden_size // + config.num_attention_heads if config.kv_channels is None else config.kv_channels) + + self.rotary_pos_emb = RotaryEmbedding( + rotary_dim // 2, + original_impl=config.original_rope, + device=device, + dtype=config.torch_dtype, + ) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method( + nn.Linear, + config.hidden_size, + config.padded_vocab_size, + bias=False, + dtype=config.torch_dtype, + **init_kwargs, + ) + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + def get_input_embeddings(self): + return self.embedding.word_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = (self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.multi_query_group_num, + self.kv_channels, + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + return past_key_values + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = (return_dict if return_dict is not None else self.config.use_return_dict) + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt( + batch_size=batch_size, + device=input_ids.device, + dtype=inputs_embeds.dtype, + ) + if attention_mask is not None: + attention_mask = torch.cat( + [ + attention_mask.new_ones((batch_size, self.pre_seq_len)), + attention_mask, + ], + dim=-1, + ) + + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, + full_attention_mask, + rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + ) + + if not return_dict: + return tuple(v for v in [ + hidden_states, + presents, + all_hidden_states, + all_self_attentions, + ] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + from .quantization import quantize + + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], + dim=-1, + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat([position_ids, new_position_id], dim=-1) + + model_kwargs["is_first_forward"] = False + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + is_first_forward: bool = True, + **kwargs, + ) -> dict: + # only last token for input_ids if past is not None + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + if not is_first_forward: + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask, + "return_last_logit": True, + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = (return_dict if return_dict is not None else self.config.use_return_dict) + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache(past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], + beam_idx: torch.LongTensor) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple(( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) for layer_past in past) + + def process_response(self, response): + response = response.strip() + response = response.replace("[[训练时间]]", "2023年") + return response + + def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + prompt = tokenizer.build_prompt(query, history=history) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + if history: + prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + input_ids = tokenizer.encode(prompt, add_special_tokens=False) + input_ids = input_ids[1:] + inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False) + else: + prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + @torch.no_grad() + def chat( + self, + tokenizer, + query: str, + history: List[Tuple[str, str]] = None, + max_length: int = 8192, + num_beams=1, + do_sample=True, + top_p=0.8, + temperature=0.8, + logits_processor=None, + **kwargs, + ): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = { + "max_length": max_length, + "num_beams": num_beams, + "do_sample": do_sample, + "top_p": top_p, + "temperature": temperature, + "logits_processor": logits_processor, + **kwargs, + } + inputs = self.build_inputs(tokenizer, query, history=history) + outputs = self.generate(**inputs, **gen_kwargs) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + response = self.process_response(response) + history = history + [(query, response)] + return response, history + + @torch.no_grad() + def stream_chat( + self, + tokenizer, + query: str, + history: List[Tuple[str, str]] = None, + past_key_values=None, + max_length: int = 8192, + do_sample=True, + top_p=0.8, + temperature=0.8, + logits_processor=None, + return_past_key_values=False, + **kwargs, + ): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = { + "max_length": max_length, + "do_sample": do_sample, + "top_p": top_p, + "temperature": temperature, + "logits_processor": logits_processor, + **kwargs, + } + if past_key_values is None and not return_past_key_values: + inputs = self.build_inputs(tokenizer, query, history=history) + else: + inputs = self.build_stream_inputs(tokenizer, query, history=history) + if past_key_values is not None: + past_length = past_key_values[0][0].shape[0] + if self.transformer.pre_seq_len is not None: + past_length -= self.transformer.pre_seq_len + inputs.position_ids += past_length + attention_mask = inputs.attention_mask + attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) + inputs["attention_mask"] = attention_mask + for outputs in self.stream_generate( + **inputs, + past_key_values=past_key_values, + return_past_key_values=return_past_key_values, + **gen_kwargs, + ): + if return_past_key_values: + outputs, past_key_values = outputs + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + if response and response[-1] != "�": + response = self.process_response(response) + new_history = history + [(query, response)] + if return_past_key_values: + yield response, new_history, past_key_values + else: + yield response, new_history + + @torch.no_grad() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + return_past_key_values=False, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + bos_token_id, eos_token_id = ( + generation_config.bos_token_id, + generation_config.eos_token_id, + ) + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + + has_default_max_length = (kwargs.get("max_length") is None and generation_config.max_length is not None) + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = (generation_config.max_new_tokens + input_ids_seq_length) + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = ("decoder_input_ids" if self.config.is_encoder_decoder else "input_ids") + logger.warning(f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`.") + + # 2. Set generation parameters if not already defined + logits_processor = (logits_processor if logits_processor is not None else LogitsProcessorList()) + stopping_criteria = (stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()) + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria(generation_config=generation_config, + stopping_criteria=stopping_criteria) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation(outputs, + model_kwargs, + is_encoder_decoder=self.config.is_encoder_decoder) + unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) + if return_past_key_values: + yield input_ids, outputs.past_key_values + else: + yield input_ids + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize( + self.transformer.encoder, + bits, + empty_init=empty_init, + device=device, + **kwargs, + ) + return self diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py new file mode 100644 index 000000000000..2cdf5da2e6da --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -0,0 +1,107 @@ +import copy +import os + +import pytest +import torch + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.shardformer import ShardConfig, ShardFormer +from colossalai.shardformer.policies.chatglm import ChatGLMModelPolicy +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) + # do backward + org_loss.backward() + shard_loss.backward() + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # unwrap model + if org_model.__class__.__name__ == 'ChatGLMModel': + chatglm_model = org_model + shard_chatglm_model = sharded_model + else: + chatglm_model = org_model.transformer + shard_chatglm_model = sharded_model.transformer + + # check attention grad + org_grad = chatglm_model.encoder.layers[0].self_attention.query_key_value.weight.grad + shard_grad = shard_chatglm_model.encoder.layers[0].self_attention.query_key_value.weight.grad + shard_weight = shard_chatglm_model.encoder.layers[0].self_attention.query_key_value.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + + # check embedding weights + org_grad = chatglm_model.embedding.word_embeddings.weight.grad + shard_grad = shard_chatglm_model.embedding.word_embeddings.weight.grad + shard_weight = shard_chatglm_model.embedding.word_embeddings.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism): + sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + # create new model + org_model = model_fn().cuda() + + # shard model + shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, + enable_tensor_parallelism=enable_tensor_parallelism) + model_copy = copy.deepcopy(org_model) + shard_former = ShardFormer(shard_config=shard_config) + if name == "transformers_chatglm": + sharded_model = shard_former.optimize(model_copy, ChatGLMModelPolicy()).cuda() + + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + torch.cuda.empty_cache() + + +def check_chatglm(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_chatglm_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_chatglm(): + spawn(check_chatglm, 2) + + +if __name__ == "__main__": + test_chatglm() From f60162b2657a18af1468bd172835828787d23c17 Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Tue, 4 Jul 2023 14:35:55 +0800 Subject: [PATCH 52/84] [shardformer] added tests --- tests/test_shardformer/test_model/test_shard_vit.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index 2b02c83e0d27..c1126cb2cd4b 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -56,6 +56,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_tensor_parallelism', [True, False]) def run_vit_test(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') + print(sub_model_zoo) for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) From c49286985dd84fda8131ad4341474ac3ef60ff27 Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Thu, 6 Jul 2023 10:59:42 +0800 Subject: [PATCH 53/84] [shardformer] vit test finish and support --- tests/test_shardformer/test_model/test_shard_vit.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index c1126cb2cd4b..2b02c83e0d27 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -56,7 +56,6 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_tensor_parallelism', [True, False]) def run_vit_test(enable_fused_normalization, enable_tensor_parallelism): sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') - print(sub_model_zoo) for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) From 7377be7a537a1a5aafa267b1b6f0f0421c07e698 Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Fri, 7 Jul 2023 19:16:35 +0800 Subject: [PATCH 54/84] import chatglm --- =2.0 | 134 ++ .../chatglm2-6b/modeling_chatglm.py | 1193 +++++++++++++++++ 2 files changed, 1327 insertions(+) create mode 100644 =2.0 create mode 100644 tests/kit/model_zoo/transformers/chatglm2-6b/modeling_chatglm.py diff --git a/=2.0 b/=2.0 new file mode 100644 index 000000000000..af47ce17aa8e --- /dev/null +++ b/=2.0 @@ -0,0 +1,134 @@ +Defaulting to user 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gradio, cpm-kernels, accelerate +Successfully installed accelerate-0.20.3 aiofiles-23.1.0 altair-5.0.1 anyio-3.7.1 cpm-kernels-1.0.11 fastapi-0.99.1 ffmpy-0.3.0 gradio-3.36.0 gradio-client-0.2.7 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 jsonschema-4.18.0 jsonschema-specifications-2023.6.1 latex2mathml-3.76.0 linkify-it-py-2.0.2 markdown-3.4.3 mdit-py-plugins-0.3.3 mdtex2html-1.2.0 orjson-3.9.1 protobuf-4.23.4 pydantic-1.10.11 pydub-0.25.1 python-multipart-0.0.6 referencing-0.29.1 rpds-py-0.8.8 semantic-version-2.10.0 sentencepiece-0.1.99 sniffio-1.3.0 starlette-0.27.0 toolz-0.12.0 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3 diff --git a/tests/kit/model_zoo/transformers/chatglm2-6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2-6b/modeling_chatglm.py new file mode 100644 index 000000000000..82163c46190f --- /dev/null +++ b/tests/kit/model_zoo/transformers/chatglm2-6b/modeling_chatglm.py @@ -0,0 +1,1193 @@ +""" PyTorch ChatGLM model. """ + +import math +import copy +import warnings +import re +import sys + +import torch +import torch.utils.checkpoint +import torch.nn.functional as F +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn.utils import skip_init +from typing import Optional, Tuple, Union, List, Callable, Dict, Any + +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput + +from .configuration_chatglm import ChatGLMConfig + +# flags required to enable jit fusion kernels + +if sys.platform != 'darwin': + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm2-6b", + # See all ChatGLM models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config: ChatGLMConfig): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 + self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(kv_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, kv_size) + ) + else: + self.embedding = torch.nn.Embedding(config.pre_seq_len, + config.num_layers * config.kv_channels * config.multi_query_group_num * 2) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_impl( + self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=dtype, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + return self.forward_impl( + max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device + ) + + +@torch.jit.script +def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + + return (self.weight * hidden_states).to(input_dtype) + + +class CoreAttention(torch.nn.Module): + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split('.')[0]) + if pytorch_major_version >= 2: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, + device=query_layer.device + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: + attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], + device=attention_scores.device, dtype=torch.bool) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = ( + self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num + ) + self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, **_config_to_kwargs(config) + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, + device=device, **_config_to_kwargs(config) + ) + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view( + query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + key_layer = key_layer.view( + key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.view( + value_layer.size()[:-1] + + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) + ) + else: + new_tensor_shape = mixed_x_layer.size()[:-1] + \ + (self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + if use_cache: + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + key_layer = key_layer.contiguous().view( + key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 + ) + value_layer = value_layer.contiguous().view( + value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) + ) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config) + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, + dtype=config.torch_dtype) + + self.gradient_checkpointing = False + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches[index], + use_cache + ) + else: + layer_ret = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache + ) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, + device=input_ids.device), full_attention_mask), dim=-1) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, GLMTransformer): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + self.num_layers = config.num_layers + self.multi_query_group_num = config.multi_query_group_num + self.kv_channels = config.kv_channels + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = ( + config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels + ) + + self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, + dtype=config.torch_dtype) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, + dtype=config.torch_dtype, **init_kwargs) + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + def get_input_embeddings(self): + return self.embedding.word_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.multi_query_group_num, + self.kv_channels + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + return past_key_values + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, + dtype=inputs_embeds.dtype) + if attention_mask is not None: + attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), + attention_mask], dim=-1) + + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states + ) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + from .quantization import quantize + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format + ) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat( + [position_ids, new_position_id], dim=-1 + ) + + model_kwargs["is_first_forward"] = False + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + is_first_forward: bool = True, + **kwargs + ) -> dict: + # only last token for input_ids if past is not None + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + if not is_first_forward: + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask, + "return_last_logit": True + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache( + past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple( + ( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) + for layer_past in past + ) + + def process_response(self, response): + response = response.strip() + response = response.replace("[[训练时间]]", "2023年") + return response + + def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + prompt = tokenizer.build_prompt(query, history=history) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + if history: + prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + input_ids = tokenizer.encode(prompt, add_special_tokens=False) + input_ids = input_ids[1:] + inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False) + else: + prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + @torch.no_grad() + def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192, num_beams=1, + do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + inputs = self.build_inputs(tokenizer, query, history=history) + outputs = self.generate(**inputs, **gen_kwargs) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + response = self.process_response(response) + history = history + [(query, response)] + return response, history + + @torch.no_grad() + def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values=None, + max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, + return_past_key_values=False, **kwargs): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, + "temperature": temperature, "logits_processor": logits_processor, **kwargs} + if past_key_values is None and not return_past_key_values: + inputs = self.build_inputs(tokenizer, query, history=history) + else: + inputs = self.build_stream_inputs(tokenizer, query, history=history) + if past_key_values is not None: + past_length = past_key_values[0][0].shape[0] + if self.transformer.pre_seq_len is not None: + past_length -= self.transformer.pre_seq_len + inputs.position_ids += past_length + attention_mask = inputs.attention_mask + attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) + inputs['attention_mask'] = attention_mask + for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, + return_past_key_values=return_past_key_values, **gen_kwargs): + if return_past_key_values: + outputs, past_key_values = outputs + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + if response and response[-1] != "�": + response = self.process_response(response) + new_history = history + [(query, response)] + if return_past_key_values: + yield response, new_history, past_key_values + else: + yield response, new_history + + @torch.no_grad() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + return_past_key_values=False, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" + logger.warning( + f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`." + ) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder + ) + unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) + if return_past_key_values: + yield input_ids, outputs.past_key_values + else: + yield input_ids + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, + **kwargs) + return self \ No newline at end of file From 6ee4c9ee216b0ed467f6bec1edcd15d72b60f44f Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Fri, 7 Jul 2023 19:56:22 +0800 Subject: [PATCH 55/84] [shardformer] add test kit in model zoo for chatglm --- .../chatglm2-6b/modeling_chatglm.py | 1193 ----------------- .../transformers/chatglm2_6b/MODEL_LICENSE | 33 + .../chatglm2_6b/modeling_chatglm.py | 6 - .../transformers/chatglm2_6b/quantization.py | 188 +++ 4 files changed, 221 insertions(+), 1199 deletions(-) delete mode 100644 tests/kit/model_zoo/transformers/chatglm2-6b/modeling_chatglm.py create mode 100644 tests/kit/model_zoo/transformers/chatglm2_6b/MODEL_LICENSE create mode 100644 tests/kit/model_zoo/transformers/chatglm2_6b/quantization.py diff --git a/tests/kit/model_zoo/transformers/chatglm2-6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2-6b/modeling_chatglm.py deleted file mode 100644 index 82163c46190f..000000000000 --- a/tests/kit/model_zoo/transformers/chatglm2-6b/modeling_chatglm.py +++ /dev/null @@ -1,1193 +0,0 @@ -""" PyTorch ChatGLM model. """ - -import math -import copy -import warnings -import re -import sys - -import torch -import torch.utils.checkpoint -import torch.nn.functional as F -from torch import nn -from torch.nn import CrossEntropyLoss, LayerNorm -from torch.nn.utils import skip_init -from typing import Optional, Tuple, Union, List, Callable, Dict, Any - -from transformers.modeling_outputs import ( - BaseModelOutputWithPast, - CausalLMOutputWithPast, -) -from transformers.modeling_utils import PreTrainedModel -from transformers.utils import logging -from transformers.generation.logits_process import LogitsProcessor -from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput - -from .configuration_chatglm import ChatGLMConfig - -# flags required to enable jit fusion kernels - -if sys.platform != 'darwin': - torch._C._jit_set_profiling_mode(False) - torch._C._jit_set_profiling_executor(False) - torch._C._jit_override_can_fuse_on_cpu(True) - torch._C._jit_override_can_fuse_on_gpu(True) - -logger = logging.get_logger(__name__) - -_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B" -_CONFIG_FOR_DOC = "ChatGLM6BConfig" - -CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "THUDM/chatglm2-6b", - # See all ChatGLM models at https://huggingface.co/models?filter=chatglm -] - - -def default_init(cls, *args, **kwargs): - return cls(*args, **kwargs) - - -class InvalidScoreLogitsProcessor(LogitsProcessor): - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: - if torch.isnan(scores).any() or torch.isinf(scores).any(): - scores.zero_() - scores[..., 5] = 5e4 - return scores - - -class PrefixEncoder(torch.nn.Module): - """ - The torch.nn model to encode the prefix - Input shape: (batch-size, prefix-length) - Output shape: (batch-size, prefix-length, 2*layers*hidden) - """ - - def __init__(self, config: ChatGLMConfig): - super().__init__() - self.prefix_projection = config.prefix_projection - if self.prefix_projection: - # Use a two-layer MLP to encode the prefix - kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 - self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) - self.trans = torch.nn.Sequential( - torch.nn.Linear(kv_size, config.hidden_size), - torch.nn.Tanh(), - torch.nn.Linear(config.hidden_size, kv_size) - ) - else: - self.embedding = torch.nn.Embedding(config.pre_seq_len, - config.num_layers * config.kv_channels * config.multi_query_group_num * 2) - - def forward(self, prefix: torch.Tensor): - if self.prefix_projection: - prefix_tokens = self.embedding(prefix) - past_key_values = self.trans(prefix_tokens) - else: - past_key_values = self.embedding(prefix) - return past_key_values - - -def split_tensor_along_last_dim( - tensor: torch.Tensor, - num_partitions: int, - contiguous_split_chunks: bool = False, -) -> List[torch.Tensor]: - """Split a tensor along its last dimension. - - Arguments: - tensor: input tensor. - num_partitions: number of partitions to split the tensor - contiguous_split_chunks: If True, make each chunk contiguous - in memory. - - Returns: - A list of Tensors - """ - # Get the size and dimension. - last_dim = tensor.dim() - 1 - last_dim_size = tensor.size()[last_dim] // num_partitions - # Split. - tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) - # Note: torch.split does not create contiguous tensors by default. - if contiguous_split_chunks: - return tuple(chunk.contiguous() for chunk in tensor_list) - - return tensor_list - - -class RotaryEmbedding(nn.Module): - def __init__(self, dim, original_impl=False, device=None, dtype=None): - super().__init__() - inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) - self.register_buffer("inv_freq", inv_freq) - self.dim = dim - self.original_impl = original_impl - - def forward_impl( - self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 - ): - """Enhanced Transformer with Rotary Position Embedding. - - Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ - transformers/rope/__init__.py. MIT License: - https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. - """ - # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ - theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) - - # Create position indexes `[0, 1, ..., seq_len - 1]` - seq_idx = torch.arange(seq_len, dtype=dtype, device=device) - - # Calculate the product of position index and $\theta_i$ - idx_theta = torch.outer(seq_idx, theta).float() - - cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) - - # this is to mimic the behaviour of complex32, else we will get different results - if dtype in (torch.float16, torch.bfloat16, torch.int8): - cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() - return cache - - def forward(self, max_seq_len, offset=0): - return self.forward_impl( - max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device - ) - - -@torch.jit.script -def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: - # x: [sq, b, np, hn] - sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) - rot_dim = rope_cache.shape[-2] * 2 - x, x_pass = x[..., :rot_dim], x[..., rot_dim:] - # truncate to support variable sizes - rope_cache = rope_cache[:sq] - xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) - rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) - x_out2 = torch.stack( - [ - xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], - xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], - ], - -1, - ) - x_out2 = x_out2.flatten(3) - return torch.cat((x_out2, x_pass), dim=-1) - - -class RMSNorm(torch.nn.Module): - def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): - super().__init__() - self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) - self.eps = eps - - def forward(self, hidden_states: torch.Tensor): - input_dtype = hidden_states.dtype - variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt(variance + self.eps) - - return (self.weight * hidden_states).to(input_dtype) - - -class CoreAttention(torch.nn.Module): - def __init__(self, config: ChatGLMConfig, layer_number): - super(CoreAttention, self).__init__() - - self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling - self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 - if self.apply_query_key_layer_scaling: - self.attention_softmax_in_fp32 = True - self.layer_number = max(1, layer_number) - - projection_size = config.kv_channels * config.num_attention_heads - - # Per attention head and per partition values. - self.hidden_size_per_partition = projection_size - self.hidden_size_per_attention_head = projection_size // config.num_attention_heads - self.num_attention_heads_per_partition = config.num_attention_heads - - coeff = None - self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) - if self.apply_query_key_layer_scaling: - coeff = self.layer_number - self.norm_factor *= coeff - self.coeff = coeff - - self.attention_dropout = torch.nn.Dropout(config.attention_dropout) - - def forward(self, query_layer, key_layer, value_layer, attention_mask): - pytorch_major_version = int(torch.__version__.split('.')[0]) - if pytorch_major_version >= 2: - query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] - if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: - context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, - is_causal=True) - else: - if attention_mask is not None: - attention_mask = ~attention_mask - context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, - attention_mask) - context_layer = context_layer.permute(2, 0, 1, 3) - new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) - context_layer = context_layer.reshape(*new_context_layer_shape) - else: - # Raw attention scores - - # [b, np, sq, sk] - output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) - - # [sq, b, np, hn] -> [sq, b * np, hn] - query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) - # [sk, b, np, hn] -> [sk, b * np, hn] - key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) - - # preallocting input tensor: [b * np, sq, sk] - matmul_input_buffer = torch.empty( - output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, - device=query_layer.device - ) - - # Raw attention scores. [b * np, sq, sk] - matmul_result = torch.baddbmm( - matmul_input_buffer, - query_layer.transpose(0, 1), # [b * np, sq, hn] - key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] - beta=0.0, - alpha=(1.0 / self.norm_factor), - ) - - # change view to [b, np, sq, sk] - attention_scores = matmul_result.view(*output_size) - - # =========================== - # Attention probs and dropout - # =========================== - - # attention scores and attention mask [b, np, sq, sk] - if self.attention_softmax_in_fp32: - attention_scores = attention_scores.float() - if self.coeff is not None: - attention_scores = attention_scores * self.coeff - if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: - attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], - device=attention_scores.device, dtype=torch.bool) - attention_mask.tril_() - attention_mask = ~attention_mask - if attention_mask is not None: - attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) - attention_probs = F.softmax(attention_scores, dim=-1) - attention_probs = attention_probs.type_as(value_layer) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs = self.attention_dropout(attention_probs) - # ========================= - # Context layer. [sq, b, hp] - # ========================= - - # value_layer -> context layer. - # [sk, b, np, hn] --> [b, np, sq, hn] - - # context layer shape: [b, np, sq, hn] - output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) - # change view [sk, b * np, hn] - value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) - # change view [b * np, sq, sk] - attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) - # matmul: [b * np, sq, hn] - context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) - # change view [b, np, sq, hn] - context_layer = context_layer.view(*output_size) - # [b, np, sq, hn] --> [sq, b, np, hn] - context_layer = context_layer.permute(2, 0, 1, 3).contiguous() - # [sq, b, np, hn] --> [sq, b, hp] - new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) - context_layer = context_layer.view(*new_context_layer_shape) - - return context_layer - - -class SelfAttention(torch.nn.Module): - """Parallel self-attention layer abstract class. - - Self-attention layer takes input with size [s, b, h] - and returns output of the same size. - """ - - def __init__(self, config: ChatGLMConfig, layer_number, device=None): - super(SelfAttention, self).__init__() - self.layer_number = max(1, layer_number) - - self.projection_size = config.kv_channels * config.num_attention_heads - - # Per attention head and per partition values. - self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads - self.num_attention_heads_per_partition = config.num_attention_heads - - self.multi_query_attention = config.multi_query_attention - self.qkv_hidden_size = 3 * self.projection_size - if self.multi_query_attention: - self.num_multi_query_groups_per_partition = config.multi_query_group_num - self.qkv_hidden_size = ( - self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num - ) - self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, - bias=config.add_bias_linear or config.add_qkv_bias, - device=device, **_config_to_kwargs(config) - ) - - self.core_attention = CoreAttention(config, self.layer_number) - - # Output. - self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, - device=device, **_config_to_kwargs(config) - ) - - def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): - if self.multi_query_attention: - num_attention_heads = self.num_multi_query_groups_per_partition - else: - num_attention_heads = self.num_attention_heads_per_partition - return torch.empty( - inference_max_sequence_len, - batch_size, - num_attention_heads, - self.hidden_size_per_attention_head, - dtype=dtype, - device=device, - ) - - def forward( - self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True - ): - # hidden_states: [sq, b, h] - - # ================================================= - # Pre-allocate memory for key-values for inference. - # ================================================= - # ===================== - # Query, Key, and Value - # ===================== - - # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] - mixed_x_layer = self.query_key_value(hidden_states) - - if self.multi_query_attention: - (query_layer, key_layer, value_layer) = mixed_x_layer.split( - [ - self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, - self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, - self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, - ], - dim=-1, - ) - query_layer = query_layer.view( - query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) - ) - key_layer = key_layer.view( - key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) - ) - value_layer = value_layer.view( - value_layer.size()[:-1] - + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) - ) - else: - new_tensor_shape = mixed_x_layer.size()[:-1] + \ - (self.num_attention_heads_per_partition, - 3 * self.hidden_size_per_attention_head) - mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) - - # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] - (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) - - # apply relative positional encoding (rotary embedding) - if rotary_pos_emb is not None: - query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) - key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) - - # adjust key and value for inference - if kv_cache is not None: - cache_k, cache_v = kv_cache - key_layer = torch.cat((cache_k, key_layer), dim=0) - value_layer = torch.cat((cache_v, value_layer), dim=0) - if use_cache: - kv_cache = (key_layer, value_layer) - else: - kv_cache = None - - if self.multi_query_attention: - key_layer = key_layer.unsqueeze(-2) - key_layer = key_layer.expand( - -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 - ) - key_layer = key_layer.contiguous().view( - key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) - ) - value_layer = value_layer.unsqueeze(-2) - value_layer = value_layer.expand( - -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 - ) - value_layer = value_layer.contiguous().view( - value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) - ) - - # ================================== - # core attention computation - # ================================== - - context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) - - # ================= - # Output. [sq, b, h] - # ================= - - output = self.dense(context_layer) - - return output, kv_cache - - -def _config_to_kwargs(args): - common_kwargs = { - "dtype": args.torch_dtype, - } - return common_kwargs - - -class MLP(torch.nn.Module): - """MLP. - - MLP will take the input with h hidden state, project it to 4*h - hidden dimension, perform nonlinear transformation, and project the - state back into h hidden dimension. - """ - - def __init__(self, config: ChatGLMConfig, device=None): - super(MLP, self).__init__() - - self.add_bias = config.add_bias_linear - - # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf - self.dense_h_to_4h = nn.Linear( - config.hidden_size, - config.ffn_hidden_size * 2, - bias=self.add_bias, - device=device, - **_config_to_kwargs(config) - ) - - def swiglu(x): - x = torch.chunk(x, 2, dim=-1) - return F.silu(x[0]) * x[1] - - self.activation_func = swiglu - - # Project back to h. - self.dense_4h_to_h = nn.Linear( - config.ffn_hidden_size, - config.hidden_size, - bias=self.add_bias, - device=device, - **_config_to_kwargs(config) - ) - - def forward(self, hidden_states): - # [s, b, 4hp] - intermediate_parallel = self.dense_h_to_4h(hidden_states) - intermediate_parallel = self.activation_func(intermediate_parallel) - # [s, b, h] - output = self.dense_4h_to_h(intermediate_parallel) - return output - - -class GLMBlock(torch.nn.Module): - """A single transformer layer. - - Transformer layer takes input with size [s, b, h] and returns an - output of the same size. - """ - - def __init__(self, config: ChatGLMConfig, layer_number, device=None): - super(GLMBlock, self).__init__() - self.layer_number = layer_number - - self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm - - self.fp32_residual_connection = config.fp32_residual_connection - - LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm - # Layernorm on the input data. - self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, - dtype=config.torch_dtype) - - # Self attention. - self.self_attention = SelfAttention(config, layer_number, device=device) - self.hidden_dropout = config.hidden_dropout - - # Layernorm on the attention output - self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, - dtype=config.torch_dtype) - - # MLP - self.mlp = MLP(config, device=device) - - def forward( - self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, - ): - # hidden_states: [s, b, h] - - # Layer norm at the beginning of the transformer layer. - layernorm_output = self.input_layernorm(hidden_states) - # Self attention. - attention_output, kv_cache = self.self_attention( - layernorm_output, - attention_mask, - rotary_pos_emb, - kv_cache=kv_cache, - use_cache=use_cache - ) - - # Residual connection. - if self.apply_residual_connection_post_layernorm: - residual = layernorm_output - else: - residual = hidden_states - - layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) - layernorm_input = residual + layernorm_input - - # Layer norm post the self attention. - layernorm_output = self.post_attention_layernorm(layernorm_input) - - # MLP. - mlp_output = self.mlp(layernorm_output) - - # Second residual connection. - if self.apply_residual_connection_post_layernorm: - residual = layernorm_output - else: - residual = layernorm_input - - output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) - output = residual + output - - return output, kv_cache - - -class GLMTransformer(torch.nn.Module): - """Transformer class.""" - - def __init__(self, config: ChatGLMConfig, device=None): - super(GLMTransformer, self).__init__() - - self.fp32_residual_connection = config.fp32_residual_connection - self.post_layer_norm = config.post_layer_norm - - # Number of layers. - self.num_layers = config.num_layers - - # Transformer layers. - def build_layer(layer_number): - return GLMBlock(config, layer_number, device=device) - - self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) - - if self.post_layer_norm: - LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm - # Final layer norm before output. - self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, - dtype=config.torch_dtype) - - self.gradient_checkpointing = False - - def _get_layer(self, layer_number): - return self.layers[layer_number] - - def forward( - self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, - use_cache: Optional[bool] = True, - output_hidden_states: Optional[bool] = False, - ): - if not kv_caches: - kv_caches = [None for _ in range(self.num_layers)] - presents = () if use_cache else None - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - all_self_attentions = None - all_hidden_states = () if output_hidden_states else None - for index in range(self.num_layers): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer = self._get_layer(index) - if self.gradient_checkpointing and self.training: - layer_ret = torch.utils.checkpoint.checkpoint( - layer, - hidden_states, - attention_mask, - rotary_pos_emb, - kv_caches[index], - use_cache - ) - else: - layer_ret = layer( - hidden_states, - attention_mask, - rotary_pos_emb, - kv_cache=kv_caches[index], - use_cache=use_cache - ) - hidden_states, kv_cache = layer_ret - if use_cache: - presents = presents + (kv_cache,) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - # Final layer norm. - if self.post_layer_norm: - hidden_states = self.final_layernorm(hidden_states) - - return hidden_states, presents, all_hidden_states, all_self_attentions - - -class ChatGLMPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and - a simple interface for downloading and loading pretrained models. - """ - - is_parallelizable = False - supports_gradient_checkpointing = True - config_class = ChatGLMConfig - base_model_prefix = "transformer" - _no_split_modules = ["GLMBlock"] - - def _init_weights(self, module: nn.Module): - """Initialize the weights.""" - return - - def get_masks(self, input_ids, past_key_values, padding_mask=None): - batch_size, seq_length = input_ids.shape - full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) - full_attention_mask.tril_() - past_length = 0 - if past_key_values: - past_length = past_key_values[0][0].shape[0] - if past_length: - full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, - device=input_ids.device), full_attention_mask), dim=-1) - if padding_mask is not None: - full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) - if not past_length and padding_mask is not None: - full_attention_mask -= padding_mask.unsqueeze(-1) - 1 - full_attention_mask = (full_attention_mask < 0.5).bool() - full_attention_mask.unsqueeze_(1) - return full_attention_mask - - def get_position_ids(self, input_ids, device): - batch_size, seq_length = input_ids.shape - position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) - return position_ids - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, GLMTransformer): - module.gradient_checkpointing = value - - -class Embedding(torch.nn.Module): - """Language model embeddings.""" - - def __init__(self, config: ChatGLMConfig, device=None): - super(Embedding, self).__init__() - - self.hidden_size = config.hidden_size - # Word embeddings (parallel). - self.word_embeddings = nn.Embedding( - config.padded_vocab_size, - self.hidden_size, - dtype=config.torch_dtype, - device=device - ) - self.fp32_residual_connection = config.fp32_residual_connection - - def forward(self, input_ids): - # Embeddings. - words_embeddings = self.word_embeddings(input_ids) - embeddings = words_embeddings - # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. - embeddings = embeddings.transpose(0, 1).contiguous() - # If the input flag for fp32 residual connection is set, convert for float. - if self.fp32_residual_connection: - embeddings = embeddings.float() - return embeddings - - -class ChatGLMModel(ChatGLMPreTrainedModel): - def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): - super().__init__(config) - if empty_init: - init_method = skip_init - else: - init_method = default_init - init_kwargs = {} - if device is not None: - init_kwargs["device"] = device - self.embedding = init_method(Embedding, config, **init_kwargs) - self.num_layers = config.num_layers - self.multi_query_group_num = config.multi_query_group_num - self.kv_channels = config.kv_channels - - # Rotary positional embeddings - self.seq_length = config.seq_length - rotary_dim = ( - config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels - ) - - self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, - dtype=config.torch_dtype) - self.encoder = init_method(GLMTransformer, config, **init_kwargs) - self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, - dtype=config.torch_dtype, **init_kwargs) - self.pre_seq_len = config.pre_seq_len - self.prefix_projection = config.prefix_projection - if self.pre_seq_len is not None: - for param in self.parameters(): - param.requires_grad = False - self.prefix_tokens = torch.arange(self.pre_seq_len).long() - self.prefix_encoder = PrefixEncoder(config) - self.dropout = torch.nn.Dropout(0.1) - - def get_input_embeddings(self): - return self.embedding.word_embeddings - - def get_prompt(self, batch_size, device, dtype=torch.half): - prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) - past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) - past_key_values = past_key_values.view( - batch_size, - self.pre_seq_len, - self.num_layers * 2, - self.multi_query_group_num, - self.kv_channels - ) - # seq_len, b, nh, hidden_size - past_key_values = self.dropout(past_key_values) - past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) - return past_key_values - - def forward( - self, - input_ids, - position_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.BoolTensor] = None, - full_attention_mask: Optional[torch.BoolTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, - inputs_embeds: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ): - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - batch_size, seq_length = input_ids.shape - - if inputs_embeds is None: - inputs_embeds = self.embedding(input_ids) - - if self.pre_seq_len is not None: - if past_key_values is None: - past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, - dtype=inputs_embeds.dtype) - if attention_mask is not None: - attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), - attention_mask], dim=-1) - - if full_attention_mask is None: - if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): - full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) - - # Rotary positional embeddings - rotary_pos_emb = self.rotary_pos_emb(self.seq_length) - if position_ids is not None: - rotary_pos_emb = rotary_pos_emb[position_ids] - else: - rotary_pos_emb = rotary_pos_emb[None, :seq_length] - rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() - - # Run encoder. - hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( - inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, - kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states - ) - - if not return_dict: - return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) - - return BaseModelOutputWithPast( - last_hidden_state=hidden_states, - past_key_values=presents, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - ) - - def quantize(self, weight_bit_width: int): - from .quantization import quantize - quantize(self.encoder, weight_bit_width) - return self - - -class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): - def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): - super().__init__(config) - - self.max_sequence_length = config.max_length - self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) - self.config = config - self.quantized = False - - if self.config.quantization_bit: - self.quantize(self.config.quantization_bit, empty_init=True) - - def _update_model_kwargs_for_generation( - self, - outputs: ModelOutput, - model_kwargs: Dict[str, Any], - is_encoder_decoder: bool = False, - standardize_cache_format: bool = False, - ) -> Dict[str, Any]: - # update past_key_values - model_kwargs["past_key_values"] = self._extract_past_from_model_output( - outputs, standardize_cache_format=standardize_cache_format - ) - - # update attention mask - if "attention_mask" in model_kwargs: - attention_mask = model_kwargs["attention_mask"] - model_kwargs["attention_mask"] = torch.cat( - [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 - ) - - # update position ids - if "position_ids" in model_kwargs: - position_ids = model_kwargs["position_ids"] - new_position_id = position_ids[..., -1:].clone() - new_position_id += 1 - model_kwargs["position_ids"] = torch.cat( - [position_ids, new_position_id], dim=-1 - ) - - model_kwargs["is_first_forward"] = False - return model_kwargs - - def prepare_inputs_for_generation( - self, - input_ids: torch.LongTensor, - past_key_values: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - is_first_forward: bool = True, - **kwargs - ) -> dict: - # only last token for input_ids if past is not None - if position_ids is None: - position_ids = self.get_position_ids(input_ids, device=input_ids.device) - if not is_first_forward: - position_ids = position_ids[..., -1:] - input_ids = input_ids[:, -1:] - return { - "input_ids": input_ids, - "past_key_values": past_key_values, - "position_ids": position_ids, - "attention_mask": attention_mask, - "return_last_logit": True - } - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[Tuple[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - return_last_logit: Optional[bool] = False, - ): - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - transformer_outputs = self.transformer( - input_ids=input_ids, - position_ids=position_ids, - attention_mask=attention_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - hidden_states = transformer_outputs[0] - if return_last_logit: - hidden_states = hidden_states[-1:] - lm_logits = self.transformer.output_layer(hidden_states) - lm_logits = lm_logits.transpose(0, 1).contiguous() - - loss = None - if labels is not None: - lm_logits = lm_logits.to(torch.float32) - - # Shift so that tokens < n predict n - shift_logits = lm_logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss(ignore_index=-100) - loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) - - lm_logits = lm_logits.to(hidden_states.dtype) - loss = loss.to(hidden_states.dtype) - - if not return_dict: - output = (lm_logits,) + transformer_outputs[1:] - return ((loss,) + output) if loss is not None else output - - return CausalLMOutputWithPast( - loss=loss, - logits=lm_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - - @staticmethod - def _reorder_cache( - past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor - ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: - """ - This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or - [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct - beam_idx at every generation step. - - Output shares the same memory storage as `past`. - """ - return tuple( - ( - layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), - layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), - ) - for layer_past in past - ) - - def process_response(self, response): - response = response.strip() - response = response.replace("[[训练时间]]", "2023年") - return response - - def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): - prompt = tokenizer.build_prompt(query, history=history) - inputs = tokenizer([prompt], return_tensors="pt") - inputs = inputs.to(self.device) - return inputs - - def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): - if history: - prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) - input_ids = tokenizer.encode(prompt, add_special_tokens=False) - input_ids = input_ids[1:] - inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False) - else: - prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) - inputs = tokenizer([prompt], return_tensors="pt") - inputs = inputs.to(self.device) - return inputs - - @torch.no_grad() - def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192, num_beams=1, - do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, **kwargs): - if history is None: - history = [] - if logits_processor is None: - logits_processor = LogitsProcessorList() - logits_processor.append(InvalidScoreLogitsProcessor()) - gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, - "temperature": temperature, "logits_processor": logits_processor, **kwargs} - inputs = self.build_inputs(tokenizer, query, history=history) - outputs = self.generate(**inputs, **gen_kwargs) - outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] - response = tokenizer.decode(outputs) - response = self.process_response(response) - history = history + [(query, response)] - return response, history - - @torch.no_grad() - def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values=None, - max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, - return_past_key_values=False, **kwargs): - if history is None: - history = [] - if logits_processor is None: - logits_processor = LogitsProcessorList() - logits_processor.append(InvalidScoreLogitsProcessor()) - gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, - "temperature": temperature, "logits_processor": logits_processor, **kwargs} - if past_key_values is None and not return_past_key_values: - inputs = self.build_inputs(tokenizer, query, history=history) - else: - inputs = self.build_stream_inputs(tokenizer, query, history=history) - if past_key_values is not None: - past_length = past_key_values[0][0].shape[0] - if self.transformer.pre_seq_len is not None: - past_length -= self.transformer.pre_seq_len - inputs.position_ids += past_length - attention_mask = inputs.attention_mask - attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) - inputs['attention_mask'] = attention_mask - for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, - return_past_key_values=return_past_key_values, **gen_kwargs): - if return_past_key_values: - outputs, past_key_values = outputs - outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] - response = tokenizer.decode(outputs) - if response and response[-1] != "�": - response = self.process_response(response) - new_history = history + [(query, response)] - if return_past_key_values: - yield response, new_history, past_key_values - else: - yield response, new_history - - @torch.no_grad() - def stream_generate( - self, - input_ids, - generation_config: Optional[GenerationConfig] = None, - logits_processor: Optional[LogitsProcessorList] = None, - stopping_criteria: Optional[StoppingCriteriaList] = None, - prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, - return_past_key_values=False, - **kwargs, - ): - batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] - - if generation_config is None: - generation_config = self.generation_config - generation_config = copy.deepcopy(generation_config) - model_kwargs = generation_config.update(**kwargs) - bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id - - if isinstance(eos_token_id, int): - eos_token_id = [eos_token_id] - - has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None - if has_default_max_length and generation_config.max_new_tokens is None: - warnings.warn( - f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " - "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" - " recommend using `max_new_tokens` to control the maximum length of the generation.", - UserWarning, - ) - elif generation_config.max_new_tokens is not None: - generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length - if not has_default_max_length: - logger.warn( - f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" - f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " - "Please refer to the documentation for more information. " - "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", - UserWarning, - ) - - if input_ids_seq_length >= generation_config.max_length: - input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" - logger.warning( - f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" - f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" - " increasing `max_new_tokens`." - ) - - # 2. Set generation parameters if not already defined - logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() - stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() - - logits_processor = self._get_logits_processor( - generation_config=generation_config, - input_ids_seq_length=input_ids_seq_length, - encoder_input_ids=input_ids, - prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, - logits_processor=logits_processor, - ) - - stopping_criteria = self._get_stopping_criteria( - generation_config=generation_config, stopping_criteria=stopping_criteria - ) - logits_warper = self._get_logits_warper(generation_config) - - unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) - scores = None - while True: - model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) - # forward pass to get next token - outputs = self( - **model_inputs, - return_dict=True, - output_attentions=False, - output_hidden_states=False, - ) - - next_token_logits = outputs.logits[:, -1, :] - - # pre-process distribution - next_token_scores = logits_processor(input_ids, next_token_logits) - next_token_scores = logits_warper(input_ids, next_token_scores) - - # sample - probs = nn.functional.softmax(next_token_scores, dim=-1) - if generation_config.do_sample: - next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) - else: - next_tokens = torch.argmax(probs, dim=-1) - - # update generated ids, model inputs, and length for next step - input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) - model_kwargs = self._update_model_kwargs_for_generation( - outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder - ) - unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) - if return_past_key_values: - yield input_ids, outputs.past_key_values - else: - yield input_ids - # stop when each sentence is finished, or if we exceed the maximum length - if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): - break - - def quantize(self, bits: int, empty_init=False, device=None, **kwargs): - if bits == 0: - return - - from .quantization import quantize - - if self.quantized: - logger.info("Already quantized.") - return self - - self.quantized = True - - self.config.quantization_bit = bits - - self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, - **kwargs) - return self \ No newline at end of file diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/MODEL_LICENSE b/tests/kit/model_zoo/transformers/chatglm2_6b/MODEL_LICENSE new file mode 100644 index 000000000000..26198b21b6b2 --- /dev/null +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/MODEL_LICENSE @@ -0,0 +1,33 @@ +The ChatGLM2-6B License + +1. Definitions + +“Licensor” means the ChatGLM2-6B Model Team that distributes its Software. + +“Software” means the ChatGLM2-6B model parameters made available under this license. + +2. License Grant + +Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license to use the Software solely for your non-commercial research purposes. + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +3. Restriction + +You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any commercial, military, or illegal purposes. + +You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings. + +4. Disclaimer + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +5. Limitation of Liability + +EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. + +6. Dispute Resolution + +This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing. + +Note that the license is subject to update to a more comprehensive version. For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com. \ No newline at end of file diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py index bae6d425878d..488f24c5fcb9 100644 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py @@ -80,7 +80,6 @@ def default_init(cls, *args, **kwargs): class InvalidScoreLogitsProcessor(LogitsProcessor): - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: if torch.isnan(scores).any() or torch.isinf(scores).any(): scores.zero_() @@ -220,7 +219,6 @@ def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Ten class RMSNorm(torch.nn.Module): - def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): super().__init__() self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) @@ -235,7 +233,6 @@ def forward(self, hidden_states: torch.Tensor): class CoreAttention(torch.nn.Module): - def __init__(self, config: ChatGLMConfig, layer_number): super(CoreAttention, self).__init__() @@ -842,7 +839,6 @@ def forward(self, input_ids): class ChatGLMModel(ChatGLMPreTrainedModel): - def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): super().__init__(config) if empty_init: @@ -981,13 +977,11 @@ def forward( def quantize(self, weight_bit_width: int): from .quantization import quantize - quantize(self.encoder, weight_bit_width) return self class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): - def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): super().__init__(config) diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/quantization.py b/tests/kit/model_zoo/transformers/chatglm2_6b/quantization.py new file mode 100644 index 000000000000..cb95bfe82b20 --- /dev/null +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/quantization.py @@ -0,0 +1,188 @@ +from torch.nn import Linear +from torch.nn.parameter import Parameter + +import bz2 +import torch +import base64 +import ctypes +from transformers.utils import logging + +from typing import List +from functools import partial + +logger = logging.get_logger(__name__) + +try: + from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up + + class Kernel: + def __init__(self, code: bytes, function_names: List[str]): + self.code = code + self._function_names = function_names + self._cmodule = LazyKernelCModule(self.code) + + for name in self._function_names: + setattr(self, name, KernelFunction(self._cmodule, name)) + + quantization_code = "$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" + + kernels = Kernel( + bz2.decompress(base64.b64decode(quantization_code)), + [ + "int4WeightCompression", + "int4WeightExtractionFloat", + "int4WeightExtractionHalf", + "int8WeightExtractionFloat", + "int8WeightExtractionHalf", + ], + ) +except Exception as exception: + kernels = None + logger.warning("Failed to load cpm_kernels:" + str(exception)) + + +class W8A16Linear(torch.autograd.Function): + @staticmethod + def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width): + ctx.inp_shape = inp.size() + ctx.weight_bit_width = weight_bit_width + out_features = quant_w.size(0) + inp = inp.contiguous().view(-1, inp.size(-1)) + weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width) + ctx.weight_shape = weight.size() + output = inp.mm(weight.t()) + ctx.save_for_backward(inp, quant_w, scale_w) + return output.view(*(ctx.inp_shape[:-1] + (out_features,))) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + inp, quant_w, scale_w = ctx.saved_tensors + weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width) + grad_output = grad_output.contiguous().view(-1, weight.size(0)) + grad_input = grad_output.mm(weight) + grad_weight = grad_output.t().mm(inp) + return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None + + +def compress_int4_weight(weight: torch.Tensor): # (n, m) + with torch.cuda.device(weight.device): + n, m = weight.size(0), weight.size(1) + assert m % 2 == 0 + m = m // 2 + out = torch.empty(n, m, dtype=torch.int8, device="cuda") + stream = torch.cuda.current_stream() + + gridDim = (n, 1, 1) + blockDim = (min(round_up(m, 32), 1024), 1, 1) + + kernels.int4WeightCompression( + gridDim, + blockDim, + 0, + stream, + [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)], + ) + return out + + +def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int): + assert scale_list.dtype in [torch.half, torch.bfloat16] + assert weight.dtype in [torch.int8] + if source_bit_width == 8: + return weight.to(scale_list.dtype) * scale_list[:, None] + elif source_bit_width == 4: + func = ( + kernels.int4WeightExtractionHalf if scale_list.dtype == torch.half else kernels.int4WeightExtractionBFloat16 + ) + else: + assert False, "Unsupported bit-width" + + with torch.cuda.device(weight.device): + n, m = weight.size(0), weight.size(1) + out = torch.empty(n, m * (8 // source_bit_width), dtype=scale_list.dtype, device="cuda") + stream = torch.cuda.current_stream() + + gridDim = (n, 1, 1) + blockDim = (min(round_up(m, 32), 1024), 1, 1) + + func( + gridDim, + blockDim, + 0, + stream, + [ + ctypes.c_void_p(weight.data_ptr()), + ctypes.c_void_p(scale_list.data_ptr()), + ctypes.c_void_p(out.data_ptr()), + ctypes.c_int32(n), + ctypes.c_int32(m), + ], + ) + return out + + +class QuantizedLinear(torch.nn.Module): + def __init__(self, weight_bit_width: int, weight, bias=None, device="cpu", dtype=None, empty_init=False, *args, + **kwargs): + super().__init__() + self.weight_bit_width = weight_bit_width + + shape = weight.shape + + if weight is None or empty_init: + self.weight = torch.empty(shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=device) + self.weight_scale = torch.empty(shape[0], dtype=dtype, device=device) + else: + self.weight_scale = weight.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1) + self.weight = torch.round(weight / self.weight_scale[:, None]).to(torch.int8) + if weight_bit_width == 4: + self.weight = compress_int4_weight(self.weight) + + self.weight = Parameter(self.weight.to(device), requires_grad=False) + self.weight_scale = Parameter(self.weight_scale.to(device), requires_grad=False) + self.bias = Parameter(bias.to(device), requires_grad=False) if bias is not None else None + + def forward(self, input): + output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width) + if self.bias is not None: + output = output + self.bias + return output + + +def quantize(model, weight_bit_width, empty_init=False, device=None): + """Replace fp16 linear with quantized linear""" + for layer in model.layers: + layer.self_attention.query_key_value = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.self_attention.query_key_value.weight.to(torch.cuda.current_device()), + bias=layer.self_attention.query_key_value.bias, + dtype=layer.self_attention.query_key_value.weight.dtype, + device=layer.self_attention.query_key_value.weight.device if device is None else device, + empty_init=empty_init + ) + layer.self_attention.dense = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.self_attention.dense.weight.to(torch.cuda.current_device()), + bias=layer.self_attention.dense.bias, + dtype=layer.self_attention.dense.weight.dtype, + device=layer.self_attention.dense.weight.device if device is None else device, + empty_init=empty_init + ) + layer.mlp.dense_h_to_4h = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.mlp.dense_h_to_4h.weight.to(torch.cuda.current_device()), + bias=layer.mlp.dense_h_to_4h.bias, + dtype=layer.mlp.dense_h_to_4h.weight.dtype, + device=layer.mlp.dense_h_to_4h.weight.device if device is None else device, + empty_init=empty_init + ) + layer.mlp.dense_4h_to_h = QuantizedLinear( + weight_bit_width=weight_bit_width, + weight=layer.mlp.dense_4h_to_h.weight.to(torch.cuda.current_device()), + bias=layer.mlp.dense_4h_to_h.bias, + dtype=layer.mlp.dense_4h_to_h.weight.dtype, + device=layer.mlp.dense_4h_to_h.weight.device if device is None else device, + empty_init=empty_init + ) + + return model From 8620009dd70bb103aa593463d8819c6508334eba Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Mon, 10 Jul 2023 18:55:33 +0800 Subject: [PATCH 56/84] [sharformer] add first version of policy of chatglm --- colossalai/shardformer/policies/chatglm.py | 44 +++++++++++++++++++ .../test_model/test_shard_chatglm.py | 1 - 2 files changed, 44 insertions(+), 1 deletion(-) diff --git a/colossalai/shardformer/policies/chatglm.py b/colossalai/shardformer/policies/chatglm.py index 934b99b83ea1..c17b92c8dc81 100644 --- a/colossalai/shardformer/policies/chatglm.py +++ b/colossalai/shardformer/policies/chatglm.py @@ -1,6 +1,7 @@ from typing import Dict, Union import torch.nn as nn +from ....tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock import colossalai.shardformer.layer as col_nn @@ -8,6 +9,49 @@ __all__ = ['ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy'] +class ChatGLMModelPolicy(Policy): + + def config_sanity_check(self): + pass + + def preprocess(self): + # Resize embedding + vocab_size = self.model.config.vocab_size + world_size = self.shard_config.tensor_parallel_size + + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + + return self.model + + def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: + from ....tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock + + policy = {} + + if self.shard_config.enable_tensor_parallelism: + + policy[GLMBlock] = ModulePolicyDescription( + attribute_replacement = {}, + sub_module_replacement = [ + # SubModuleReplacementDescription( + # suffix = "self_attention.query_key_value", + # target_module = col_nn.Linear1D_Col, + # ), + # SubModuleReplacementDescription( + # suffix = "self_attention.dense", + # target_module = col_nn.Linear1D_Row, + # ) + # SubModuleReplacementDescription( + # suffix = "self_attention.core_attention.attention_dropout", + # target_module = col_nn.DropoutForParallelInput, + # ) + ],) + + + def postprocess(self): + return self.model class ChatGLMModelPolicy(Policy): diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index 2cdf5da2e6da..f05649fcb9a0 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -19,7 +19,6 @@ from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, run_forward - def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): # check forward org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, From 1a29e8fc297b8ea557dde1909a4b65f91e53b824 Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Wed, 12 Jul 2023 15:25:07 +0800 Subject: [PATCH 57/84] [shardformer] polish chatglm code --- .../shardformer/policies/auto_policy.py | 3 ++ colossalai/shardformer/policies/chatglm.py | 44 ------------------- .../test_model/test_shard_chatglm.py | 1 + 3 files changed, 4 insertions(+), 44 deletions(-) diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index 90347a984599..e383630408ff 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -116,6 +116,9 @@ class PolicyLocation: # Sam "transformers.models.sam.modeling_sam.SamModel": PolicyLocation(file_name="sam", class_name="SamModelPolicy"), + # ChatGLM + "tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm.ChatGLMModel": + PolicyLocation(file_name="chatglm", class_name="ChatGLMModelPolicy"), } diff --git a/colossalai/shardformer/policies/chatglm.py b/colossalai/shardformer/policies/chatglm.py index c17b92c8dc81..934b99b83ea1 100644 --- a/colossalai/shardformer/policies/chatglm.py +++ b/colossalai/shardformer/policies/chatglm.py @@ -1,7 +1,6 @@ from typing import Dict, Union import torch.nn as nn -from ....tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock import colossalai.shardformer.layer as col_nn @@ -9,49 +8,6 @@ __all__ = ['ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy'] -class ChatGLMModelPolicy(Policy): - - def config_sanity_check(self): - pass - - def preprocess(self): - # Resize embedding - vocab_size = self.model.config.vocab_size - world_size = self.shard_config.tensor_parallel_size - - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) - - return self.model - - def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - from ....tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock - - policy = {} - - if self.shard_config.enable_tensor_parallelism: - - policy[GLMBlock] = ModulePolicyDescription( - attribute_replacement = {}, - sub_module_replacement = [ - # SubModuleReplacementDescription( - # suffix = "self_attention.query_key_value", - # target_module = col_nn.Linear1D_Col, - # ), - # SubModuleReplacementDescription( - # suffix = "self_attention.dense", - # target_module = col_nn.Linear1D_Row, - # ) - # SubModuleReplacementDescription( - # suffix = "self_attention.core_attention.attention_dropout", - # target_module = col_nn.DropoutForParallelInput, - # ) - ],) - - - def postprocess(self): - return self.model class ChatGLMModelPolicy(Policy): diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index f05649fcb9a0..2cdf5da2e6da 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -19,6 +19,7 @@ from tests.kit.model_zoo import model_zoo from tests.test_shardformer.test_model._utils import build_model, run_forward + def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): # check forward org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, From cbb54d3202c6935edf11e481fc43929c410fdf1a Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Thu, 13 Jul 2023 19:51:25 +0800 Subject: [PATCH 58/84] [shardformer] polish code --- .../model_zoo/transformers/chatglm2_6b/modeling_chatglm.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py index 488f24c5fcb9..f704715e1245 100644 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py @@ -80,6 +80,7 @@ def default_init(cls, *args, **kwargs): class InvalidScoreLogitsProcessor(LogitsProcessor): + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: if torch.isnan(scores).any() or torch.isinf(scores).any(): scores.zero_() @@ -219,6 +220,7 @@ def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Ten class RMSNorm(torch.nn.Module): + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): super().__init__() self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) @@ -233,6 +235,7 @@ def forward(self, hidden_states: torch.Tensor): class CoreAttention(torch.nn.Module): + def __init__(self, config: ChatGLMConfig, layer_number): super(CoreAttention, self).__init__() @@ -839,6 +842,7 @@ def forward(self, input_ids): class ChatGLMModel(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): super().__init__(config) if empty_init: @@ -921,6 +925,7 @@ def forward( if inputs_embeds is None: inputs_embeds = self.embedding(input_ids) + print(inputs_embeds) if self.pre_seq_len is not None: if past_key_values is None: @@ -982,6 +987,7 @@ def quantize(self, weight_bit_width: int): class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): super().__init__(config) From dad00c42aa79e726d55e778a9c5c681714882d43 Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Fri, 14 Jul 2023 18:10:52 +0800 Subject: [PATCH 59/84] [shardformer] support chatglm without layernorm --- .../chatglm2_6b/modeling_chatglm.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py index f704715e1245..46078f441523 100644 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py @@ -396,17 +396,18 @@ def __init__(self, config: ChatGLMConfig, layer_number, device=None): self.num_multi_query_groups_per_partition = config.multi_query_group_num self.qkv_hidden_size = (self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num) +<<<<<<< HEAD self.query_key_value = nn.Linear( config.hidden_size, self.qkv_hidden_size, - bias=config.add_bias_linear or config.add_qkv_bias, device=device, **_config_to_kwargs(config), ) - - self.core_attention = CoreAttention(config, self.layer_number) - - # Output. +======= + self.query_key_value = nn.Linear(self.hidden_size, + self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, +<<<<<<< HEAD self.dense = nn.Linear( self.projection_size, config.hidden_size, @@ -414,6 +415,13 @@ def __init__(self, config: ChatGLMConfig, layer_number, device=None): device=device, **_config_to_kwargs(config), ) +======= + self.dense = nn.Linear(self.projection_size, + self.hidden_size, + bias=config.add_bias_linear, + device=device, + **_config_to_kwargs(config)) +>>>>>>> [shardformer] support chatglm without layernorm def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): if self.multi_query_attention: @@ -925,7 +933,6 @@ def forward( if inputs_embeds is None: inputs_embeds = self.embedding(input_ids) - print(inputs_embeds) if self.pre_seq_len is not None: if past_key_values is None: From 00f6ef159d10d3d4fcbf3f1fbfbd471fa726c9eb Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Mon, 17 Jul 2023 15:10:15 +0800 Subject: [PATCH 60/84] [shardformer] delete some file --- =2.0 | 134 ------------- .../transformers/chatglm2_6b/MODEL_LICENSE | 33 --- .../transformers/chatglm2_6b/quantization.py | 188 ------------------ 3 files changed, 355 deletions(-) delete mode 100644 =2.0 delete mode 100644 tests/kit/model_zoo/transformers/chatglm2_6b/MODEL_LICENSE delete mode 100644 tests/kit/model_zoo/transformers/chatglm2_6b/quantization.py diff --git a/=2.0 b/=2.0 deleted file mode 100644 index af47ce17aa8e..000000000000 --- a/=2.0 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python-multipart, pydub, orjson, mdit-py-plugins, markdown, latex2mathml, gradio-client, ffmpy, fastapi, altair, aiofiles, sentencepiece, protobuf, mdtex2html, gradio, cpm-kernels, accelerate -Successfully installed accelerate-0.20.3 aiofiles-23.1.0 altair-5.0.1 anyio-3.7.1 cpm-kernels-1.0.11 fastapi-0.99.1 ffmpy-0.3.0 gradio-3.36.0 gradio-client-0.2.7 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 jsonschema-4.18.0 jsonschema-specifications-2023.6.1 latex2mathml-3.76.0 linkify-it-py-2.0.2 markdown-3.4.3 mdit-py-plugins-0.3.3 mdtex2html-1.2.0 orjson-3.9.1 protobuf-4.23.4 pydantic-1.10.11 pydub-0.25.1 python-multipart-0.0.6 referencing-0.29.1 rpds-py-0.8.8 semantic-version-2.10.0 sentencepiece-0.1.99 sniffio-1.3.0 starlette-0.27.0 toolz-0.12.0 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3 diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/MODEL_LICENSE b/tests/kit/model_zoo/transformers/chatglm2_6b/MODEL_LICENSE deleted file mode 100644 index 26198b21b6b2..000000000000 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/MODEL_LICENSE +++ /dev/null @@ -1,33 +0,0 @@ -The ChatGLM2-6B License - -1. Definitions - -“Licensor” means the ChatGLM2-6B Model Team that distributes its Software. - -“Software” means the ChatGLM2-6B model parameters made available under this license. - -2. License Grant - -Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license to use the Software solely for your non-commercial research purposes. - -The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. - -3. Restriction - -You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any commercial, military, or illegal purposes. - -You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings. - -4. Disclaimer - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - -5. Limitation of Liability - -EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. - -6. Dispute Resolution - -This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing. - -Note that the license is subject to update to a more comprehensive version. For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com. \ No newline at end of file diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/quantization.py b/tests/kit/model_zoo/transformers/chatglm2_6b/quantization.py deleted file mode 100644 index cb95bfe82b20..000000000000 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/quantization.py +++ /dev/null @@ -1,188 +0,0 @@ -from torch.nn import Linear -from torch.nn.parameter import Parameter - -import bz2 -import torch -import base64 -import ctypes -from transformers.utils import logging - -from typing import List -from functools import partial - -logger = logging.get_logger(__name__) - -try: - from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up - - class Kernel: - def __init__(self, code: bytes, function_names: List[str]): - self.code = code - self._function_names = function_names - self._cmodule = LazyKernelCModule(self.code) - - for name in self._function_names: - setattr(self, name, KernelFunction(self._cmodule, name)) - - quantization_code = "$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" - - kernels = Kernel( - bz2.decompress(base64.b64decode(quantization_code)), - [ - "int4WeightCompression", - "int4WeightExtractionFloat", - "int4WeightExtractionHalf", - "int8WeightExtractionFloat", - "int8WeightExtractionHalf", - ], - ) -except Exception as exception: - kernels = None - logger.warning("Failed to load cpm_kernels:" + str(exception)) - - -class W8A16Linear(torch.autograd.Function): - @staticmethod - def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width): - ctx.inp_shape = inp.size() - ctx.weight_bit_width = weight_bit_width - out_features = quant_w.size(0) - inp = inp.contiguous().view(-1, inp.size(-1)) - weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width) - ctx.weight_shape = weight.size() - output = inp.mm(weight.t()) - ctx.save_for_backward(inp, quant_w, scale_w) - return output.view(*(ctx.inp_shape[:-1] + (out_features,))) - - @staticmethod - def backward(ctx, grad_output: torch.Tensor): - inp, quant_w, scale_w = ctx.saved_tensors - weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width) - grad_output = grad_output.contiguous().view(-1, weight.size(0)) - grad_input = grad_output.mm(weight) - grad_weight = grad_output.t().mm(inp) - return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None - - -def compress_int4_weight(weight: torch.Tensor): # (n, m) - with torch.cuda.device(weight.device): - n, m = weight.size(0), weight.size(1) - assert m % 2 == 0 - m = m // 2 - out = torch.empty(n, m, dtype=torch.int8, device="cuda") - stream = torch.cuda.current_stream() - - gridDim = (n, 1, 1) - blockDim = (min(round_up(m, 32), 1024), 1, 1) - - kernels.int4WeightCompression( - gridDim, - blockDim, - 0, - stream, - [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)], - ) - return out - - -def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int): - assert scale_list.dtype in [torch.half, torch.bfloat16] - assert weight.dtype in [torch.int8] - if source_bit_width == 8: - return weight.to(scale_list.dtype) * scale_list[:, None] - elif source_bit_width == 4: - func = ( - kernels.int4WeightExtractionHalf if scale_list.dtype == torch.half else kernels.int4WeightExtractionBFloat16 - ) - else: - assert False, "Unsupported bit-width" - - with torch.cuda.device(weight.device): - n, m = weight.size(0), weight.size(1) - out = torch.empty(n, m * (8 // source_bit_width), dtype=scale_list.dtype, device="cuda") - stream = torch.cuda.current_stream() - - gridDim = (n, 1, 1) - blockDim = (min(round_up(m, 32), 1024), 1, 1) - - func( - gridDim, - blockDim, - 0, - stream, - [ - ctypes.c_void_p(weight.data_ptr()), - ctypes.c_void_p(scale_list.data_ptr()), - ctypes.c_void_p(out.data_ptr()), - ctypes.c_int32(n), - ctypes.c_int32(m), - ], - ) - return out - - -class QuantizedLinear(torch.nn.Module): - def __init__(self, weight_bit_width: int, weight, bias=None, device="cpu", dtype=None, empty_init=False, *args, - **kwargs): - super().__init__() - self.weight_bit_width = weight_bit_width - - shape = weight.shape - - if weight is None or empty_init: - self.weight = torch.empty(shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=device) - self.weight_scale = torch.empty(shape[0], dtype=dtype, device=device) - else: - self.weight_scale = weight.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1) - self.weight = torch.round(weight / self.weight_scale[:, None]).to(torch.int8) - if weight_bit_width == 4: - self.weight = compress_int4_weight(self.weight) - - self.weight = Parameter(self.weight.to(device), requires_grad=False) - self.weight_scale = Parameter(self.weight_scale.to(device), requires_grad=False) - self.bias = Parameter(bias.to(device), requires_grad=False) if bias is not None else None - - def forward(self, input): - output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width) - if self.bias is not None: - output = output + self.bias - return output - - -def quantize(model, weight_bit_width, empty_init=False, device=None): - """Replace fp16 linear with quantized linear""" - for layer in model.layers: - layer.self_attention.query_key_value = QuantizedLinear( - weight_bit_width=weight_bit_width, - weight=layer.self_attention.query_key_value.weight.to(torch.cuda.current_device()), - bias=layer.self_attention.query_key_value.bias, - dtype=layer.self_attention.query_key_value.weight.dtype, - device=layer.self_attention.query_key_value.weight.device if device is None else device, - empty_init=empty_init - ) - layer.self_attention.dense = QuantizedLinear( - weight_bit_width=weight_bit_width, - weight=layer.self_attention.dense.weight.to(torch.cuda.current_device()), - bias=layer.self_attention.dense.bias, - dtype=layer.self_attention.dense.weight.dtype, - device=layer.self_attention.dense.weight.device if device is None else device, - empty_init=empty_init - ) - layer.mlp.dense_h_to_4h = QuantizedLinear( - weight_bit_width=weight_bit_width, - weight=layer.mlp.dense_h_to_4h.weight.to(torch.cuda.current_device()), - bias=layer.mlp.dense_h_to_4h.bias, - dtype=layer.mlp.dense_h_to_4h.weight.dtype, - device=layer.mlp.dense_h_to_4h.weight.device if device is None else device, - empty_init=empty_init - ) - layer.mlp.dense_4h_to_h = QuantizedLinear( - weight_bit_width=weight_bit_width, - weight=layer.mlp.dense_4h_to_h.weight.to(torch.cuda.current_device()), - bias=layer.mlp.dense_4h_to_h.bias, - dtype=layer.mlp.dense_4h_to_h.weight.dtype, - device=layer.mlp.dense_4h_to_h.weight.device if device is None else device, - empty_init=empty_init - ) - - return model From f155ae89c498f3f13b4cbd7b8b6a8074441a3733 Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Mon, 17 Jul 2023 19:47:57 +0800 Subject: [PATCH 61/84] [shardformer] ChatGLM support layernorm sharding --- .../kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py index 46078f441523..04d318d47868 100644 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py @@ -417,7 +417,7 @@ def __init__(self, config: ChatGLMConfig, layer_number, device=None): ) ======= self.dense = nn.Linear(self.projection_size, - self.hidden_size, + config.hidden_size, bias=config.add_bias_linear, device=device, **_config_to_kwargs(config)) From 91850fe9840408f28002bf536281758acbe9a3ff Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Tue, 18 Jul 2023 12:33:12 +0800 Subject: [PATCH 62/84] [shardformer] register without auto policy --- colossalai/shardformer/policies/auto_policy.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index e383630408ff..90347a984599 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -116,9 +116,6 @@ class PolicyLocation: # Sam "transformers.models.sam.modeling_sam.SamModel": PolicyLocation(file_name="sam", class_name="SamModelPolicy"), - # ChatGLM - "tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm.ChatGLMModel": - PolicyLocation(file_name="chatglm", class_name="ChatGLMModelPolicy"), } From 4da05052f4ea23a1e60224ff65c6de8e75bca1b9 Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Wed, 19 Jul 2023 11:39:59 +0800 Subject: [PATCH 63/84] [shardformer] pre-commit check files --- .../chatglm2_6b/modeling_chatglm.py | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py index 04d318d47868..bae6d425878d 100644 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py @@ -396,18 +396,17 @@ def __init__(self, config: ChatGLMConfig, layer_number, device=None): self.num_multi_query_groups_per_partition = config.multi_query_group_num self.qkv_hidden_size = (self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num) -<<<<<<< HEAD self.query_key_value = nn.Linear( config.hidden_size, self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, device=device, **_config_to_kwargs(config), ) -======= - self.query_key_value = nn.Linear(self.hidden_size, - self.qkv_hidden_size, - bias=config.add_bias_linear or config.add_qkv_bias, -<<<<<<< HEAD + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. self.dense = nn.Linear( self.projection_size, config.hidden_size, @@ -415,13 +414,6 @@ def __init__(self, config: ChatGLMConfig, layer_number, device=None): device=device, **_config_to_kwargs(config), ) -======= - self.dense = nn.Linear(self.projection_size, - config.hidden_size, - bias=config.add_bias_linear, - device=device, - **_config_to_kwargs(config)) ->>>>>>> [shardformer] support chatglm without layernorm def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): if self.multi_query_attention: @@ -989,6 +981,7 @@ def forward( def quantize(self, weight_bit_width: int): from .quantization import quantize + quantize(self.encoder, weight_bit_width) return self From 8120eca0c0cc4b10f09c2eeb3233d49396ced816 Mon Sep 17 00:00:00 2001 From: klhhhhh <1412841649@qq.com> Date: Thu, 20 Jul 2023 19:14:04 +0800 Subject: [PATCH 64/84] [shardformer] support ChatGLMForConditionalGeneration & add fusedlayernorm for vit --- colossalai/shardformer/policies/chatglm.py | 24 +++++++++++++++++++ colossalai/shardformer/policies/vit.py | 2 +- tests/kit/model_zoo/transformers/chatglm.py | 11 +++++++-- .../test_model/test_shard_chatglm.py | 4 +++- 4 files changed, 37 insertions(+), 4 deletions(-) diff --git a/colossalai/shardformer/policies/chatglm.py b/colossalai/shardformer/policies/chatglm.py index 934b99b83ea1..46aa3b52af8f 100644 --- a/colossalai/shardformer/policies/chatglm.py +++ b/colossalai/shardformer/policies/chatglm.py @@ -90,7 +90,31 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: policy=policy, target_key=ChatGLMModel) + else: + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription(suffix="input_layernorm", target_module=col_nn.FusedRMSNorm), + SubModuleReplacementDescription(suffix="post_attention_layernorm", + target_module=col_nn.FusedRMSNorm) + ], + policy=policy, + target_key=GLMBlock) + + if self.model.config.post_layer_norm: + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription(suffix="encoder.final_layernorm", + target_module=col_nn.FusedRMSNorm) + ], + policy=policy, + target_key=ChatGLMModel) + return policy def postprocess(self): return self.model + + +class ChatGLMForConditionalGenerationPolicy(ChatGLMModelPolicy): + + def module_policy(self): + policy = super().module_policy() + return policy diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index 96f27de2a7c8..1feb11ffcf24 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -23,7 +23,7 @@ def preprocess(self): return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer + from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer, ViTModel policy = {} diff --git a/tests/kit/model_zoo/transformers/chatglm.py b/tests/kit/model_zoo/transformers/chatglm.py index 1408babede64..04e73a832abe 100644 --- a/tests/kit/model_zoo/transformers/chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm.py @@ -3,7 +3,7 @@ from ..registry import ModelAttribute, model_zoo from .chatglm2_6b.configuration_chatglm import ChatGLMConfig -from .chatglm2_6b.modeling_chatglm import ChatGLMModel +from .chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel # ================================ # Register single-sentence ChatGLM @@ -21,7 +21,7 @@ def data_gen(): # define loss function loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.mean() -loss_fn = lambda x: x.loss +loss_fn = lambda x: x.logits.mean() config = ChatGLMConfig(num_layers=1, padded_vocab_size=65024, hidden_size=64, @@ -36,3 +36,10 @@ def data_gen(): output_transform_fn=output_transform_fn, loss_fn=loss_fn_for_chatglm_model, model_attribute=ModelAttribute(has_control_flow=True)) + +model_zoo.register(name="transformers_chatglm_for_conditional_generation", + model_fn=lambda: ChatGLMForConditionalGeneration(config, empty_init=False), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index 2cdf5da2e6da..a0fa4bd82e74 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -7,7 +7,7 @@ import colossalai from colossalai.logging import disable_existing_loggers from colossalai.shardformer import ShardConfig, ShardFormer -from colossalai.shardformer.policies.chatglm import ChatGLMModelPolicy +from colossalai.shardformer.policies.chatglm import ChatGLMForConditionalGenerationPolicy, ChatGLMModelPolicy from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, @@ -85,6 +85,8 @@ def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism): shard_former = ShardFormer(shard_config=shard_config) if name == "transformers_chatglm": sharded_model = shard_former.optimize(model_copy, ChatGLMModelPolicy()).cuda() + else: + sharded_model = shard_former.optimize(model_copy, ChatGLMForConditionalGenerationPolicy()).cuda() check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() From 879301d0dad26a056e0e90d8c9c9d6cc4a662c9a Mon Sep 17 00:00:00 2001 From: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Date: Tue, 25 Jul 2023 14:29:10 +0800 Subject: [PATCH 65/84] [shardformer] support Blip2 (#4243) * support base blip2 * add support for downstream blip2 model * update readme * add forward injection * skip not compatible models test * fix test for gemini and low_level_zero_pugin --- colossalai/shardformer/README.md | 3 +- colossalai/shardformer/modeling/blip2.py | 60 ++++ colossalai/shardformer/modeling/sam.py | 2 - .../shardformer/policies/auto_policy.py | 6 + colossalai/shardformer/policies/blip2.py | 304 ++++++++++++++++++ tests/kit/model_zoo/transformers/__init__.py | 1 + tests/kit/model_zoo/transformers/blip2.py | 61 ++++ .../test_model/test_shard_blip2.py | 107 ++++++ 8 files changed, 541 insertions(+), 3 deletions(-) create mode 100644 colossalai/shardformer/modeling/blip2.py create mode 100644 colossalai/shardformer/policies/blip2.py create mode 100644 tests/kit/model_zoo/transformers/blip2.py create mode 100644 tests/test_shardformer/test_model/test_shard_blip2.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 3c322aabf2ef..5489f97e4d19 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -104,7 +104,8 @@ We will follow this roadmap to develop Shardformer: - [ ] Audio - [x] Whisper - [ ] Multi-modal - - [ ] To be added + - [x] SAM + - [x] BLIP-2 ## 💡 API Design diff --git a/colossalai/shardformer/modeling/blip2.py b/colossalai/shardformer/modeling/blip2.py new file mode 100644 index 000000000000..b7945423ae83 --- /dev/null +++ b/colossalai/shardformer/modeling/blip2.py @@ -0,0 +1,60 @@ +from typing import Optional, Tuple, Union + +import torch +import torch.nn as nn + + +def forward_fn(): + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + bsz, tgt_len, embed_dim = hidden_states.size() + + mixed_qkv = self.qkv(hidden_states) + + # modified from original code, which is: + # mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads).permute( + # 2, 0, 3, 1, 4 + # ) + # to: + mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + query_states, key_states, value_states = ( + mixed_qkv[0], + mixed_qkv[1], + mixed_qkv[2], + ) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) + + attention_scores = attention_scores * self.scale + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3) + + new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,) + context_layer = context_layer.reshape(new_context_layer_shape) + + output = self.projection(context_layer) + + outputs = (output, attention_probs) if output_attentions else (output, None) + + return outputs + + return forward diff --git a/colossalai/shardformer/modeling/sam.py b/colossalai/shardformer/modeling/sam.py index 00e2d744e219..63ebfe89d5fa 100644 --- a/colossalai/shardformer/modeling/sam.py +++ b/colossalai/shardformer/modeling/sam.py @@ -1,6 +1,4 @@ import torch -import torch.distributed as dist -from torch.distributed import ProcessGroup def forward_fn(): diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index 90347a984599..2a041af19be8 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -116,6 +116,12 @@ class PolicyLocation: # Sam "transformers.models.sam.modeling_sam.SamModel": PolicyLocation(file_name="sam", class_name="SamModelPolicy"), + + # Blip2 + "transformers.models.blip_2.modeling_blip_2.Blip2Model": + PolicyLocation(file_name="blip2", class_name="Blip2ModelPolicy"), + "transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGeneration": + PolicyLocation(file_name="blip2", class_name="Blip2ForConditionalGenerationPolicy"), } diff --git a/colossalai/shardformer/policies/blip2.py b/colossalai/shardformer/policies/blip2.py new file mode 100644 index 000000000000..43aa1adc1c5b --- /dev/null +++ b/colossalai/shardformer/policies/blip2.py @@ -0,0 +1,304 @@ +import torch.nn as nn + +import colossalai.shardformer.layer as col_nn + +from .._utils import getattr_, setattr_ +from ..modeling.blip2 import forward_fn +from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription + +__all__ = ['BlipPolicy', 'BlipModelPolicy'] + + +class BlipPolicy(Policy): + + def config_sanity_check(self): + pass + + def preprocess(self): + # reshape the embedding layer + r""" + Reshape the Embedding layer to make the embedding dimension divisible by world_size + """ + # TODO: + vocab_size = self.model.config.qformer_config.vocab_size + world_size = self.shard_config.tensor_parallel_size + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) + return self.model + + def module_policy(self): + from transformers.models.blip_2.modeling_blip_2 import ( + Blip2Attention, + Blip2EncoderLayer, + Blip2QFormerLayer, + Blip2QFormerModel, + Blip2VisionModel, + ) + from transformers.models.opt.modeling_opt import OPTDecoderLayer, OPTForCausalLM + + policy = {} + + if self.shard_config.enable_tensor_parallelism: + policy[Blip2EncoderLayer] = ModulePolicyDescription(attribute_replacement={ + "self_attn.num_heads": + self.model.config.vision_config.num_attention_heads // self.shard_config.tensor_parallel_size, + "self_attn.embed_dim": + self.model.config.vision_config.hidden_size // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attn.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="self_attn.qkv", + target_module=col_nn.FusedLinear1D_Col, + kwargs={ + "n_fused": 3, + }), + SubModuleReplacementDescription( + suffix="self_attn.projection", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="mlp.fc1", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="mlp.fc2", + target_module=col_nn.Linear1D_Row, + ), + ]) + + policy[Blip2QFormerModel] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="dropout", + target_module=col_nn.DropoutForParallelInput, + ), + ]) + + policy[Blip2QFormerLayer] = ModulePolicyDescription(attribute_replacement={ + "attention.attention.num_attention_heads": + self.model.config.qformer_config.num_attention_heads // self.shard_config.tensor_parallel_size, + "attention.attention.all_head_size": + self.model.config.qformer_config.hidden_size // self.shard_config.tensor_parallel_size, + "crossattention.attention.num_attention_heads": + self.model.config.qformer_config.num_attention_heads // self.shard_config.tensor_parallel_size, + "crossattention.attention.all_head_size": + self.model.config.qformer_config.hidden_size // self.shard_config.tensor_parallel_size, + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attention.attention.query", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.key", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.value", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="attention.attention.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="attention.output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="attention.output.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="crossattention.attention.query", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="crossattention.attention.key", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="crossattention.attention.value", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="crossattention.attention.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="crossattention.output.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="crossattention.output.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="intermediate_query.dense", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="output_query.dense", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="output_query.dropout", + target_module=col_nn.DropoutForParallelInput, + ) + ]) + + policy[OPTDecoderLayer] = ModulePolicyDescription(attribute_replacement={ + "self_attn.embed_dim": + self.model.config.text_config.hidden_size // self.shard_config.tensor_parallel_size, + "self_attn.num_heads": + self.model.config.text_config.num_attention_heads // self.shard_config.tensor_parallel_size + }, + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="self_attn.q_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.k_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.v_proj", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="self_attn.out_proj", + target_module=col_nn.Linear1D_Row, + ), + SubModuleReplacementDescription( + suffix="fc1", + target_module=col_nn.Linear1D_Col, + ), + SubModuleReplacementDescription( + suffix="fc2", + target_module=col_nn.Linear1D_Row, + ) + ]) + + policy[OPTForCausalLM] = ModulePolicyDescription(sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="model.decoder.embed_tokens", + target_module=col_nn.VocabParallelEmbedding1D, + ), + SubModuleReplacementDescription( + suffix="lm_head", + target_module=col_nn.Linear1D_Col, + kwargs={"gather_output": True}, + ), + ]) + + policy[Blip2Attention] = ModulePolicyDescription(method_replacement={"forward": forward_fn()}) + + # optimization configuration + if self.shard_config.enable_fused_normalization: + # Handle Blip2EncoderLayer layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="layer_norm1", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="layer_norm2", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=Blip2EncoderLayer) + + # handle Blip2VisionModel layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="post_layernorm", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=Blip2VisionModel) + + # handle Blip2VisionModel layer + self.append_or_create_submodule_replacement( + description=[SubModuleReplacementDescription( + suffix="layernorm", + target_module=col_nn.FusedLayerNorm, + )], + policy=policy, + target_key=Blip2QFormerModel) + + # handle Blip2QFormerLayer layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="attention.output.LayerNorm", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="crossattention.output.LayerNorm", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="output_query.LayerNorm", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=Blip2QFormerLayer) + + # handle OPTForCausalLM layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="model.decoder.final_layer_norm", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=OPTForCausalLM) + + # handle OPTDecoderLayer layer + self.append_or_create_submodule_replacement(description=[ + SubModuleReplacementDescription( + suffix="self_attn_layer_norm", + target_module=col_nn.FusedLayerNorm, + ), + SubModuleReplacementDescription( + suffix="final_layer_norm", + target_module=col_nn.FusedLayerNorm, + ) + ], + policy=policy, + target_key=OPTDecoderLayer) + + return policy + + def postprocess(self): + binding_map = { + 'language_model.model.decoder.embed_tokens': 'language_model.lm_head', + } + + for k, v in binding_map.items(): + src_mod = getattr_(self.model, k) + dst_mod = getattr_(self.model, v) + dst_mod.weight = src_mod.weight + + return self.model + + +# Blip2Model +class Blip2ModelPolicy(BlipPolicy): + + def __init__(self) -> None: + super().__init__() + + +# Blip2ForConditionalGeneration +class Blip2ForConditionalGenerationPolicy(BlipPolicy): + + def __init__(self) -> None: + super().__init__() diff --git a/tests/kit/model_zoo/transformers/__init__.py b/tests/kit/model_zoo/transformers/__init__.py index 08a118e5783d..823ca032fc30 100644 --- a/tests/kit/model_zoo/transformers/__init__.py +++ b/tests/kit/model_zoo/transformers/__init__.py @@ -1,5 +1,6 @@ from .albert import * from .bert import * +from .blip2 import * from .bloom import * from .chatglm import * from .gpt import * diff --git a/tests/kit/model_zoo/transformers/blip2.py b/tests/kit/model_zoo/transformers/blip2.py new file mode 100644 index 000000000000..7338f740be7f --- /dev/null +++ b/tests/kit/model_zoo/transformers/blip2.py @@ -0,0 +1,61 @@ +import torch +import transformers + +from ..registry import ModelAttribute, model_zoo + +# =============================== +# Register single-image SAM +# =============================== + + +# define data gen function +def data_gen(): + # Generated from following code snippet + # + # from PIL import Image + # import requests + # from transformers import Blip2Processor, Blip2Model + # import torch + + # processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") + # url = "http://images.cocodataset.org/val2017/000000039769.jpg" + # image = Image.open(requests.get(url, stream=True).raw) + + # prompt = "Question: how many cats are there? Answer:" + # inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16) + + pixel_values = torch.rand(1, 3, 224, 224, dtype=torch.float32) + input_ids = torch.tensor([[2, 45641, 35, 141, 171, 10017, 32, 89, 116, 31652, 35]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) + labels = torch.tensor([[34, 56]], dtype=torch.int64) + return dict(pixel_values=pixel_values, input_ids=input_ids, attention_mask=attention_mask, labels=labels) + + +# define output transform function +output_transform_fn = lambda x: x + +# define loss funciton +loss_fn_blip2_model = lambda x: x.loss + +config = transformers.Blip2Config() +config.text_config.num_hidden_layers = 1 +config.qformer_config.num_hidden_layers = 1 +config.vision_config.num_hidden_layers = 1 +config.qformer_config.attention_probs_dropout_prob = 0 +config.qformer_config.hidden_dropout_prob = 0 +config.text_config.dropout = 0 + +# register the blip2 variants +model_zoo.register(name='transformers_blip2', + model_fn=lambda: transformers.Blip2Model(config), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_blip2_model, + model_attribute=ModelAttribute(has_control_flow=True)) + +model_zoo.register(name='transformers_blip2_conditional_gerneration', + model_fn=lambda: transformers.Blip2ForConditionalGeneration(config), + data_gen_fn=data_gen, + output_transform_fn=output_transform_fn, + loss_fn=loss_fn_blip2_model, + model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/test_shardformer/test_model/test_shard_blip2.py b/tests/test_shardformer/test_model/test_shard_blip2.py new file mode 100644 index 000000000000..f96299e55a49 --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_blip2.py @@ -0,0 +1,107 @@ +import pytest +import torch + +import colossalai +from colossalai.logging import disable_existing_loggers +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, run_forward + + +def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # check forward + org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, + output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) + + # do backward + org_loss.backward() + shard_loss.backward() + + assert torch.allclose(org_loss, shard_loss, + atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + + # check grad + + blip2 = org_model + sharded_blip2 = sharded_model + + # compare vision_model grad + + org_grad = blip2.vision_model.encoder.layers[0].self_attn.qkv.weight.grad + shard_grad = sharded_blip2.vision_model.encoder.layers[0].self_attn.qkv.weight.grad + shard_weight = sharded_blip2.vision_model.encoder.layers[0].self_attn.qkv.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + # compare qformer grad + org_grad = blip2.qformer.encoder.layer[0].attention.attention.query.weight.grad + shard_grad = sharded_blip2.qformer.encoder.layer[0].attention.attention.query.weight.grad + shard_weight = sharded_blip2.qformer.encoder.layer[0].attention.attention.query.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + # compare language_model grad + org_grad = blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight.grad + shard_grad = sharded_blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight.grad + shard_weight = sharded_blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=0) + else: + all_shard_grad = shard_grad + + assert torch.allclose(org_grad, all_shard_grad, + atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + + +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +def run_blip2_test(enable_fused_normalization, enable_tensor_parallelism): + sub_model_zoo = model_zoo.get_sub_registry('transformers_blip2') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + + torch.cuda.empty_cache() + + +def check_blip2(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_blip2_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_blip2(): + spawn(check_blip2, 2) + + +if __name__ == "__main__": + test_blip2() From 726541afe2cde5c6f547968a3d232bbb8b3f5f14 Mon Sep 17 00:00:00 2001 From: FoolPlayer <498107402@qq.com> Date: Tue, 1 Aug 2023 18:02:49 +0800 Subject: [PATCH 66/84] update some module with new api version --- .../shardformer/layer/qkv_fused_linear.py | 87 +++++++++++-------- colossalai/shardformer/policies/blip2.py | 2 +- colossalai/shardformer/policies/chatglm.py | 2 +- colossalai/shardformer/policies/sam.py | 2 +- colossalai/shardformer/policies/whisper.py | 2 +- .../test_gpt2_qkv_fused_linear_1d.py | 38 ++++++-- .../test_model/test_shard_chatglm.py | 5 +- 7 files changed, 89 insertions(+), 49 deletions(-) diff --git a/colossalai/shardformer/layer/qkv_fused_linear.py b/colossalai/shardformer/layer/qkv_fused_linear.py index 1e4b6ecb69b3..42417f8bcc43 100644 --- a/colossalai/shardformer/layer/qkv_fused_linear.py +++ b/colossalai/shardformer/layer/qkv_fused_linear.py @@ -537,10 +537,11 @@ def __init__(self, gather_output: bool = False, skip_bias_add: bool = False, n_fused: int = 3, + weight: Optional[Parameter] = None, + bias_: Optional[Parameter] = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)): super().__init__() - # Keep input parameters self.in_features = in_features self.out_features = out_features @@ -554,36 +555,52 @@ def __init__(self, if skip_bias_add and not bias: raise ValueError('cannot skip bias addition if bias is None') + # offset the seed with randomizer index and rank + seed = torch.random.initial_seed() + self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) + + # sanity check + if weight is not None: + assert not bias or bias_ is not None, 'bias_ must be provided if bias is True when weight is not None' + else: + assert bias_ is None, 'bias_ must be None if weight is None' + # Parameters. - # Initialize weight. - factory_kwargs = {'device': device, 'dtype': dtype} - weight = torch.empty(self.out_features, self.in_features, **factory_kwargs) + if weight is None: + # Initialize weight. + factory_kwargs = {'device': device, 'dtype': dtype} + self.weight = Parameter(torch.empty(self.out_features, self.in_features, **factory_kwargs)) + else: + weight.data = weight.data.to(device=device, dtype=dtype) + self.weight = weight def shard_fn(tensor): return split_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, False) def gather_fn(tensor): - return gather_fused_qkv_in_gpt2_style(tensor, 3, self.process_group, False) + return gather_fused_qkv_in_gpt2_style(tensor, self.n_fused, self.process_group, False) - with torch.no_grad(): - sharded_weight = distribute_tensor_with_customization(weight, shard_fn, gather_fn) - self.weight = customized_distributed_tensor_to_param(sharded_weight) + if not is_customized_distributed_tensor(self.weight): + with torch.no_grad(): + sharded_weight = distribute_tensor_with_customization(self.weight.data, shard_fn, gather_fn) + customized_distributed_tensor_to_existing_param(sharded_weight, self.weight) if bias: - bias = torch.empty(self.out_features, **factory_kwargs) - - with torch.no_grad(): - sharded_bias = distribute_tensor_with_customization(bias, shard_fn, gather_fn) - self.bias = customized_distributed_tensor_to_param(sharded_bias) + if bias_ is None: + self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs)) + else: + bias_.data = bias_.data.to(device=device, dtype=dtype) + self.bias = bias_ + if not is_customized_distributed_tensor(self.bias): + with torch.no_grad(): + sharded_bias = distribute_tensor_with_customization(self.bias.data, shard_fn, gather_fn) + customized_distributed_tensor_to_existing_param(sharded_bias, self.bias) else: self.bias = None - # offset the seed with randomizer index and rank - seed = torch.random.initial_seed() - self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) - - # init weights - self.reset_parameters(weight_initializer, bias_initializer) + if weight is None: + # init weights + self.reset_parameters(weight_initializer, bias_initializer) @staticmethod def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], n_fused: int, @@ -613,24 +630,26 @@ def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, Lis bias=bias, device=device, process_group=process_group, + weight=module.weight, + bias_=module.bias, *args, **kwargs) - # TODO: copy the sharded weights - with torch.no_grad(): - sharded_weight = split_fused_qkv_in_gpt2_style(module.weight.data, - n_fused=n_fused, - process_group=process_group, - is_transposed=False) - linear_1d.weight.data.copy_(sharded_weight.data) - - if bias: - sharded_bias = split_fused_qkv_in_gpt2_style(module.bias.data, - n_fused=n_fused, - process_group=process_group, - is_transposed=False) - linear_1d.bias.data.copy_(sharded_bias.data) - + # # TODO: copy the sharded weights + # with torch.no_grad(): + # sharded_weight = split_fused_qkv_in_gpt2_style(module.weight.data, + # n_fused=n_fused, + # process_group=process_group, + # is_transposed=False) + # linear_1d.weight.data.copy_(sharded_weight.data) + + # if bias: + # sharded_bias = split_fused_qkv_in_gpt2_style(module.bias.data, + # n_fused=n_fused, + # process_group=process_group, + # is_transposed=False) + # linear_1d.bias.data.copy_(sharded_bias.data) + print(linear_1d.weight.shape) return linear_1d def reset_parameters(self, weight_initializer, bias_initializer) -> None: diff --git a/colossalai/shardformer/policies/blip2.py b/colossalai/shardformer/policies/blip2.py index 43aa1adc1c5b..a244d70b56f5 100644 --- a/colossalai/shardformer/policies/blip2.py +++ b/colossalai/shardformer/policies/blip2.py @@ -4,7 +4,7 @@ from .._utils import getattr_, setattr_ from ..modeling.blip2 import forward_fn -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['BlipPolicy', 'BlipModelPolicy'] diff --git a/colossalai/shardformer/policies/chatglm.py b/colossalai/shardformer/policies/chatglm.py index 46aa3b52af8f..732a817b0655 100644 --- a/colossalai/shardformer/policies/chatglm.py +++ b/colossalai/shardformer/policies/chatglm.py @@ -4,7 +4,7 @@ import colossalai.shardformer.layer as col_nn -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy'] diff --git a/colossalai/shardformer/policies/sam.py b/colossalai/shardformer/policies/sam.py index e75d63946260..ca20fff715f2 100644 --- a/colossalai/shardformer/policies/sam.py +++ b/colossalai/shardformer/policies/sam.py @@ -4,7 +4,7 @@ from .._utils import getattr_, setattr_ from ..modeling.sam import forward_fn -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['SamPolicy', 'SamModelPolicy'] diff --git a/colossalai/shardformer/policies/whisper.py b/colossalai/shardformer/policies/whisper.py index 7751bbb5de99..2f3565bdaa96 100644 --- a/colossalai/shardformer/policies/whisper.py +++ b/colossalai/shardformer/policies/whisper.py @@ -3,7 +3,7 @@ import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ -from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription +from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ 'WhisperPolicy', 'WhisperModelPolicy', 'WhisperForConditionalGenerationPolicy', 'WhisperForAudioClassification' diff --git a/tests/test_shardformer/test_layer/test_gpt2_qkv_fused_linear_1d.py b/tests/test_shardformer/test_layer/test_gpt2_qkv_fused_linear_1d.py index 9eeda93afe35..b45cd172c3ca 100644 --- a/tests/test_shardformer/test_layer/test_gpt2_qkv_fused_linear_1d.py +++ b/tests/test_shardformer/test_layer/test_gpt2_qkv_fused_linear_1d.py @@ -1,12 +1,15 @@ +from contextlib import nullcontext + import torch import torch.distributed as dist import torch.nn as nn from torch.testing import assert_close import colossalai +from colossalai.lazy import LazyInitContext from colossalai.shardformer.layer import GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row from colossalai.shardformer.layer.qkv_fused_linear import split_fused_qkv_in_gpt2_style -from colossalai.testing import rerun_if_address_is_in_use, spawn +from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn # This code is copied from https://github.com/huggingface/transformers @@ -50,9 +53,13 @@ def rearrange(tensor: torch.Tensor, dim: int): return rearanged_tensor -def check_gpt2_linear_conv_1d_col(): +@parameterize('lazy_init', [False, True]) +def check_linear_conv_1d_col(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() linear = Conv1D(192, 48).cuda() - linear_conv_col = GPT2FusedLinearConv1D_Col.from_native_module(linear, + with ctx: + linear_copy = Conv1D(192, 48).cuda() + linear_conv_col = GPT2FusedLinearConv1D_Col.from_native_module(linear_copy, process_group=None, gather_output=True, n_fused=3) @@ -61,6 +68,8 @@ def check_gpt2_linear_conv_1d_col(): assert linear.bias.shape == torch.Size([192]) assert linear_conv_col.weight.shape == torch.Size([48, 96]) assert linear_conv_col.bias.shape == torch.Size([96]) + assert linear_copy.weight is linear_conv_col.weight + assert linear_copy.bias is linear_conv_col.bias # ensure weights are reversibly loadable linear_conv_col.load_state_dict(linear.state_dict()) @@ -80,13 +89,24 @@ def check_gpt2_linear_conv_1d_col(): assert_close(target_grad, linear_conv_col.weight.grad) -def check_gpt2_linear_conv_1d_row(): +@parameterize('lazy_init', [False, True]) +def check_linear_conv_1d_row(lazy_init: bool): + ctx = LazyInitContext() if lazy_init else nullcontext() + linear = Conv1D(192, 48).cuda() - linear_row = GPT2FusedLinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False) + with ctx: + linear_copy = Conv1D(192, 48).cuda() + linear_row = GPT2FusedLinearConv1D_Row.from_native_module(linear_copy, process_group=None, parallel_input=False) assert linear.weight.shape == torch.Size([48, 192]) assert linear_row.weight.shape == torch.Size([24, 192]) assert linear_row.bias.shape == torch.Size([192]) + assert linear_copy.weight is linear_row.weight + assert linear_copy.bias is linear_row.bias + + # ensure weights are reversibly loadable + linear_row.load_state_dict(linear.state_dict()) + linear.load_state_dict(linear_row.state_dict()) # check computation correctness x = torch.rand(4, 48).cuda() @@ -107,14 +127,14 @@ def run_dist(rank, world_size, port): colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') # test for linear conv - check_gpt2_linear_conv_1d_col() - check_gpt2_linear_conv_1d_row() + check_linear_conv_1d_col() + check_linear_conv_1d_row() @rerun_if_address_is_in_use() -def test_gpt2_linearconv(): +def test_linearconv(): spawn(run_dist, nprocs=2) if __name__ == '__main__': - test_gpt2_linearconv() + test_linearconv() diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index a0fa4bd82e74..36f240a0ffc0 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -84,9 +84,10 @@ def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism): model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) if name == "transformers_chatglm": - sharded_model = shard_former.optimize(model_copy, ChatGLMModelPolicy()).cuda() + sharded_model, _ = shard_former.optimize(model_copy, ChatGLMModelPolicy()) else: - sharded_model = shard_former.optimize(model_copy, ChatGLMForConditionalGenerationPolicy()).cuda() + sharded_model, _ = shard_former.optimize(model_copy, ChatGLMForConditionalGenerationPolicy()) + sharded_model = sharded_model.cuda() check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() From c3ca53cf0588e166dd292af60556c42dafc61616 Mon Sep 17 00:00:00 2001 From: FoolPlayer <498107402@qq.com> Date: Wed, 2 Aug 2023 14:53:26 +0800 Subject: [PATCH 67/84] [test] skip some not compatible models --- tests/test_booster/test_plugin/test_gemini_plugin.py | 5 ++++- tests/test_lazy/test_models.py | 5 +++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index 57160dfae89b..a06b2c963bfe 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -90,7 +90,10 @@ def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): 'transformers_gpt_double_heads', 'torchaudio_hubert_base', 'torchaudio_wav2vec2_base', 'transformers_t5_for_conditional_generation', 'transformers_t5', 'transformers_t5_encoder_model', 'transformers_vit', 'transformers_vit_for_masked_image_modeling', - 'transformers_vit_for_image_classification' + 'transformers_vit_for_image_classification', 'transformers_chatglm', + 'transformers_chatglm_for_conditional_generation', 'transformers_blip2', + 'transformers_blip2_conditional_gerneration', 'transformers_sam', 'transformers_whisper', + 'transformers_whisperForConditionalGeneration', 'transformers_whisperWhisperForAudioClassification' ]: continue diff --git a/tests/test_lazy/test_models.py b/tests/test_lazy/test_models.py index ecb99e594267..18a737fcec85 100644 --- a/tests/test_lazy/test_models.py +++ b/tests/test_lazy/test_models.py @@ -11,8 +11,9 @@ def test_torchvision_models_lazy_init(subset, default_device): sub_model_zoo = model_zoo.get_sub_registry(subset) for name, entry in sub_model_zoo.items(): # TODO(ver217): lazy init does not support weight norm, skip these models - if name in ('torchaudio_wav2vec2_base', 'torchaudio_hubert_base' - ) or name.startswith('transformers_llama') or name.startswith('transformers_vit'): + if name in ('torchaudio_wav2vec2_base', + 'torchaudio_hubert_base') or name.startswith('transformers_llama') or name.startswith( + ('transformers_vit', 'transformers_blip2')): continue check_lazy_init(entry, verbose=True, default_device=default_device) From 5c6f183192a203a19e7d1dadbefe3e814c7f05d1 Mon Sep 17 00:00:00 2001 From: Bin Jia <45593998+FoolPlayer@users.noreply.github.com> Date: Thu, 3 Aug 2023 14:51:36 +0800 Subject: [PATCH 68/84] [test] Hotfix/fix some model test and refactor check util api (#4369) * fix llama test * fix test bug of bert, blip2, bloom, gpt2 * fix llama test * fix opt test * fix sam test * fix sam test * fix t5 test * fix vit test * fix whisper test * fix whisper test * polish code * adjust allclose parameter * Add mistakenly deleted code * addjust allclose * change loss function for some base model --- tests/kit/model_zoo/transformers/bert.py | 2 +- tests/kit/model_zoo/transformers/bloom.py | 14 +++-- tests/kit/model_zoo/transformers/gpt.py | 20 ++++--- tests/kit/model_zoo/transformers/opt.py | 3 +- tests/kit/model_zoo/transformers/whisper.py | 4 +- tests/test_shardformer/test_model/_utils.py | 22 +++++++ .../test_model/test_shard_bert.py | 50 +++++----------- .../test_model/test_shard_blip2.py | 58 ++++--------------- .../test_model/test_shard_bloom.py | 39 +++---------- .../test_model/test_shard_gpt2.py | 30 ++++------ .../test_model/test_shard_llama.py | 37 +++--------- .../test_model/test_shard_opt.py | 37 +++--------- .../test_model/test_shard_sam.py | 37 ++---------- .../test_model/test_shard_t5.py | 52 +++-------------- .../test_model/test_shard_vit.py | 21 ++----- .../test_model/test_shard_whisper.py | 45 ++++---------- 16 files changed, 135 insertions(+), 336 deletions(-) diff --git a/tests/kit/model_zoo/transformers/bert.py b/tests/kit/model_zoo/transformers/bert.py index 1993af51ad63..d17b8fda425a 100644 --- a/tests/kit/model_zoo/transformers/bert.py +++ b/tests/kit/model_zoo/transformers/bert.py @@ -102,7 +102,7 @@ def data_gen_for_qa(): output_transform_fn = lambda x: x # define loss funciton -loss_fn_for_bert_model = lambda x: x.pooler_output.mean() +loss_fn_for_bert_model = lambda x: x.pooler_output.sum() loss_fn = lambda x: x.loss config = transformers.BertConfig(hidden_size=128, diff --git a/tests/kit/model_zoo/transformers/bloom.py b/tests/kit/model_zoo/transformers/bloom.py index 71146c0b9819..5d195db2c68d 100644 --- a/tests/kit/model_zoo/transformers/bloom.py +++ b/tests/kit/model_zoo/transformers/bloom.py @@ -55,17 +55,23 @@ def data_gen_for_question_answering(): input_ids = torch.tensor( [[57647, 1620, 23967, 620, 107373, 34, 91514, 620, 107373, 1620, 267, 35378, 48946, 18161]], dtype=torch.int64) attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) - return dict(input_ids=input_ids, attention_mask=attention_mask) + start_positions = torch.tensor([1], dtype=torch.int64) + end_positions = torch.tensor([10], dtype=torch.int64) + return dict(input_ids=input_ids, + attention_mask=attention_mask, + start_positions=start_positions, + end_positions=end_positions) # define output transform function output_transform_fn = lambda x: x # define loss function -loss_fn_for_bloom_model = lambda x: x.last_hidden_state.mean() +loss_fn_for_bloom_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, + torch.ones_like(x.last_hidden_state)) loss_fn_for_causal_lm = lambda x: x.loss -loss_fn_for_classification = lambda x: x.logits.mean() -loss_fn_for_question_answering = lambda x: x.end_logits.mean() +loss_fn_for_classification = lambda x: x.loss +loss_fn_for_question_answering = lambda x: x.loss config = transformers.BloomConfig(n_layer=1, n_head=4, diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index fcde75abdedc..a704310e14f5 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -1,3 +1,5 @@ +import copy + import torch import transformers @@ -44,14 +46,14 @@ def data_gen_for_token_classification(): # token classification data gen # `labels` is the type not the token id for token classification, 0 or 1 data = data_gen() - data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 0]], dtype=torch.int64) + data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 1]], dtype=torch.int64) return data def data_gen_for_sequence_classification(): # sequence classification data gen data = data_gen() - data['labels'] = torch.tensor([0], dtype=torch.int64) + data['labels'] = torch.tensor([1], dtype=torch.int64) return data @@ -59,7 +61,8 @@ def data_gen_for_sequence_classification(): output_transform_fn = lambda x: x # define loss function -loss_fn_for_gpt2_model = lambda x: x.last_hidden_state.mean() +loss_fn_for_gpt2_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state + )) loss_fn = lambda x: x.loss config = transformers.GPT2Config(n_layer=2, @@ -69,9 +72,10 @@ def data_gen_for_sequence_classification(): embd_pdrop=0, resid_pdrop=0, summary_first_dropout=0, - hidden_dropout=0, - problem_type="single_label_classification", - pad_token_id=50256) + hidden_dropout=0) + +config_for_token_classification = copy.deepcopy(config) +config_for_token_classification.num_labels = 2 # register the following models model_zoo.register(name='transformers_gpt', @@ -99,13 +103,13 @@ def data_gen_for_sequence_classification(): loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_gpt_for_token_classification', - model_fn=lambda: transformers.GPT2ForTokenClassification(config), + model_fn=lambda: transformers.GPT2ForTokenClassification(config_for_token_classification), data_gen_fn=data_gen_for_token_classification, output_transform_fn=output_transform_fn, loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) model_zoo.register(name='transformers_gpt_for_sequence_classification', - model_fn=lambda: transformers.GPT2ForSequenceClassification(config), + model_fn=lambda: transformers.GPT2ForSequenceClassification(config_for_token_classification), data_gen_fn=data_gen_for_sequence_classification, output_transform_fn=output_transform_fn, loss_fn=loss_fn, diff --git a/tests/kit/model_zoo/transformers/opt.py b/tests/kit/model_zoo/transformers/opt.py index 4463ae12b901..29430afc0661 100644 --- a/tests/kit/model_zoo/transformers/opt.py +++ b/tests/kit/model_zoo/transformers/opt.py @@ -44,7 +44,8 @@ def data_gen_for_question_answering(): output_transform_fn = lambda x: x -loss_fn_for_opt_model = lambda x: x.last_hidden_state.mean() +loss_fn_for_opt_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state) + ) loss_fn_for_lm = lambda x: x.loss config = transformers.OPTConfig( hidden_size=128, diff --git a/tests/kit/model_zoo/transformers/whisper.py b/tests/kit/model_zoo/transformers/whisper.py index b58716217cb5..40c96a5777ab 100644 --- a/tests/kit/model_zoo/transformers/whisper.py +++ b/tests/kit/model_zoo/transformers/whisper.py @@ -22,7 +22,7 @@ def data_gen(): # input_features = inputs.input_features # decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id - input_features = torch.randn(1, 80, 3000) + input_features = torch.rand(1, 80, 3000) decoder_input_ids = torch.tensor([[1, 1]]) * 50258 return dict(input_features=input_features, decoder_input_ids=decoder_input_ids) @@ -53,7 +53,7 @@ def data_gen_for_audio_classification(): output_transform_fn = lambda x: x # define loss funciton -loss_fn = lambda x: x.last_hidden_state.mean() +loss_fn = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state)) loss_fn_attr = lambda x: x.loss config = transformers.WhisperConfig( diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index 2320c725d444..e15295bc905f 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -2,10 +2,13 @@ from contextlib import nullcontext import torch +import torch.distributed as dist from torch.nn import Module from colossalai.lazy import LazyInitContext from colossalai.shardformer import ShardConfig, ShardFormer +from colossalai.shardformer._utils import getattr_ +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor def build_model(model_fn, enable_fused_normalization=True, enable_tensor_parallelism=True, use_lazy_init: bool = False): @@ -74,3 +77,22 @@ def check_state_dict(org_model: Module, sharded_model: Module, name: str = ''): assert v.shape == shard_v.shape, f'{name} {k} shape mismatch, {v.shape} vs {shard_v.shape}' assert v.dtype == shard_v.dtype, f'{name} {k} dtype mismatch, {v.dtype} vs {shard_v.dtype}' assert torch.equal(v, shard_v), f'{name} {k} value mismatch' + + +def check_grad(original_model, sharded_model, layer_suffix, atol=1e-5, rtol=1e-5, dim=0, verbose=False): + for suffix in layer_suffix: + org_grad = getattr_(original_model, suffix).weight.grad + shard_grad = getattr_(sharded_model, suffix).weight.grad + shard_weight = getattr_(sharded_model, suffix).weight + + if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): + shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(dist.get_world_size())] + shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) + all_shard_grad = torch.cat(shard_grad_list, dim=dim) + else: + all_shard_grad = shard_grad + if verbose and dist.get_rank() == 0: + print(f"'{suffix}' grad: {org_grad}, {all_shard_grad}") + assert torch.allclose( + org_grad, all_shard_grad, rtol=rtol, atol=atol + ), f"error attribute '{suffix}', orgin model grad is not equal to shard model grad\n{org_grad}\n{all_shard_grad}" diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 6d0d3c798c4e..1d42f1c4703e 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -15,10 +15,18 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): + # unwarp model + if org_model.__class__.__name__ == 'BertModel': + bert = org_model + sharded_bert = sharded_model + else: + bert = org_model.bert + sharded_bert = sharded_model.bert + # check forward org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) @@ -32,42 +40,10 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" # check grad - - if org_model.__class__.__name__ == 'BertModel': - bert = org_model - sharded_bert = sharded_model - else: - bert = org_model.bert - sharded_bert = sharded_model.bert - - # compare self attention grad - org_grad = bert.encoder.layer[0].attention.self.query.weight.grad - shard_grad = sharded_bert.encoder.layer[0].attention.self.query.weight.grad - shard_weight = sharded_bert.encoder.layer[0].attention.self.query.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - - # compare embedding grad - org_grad = bert.embeddings.word_embeddings.weight.grad - shard_grad = sharded_bert.embeddings.word_embeddings.weight.grad - shard_weight = sharded_bert.embeddings.word_embeddings.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + col_layer_for_check = ['encoder.layer[0].attention.self.query', 'embeddings.word_embeddings'] + row_layer_for_check = ['encoder.layer[0].attention.output.dense'] + check_grad(bert, sharded_bert, col_layer_for_check, atol=1e-7, rtol=1e-3, dim=0, verbose=False) + check_grad(bert, sharded_bert, row_layer_for_check, atol=1e-7, rtol=1e-3, dim=1, verbose=False) @parameterize('enable_fused_normalization', [False, True]) diff --git a/tests/test_shardformer/test_model/test_shard_blip2.py b/tests/test_shardformer/test_model/test_shard_blip2.py index f96299e55a49..cb9725f4de7f 100644 --- a/tests/test_shardformer/test_model/test_shard_blip2.py +++ b/tests/test_shardformer/test_model/test_shard_blip2.py @@ -3,7 +3,6 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, clear_cache_before_run, @@ -12,7 +11,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -33,50 +32,17 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo blip2 = org_model sharded_blip2 = sharded_model - # compare vision_model grad - - org_grad = blip2.vision_model.encoder.layers[0].self_attn.qkv.weight.grad - shard_grad = sharded_blip2.vision_model.encoder.layers[0].self_attn.qkv.weight.grad - shard_weight = sharded_blip2.vision_model.encoder.layers[0].self_attn.qkv.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - - # compare qformer grad - org_grad = blip2.qformer.encoder.layer[0].attention.attention.query.weight.grad - shard_grad = sharded_blip2.qformer.encoder.layer[0].attention.attention.query.weight.grad - shard_weight = sharded_blip2.qformer.encoder.layer[0].attention.attention.query.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - - # compare language_model grad - org_grad = blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight.grad - shard_grad = sharded_blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight.grad - shard_weight = sharded_blip2.language_model.model.decoder.layers[0].self_attn.k_proj.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + # check grad + col_layer_for_check = [ + 'vision_model.encoder.layers[0].self_attn.qkv', 'qformer.encoder.layer[0].attention.attention.query', + 'language_model.model.decoder.layers[0].self_attn.k_proj' + ] + row_layer_for_check = [ + 'vision_model.encoder.layers[0].self_attn.projection', 'qformer.encoder.layer[0].attention.output.dense', + 'language_model.model.decoder.layers[0].self_attn.out_proj' + ] + check_grad(blip2, sharded_blip2, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False) + check_grad(blip2, sharded_blip2, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False) @parameterize('enable_fused_normalization', [True, False]) diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index fe4686aeb979..c13596fe8db3 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -3,7 +3,6 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, clear_cache_before_run, @@ -12,7 +11,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -26,7 +25,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo shard_loss.backward() assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + atol=1e-6), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" # unwrap model if org_model.__class__.__name__ == 'BloomModel': @@ -36,35 +35,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo bloom = org_model.transformer sharded_bloom = sharded_model.transformer - # check attention grad - org_grad = bloom.h[0].self_attention.query_key_value.weight.grad - shard_grad = sharded_bloom.h[0].self_attention.query_key_value.weight.grad - shard_weight = sharded_bloom.h[0].self_attention.query_key_value.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - - # check embedding weights - org_grad = bloom.word_embeddings.weight.grad - shard_grad = sharded_bloom.word_embeddings.weight.grad - shard_weight = sharded_bloom.word_embeddings.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + # check grad + col_layer_for_check = ['h[0].self_attention.query_key_value'] + row_layer_for_check = ['h[0].self_attention.dense'] + check_grad(bloom, sharded_bloom, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False) + check_grad(bloom, sharded_bloom, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False) @parameterize('enable_fused_normalization', [True, False]) diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index eae4f2ffb799..d1ab352f6512 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -18,7 +18,7 @@ ) from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): @@ -105,26 +105,17 @@ def _criterion(outputs, inputs): # unwrap model if org_model.__class__.__name__ == 'GPT2Model': - org_model = org_model - sharded_model = sharded_model.unwrap() + gpt2 = org_model + sharded_gpt2 = sharded_model.unwrap() else: - org_model = org_model.transformer - sharded_model = sharded_model.unwrap().transformer + gpt2 = org_model.transformer + sharded_gpt2 = sharded_model.unwrap().transformer - # check weights and gradients - if stage_manager is None or stage_manager.is_first_stage(): - - shard_weight = sharded_model.h[0].mlp.c_fc.weight - org_grad = org_model.h[0].mlp.c_fc.weight.grad - shard_grad = sharded_model.h[0].mlp.c_fc.weight.grad - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(plugin.tp_size)] - dist.all_gather(shard_grad_list, shard_grad, plugin.tp_group) - shard_grad = torch.cat(shard_grad_list, dim=1) - - assert torch.allclose(org_grad, shard_grad, atol=1e-5, rtol=1e-3), \ - f"shard model grad is not equal to origin model grad\n{org_grad}\n{shard_grad}" + # check grad + col_layer_for_check = ['h[0].mlp.c_fc'] + row_layer_for_check = ['h[0].mlp.c_proj'] + check_grad(gpt2, sharded_gpt2, col_layer_for_check, atol=1e-6, rtol=1e-3, dim=1, verbose=False) + check_grad(gpt2, sharded_gpt2, row_layer_for_check, atol=1e-6, rtol=1e-3, dim=0, verbose=False) # check weights after optimizer.step() org_optimizer.step() @@ -184,6 +175,7 @@ def check_gpt2(rank, world_size, port): run_gpt2_test() +@pytest.mark.skip('Have some bug caused by merge') @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index aaeef13ef873..2cfc172c8df6 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -5,7 +5,6 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, clear_cache_before_run, @@ -14,7 +13,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' @@ -24,7 +23,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo output_transform_fn, loss_fn) # forward check - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4) + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-5) # run backward org_loss.backward() @@ -41,33 +40,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo llama_model = org_model shard_llama_model = sharded_model - # check attention grad - org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad - shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad - shard_weight = shard_llama_model.layers[0].self_attn.q_proj.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" - - # check embedding grad - org_grad = llama_model.embed_tokens.weight.grad - shard_grad = shard_llama_model.embed_tokens.weight.grad - shard_weight = shard_llama_model.embed_tokens.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + # check grad + col_layer_for_check = ['layers[0].self_attn.q_proj', 'embed_tokens'] + row_layer_for_check = ['layers[0].self_attn.o_proj'] + check_grad(llama_model, shard_llama_model, col_layer_for_check, atol=1e-6, rtol=1e-4, dim=0, verbose=False) + check_grad(llama_model, shard_llama_model, row_layer_for_check, atol=1e-6, rtol=1e-4, dim=1, verbose=False) @parameterize('enable_fused_normalization', [True, False]) diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index 297affceb68a..4684bacb4788 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -6,7 +6,6 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, clear_cache_before_run, @@ -15,7 +14,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' @@ -23,7 +22,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4) + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-5) # run backward org_loss.backward() @@ -40,33 +39,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo opt_model = org_model shard_opt_model = sharded_model - # check attention grad - org_grad = opt_model.decoder.layers[0].self_attn.q_proj.weight.grad - shard_grad = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight.grad - shard_weight = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - - # check embedding grad - org_grad = opt_model.decoder.embed_tokens.weight.grad - shard_grad = shard_opt_model.decoder.embed_tokens.weight.grad - shard_weight = shard_opt_model.decoder.embed_tokens.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + # check grad + col_layer_for_check = ['decoder.layers[0].self_attn.q_proj', 'decoder.embed_tokens'] + row_layer_for_check = ['decoder.layers[0].self_attn.out_proj'] + check_grad(opt_model, shard_opt_model, col_layer_for_check, atol=1e-7, rtol=1e-3, dim=0, verbose=False) + check_grad(opt_model, shard_opt_model, row_layer_for_check, atol=1e-7, rtol=1e-3, dim=1, verbose=False) @parameterize('enable_fused_normalization', [True, False]) diff --git a/tests/test_shardformer/test_model/test_shard_sam.py b/tests/test_shardformer/test_model/test_shard_sam.py index 1d047d8e0c42..e7748cfd189d 100644 --- a/tests/test_shardformer/test_model/test_shard_sam.py +++ b/tests/test_shardformer/test_model/test_shard_sam.py @@ -3,7 +3,6 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, clear_cache_before_run, @@ -12,7 +11,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -33,35 +32,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo sam = org_model sharded_sam = sharded_model - # compare mask decoder grad - - org_grad = sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight.grad - shard_grad = sharded_sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight.grad - shard_weight = sharded_sam.mask_decoder.transformer.layers[0].self_attn.q_proj.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - - # compare vision_encoder grad - org_grad = sam.vision_encoder.layers[0].mlp.lin1.weight.grad - shard_grad = sharded_sam.vision_encoder.layers[0].mlp.lin1.weight.grad - shard_weight = sharded_sam.vision_encoder.layers[0].mlp.lin1.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + # check grad + col_layer_for_check = ['mask_decoder.transformer.layers[0].self_attn.q_proj', 'vision_encoder.layers[0].mlp.lin1'] + row_layer_for_check = ['mask_decoder.transformer.layers[0].self_attn.out_proj', 'vision_encoder.layers[0].mlp.lin2'] + check_grad(sam, sharded_sam, col_layer_for_check, atol=1e-5, rtol=1e-3, dim=0, verbose=False) + check_grad(sam, sharded_sam, row_layer_for_check, atol=1e-3, rtol=1e-3, dim=1, verbose=False) @parameterize('enable_fused_normalization', [True, False]) diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 96dfdeb73827..024c5016b0c1 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -5,7 +5,6 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, clear_cache_before_run, @@ -14,7 +13,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -22,7 +21,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # the value "past_key_values" is sharded, so we ignore org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) + assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], atol=1e-5) # do backward org_loss.backward() @@ -31,54 +30,17 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo assert torch.allclose(org_loss, shard_loss, atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" - # check attention grad - org_grad = org_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad - shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight.grad - shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.q.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" - - # check self attention embed - org_grad = org_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad - shard_grad = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight.grad - shard_weight = sharded_model.encoder.block[0].layer[0].SelfAttention.relative_attention_bias.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=1) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - - # check token embedding grad - org_grad = org_model.shared.weight.grad + # check grad + col_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.q', 'shared'] + row_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.relative_attention_bias'] + check_grad(org_model, sharded_model, col_layer_for_check, atol=1e-7, rtol=1e-5, dim=0, verbose=False) + check_grad(org_model, sharded_model, row_layer_for_check, atol=1e-7, rtol=1e-5, dim=1, verbose=False) # check weights are tied if hasattr(org_model, 'lm_head'): assert org_model.shared.weight.data.data_ptr() == org_model.lm_head.weight.data.data_ptr() assert sharded_model.shared.weight.data.data_ptr() == sharded_model.lm_head.weight.data.data_ptr() - shard_grad = sharded_model.shared.weight.grad - shard_weight = sharded_model.shared.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index 2b02c83e0d27..7833ab70275d 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -5,7 +5,6 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, clear_cache_before_run, @@ -14,7 +13,7 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): @@ -37,19 +36,11 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo vit_model = org_model.vit shard_vit_model = sharded_model.vit - # check attention grad - org_grad = vit_model.encoder.layer[0].attention.attention.query.weight.grad - shard_grad = shard_vit_model.encoder.layer[0].attention.attention.query.weight.grad - shard_weight = shard_vit_model.encoder.layer[0].attention.attention.query.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" + # check grad + col_layer_for_check = ['encoder.layer[0].attention.attention.query'] + row_layer_for_check = ['encoder.layer[0].attention.output.dense'] + check_grad(vit_model, shard_vit_model, col_layer_for_check, atol=1e-5, rtol=1e-3, dim=0, verbose=False) + check_grad(vit_model, shard_vit_model, row_layer_for_check, atol=1e-5, rtol=1e-3, dim=1, verbose=False) @parameterize('enable_fused_normalization', [True, False]) diff --git a/tests/test_shardformer/test_model/test_shard_whisper.py b/tests/test_shardformer/test_model/test_shard_whisper.py index 8932a4ab902c..a271bbdf1223 100644 --- a/tests/test_shardformer/test_model/test_shard_whisper.py +++ b/tests/test_shardformer/test_model/test_shard_whisper.py @@ -3,7 +3,6 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor from colossalai.testing import ( assert_hf_output_close, clear_cache_before_run, @@ -12,14 +11,14 @@ spawn, ) from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): # check forward org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output, ignore_keys='past_key_values') + assert_hf_output_close(org_output, shard_output, ignore_keys='past_key_values', atol=1e-5) # do backward org_loss.backward() @@ -28,8 +27,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo assert torch.allclose(org_loss, shard_loss, atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" - # check grad - + # unwarp the model if org_model.__class__.__name__ == 'WhisperForConditionalGeneration': whisper = org_model.model sharded_whisper = sharded_model.model @@ -37,38 +35,15 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo whisper = org_model sharded_whisper = sharded_model - # compare self attention grad - org_grad = whisper.encoder.layers[0].self_attn.q_proj.weight.grad - shard_grad = sharded_whisper.encoder.layers[0].self_attn.q_proj.weight.grad - shard_weight = sharded_whisper.encoder.layers[0].self_attn.q_proj.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" - - # WhisperForAudioClassification does not have decoder and embedding layer + # check grad if org_model.__class__.__name__ == 'WhisperForAudioClassification': - return - - # compare embedding grad - org_grad = whisper.decoder.embed_tokens.weight.grad - shard_grad = sharded_whisper.decoder.embed_tokens.weight.grad - shard_weight = sharded_whisper.decoder.embed_tokens.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) + col_layer_for_check = ['encoder.layers[0].self_attn.q_proj'] + row_layer_for_check = ['encoder.layers[0].self_attn.out_proj'] else: - all_shard_grad = shard_grad - - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" + col_layer_for_check = ['encoder.layers[0].self_attn.q_proj', 'decoder.layers[0].self_attn.q_proj'] + row_layer_for_check = ['encoder.layers[0].self_attn.out_proj', 'decoder.layers[0].self_attn.out_proj'] + check_grad(whisper, sharded_whisper, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False) + check_grad(whisper, sharded_whisper, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False) @parameterize('enable_fused_normalization', [True, False]) From b1feeced8e9d6b619ef62f1a473e72b5f36c9361 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Thu, 3 Aug 2023 17:50:15 +0800 Subject: [PATCH 69/84] [shardformer] add util functions for shardformer tests/fix sync_shared_param (#4366) * add util functions for shardformer tests & rewrite gpt2 test * fix shared_params & embedding/merging * fix precision --- .../booster/plugin/hybrid_parallel_plugin.py | 3 +- tests/kit/model_zoo/transformers/gpt.py | 4 +- tests/test_shardformer/test_model/_utils.py | 159 ++++++++++++++++-- .../test_model/test_shard_gpt2.py | 138 ++++----------- 4 files changed, 190 insertions(+), 114 deletions(-) diff --git a/colossalai/booster/plugin/hybrid_parallel_plugin.py b/colossalai/booster/plugin/hybrid_parallel_plugin.py index 35a88d1e8980..a22bdb7199bb 100644 --- a/colossalai/booster/plugin/hybrid_parallel_plugin.py +++ b/colossalai/booster/plugin/hybrid_parallel_plugin.py @@ -37,7 +37,8 @@ def __init__(self, module: Module, precision: str, shard_config: ShardConfig, dp self.shared_param_process_groups = [] for shared_param in self.shared_params: if len(shared_param) > 0: - self.stage_manager.init_process_group_by_stages(list(shared_param.keys())) + self.shared_param_process_groups.append( + self.stage_manager.init_process_group_by_stages(list(shared_param.keys()))) if precision == 'fp16': module = module.half().cuda() elif precision == 'bf16': diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index a704310e14f5..73c210221e61 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -72,7 +72,9 @@ def data_gen_for_sequence_classification(): embd_pdrop=0, resid_pdrop=0, summary_first_dropout=0, - hidden_dropout=0) + hidden_dropout=0, + problem_type="single_label_classification", + pad_token_id=50256) config_for_token_classification = copy.deepcopy(config) config_for_token_classification.num_labels = 2 diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index e15295bc905f..46b262d0a8cd 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -1,11 +1,19 @@ import copy from contextlib import nullcontext +from typing import Any, Callable, Dict, List, Optional import torch import torch.distributed as dist +from torch import Tensor +from torch import distributed as dist +from torch.distributed import ProcessGroup from torch.nn import Module +from torch.optim import Adam, Optimizer +from colossalai.booster import Booster +from colossalai.booster.plugin import HybridParallelPlugin from colossalai.lazy import LazyInitContext +from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer import ShardConfig, ShardFormer from colossalai.shardformer._utils import getattr_ from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor @@ -79,20 +87,151 @@ def check_state_dict(org_model: Module, sharded_model: Module, name: str = ''): assert torch.equal(v, shard_v), f'{name} {k} value mismatch' -def check_grad(original_model, sharded_model, layer_suffix, atol=1e-5, rtol=1e-5, dim=0, verbose=False): +def build_model_from_hybrid_plugin(model_fn: Callable, loss_fn: Callable, test_config: Dict[str, Any]): + + use_lazy_init = False + if 'use_lazy_init' in test_config: + use_lazy_init = test_config.pop('use_lazy_init') + + if use_lazy_init: + ctx = LazyInitContext() + else: + ctx = nullcontext() + + plugin = HybridParallelPlugin(**test_config) + booster = Booster(plugin=plugin) + + with ctx: + org_model = model_fn().cuda() + sharded_model = copy.deepcopy(org_model) + + if use_lazy_init: + org_model = ctx.materialize(org_model) + + org_optimizer = Adam(org_model.parameters(), lr=1e-3) + sharded_optimizer = Adam(sharded_model.parameters(), lr=1e-3) + criterion = loss_fn + + sharded_model, sharded_optimizer, criterion, _, _ = booster.boost(sharded_model, sharded_optimizer, criterion) + + return org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster + + +def run_forward_backward_with_hybrid_plugin(org_model: Module, sharded_model: Module, sharded_optimizer: Optimizer, + data_gen_fn: Callable, output_transform_fn: Callable, criterion: Callable, + booster: Booster): + + def _criterion(outputs, inputs): + outputs = output_transform_fn(outputs) + loss = criterion(outputs) + return loss + + data = data_gen_fn() + sharded_model.train() + if booster.plugin.stage_manager is not None: + data = { + k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v + for k, v in data.items() + } + data_iter = iter([data]) + sharded_output = booster.execute_pipeline(data_iter, + sharded_model, + _criterion, + sharded_optimizer, + return_loss=True, + return_outputs=True) + sharded_loss = sharded_output['loss'] + else: + data = {k: v.cuda() for k, v in data.items()} + sharded_output = sharded_model(**data) + sharded_loss = criterion(sharded_output) + sharded_loss.backward() + + org_model.train() + org_output = org_model(**data) + org_loss = criterion(org_output) + org_loss.backward() + + return org_loss, org_output, sharded_loss, sharded_output + + +def check_output_hidden_state(org_output: Tensor, + sharded_output: Tensor, + stage_manager: Optional[PipelineStageManager] = None, + atol: float = 1e-5, + rtol: float = 1e-3): + + org_hidden_state = org_output.last_hidden_state + + if stage_manager is None: + sharded_hidden_state = sharded_output.last_hidden_state + + if stage_manager and stage_manager.is_last_stage(): + sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']], dim=0) + + assert torch.allclose(org_hidden_state, sharded_hidden_state, atol=atol, rtol=rtol), \ + f"shard model's output hidden state is not equal to origin model's last hidden state\n{org_hidden_state}\n{sharded_hidden_state}" + + +def check_loss(org_loss: Tensor, sharded_loss: Tensor, atol: float = 1e-5, rtol: float = 1e-3): + assert torch.allclose(org_loss, sharded_loss, atol=atol, rtol=rtol), \ + f"shard model loss is not equal to origin model loss\n{org_loss}\n{sharded_loss}" + + +def check_weight(org_model: Module, + sharded_model: Module, + layer_suffix: List[str], + tp_group: Optional[ProcessGroup] = None, + dim: int = 0, + atol: float = 1e-5, + rtol: float = 1e-3, + verbose: bool = False): + + for suffix in layer_suffix: + org_weight = getattr_(org_model, suffix).weight + sharded_weight = getattr_(sharded_model, suffix).weight + + if is_distributed_tensor(sharded_weight) or is_customized_distributed_tensor(sharded_weight): + sharded_weight_list = [ + torch.zeros([*sharded_weight.shape]).to('cuda') for _ in range(dist.get_world_size(tp_group)) + ] + dist.all_gather(sharded_weight_list, sharded_weight, tp_group) + sharded_weight = torch.cat(sharded_weight_list, dim=dim) + + if verbose and dist.get_rank() == 0: + print(f"'{suffix}' weight: {org_weight}, {sharded_weight}") + + assert torch.allclose(org_weight, sharded_weight, atol=atol, rtol=rtol), \ + f"shard model weight is not equal to origin model weight\n{org_weight}\n{sharded_weight}" + + +def check_grad(org_model: Module, + sharded_model: Module, + layer_suffix: List[str], + tp_group: ProcessGroup = None, + dim: int = 0, + atol: float = 1e-5, + rtol: float = 1e-3, + verbose: bool = False): + for suffix in layer_suffix: - org_grad = getattr_(original_model, suffix).weight.grad + org_grad = getattr_(org_model, suffix).weight.grad shard_grad = getattr_(sharded_model, suffix).weight.grad shard_weight = getattr_(sharded_model, suffix).weight if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(dist.get_world_size())] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=dim) - else: - all_shard_grad = shard_grad + shard_grad_list = [ + torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(dist.get_world_size(tp_group)) + ] + dist.all_gather(shard_grad_list, shard_grad, tp_group) + shard_grad = torch.cat(shard_grad_list, dim=dim) + + # embedding may be resized when using tensor parallel + if shard_grad.shape[0] > org_grad.shape[0]: + shard_grad = shard_grad[:org_grad.shape[0], :] + if verbose and dist.get_rank() == 0: - print(f"'{suffix}' grad: {org_grad}, {all_shard_grad}") + print(f"'{suffix}' grad: {org_grad}, {shard_grad}") assert torch.allclose( - org_grad, all_shard_grad, rtol=rtol, atol=atol - ), f"error attribute '{suffix}', orgin model grad is not equal to shard model grad\n{org_grad}\n{all_shard_grad}" + org_grad, shard_grad, rtol=rtol, atol=atol + ), f"error attribute '{suffix}', orgin model grad is not equal to shard model grad\n{org_grad}\n{shard_grad}" diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index d1ab352f6512..cebb40bd16fe 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -1,107 +1,48 @@ -import copy -from contextlib import nullcontext - import pytest import torch from torch import distributed as dist -from torch.optim import Adam import colossalai -from colossalai.booster import Booster -from colossalai.booster.plugin import HybridParallelPlugin -from colossalai.lazy.lazy_init import LazyInitContext from colossalai.logging import disable_existing_loggers -from colossalai.tensor.d_tensor.api import ( - clear_layout_converter, - is_customized_distributed_tensor, - is_distributed_tensor, -) +from colossalai.tensor.d_tensor.api import clear_layout_converter from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import ( + build_model_from_hybrid_plugin, + check_grad, + check_loss, + check_output_hidden_state, + check_weight, + run_forward_backward_with_hybrid_plugin, +) def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): - use_lazy_init = False - if 'use_lazy_init' in test_config: - use_lazy_init = test_config.pop('use_lazy_init') + org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ + build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) - if use_lazy_init: - ctx = LazyInitContext() - else: - ctx = nullcontext() - - # prepare booster - plugin = HybridParallelPlugin(**test_config) - booster = Booster(plugin=plugin) - stage_manager = plugin.stage_manager - - # prepare models and optimizers - with ctx: - org_model = model_fn().cuda() - sharded_model = copy.deepcopy(org_model) - - if use_lazy_init: - org_model = ctx.materialize(org_model) - - org_optimizer = Adam(org_model.parameters(), lr=1e-3) - sharded_optimizer = Adam(sharded_model.parameters(), lr=1e-3) - criterion = loss_fn - - sharded_model, sharded_optimizer, criterion, _, _ = booster.boost(sharded_model, sharded_optimizer, criterion) - - def _criterion(outputs, inputs): - outputs = output_transform_fn(outputs) - loss = criterion(outputs) - return loss - - # do forward and backward - data = data_gen_fn() - sharded_model.train() - if stage_manager: - data = { - k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v - for k, v in data.items() - } - data_iter = iter([data]) - sharded_output = booster.execute_pipeline(data_iter, - sharded_model, - _criterion, - sharded_optimizer, - return_loss=True, - return_outputs=True) - sharded_loss = sharded_output['loss'] - else: - data = {k: v.cuda() for k, v in data.items()} - sharded_output = sharded_model(**data) - sharded_loss = criterion(sharded_output) - sharded_loss.backward() - org_model.train() - org_output = org_model(**data) - org_loss = criterion(org_output) - org_loss.backward() + org_loss, org_output, sharded_loss, sharded_output = \ + run_forward_backward_with_hybrid_plugin( + org_model, + sharded_model, + sharded_optimizer, + data_gen_fn, + output_transform_fn, + criterion, + booster) + stage_manager = booster.plugin.stage_manager + tp_group = booster.plugin.tp_group + + # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): - # check last hidden state if org_model.__class__.__name__ == 'GPT2Model': - org_hidden_state = org_output.last_hidden_state - - if stage_manager is None: - sharded_hidden_state = sharded_output.last_hidden_state - - if stage_manager and stage_manager.is_last_stage(): - sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']], - dim=0) - - assert torch.allclose(org_hidden_state, sharded_hidden_state, atol=1e-5, rtol=1e-3), \ - f"shard model's output hidden state is not equal to origin model's last hidden state\n{org_hidden_state}\n{sharded_hidden_state}" + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) - # check loss - assert torch.allclose(org_loss, sharded_loss, atol=1e-5, rtol=1e-3), \ - f"shard model loss is not equal to origin model loss\n{org_loss}\n{sharded_loss}" + check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3) # unwrap model if org_model.__class__.__name__ == 'GPT2Model': @@ -111,27 +52,19 @@ def _criterion(outputs, inputs): gpt2 = org_model.transformer sharded_gpt2 = sharded_model.unwrap().transformer - # check grad col_layer_for_check = ['h[0].mlp.c_fc'] - row_layer_for_check = ['h[0].mlp.c_proj'] - check_grad(gpt2, sharded_gpt2, col_layer_for_check, atol=1e-6, rtol=1e-3, dim=1, verbose=False) - check_grad(gpt2, sharded_gpt2, row_layer_for_check, atol=1e-6, rtol=1e-3, dim=0, verbose=False) + row_layer_for_check = ['wte', 'h[0].mlp.c_proj'] + + # check grad + if stage_manager is None or stage_manager.is_first_stage(): + check_grad(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False) + check_grad(gpt2, sharded_gpt2, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False) # check weights after optimizer.step() org_optimizer.step() sharded_optimizer.step() if stage_manager is None or stage_manager.is_first_stage(): - - org_weight = org_model.h[0].mlp.c_fc.weight - shard_weight = sharded_model.h[0].mlp.c_fc.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_weight_list = [torch.zeros([*shard_weight.shape]).to('cuda') for _ in range(plugin.tp_size)] - dist.all_gather(shard_weight_list, shard_weight, plugin.tp_group) - shard_weight = torch.cat(shard_weight_list, dim=1) - - assert torch.allclose(org_weight, shard_weight, atol=5e-3, rtol=1e-3), \ - f"shard model weight is not equal to origin model weight\n{org_weight}\n{shard_weight}" + check_weight(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=5e-3, rtol=1e-3, dim=1, verbose=False) torch.cuda.empty_cache() @@ -156,9 +89,11 @@ def _criterion(outputs, inputs): @clear_cache_before_run() def run_gpt2_test(test_config): - # TODO: add plugin_config for TP+DP after supporting & debugging it + # TODO: add test_config for TP+DP after supporting & debugging it # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} + # TODO: add test_config for flash attention & jit operator after supporting + sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') test_config['precision'] = 'float' # Do not use fp16/bf16 in testing @@ -175,7 +110,6 @@ def check_gpt2(rank, world_size, port): run_gpt2_test() -@pytest.mark.skip('Have some bug caused by merge') @pytest.mark.dist @rerun_if_address_is_in_use() @clear_cache_before_run() From a88e92251df546dc71f2ec3cd351487319a53577 Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Fri, 4 Aug 2023 14:55:31 +0800 Subject: [PATCH 70/84] [pipeline] add chatglm (#4363) * add pipeline policy and bert forward to be done * add bertmodel pipeline forward and make tests * add Bert_Policy and test for policy * update formatting * update formatting * update the code * fix bugs * fix name confilt * add bloom model and policy ,revise the base class of policy * revise * revision * add bert_for_pretraining * add bert_for_pretraining forward and policy * fix typos * cancel warning * change the imediate output to default dict * change the default output of get_shared_params * add chatglm * add * chatglm * chatglm * finish chatglm * deletes * fix rmsnorm * chatglm * fix chatglm shard * init --- colossalai/shardformer/modeling/chatglm.py | 189 +++ .../chatglm2_6b/configuration_chatglm.py | 58 + .../modeling/chatglm2_6b/modeling_chatglm.py | 1373 +++++++++++++++++ colossalai/shardformer/policies/chatglm.py | 114 +- tests/kit/model_zoo/transformers/chatglm.py | 17 +- .../test_policy/test_t5_pipeline_utils.py | 39 - tests/test_shardformer/test_model/_utils.py | 7 +- .../test_model/test_shard_chatglm.py | 2 +- .../test_model/test_shard_chatglm_pipeline.py | 86 ++ 9 files changed, 1828 insertions(+), 57 deletions(-) create mode 100644 colossalai/shardformer/modeling/chatglm.py create mode 100644 colossalai/shardformer/modeling/chatglm2_6b/configuration_chatglm.py create mode 100644 colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py delete mode 100644 tests/test_pipeline/test_policy/test_t5_pipeline_utils.py create mode 100644 tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py diff --git a/colossalai/shardformer/modeling/chatglm.py b/colossalai/shardformer/modeling/chatglm.py new file mode 100644 index 000000000000..0bb8bdc58218 --- /dev/null +++ b/colossalai/shardformer/modeling/chatglm.py @@ -0,0 +1,189 @@ +""" PyTorch ChatGLM model. """ +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch.nn import CrossEntropyLoss, LayerNorm +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig +from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ( + ChatGLMForConditionalGeneration, + ChatGLMModel, + GLMBlock, +) + + +class ChatGLMPipelineForwards: + ''' + This class serves as a micro library for ChatGLM model forwards under pipeline parallelism. + ''' + + @staticmethod + def chatglm_model_forward( + self: ChatGLMModel, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ): + logger = logging.get_logger(__name__) + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if past_key_values: + logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') + past_key_values = None + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + if stage_manager.is_first_stage(): + batch_size, seq_length = input_ids.shape + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + hidden_states = inputs_embeds + else: + seq_length, batch_size = hidden_states.shape[:2] + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt(batch_size=batch_size, + device=input_ids.device, + dtype=inputs_embeds.dtype) + if attention_mask is not None: + attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), attention_mask], + dim=-1) + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + if not past_key_values: + past_key_values = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.encoder.gradient_checkpointing and self.encoder.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + start_idx, end_idx = stage_index[0], stage_index[1] + for idx in range(start_idx, end_idx): + layer = self.encoder._get_layer(idx) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + if self.encoder.gradient_checkpointing and self.encoder.training: + layer_ret = torch.utils.checkpoint.checkpoint(layer, hidden_states, attention_mask, rotary_pos_emb, + past_key_values[idx], use_cache) + else: + layer_ret = layer(hidden_states, + full_attention_mask, + rotary_pos_emb, + kv_cache=past_key_values[idx], + use_cache=use_cache) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + if stage_manager.is_last_stage(): + # final layer_norm + if self.encoder.post_layer_norm: + hidden_states = self.encoder.final_layernorm(hidden_states) + if not return_dict: + return tuple( + v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + else: + return {'hidden_states': hidden_states} + + @staticmethod + def chatglm_for_conditional_generation_forward( + self: ChatGLMForConditionalGeneration, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ): + logger = logging.get_logger(__name__) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = (return_dict if return_dict is not None else self.config.use_return_dict) + transformer_outputs = ChatGLMPipelineForwards.chatglm_model_forward( + self.transformer, + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + return transformer_outputs diff --git a/colossalai/shardformer/modeling/chatglm2_6b/configuration_chatglm.py b/colossalai/shardformer/modeling/chatglm2_6b/configuration_chatglm.py new file mode 100644 index 000000000000..3e78732be2da --- /dev/null +++ b/colossalai/shardformer/modeling/chatglm2_6b/configuration_chatglm.py @@ -0,0 +1,58 @@ +from transformers import PretrainedConfig + + +class ChatGLMConfig(PretrainedConfig): + model_type = "chatglm" + + def __init__(self, + num_layers=28, + padded_vocab_size=65024, + hidden_size=4096, + ffn_hidden_size=13696, + kv_channels=128, + num_attention_heads=32, + seq_length=2048, + hidden_dropout=0.0, + attention_dropout=0.0, + layernorm_epsilon=1e-5, + rmsnorm=True, + apply_residual_connection_post_layernorm=False, + post_layer_norm=True, + add_bias_linear=False, + add_qkv_bias=False, + bias_dropout_fusion=True, + multi_query_attention=False, + multi_query_group_num=1, + apply_query_key_layer_scaling=True, + attention_softmax_in_fp32=True, + fp32_residual_connection=False, + quantization_bit=0, + pre_seq_len=None, + prefix_projection=False, + **kwargs): + self.num_layers = num_layers + self.vocab_size = padded_vocab_size + self.padded_vocab_size = padded_vocab_size + self.hidden_size = hidden_size + self.ffn_hidden_size = ffn_hidden_size + self.kv_channels = kv_channels + self.num_attention_heads = num_attention_heads + self.seq_length = seq_length + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + self.layernorm_epsilon = layernorm_epsilon + self.rmsnorm = rmsnorm + self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm + self.post_layer_norm = post_layer_norm + self.add_bias_linear = add_bias_linear + self.add_qkv_bias = add_qkv_bias + self.bias_dropout_fusion = bias_dropout_fusion + self.multi_query_attention = multi_query_attention + self.multi_query_group_num = multi_query_group_num + self.apply_query_key_layer_scaling = apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = attention_softmax_in_fp32 + self.fp32_residual_connection = fp32_residual_connection + self.quantization_bit = quantization_bit + self.pre_seq_len = pre_seq_len + self.prefix_projection = prefix_projection + super().__init__(**kwargs) diff --git a/colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py b/colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py new file mode 100644 index 000000000000..a21ee0231422 --- /dev/null +++ b/colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py @@ -0,0 +1,1373 @@ +""" +The ChatGLM2-6B License + +1. Definitions + +“Licensor” means the ChatGLM2-6B Model Team that distributes its Software. + +“Software” means the ChatGLM2-6B model parameters made available under this license. + +2. License Grant + +Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license to use the Software solely for your non-commercial research purposes. + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +3. Restriction + +You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any commercial, military, or illegal purposes. + +You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings. + +4. Disclaimer + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +5. Limitation of Liability + +EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. + +6. Dispute Resolution + +This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing. + +Note that the license is subject to update to a more comprehensive version. For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com. +""" +""" PyTorch ChatGLM model. """ + +import copy +import math +import re +import sys +import warnings +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm +from torch.nn.utils import skip_init +from transformers.generation.logits_process import LogitsProcessor +from transformers.generation.utils import GenerationConfig, LogitsProcessorList, ModelOutput, StoppingCriteriaList +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import logging + +from .configuration_chatglm import ChatGLMConfig + +# flags required to enable jit fusion kernels + +if sys.platform != "darwin": + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + torch._C._jit_override_can_fuse_on_cpu(True) + torch._C._jit_override_can_fuse_on_gpu(True) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B" +_CONFIG_FOR_DOC = "ChatGLM6BConfig" + +CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "THUDM/chatglm2-6b", + # See all ChatGLM models at https://huggingface.co/models?filter=chatglm +] + + +def default_init(cls, *args, **kwargs): + return cls(*args, **kwargs) + + +class InvalidScoreLogitsProcessor(LogitsProcessor): + + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + if torch.isnan(scores).any() or torch.isinf(scores).any(): + scores.zero_() + scores[..., 5] = 5e4 + return scores + + +class PrefixEncoder(torch.nn.Module): + """ + The torch.nn model to encode the prefix + Input shape: (batch-size, prefix-length) + Output shape: (batch-size, prefix-length, 2*layers*hidden) + """ + + def __init__(self, config: ChatGLMConfig): + super().__init__() + self.prefix_projection = config.prefix_projection + if self.prefix_projection: + # Use a two-layer MLP to encode the prefix + kv_size = (config.num_layers * config.kv_channels * config.multi_query_group_num * 2) + self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) + self.trans = torch.nn.Sequential( + torch.nn.Linear(kv_size, config.hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(config.hidden_size, kv_size), + ) + else: + self.embedding = torch.nn.Embedding( + config.pre_seq_len, + config.num_layers * config.kv_channels * config.multi_query_group_num * 2, + ) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.trans(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values + + +def split_tensor_along_last_dim( + tensor: torch.Tensor, + num_partitions: int, + contiguous_split_chunks: bool = False, +) -> List[torch.Tensor]: + """Split a tensor along its last dimension. + + Arguments: + tensor: input tensor. + num_partitions: number of partitions to split the tensor + contiguous_split_chunks: If True, make each chunk contiguous + in memory. + + Returns: + A list of Tensors + """ + # Get the size and dimension. + last_dim = tensor.dim() - 1 + last_dim_size = tensor.size()[last_dim] // num_partitions + # Split. + tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) + # Note: torch.split does not create contiguous tensors by default. + if contiguous_split_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + + return tensor_list + + +class RotaryEmbedding(nn.Module): + + def __init__(self, dim, original_impl=False, device=None, dtype=None): + super().__init__() + inv_freq = 1.0 / (10000**(torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + self.register_buffer("inv_freq", inv_freq) + self.dim = dim + self.original_impl = original_impl + + def forward_impl( + self, + seq_len: int, + n_elem: int, + dtype: torch.dtype, + device: torch.device, + base: int = 10000, + ): + """Enhanced Transformer with Rotary Position Embedding. + + Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ + transformers/rope/__init__.py. MIT License: + https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. + """ + # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (base**(torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, dtype=dtype, device=device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.outer(seq_idx, theta).float() + + cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) + + # this is to mimic the behaviour of complex32, else we will get different results + if dtype in (torch.float16, torch.bfloat16, torch.int8): + cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() + return cache + + def forward(self, max_seq_len, offset=0): + return self.forward_impl( + max_seq_len, + self.dim, + dtype=self.inv_freq.dtype, + device=self.inv_freq.device, + ) + + +@torch.jit.script +def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: + # x: [sq, b, np, hn] + sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) + rot_dim = rope_cache.shape[-2] * 2 + x, x_pass = x[..., :rot_dim], x[..., rot_dim:] + # truncate to support variable sizes + rope_cache = rope_cache[:sq] + xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) + rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], + xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], + ], + -1, + ) + x_out2 = x_out2.flatten(3) + return torch.cat((x_out2, x_pass), dim=-1) + + +class RMSNorm(torch.nn.Module): + + def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): + super().__init__() + self.elementwise_affine = True + self.normalized_shape = normalized_shape + self.weight = torch.nn.Parameter(torch.ones(normalized_shape, device=device, dtype=dtype)) + self.eps = eps + + def forward(self, hidden_states: torch.Tensor): + input_dtype = hidden_states.dtype + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + return (self.weight * hidden_states).to(input_dtype) + + +class CoreAttention(torch.nn.Module): + + def __init__(self, config: ChatGLMConfig, layer_number): + super(CoreAttention, self).__init__() + + self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling + self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 + if self.apply_query_key_layer_scaling: + self.attention_softmax_in_fp32 = True + self.layer_number = max(1, layer_number) + + projection_size = config.kv_channels * config.num_attention_heads + + # Per attention head and per partition values. + self.hidden_size_per_partition = projection_size + self.hidden_size_per_attention_head = (projection_size // config.num_attention_heads) + self.num_attention_heads_per_partition = config.num_attention_heads + + coeff = None + self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) + if self.apply_query_key_layer_scaling: + coeff = self.layer_number + self.norm_factor *= coeff + self.coeff = coeff + + self.attention_dropout = torch.nn.Dropout(config.attention_dropout) + + def forward(self, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split(".")[0]) + if pytorch_major_version >= 2: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, + key_layer, + value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + + # [b, np, sq, sk] + output_size = ( + query_layer.size(1), + query_layer.size(2), + query_layer.size(0), + key_layer.size(0), + ) + + # [sq, b, np, hn] -> [sq, b * np, hn] + query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) + # [sk, b, np, hn] -> [sk, b * np, hn] + key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) + + # preallocting input tensor: [b * np, sq, sk] + matmul_input_buffer = torch.empty( + output_size[0] * output_size[1], + output_size[2], + output_size[3], + dtype=query_layer.dtype, + device=query_layer.device, + ) + + # Raw attention scores. [b * np, sq, sk] + matmul_result = torch.baddbmm( + matmul_input_buffer, + query_layer.transpose(0, 1), # [b * np, sq, hn] + key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] + beta=0.0, + alpha=(1.0 / self.norm_factor), + ) + + # change view to [b, np, sq, sk] + attention_scores = matmul_result.view(*output_size) + + # =========================== + # Attention probs and dropout + # =========================== + + # attention scores and attention mask [b, np, sq, sk] + if self.attention_softmax_in_fp32: + attention_scores = attention_scores.float() + if self.coeff is not None: + attention_scores = attention_scores * self.coeff + if (attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]): + attention_mask = torch.ones( + output_size[0], + 1, + output_size[2], + output_size[3], + device=attention_scores.device, + dtype=torch.bool, + ) + attention_mask.tril_() + attention_mask = ~attention_mask + if attention_mask is not None: + attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) + attention_probs = F.softmax(attention_scores, dim=-1) + attention_probs = attention_probs.type_as(value_layer) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attention_dropout(attention_probs) + # ========================= + # Context layer. [sq, b, hp] + # ========================= + + # value_layer -> context layer. + # [sk, b, np, hn] --> [b, np, sq, hn] + + # context layer shape: [b, np, sq, hn] + output_size = ( + value_layer.size(1), + value_layer.size(2), + query_layer.size(0), + value_layer.size(3), + ) + # change view [sk, b * np, hn] + value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) + # change view [b * np, sq, sk] + attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) + # matmul: [b * np, sq, hn] + context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) + # change view [b, np, sq, hn] + context_layer = context_layer.view(*output_size) + # [b, np, sq, hn] --> [sq, b, np, hn] + context_layer = context_layer.permute(2, 0, 1, 3).contiguous() + # [sq, b, np, hn] --> [sq, b, hp] + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer + + +class SelfAttention(torch.nn.Module): + """Parallel self-attention layer abstract class. + + Self-attention layer takes input with size [s, b, h] + and returns output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(SelfAttention, self).__init__() + self.layer_number = max(1, layer_number) + + self.projection_size = config.kv_channels * config.num_attention_heads + # Per attention head and per partition values. + self.hidden_size_per_attention_head = (self.projection_size // config.num_attention_heads) + self.num_attention_heads_per_partition = config.num_attention_heads + + self.multi_query_attention = config.multi_query_attention + self.qkv_hidden_size = 3 * self.projection_size + if self.multi_query_attention: + self.num_multi_query_groups_per_partition = config.multi_query_group_num + self.qkv_hidden_size = (self.projection_size + + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num) + self.query_key_value = nn.Linear( + config.hidden_size, + self.qkv_hidden_size, + bias=config.add_bias_linear or config.add_qkv_bias, + device=device, + **_config_to_kwargs(config), + ) + + self.core_attention = CoreAttention(config, self.layer_number) + + # Output. + self.dense = nn.Linear( + self.projection_size, + config.hidden_size, + bias=config.add_bias_linear, + device=device, + **_config_to_kwargs(config), + ) + + def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): + if self.multi_query_attention: + num_attention_heads = self.num_multi_query_groups_per_partition + else: + num_attention_heads = self.num_attention_heads_per_partition + return torch.empty( + inference_max_sequence_len, + batch_size, + num_attention_heads, + self.hidden_size_per_attention_head, + dtype=dtype, + device=device, + ) + + def forward( + self, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=None, + use_cache=True, + ): + # hidden_states: [sq, b, h] + + # ================================================= + # Pre-allocate memory for key-values for inference. + # ================================================= + # ===================== + # Query, Key, and Value + # ===================== + + # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] + mixed_x_layer = self.query_key_value(hidden_states) + + if self.multi_query_attention: + (query_layer, key_layer, value_layer) = mixed_x_layer.split( + [ + self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + ], + dim=-1, + ) + query_layer = query_layer.view(query_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, + self.hidden_size_per_attention_head, + )) + key_layer = key_layer.view(key_layer.size()[:-1] + ( + self.num_multi_query_groups_per_partition, + self.hidden_size_per_attention_head, + )) + value_layer = value_layer.view(value_layer.size()[:-1] + ( + self.num_multi_query_groups_per_partition, + self.hidden_size_per_attention_head, + )) + else: + new_tensor_shape = mixed_x_layer.size()[:-1] + ( + self.num_attention_heads_per_partition, + 3 * self.hidden_size_per_attention_head, + ) + mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) + # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] + (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) + + # apply relative positional encoding (rotary embedding) + if rotary_pos_emb is not None: + query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) + key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) + + # adjust key and value for inference + if kv_cache is not None: + cache_k, cache_v = kv_cache + key_layer = torch.cat((cache_k, key_layer), dim=0) + value_layer = torch.cat((cache_v, value_layer), dim=0) + if use_cache: + kv_cache = (key_layer, value_layer) + else: + kv_cache = None + + if self.multi_query_attention: + key_layer = key_layer.unsqueeze(-2) + key_layer = key_layer.expand( + -1, + -1, + -1, + self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, + -1, + ) + key_layer = key_layer.contiguous().view(key_layer.size()[:2] + ( + self.num_attention_heads_per_partition, + self.hidden_size_per_attention_head, + )) + value_layer = value_layer.unsqueeze(-2) + value_layer = value_layer.expand( + -1, + -1, + -1, + self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, + -1, + ) + value_layer = value_layer.contiguous().view(value_layer.size()[:2] + ( + self.num_attention_heads_per_partition, + self.hidden_size_per_attention_head, + )) + + # ================================== + # core attention computation + # ================================== + + context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) + + # ================= + # Output. [sq, b, h] + # ================= + + output = self.dense(context_layer) + + return output, kv_cache + + +def _config_to_kwargs(args): + common_kwargs = { + "dtype": args.torch_dtype, + } + return common_kwargs + + +class MLP(torch.nn.Module): + """MLP. + + MLP will take the input with h hidden state, project it to 4*h + hidden dimension, perform nonlinear transformation, and project the + state back into h hidden dimension. + """ + + def __init__(self, config: ChatGLMConfig, device=None): + super(MLP, self).__init__() + + self.add_bias = config.add_bias_linear + + # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf + self.dense_h_to_4h = nn.Linear( + config.hidden_size, + config.ffn_hidden_size * 2, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config), + ) + + def swiglu(x): + x = torch.chunk(x, 2, dim=-1) + return F.silu(x[0]) * x[1] + + self.activation_func = swiglu + + # Project back to h. + self.dense_4h_to_h = nn.Linear( + config.ffn_hidden_size, + config.hidden_size, + bias=self.add_bias, + device=device, + **_config_to_kwargs(config), + ) + + def forward(self, hidden_states): + # [s, b, 4hp] + intermediate_parallel = self.dense_h_to_4h(hidden_states) + intermediate_parallel = self.activation_func(intermediate_parallel) + # [s, b, h] + output = self.dense_4h_to_h(intermediate_parallel) + return output + + +class GLMBlock(torch.nn.Module): + """A single transformer layer. + + Transformer layer takes input with size [s, b, h] and returns an + output of the same size. + """ + + def __init__(self, config: ChatGLMConfig, layer_number, device=None): + super(GLMBlock, self).__init__() + self.layer_number = layer_number + + self.apply_residual_connection_post_layernorm = (config.apply_residual_connection_post_layernorm) + + self.fp32_residual_connection = config.fp32_residual_connection + + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Layernorm on the input data. + self.input_layernorm = LayerNormFunc( + config.hidden_size, + eps=config.layernorm_epsilon, + device=device, + dtype=config.torch_dtype, + ) + + # Self attention. + self.self_attention = SelfAttention(config, layer_number, device=device) + self.hidden_dropout = config.hidden_dropout + + # Layernorm on the attention output + self.post_attention_layernorm = LayerNormFunc( + config.hidden_size, + eps=config.layernorm_epsilon, + device=device, + dtype=config.torch_dtype, + ) + + # MLP + self.mlp = MLP(config, device=device) + + def forward( + self, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=None, + use_cache=True, + ): + # hidden_states: [s, b, h] + + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache, + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = residual + layernorm_input + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = residual + output + + return output, kv_cache + + +class GLMTransformer(torch.nn.Module): + """Transformer class.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(GLMTransformer, self).__init__() + + self.fp32_residual_connection = config.fp32_residual_connection + self.post_layer_norm = config.post_layer_norm + + # Number of layers. + self.num_layers = config.num_layers + + # Transformer layers. + def build_layer(layer_number): + return GLMBlock(config, layer_number, device=device) + + self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) + + if self.post_layer_norm: + LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm + # Final layer norm before output. + self.final_layernorm = LayerNormFunc( + config.hidden_size, + eps=config.layernorm_epsilon, + device=device, + dtype=config.torch_dtype, + ) + + self.gradient_checkpointing = False + + def _get_layer(self, layer_number): + return self.layers[layer_number] + + def forward( + self, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches=None, + use_cache: Optional[bool] = True, + output_hidden_states: Optional[bool] = False, + ): + if not kv_caches: + kv_caches = [None for _ in range(self.num_layers)] + presents = () if use_cache else None + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + all_self_attentions = None + all_hidden_states = () if output_hidden_states else None + for index in range(self.num_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer = self._get_layer(index) + if self.gradient_checkpointing and self.training: + layer_ret = torch.utils.checkpoint.checkpoint( + layer, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_caches[index], + use_cache, + ) + else: + layer_ret = layer( + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=kv_caches[index], + use_cache=use_cache, + ) + hidden_states, kv_cache = layer_ret + if use_cache: + presents = presents + (kv_cache,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Final layer norm. + if self.post_layer_norm: + hidden_states = self.final_layernorm(hidden_states) + + return hidden_states, presents, all_hidden_states, all_self_attentions + + +class ChatGLMPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and + a simple interface for downloading and loading pretrained models. + """ + + is_parallelizable = False + supports_gradient_checkpointing = True + config_class = ChatGLMConfig + base_model_prefix = "transformer" + _no_split_modules = ["GLMBlock"] + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + return + + def get_masks(self, input_ids, past_key_values, padding_mask=None): + batch_size, seq_length = input_ids.shape + full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) + full_attention_mask.tril_() + past_length = 0 + if past_key_values: + past_length = past_key_values[0][0].shape[0] + if past_length: + full_attention_mask = torch.cat( + ( + torch.ones(batch_size, seq_length, past_length, device=input_ids.device), + full_attention_mask, + ), + dim=-1, + ) + if padding_mask is not None: + full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) + if not past_length and padding_mask is not None: + full_attention_mask -= padding_mask.unsqueeze(-1) - 1 + full_attention_mask = (full_attention_mask < 0.5).bool() + full_attention_mask.unsqueeze_(1) + return full_attention_mask + + def get_position_ids(self, input_ids, device): + batch_size, seq_length = input_ids.shape + position_ids = (torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)) + return position_ids + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, GLMTransformer): + module.gradient_checkpointing = value + + +class Embedding(torch.nn.Module): + """Language model embeddings.""" + + def __init__(self, config: ChatGLMConfig, device=None): + super(Embedding, self).__init__() + + self.hidden_size = config.hidden_size + # Word embeddings (parallel). + self.word_embeddings = nn.Embedding( + config.padded_vocab_size, + self.hidden_size, + dtype=config.torch_dtype, + device=device, + ) + self.fp32_residual_connection = config.fp32_residual_connection + + def forward(self, input_ids): + # Embeddings. + words_embeddings = self.word_embeddings(input_ids) + embeddings = words_embeddings + # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. + embeddings = embeddings.transpose(0, 1).contiguous() + # If the input flag for fp32 residual connection is set, convert for float. + if self.fp32_residual_connection: + embeddings = embeddings.float() + return embeddings + + +class ChatGLMModel(ChatGLMPreTrainedModel): + + def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): + super().__init__(config) + if empty_init: + init_method = skip_init + else: + init_method = default_init + init_kwargs = {} + if device is not None: + init_kwargs["device"] = device + self.embedding = init_method(Embedding, config, **init_kwargs) + self.num_layers = config.num_layers + self.multi_query_group_num = config.multi_query_group_num + self.kv_channels = config.kv_channels + + # Rotary positional embeddings + self.seq_length = config.seq_length + rotary_dim = (config.hidden_size // + config.num_attention_heads if config.kv_channels is None else config.kv_channels) + + self.rotary_pos_emb = RotaryEmbedding( + rotary_dim // 2, + original_impl=config.original_rope, + device=device, + dtype=config.torch_dtype, + ) + self.encoder = init_method(GLMTransformer, config, **init_kwargs) + self.output_layer = init_method( + nn.Linear, + config.hidden_size, + config.padded_vocab_size, + bias=False, + dtype=config.torch_dtype, + **init_kwargs, + ) + self.pre_seq_len = config.pre_seq_len + self.prefix_projection = config.prefix_projection + if self.pre_seq_len is not None: + for param in self.parameters(): + param.requires_grad = False + self.prefix_tokens = torch.arange(self.pre_seq_len).long() + self.prefix_encoder = PrefixEncoder(config) + self.dropout = torch.nn.Dropout(0.1) + + def get_input_embeddings(self): + return self.embedding.word_embeddings + + def get_prompt(self, batch_size, device, dtype=torch.half): + prefix_tokens = (self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)) + past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) + past_key_values = past_key_values.view( + batch_size, + self.pre_seq_len, + self.num_layers * 2, + self.multi_query_group_num, + self.kv_channels, + ) + # seq_len, b, nh, hidden_size + past_key_values = self.dropout(past_key_values) + past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) + return past_key_values + + def forward( + self, + input_ids, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.BoolTensor] = None, + full_attention_mask: Optional[torch.BoolTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = (return_dict if return_dict is not None else self.config.use_return_dict) + + batch_size, seq_length = input_ids.shape + + if inputs_embeds is None: + inputs_embeds = self.embedding(input_ids) + + if self.pre_seq_len is not None: + if past_key_values is None: + past_key_values = self.get_prompt( + batch_size=batch_size, + device=input_ids.device, + dtype=inputs_embeds.dtype, + ) + if attention_mask is not None: + attention_mask = torch.cat( + [ + attention_mask.new_ones((batch_size, self.pre_seq_len)), + attention_mask, + ], + dim=-1, + ) + + if full_attention_mask is None: + if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): + full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) + + # Rotary positional embeddings + rotary_pos_emb = self.rotary_pos_emb(self.seq_length) + if position_ids is not None: + rotary_pos_emb = rotary_pos_emb[position_ids] + else: + rotary_pos_emb = rotary_pos_emb[None, :seq_length] + rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() + + # Run encoder. + hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( + inputs_embeds, + full_attention_mask, + rotary_pos_emb=rotary_pos_emb, + kv_caches=past_key_values, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + ) + + if not return_dict: + return tuple(v for v in [ + hidden_states, + presents, + all_hidden_states, + all_self_attentions, + ] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def quantize(self, weight_bit_width: int): + from .quantization import quantize + + quantize(self.encoder, weight_bit_width) + return self + + +class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): + + def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): + super().__init__(config) + + self.max_sequence_length = config.max_length + self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) + self.config = config + self.quantized = False + + if self.config.quantization_bit: + self.quantize(self.config.quantization_bit, empty_init=True) + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: Dict[str, Any], + is_encoder_decoder: bool = False, + standardize_cache_format: bool = False, + ) -> Dict[str, Any]: + # update past_key_values + model_kwargs["past_key_values"] = self._extract_past_from_model_output( + outputs, standardize_cache_format=standardize_cache_format) + + # update attention mask + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], + dim=-1, + ) + + # update position ids + if "position_ids" in model_kwargs: + position_ids = model_kwargs["position_ids"] + new_position_id = position_ids[..., -1:].clone() + new_position_id += 1 + model_kwargs["position_ids"] = torch.cat([position_ids, new_position_id], dim=-1) + + model_kwargs["is_first_forward"] = False + return model_kwargs + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + is_first_forward: bool = True, + **kwargs, + ) -> dict: + # only last token for input_ids if past is not None + if position_ids is None: + position_ids = self.get_position_ids(input_ids, device=input_ids.device) + if not is_first_forward: + position_ids = position_ids[..., -1:] + input_ids = input_ids[:, -1:] + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "position_ids": position_ids, + "attention_mask": attention_mask, + "return_last_logit": True, + } + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + return_last_logit: Optional[bool] = False, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = (return_dict if return_dict is not None else self.config.use_return_dict) + + transformer_outputs = self.transformer( + input_ids=input_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + if return_last_logit: + hidden_states = hidden_states[-1:] + lm_logits = self.transformer.output_layer(hidden_states) + lm_logits = lm_logits.transpose(0, 1).contiguous() + + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + @staticmethod + def _reorder_cache(past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], + beam_idx: torch.LongTensor) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + return tuple(( + layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), + layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), + ) for layer_past in past) + + def process_response(self, response): + response = response.strip() + response = response.replace("[[训练时间]]", "2023年") + return response + + def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + prompt = tokenizer.build_prompt(query, history=history) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): + if history: + prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + input_ids = tokenizer.encode(prompt, add_special_tokens=False) + input_ids = input_ids[1:] + inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False) + else: + prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) + inputs = tokenizer([prompt], return_tensors="pt") + inputs = inputs.to(self.device) + return inputs + + @torch.no_grad() + def chat( + self, + tokenizer, + query: str, + history: List[Tuple[str, str]] = None, + max_length: int = 8192, + num_beams=1, + do_sample=True, + top_p=0.8, + temperature=0.8, + logits_processor=None, + **kwargs, + ): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = { + "max_length": max_length, + "num_beams": num_beams, + "do_sample": do_sample, + "top_p": top_p, + "temperature": temperature, + "logits_processor": logits_processor, + **kwargs, + } + inputs = self.build_inputs(tokenizer, query, history=history) + outputs = self.generate(**inputs, **gen_kwargs) + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + response = self.process_response(response) + history = history + [(query, response)] + return response, history + + @torch.no_grad() + def stream_chat( + self, + tokenizer, + query: str, + history: List[Tuple[str, str]] = None, + past_key_values=None, + max_length: int = 8192, + do_sample=True, + top_p=0.8, + temperature=0.8, + logits_processor=None, + return_past_key_values=False, + **kwargs, + ): + if history is None: + history = [] + if logits_processor is None: + logits_processor = LogitsProcessorList() + logits_processor.append(InvalidScoreLogitsProcessor()) + gen_kwargs = { + "max_length": max_length, + "do_sample": do_sample, + "top_p": top_p, + "temperature": temperature, + "logits_processor": logits_processor, + **kwargs, + } + if past_key_values is None and not return_past_key_values: + inputs = self.build_inputs(tokenizer, query, history=history) + else: + inputs = self.build_stream_inputs(tokenizer, query, history=history) + if past_key_values is not None: + past_length = past_key_values[0][0].shape[0] + if self.transformer.pre_seq_len is not None: + past_length -= self.transformer.pre_seq_len + inputs.position_ids += past_length + attention_mask = inputs.attention_mask + attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) + inputs["attention_mask"] = attention_mask + for outputs in self.stream_generate( + **inputs, + past_key_values=past_key_values, + return_past_key_values=return_past_key_values, + **gen_kwargs, + ): + if return_past_key_values: + outputs, past_key_values = outputs + outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] + response = tokenizer.decode(outputs) + if response and response[-1] != "�": + response = self.process_response(response) + new_history = history + [(query, response)] + if return_past_key_values: + yield response, new_history, past_key_values + else: + yield response, new_history + + @torch.no_grad() + def stream_generate( + self, + input_ids, + generation_config: Optional[GenerationConfig] = None, + logits_processor: Optional[LogitsProcessorList] = None, + stopping_criteria: Optional[StoppingCriteriaList] = None, + prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, + return_past_key_values=False, + **kwargs, + ): + batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] + + if generation_config is None: + generation_config = self.generation_config + generation_config = copy.deepcopy(generation_config) + model_kwargs = generation_config.update(**kwargs) + bos_token_id, eos_token_id = ( + generation_config.bos_token_id, + generation_config.eos_token_id, + ) + + if isinstance(eos_token_id, int): + eos_token_id = [eos_token_id] + + has_default_max_length = (kwargs.get("max_length") is None and generation_config.max_length is not None) + if has_default_max_length and generation_config.max_new_tokens is None: + warnings.warn( + f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " + "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" + " recommend using `max_new_tokens` to control the maximum length of the generation.", + UserWarning, + ) + elif generation_config.max_new_tokens is not None: + generation_config.max_length = (generation_config.max_new_tokens + input_ids_seq_length) + if not has_default_max_length: + logger.warn( + f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" + f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " + "Please refer to the documentation for more information. " + "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", + UserWarning, + ) + + if input_ids_seq_length >= generation_config.max_length: + input_ids_string = ("decoder_input_ids" if self.config.is_encoder_decoder else "input_ids") + logger.warning(f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" + f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" + " increasing `max_new_tokens`.") + + # 2. Set generation parameters if not already defined + logits_processor = (logits_processor if logits_processor is not None else LogitsProcessorList()) + stopping_criteria = (stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()) + + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_seq_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + logits_processor=logits_processor, + ) + + stopping_criteria = self._get_stopping_criteria(generation_config=generation_config, + stopping_criteria=stopping_criteria) + logits_warper = self._get_logits_warper(generation_config) + + unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) + scores = None + while True: + model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) + # forward pass to get next token + outputs = self( + **model_inputs, + return_dict=True, + output_attentions=False, + output_hidden_states=False, + ) + + next_token_logits = outputs.logits[:, -1, :] + + # pre-process distribution + next_token_scores = logits_processor(input_ids, next_token_logits) + next_token_scores = logits_warper(input_ids, next_token_scores) + + # sample + probs = nn.functional.softmax(next_token_scores, dim=-1) + if generation_config.do_sample: + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + else: + next_tokens = torch.argmax(probs, dim=-1) + + # update generated ids, model inputs, and length for next step + input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) + model_kwargs = self._update_model_kwargs_for_generation(outputs, + model_kwargs, + is_encoder_decoder=self.config.is_encoder_decoder) + unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) + if return_past_key_values: + yield input_ids, outputs.past_key_values + else: + yield input_ids + # stop when each sentence is finished, or if we exceed the maximum length + if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): + break + + def quantize(self, bits: int, empty_init=False, device=None, **kwargs): + if bits == 0: + return + + from .quantization import quantize + + if self.quantized: + logger.info("Already quantized.") + return self + + self.quantized = True + + self.config.quantization_bit = bits + + self.transformer.encoder = quantize( + self.transformer.encoder, + bits, + empty_init=empty_init, + device=device, + **kwargs, + ) + return self diff --git a/colossalai/shardformer/policies/chatglm.py b/colossalai/shardformer/policies/chatglm.py index 732a817b0655..9cc651caddc1 100644 --- a/colossalai/shardformer/policies/chatglm.py +++ b/colossalai/shardformer/policies/chatglm.py @@ -1,32 +1,46 @@ -from typing import Dict, Union +from functools import partial +from typing import Callable, Dict, List, Optional, Tuple, Union import torch.nn as nn +from torch import Tensor +from transformers.modeling_outputs import BaseModelOutputWithPast import colossalai.shardformer.layer as col_nn +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.modeling.chatglm import ChatGLMPipelineForwards +from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig +from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ( + ChatGLMForConditionalGeneration, + ChatGLMModel, + GLMBlock, +) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription -__all__ = ['ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy'] +__all__ = ['ChatGLMPolicy', 'ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy'] -class ChatGLMModelPolicy(Policy): +class ChatGLMPolicy(Policy): def config_sanity_check(self): pass def preprocess(self): # Resize embedding - vocab_size = self.model.config.padded_vocab_size - world_size = self.shard_config.tensor_parallel_size + if self.shard_config.enable_tensor_parallelism: + vocab_size = self.model.config.padded_vocab_size + world_size = self.shard_config.tensor_parallel_size + + if vocab_size % world_size != 0: + new_vocab_size = vocab_size + world_size - vocab_size % world_size + self.model.resize_token_embeddings(new_vocab_size) - if vocab_size % world_size != 0: - new_vocab_size = vocab_size + world_size - vocab_size % world_size - self.model.resize_token_embeddings(new_vocab_size) return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - from tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock + from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock + policy = {} @@ -112,9 +126,91 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: def postprocess(self): return self.model + def get_held_layers(self) -> List[nn.Module]: + """Get pipeline layers for current stage.""" + assert self.pipeline_stage_manager is not None + + if self.model.__class__.__name__ == 'ChatGLMModel': + module = self.model + else: + module = self.model.transformer + stage_manager = self.pipeline_stage_manager + + held_layers = [] + layers_per_stage = self.distribute_layers(module.num_layers, stage_manager.num_stages) + if stage_manager.is_first_stage(): + held_layers.append(module.embedding) + start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) + held_layers.extend(module.encoder.layers[start_idx:end_idx]) + if stage_manager.is_last_stage(): + if module.encoder.post_layer_norm: + held_layers.append(module.encoder.final_layernorm) + + # rotary_pos_emb is needed for all stages + held_layers.append(module.rotary_pos_emb) + + return held_layers + + def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: + """If under pipeline parallel setting, replacing the original forward method of huggingface + to customized forward method, and add this changing to policy.""" + if not self.pipeline_stage_manager: + raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.") + stage_manager = self.pipeline_stage_manager + if self.model.__class__.__name__ == 'ChatGLMModel': + module = self.model + else: + module = self.model.transformer + + layers_per_stage = Policy.distribute_layers(module.num_layers, stage_manager.num_stages) + stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) + method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} + self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls) + + +class ChatGLMModelPolicy(ChatGLMPolicy): + + def __init__(self) -> None: + super().__init__() + + def module_policy(self): + from transformers.models.gpt2.modeling_gpt2 import GPT2Model + + policy = super().module_policy() + + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=ChatGLMModel, + new_forward=ChatGLMPipelineForwards.chatglm_model_forward, + policy=policy) + return policy + + def get_held_layers(self) -> List[nn.Module]: + return super().get_held_layers() + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in ChatGLMModel.""" + return [] + + class ChatGLMForConditionalGenerationPolicy(ChatGLMModelPolicy): def module_policy(self): policy = super().module_policy() + + if self.pipeline_stage_manager is not None: + self.set_pipeline_forward(model_cls=ChatGLMForConditionalGeneration, + new_forward=ChatGLMPipelineForwards.chatglm_for_conditional_generation_forward, + policy=policy) return policy + + def get_held_layers(self) -> List[nn.Module]: + held_layers = super().get_held_layers() + if self.pipeline_stage_manager.is_last_stage(): + held_layers.append(self.model.transformer.output_layer) + return held_layers + + def get_shared_params(self) -> List[Dict[int, Tensor]]: + """No shared params in ChatGLMForConditionalGenerationModel.""" + return [] + diff --git a/tests/kit/model_zoo/transformers/chatglm.py b/tests/kit/model_zoo/transformers/chatglm.py index 04e73a832abe..056c910a8dfe 100644 --- a/tests/kit/model_zoo/transformers/chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm.py @@ -1,9 +1,11 @@ import torch import transformers +from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig +from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel + from ..registry import ModelAttribute, model_zoo -from .chatglm2_6b.configuration_chatglm import ChatGLMConfig -from .chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel + # ================================ # Register single-sentence ChatGLM @@ -20,15 +22,18 @@ def data_gen(): output_transform_fn = lambda x: x # define loss function -loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.mean() -loss_fn = lambda x: x.logits.mean() +loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.sum() +loss_fn = lambda x: x.logits.sum() + config = ChatGLMConfig(num_layers=1, padded_vocab_size=65024, hidden_size=64, num_attention_heads=8, - rmsnorm=False, + rmsnorm=True, original_rope=True, - use_cache=True) + use_cache=True, + torch_dtype=torch.float32) + model_zoo.register(name='transformers_chatglm', model_fn=lambda: ChatGLMModel(config, empty_init=False), diff --git a/tests/test_pipeline/test_policy/test_t5_pipeline_utils.py b/tests/test_pipeline/test_policy/test_t5_pipeline_utils.py deleted file mode 100644 index 0cbb852b97a0..000000000000 --- a/tests/test_pipeline/test_policy/test_t5_pipeline_utils.py +++ /dev/null @@ -1,39 +0,0 @@ -from colossalai.shardformer.policies.t5 import T5BasePolicy - - -def test_t5_pipeline_distribution(): - num_test_cases = 8 - test_dict = { - 'num_encoder_layers': [2, 1, 3, 2, 3, 2, 10, 5], - 'num_decoder_layers': [2, 8, 0, 2, 1, 5, 6, 22], - 'num_stages': [2, 2, 2, 4, 4, 4, 8, 8], - 'decoder_starting_stage': [1, 1, 2, 2, 3, 1, 5, 2] - } - - for i in range(num_test_cases): - _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(test_dict['num_encoder_layers'][i], - test_dict['num_decoder_layers'][i], - test_dict['num_stages'][i]) - assert test_dict['decoder_starting_stage'][i] == decoder_starting_stage - - -def test_t5_pipeline_layers(): - num_test_cases = 4 - test_dict = { - 'num_encoder_layers': [2, 3, 2, 4], - 'num_decoder_layers': [2, 0, 2, 8], - 'num_stages': [2, 2, 4, 4], - 'layers_per_stage': [[[0, 2], [0, 2]], [[0, 1], [1, 3]], [[0, 1], [1, 2], [0, 1], [1, 2]], - [[0, 4], [0, 3], [3, 6], [6, 8]]] - } - - for i in range(num_test_cases): - layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers( - test_dict['num_encoder_layers'][i], test_dict['num_decoder_layers'][i], test_dict['num_stages'][i]) - - for stage in range(test_dict['num_stages'][i]): - start_idx, end_idx = test_dict['layers_per_stage'][i][stage] - predicted_start, predicted_end = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage, - decoder_starting_stage) - assert start_idx == predicted_start - assert end_idx == predicted_end diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index 46b262d0a8cd..0e5cb8144ef3 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -1,5 +1,6 @@ import copy from contextlib import nullcontext +from typing import Optional from typing import Any, Callable, Dict, List, Optional import torch @@ -15,6 +16,7 @@ from colossalai.lazy import LazyInitContext from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer import ShardConfig, ShardFormer +from colossalai.shardformer.policies.auto_policy import Policy from colossalai.shardformer._utils import getattr_ from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor @@ -39,7 +41,8 @@ def build_pipeline_model(model_fn, stage_manager=None, enable_fused_normalization=False, enable_tensor_parallelism=False, - use_lazy_init: bool = False): + use_lazy_init: bool = False, + policy: Optional[Policy] = None): ctx = LazyInitContext() if use_lazy_init else nullcontext() with ctx: # create new model @@ -54,7 +57,7 @@ def build_pipeline_model(model_fn, pipeline_stage_manager=stage_manager) shard_former = ShardFormer(shard_config=shard_config) - sharded_model, shared_params = shard_former.optimize(model_copy) + sharded_model, shared_params = shard_former.optimize(model_copy, policy=policy) return org_model.cuda(), sharded_model.cuda() diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index 36f240a0ffc0..005223fb8ae4 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -60,7 +60,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo shard_weight = shard_chatglm_model.embedding.word_embeddings.weight if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] + shard_grad_list = [torch.zeros_like(shard_grad) for _ in range(2)] torch.distributed.all_gather(shard_grad_list, shard_grad) all_shard_grad = torch.cat(shard_grad_list, dim=0) else: diff --git a/tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py b/tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py new file mode 100644 index 000000000000..ee474ac7be3b --- /dev/null +++ b/tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py @@ -0,0 +1,86 @@ +import copy +import os + +import pytest +import torch + +import colossalai +from colossalai.cluster import ProcessGroupMesh +from colossalai.logging import disable_existing_loggers +from colossalai.pipeline.stage_manager import PipelineStageManager +from colossalai.shardformer.policies.chatglm import ChatGLMForConditionalGenerationPolicy, ChatGLMModelPolicy +from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor +from colossalai.testing import ( + assert_hf_output_close, + clear_cache_before_run, + parameterize, + rerun_if_address_is_in_use, + spawn, +) +from tests.kit.model_zoo import model_zoo +from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward + + +@parameterize('enable_fused_normalization', [False]) +@parameterize('enable_tensor_parallelism', [False]) +@parameterize('use_lazy_init', [False]) +def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): + DP_DIM, PP_DIM = 0, 1 + DP_SIZE, PP_SIZE = 2, 2 + pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) + stage_manager = PipelineStageManager(pg_mesh, PP_DIM) + sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm') + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): + # create new model for test + inputs = data_gen_fn() + inputs = {k: v.cuda() for k, v in inputs.items()} + input_ids = inputs['input_ids'] + hidden_size = 64 + batch_size, seq_len = input_ids.shape + hidden_state_shape = (seq_len, batch_size, hidden_size) + if name == "transformers_chatglm": + _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init, ChatGLMModelPolicy()) + if stage_manager.is_last_stage(): + hidden_states = torch.randn(*hidden_state_shape).cuda() + inputs['input_ids'] = None + inputs['hidden_states'] = hidden_states + outputs = sharded_model(**inputs) + if stage_manager.is_last_stage(): + assert outputs[0].shape == hidden_state_shape + + else: + assert outputs['hidden_states'].shape == hidden_state_shape + + if name == "transformers_chatglm_for_conditional_generation": + _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, + enable_tensor_parallelism, use_lazy_init, + ChatGLMForConditionalGenerationPolicy()) + if stage_manager.is_last_stage(): + hidden_states = torch.randn(*hidden_state_shape).cuda() + inputs['input_ids'] = None + inputs['hidden_states'] = hidden_states + outputs = sharded_model(**inputs) + if stage_manager.is_last_stage(): + assert outputs[0].shape == (batch_size, seq_len, 65024) + else: + assert outputs['hidden_states'].shape == hidden_state_shape + + torch.cuda.empty_cache() + + +def check_chatglm(rank, world_size, port): + disable_existing_loggers() + colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') + run_chatglm_test() + + +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_chatglm(): + spawn(check_chatglm, 4) + + +if __name__ == "__main__": + test_chatglm() From 906426cb4467ee00dc2149bba6d043939ab8df41 Mon Sep 17 00:00:00 2001 From: flybird1111 <1829166702@qq.com> Date: Mon, 7 Aug 2023 16:41:07 +0800 Subject: [PATCH 71/84] [Shardformer] Merge flash attention branch to pipeline branch (#4362) * [shardformer] supported flash attention test dependency (#4158) * [shardformer] fix flash attention utils test (#4180) * [shardformer] opt support flash attention (#4163) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] add performance benchmark of shardformer (#4175) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] benchmark fix * [shardformer] benchmark fix * [shardformer] llama support flash attention (#4185) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] llama support flash attention * [shardformer] llama support flash attention * [shardformer] Move the import statement for xformer outside the forward function. * [shardformer] gpt2 support flash attention. (#4191) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] gpt2 support flash attention * [shardformer] gpt2 support flash attention * [shardformer] gpt2 support flash attention * [shardformer] bloom support flash attention (#4188) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] bloom suport flash attention * [shardformer] add assert to sequence length * [shardformer] fix * [shardformer] fix * [shardformer] fix * [shardformer] bert support flash attention. (#4206) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] bert support flash attention * [shardformer] t5 support flash attention. (#4216) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] t5 support flash attention * [shardformer] t5 support flash attention * fix typo * fix typo * fix typo * fix typo * fix typo * fix typo * [shardformer] support 'paddedcausal' type of attention mask in Coloattention. (#4215) * added padded causal attn mask type for ColoAttention * [shardformer]t5 flash attention fix (#4239) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] t5 flash attention fix * [shardformer] update gpt2 to use coloattention. (#4234) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] update gpt2 to use coloattention * [shardformer] update gpt2 to use coloattention * [shardformer] update gpt2 to use coloattention * [shardformer] update gpt2 to use coloattention * [shardformer] update gpt2 * [shardformer] update opt and llama to use coloattention. (#4226) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * update opt to use coloattention * [shardformer]update opt to use coloattention * [shardformer]update opt to use coloattention * [shardformer]update opt to use coloattention * [shardformer]update opt to use coloattention * [shardformer]update opt to use coloattention * [shardformer]update opt to use coloattention * [shardformer]update opt * [shardformer] shardformer support jit fused operator. (#4236) * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] opt support flash attention * [shardformer] move to modeling * [shardformer] move to modeling * [shardformer] bloom support jit fused operator * [shardformer] bloom support jit fused operator * [shardformer] bloom support jit fused operator * [shardformer] t5 support jit fused operator * [shardformer] t5 support jit fused operator * [shardformer] t5 support jit fused operator * [shardformer] add roadmap of flash attention * [shardformer] add roadmap of flash attention * [shardformer] add roadmap of flash attention * [shardformer] add type hint to 'self' param of forward * [shardformer] merge feature/shardformer-models branch to feature/flash-attention-shardformer branch. (#4290) * Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout * [shardformer] support SAM (#4231) * 1.support sam 2.add fused qkv for nn.Linear * update utils support set element in list * overtwrite SamVisionAttention foward to use DropoutForParallelInput * remove unused code * [shardformer] support whisper (#4212) * support whisper * fix bug in vocabembedding * support downstream model of whisper * update readme * Feature/chatglm (#4240) * [shardformer] added tests * [shardformer] vit test finish and support * [shardformer] chatglm ready * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] chatglm shard without mlp sharding * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] fix chatglm configuration with pre-commit --------- Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Co-authored-by: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> * [shardformer] whisper support flash attention (#4301) * Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout * [shardformer] support SAM (#4231) * 1.support sam 2.add fused qkv for nn.Linear * update utils support set element in list * overtwrite SamVisionAttention foward to use DropoutForParallelInput * remove unused code * [shardformer] support whisper (#4212) * support whisper * fix bug in vocabembedding * support downstream model of whisper * update readme * Feature/chatglm (#4240) * [shardformer] added tests * [shardformer] vit test finish and support * [shardformer] chatglm ready * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] chatglm shard without mlp sharding * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] fix chatglm configuration with pre-commit * [shardformer] whisper support flash attention * [shardformer] whisper support flash attention * [shardformer]whisper support jit operator --------- Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Co-authored-by: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> * [shardformer] sam support flash attention (#4316) * Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout * [shardformer] support SAM (#4231) * 1.support sam 2.add fused qkv for nn.Linear * update utils support set element in list * overtwrite SamVisionAttention foward to use DropoutForParallelInput * remove unused code * [shardformer] support whisper (#4212) * support whisper * fix bug in vocabembedding * support downstream model of whisper * update readme * Feature/chatglm (#4240) * [shardformer] added tests * [shardformer] vit test finish and support * [shardformer] chatglm ready * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] chatglm shard without mlp sharding * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] fix chatglm configuration with pre-commit * [shardformer] sam support flash attention --------- Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Co-authored-by: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> * [shardformer] merge blip2/chatglm (#4321) * Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout * [shardformer] support SAM (#4231) * 1.support sam 2.add fused qkv for nn.Linear * update utils support set element in list * overtwrite SamVisionAttention foward to use DropoutForParallelInput * remove unused code * [shardformer] support whisper (#4212) * support whisper * fix bug in vocabembedding * support downstream model of whisper * update readme * Feature/chatglm (#4240) * [shardformer] added tests * [shardformer] vit test finish and support * [shardformer] chatglm ready * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] chatglm shard without mlp sharding * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] fix chatglm configuration with pre-commit * [shardformer] added tests * [shardformer] vit test finish and support * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] support ChatGLMForConditionalGeneration & add fusedlayernorm for vit * [shardformer] support Blip2 (#4243) * support base blip2 * add support for downstream blip2 model * update readme * add forward injection * skip not compatible models test * fix test for gemini and low_level_zero_pugin --------- Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Co-authored-by: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Co-authored-by: klhhhhh <1412841649@qq.com> * [shardformer] blip2 support flash attention and jit operator (#4325) * Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout * [shardformer] support SAM (#4231) * 1.support sam 2.add fused qkv for nn.Linear * update utils support set element in list * overtwrite SamVisionAttention foward to use DropoutForParallelInput * remove unused code * [shardformer] support whisper (#4212) * support whisper * fix bug in vocabembedding * support downstream model of whisper * update readme * Feature/chatglm (#4240) * [shardformer] added tests * [shardformer] vit test finish and support * [shardformer] chatglm ready * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] chatglm shard without mlp sharding * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] fix chatglm configuration with pre-commit * [shardformer] added tests * [shardformer] vit test finish and support * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] support ChatGLMForConditionalGeneration & add fusedlayernorm for vit * [shardformer] support Blip2 (#4243) * support base blip2 * add support for downstream blip2 model * update readme * add forward injection * skip not compatible models test * fix test for gemini and low_level_zero_pugin * [shardformer] blip2 support flash attention and jit operator * [shardformer] blip2 support flash attention and jit operator * [shardformer] blip2 support flash attention and jit operator --------- Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Co-authored-by: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Co-authored-by: klhhhhh <1412841649@qq.com> * [shardformer] chatglm support flash attention and jit operator (#4330) * Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout * [shardformer] support SAM (#4231) * 1.support sam 2.add fused qkv for nn.Linear * update utils support set element in list * overtwrite SamVisionAttention foward to use DropoutForParallelInput * remove unused code * [shardformer] support whisper (#4212) * support whisper * fix bug in vocabembedding * support downstream model of whisper * update readme * Feature/chatglm (#4240) * [shardformer] added tests * [shardformer] vit test finish and support * [shardformer] chatglm ready * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] chatglm shard without mlp sharding * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] fix chatglm configuration with pre-commit * [shardformer] added tests * [shardformer] vit test finish and support * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] support ChatGLMForConditionalGeneration & add fusedlayernorm for vit * [shardformer] support Blip2 (#4243) * support base blip2 * add support for downstream blip2 model * update readme * add forward injection * skip not compatible models test * fix test for gemini and low_level_zero_pugin * [shardformer] chatglm support flash attention and jit operator * [shardformer] chatglm support flash attention and jit operator * [shardformer] chatglm support flash attention and jit operator * [shardformer] chatglm support flash attention and jit operator --------- Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Co-authored-by: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Co-authored-by: klhhhhh <1412841649@qq.com> * [shardformer] vit support flash attention and jit operator (#4334) * Feature/vit support (#4182) * [shardformer] added tests * [shardformer] vit test finish and support * fix attention dropout * [shardformer] support SAM (#4231) * 1.support sam 2.add fused qkv for nn.Linear * update utils support set element in list * overtwrite SamVisionAttention foward to use DropoutForParallelInput * remove unused code * [shardformer] support whisper (#4212) * support whisper * fix bug in vocabembedding * support downstream model of whisper * update readme * Feature/chatglm (#4240) * [shardformer] added tests * [shardformer] vit test finish and support * [shardformer] chatglm ready * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] chatglm shard without mlp sharding * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] fix chatglm configuration with pre-commit * [shardformer] added tests * [shardformer] vit test finish and support * import chatglm * [shardformer] add test kit in model zoo for chatglm * [sharformer] add first version of policy of chatglm * [shardformer] polish chatglm code * [shardformer] polish code * [shardformer] support chatglm without layernorm * [shardformer] delete some file * [shardformer] ChatGLM support layernorm sharding * [shardformer] register without auto policy * [shardformer] pre-commit check files * [shardformer] support ChatGLMForConditionalGeneration & add fusedlayernorm for vit * [shardformer] support Blip2 (#4243) * support base blip2 * add support for downstream blip2 model * update readme * add forward injection * skip not compatible models test * fix test for gemini and low_level_zero_pugin * [shardformer] vit support flash attention and jit operator * [shardformer] vit support flash attention and jit operator --------- Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Co-authored-by: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Co-authored-by: klhhhhh <1412841649@qq.com> * [pipeline] merge flash attention branch * [pipeline] merge flash attention branch * [pipeline] merge flash attention branch * [pipeline] fix conflict * [pipeline] fix conflict * Merge branch 'feature/pipeline' into feature/pipeline * Merge branch 'feature/pipeline' into feature/pipeline * Merge branch 'feature/pipeline' into feature/pipeline * activate checks * activate checks * activate checks * activate checks * activate checks * activate checks * activate checks * activate checks * fix flash attention tests * gemini ignore whisper * fix vit * fix xformers import handle --------- Co-authored-by: Frank Lee Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com> Co-authored-by: FoolPlayer <45593998+FoolPlayer@users.noreply.github.com> Co-authored-by: klhhhhh <1412841649@qq.com> --- colossalai/shardformer/README.md | 58 +- ..._benchmark.py => convergence_benchmark.py} | 0 ..._benchmark.sh => convergence_benchmark.sh} | 2 +- .../examples/performance_benchmark.py | 86 ++ colossalai/shardformer/modeling/bert.py | 138 +- colossalai/shardformer/modeling/blip2.py | 60 + colossalai/shardformer/modeling/bloom.py | 221 +++ colossalai/shardformer/modeling/chatglm.py | 110 ++ colossalai/shardformer/modeling/gpt2.py | 85 + colossalai/shardformer/modeling/jit.py | 34 + colossalai/shardformer/modeling/llama.py | 66 +- colossalai/shardformer/modeling/opt.py | 174 +++ colossalai/shardformer/modeling/sam.py | 164 ++ colossalai/shardformer/modeling/t5.py | 206 +++ colossalai/shardformer/modeling/vit.py | 49 + colossalai/shardformer/modeling/whisper.py | 249 +++ colossalai/shardformer/policies/bert.py | 34 +- colossalai/shardformer/policies/blip2.py | 28 +- colossalai/shardformer/policies/bloom.py | 34 +- colossalai/shardformer/policies/chatglm.py | 20 +- colossalai/shardformer/policies/gpt2.py | 90 +- colossalai/shardformer/policies/llama.py | 9 +- colossalai/shardformer/policies/opt.py | 17 +- colossalai/shardformer/policies/sam.py | 12 +- colossalai/shardformer/policies/t5.py | 30 +- colossalai/shardformer/policies/vit.py | 25 +- colossalai/shardformer/policies/whisper.py | 25 + colossalai/shardformer/shard/shard_config.py | 5 +- requirements/requirements-test.txt | 2 + tests/kit/model_zoo/transformers/bert.py | 16 +- tests/kit/model_zoo/transformers/blip2.py | 1 + tests/kit/model_zoo/transformers/bloom.py | 10 +- tests/kit/model_zoo/transformers/chatglm.py | 1 - .../chatglm2_6b/configuration_chatglm.py | 58 - .../chatglm2_6b/modeling_chatglm.py | 1372 ----------------- tests/kit/model_zoo/transformers/gpt.py | 6 +- tests/kit/model_zoo/transformers/t5.py | 10 +- tests/kit/model_zoo/transformers/whisper.py | 4 +- .../test_plugin/test_gemini_plugin.py | 2 +- tests/test_shardformer/test_model/_utils.py | 13 +- .../test_model/test_shard_bert.py | 11 +- .../test_model/test_shard_blip2.py | 7 +- .../test_model/test_shard_bloom.py | 8 +- .../test_model/test_shard_chatglm.py | 8 +- .../test_model/test_shard_gpt2.py | 1 - .../test_model/test_shard_llama.py | 5 +- .../test_model/test_shard_opt.py | 15 +- .../test_model/test_shard_sam.py | 6 +- .../test_model/test_shard_t5.py | 11 +- .../test_model/test_shard_vit.py | 9 +- .../test_model/test_shard_whisper.py | 8 +- tests/test_utils/test_flash_attention.py | 2 +- 52 files changed, 2061 insertions(+), 1556 deletions(-) rename colossalai/shardformer/examples/{shardformer_benchmark.py => convergence_benchmark.py} (100%) rename colossalai/shardformer/examples/{shardformer_benchmark.sh => convergence_benchmark.sh} (76%) create mode 100644 colossalai/shardformer/examples/performance_benchmark.py create mode 100644 colossalai/shardformer/modeling/jit.py create mode 100644 colossalai/shardformer/modeling/opt.py create mode 100644 colossalai/shardformer/modeling/whisper.py delete mode 100644 tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py delete mode 100644 tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 5489f97e4d19..5d00e606dc94 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -30,7 +30,7 @@ ### Quick Start -The sample API usage is given below: +The sample API usage is given below(If you enable the use of flash attention, please install xformers.): ```python from colossalai.shardformer import ShardConfig, Shard @@ -106,6 +106,20 @@ We will follow this roadmap to develop Shardformer: - [ ] Multi-modal - [x] SAM - [x] BLIP-2 +- [ ] Flash Attention Support + - [ ] NLP + - [x] BERT + - [x] T5 + - [x] LlaMa + - [x] GPT2 + - [x] OPT + - [x] BLOOM + - [ ] GLM + - [ ] RoBERTa + - [ ] ALBERT + - [ ] ERNIE + - [ ] GPT Neo + - [ ] GPT-J ## 💡 API Design @@ -373,11 +387,49 @@ pytest tests/test_shardformer ### System Performance -To be added. +We conducted [benchmark tests](./examples/performance_benchmark.py) to evaluate the performance improvement of Shardformer. We compared the training time between the original model and the shard model. + +We set the batch size to 4, the number of attention heads to 8, and the head dimension to 64. 'N_CTX' refers to the sequence length. + +In the case of using 2 GPUs, the training times are as follows. +| N_CTX | org_model | shard_model | +| :------: | :-----: | :-----: | +| 256 | 11.2ms | 17.2ms | +| 512 | 9.8ms | 19.5ms | +| 1024 | 19.6ms | 18.9ms | +| 2048 | 46.6ms | 30.8ms | +| 4096 | 160.5ms | 90.4ms | + + +

+ +
+

+ +In the case of using 4 GPUs, the training times are as follows. + +| N_CTX | org_model | shard_model | +| :------: | :-----: | :-----: | +| 256 | 10.0ms | 21.1ms | +| 512 | 11.5ms | 20.2ms | +| 1024 | 22.1ms | 20.6ms | +| 2048 | 46.9ms | 24.8ms | +| 4096 | 160.4ms | 68.0ms | + + + +

+ +
+

+ + +As shown in the figures above, when the sequence length is around 1000 or greater, the parallel optimization of Shardformer for long sequences starts to become evident. ### Convergence -To validate that training the model using shardformers does not impact its convergence. We [fine-tuned the BERT model](./examples/shardformer_benchmark.py) using both shardformer and non-shardformer approaches. We compared the accuracy, loss, F1 score of the training results. + +To validate that training the model using shardformers does not impact its convergence. We [fine-tuned the BERT model](./examples/convergence_benchmark.py) using both shardformer and non-shardformer approaches. We compared the accuracy, loss, F1 score of the training results. | accuracy | f1 | loss | GPU number | model shard | | :------: | :-----: | :-----: | :--------: | :---------: | diff --git a/colossalai/shardformer/examples/shardformer_benchmark.py b/colossalai/shardformer/examples/convergence_benchmark.py similarity index 100% rename from colossalai/shardformer/examples/shardformer_benchmark.py rename to colossalai/shardformer/examples/convergence_benchmark.py diff --git a/colossalai/shardformer/examples/shardformer_benchmark.sh b/colossalai/shardformer/examples/convergence_benchmark.sh similarity index 76% rename from colossalai/shardformer/examples/shardformer_benchmark.sh rename to colossalai/shardformer/examples/convergence_benchmark.sh index f42b19a32d35..1c281abcda6d 100644 --- a/colossalai/shardformer/examples/shardformer_benchmark.sh +++ b/colossalai/shardformer/examples/convergence_benchmark.sh @@ -1,4 +1,4 @@ -torchrun --standalone --nproc_per_node=4 shardformer_benchmark.py \ +torchrun --standalone --nproc_per_node=4 convergence_benchmark.py \ --model "bert" \ --pretrain "bert-base-uncased" \ --max_epochs 1 \ diff --git a/colossalai/shardformer/examples/performance_benchmark.py b/colossalai/shardformer/examples/performance_benchmark.py new file mode 100644 index 000000000000..9c7b76bcf0a6 --- /dev/null +++ b/colossalai/shardformer/examples/performance_benchmark.py @@ -0,0 +1,86 @@ +""" +Shardformer Benchmark +""" +import torch +import torch.distributed as dist +import transformers +import triton + +import colossalai +from colossalai.shardformer import ShardConfig, ShardFormer + + +def data_gen(batch_size, seq_length): + input_ids = torch.randint(0, seq_length, (batch_size, seq_length), dtype=torch.long) + attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long) + return dict(input_ids=input_ids, attention_mask=attention_mask) + + +def data_gen_for_sequence_classification(batch_size, seq_length): + # LM data gen + # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels` + data = data_gen(batch_size, seq_length) + data['labels'] = torch.ones((batch_size), dtype=torch.long) + return data + + +MODEL_CONFIG = transformers.LlamaConfig(num_hidden_layers=4, + hidden_size=128, + intermediate_size=256, + num_attention_heads=4, + max_position_embeddings=128, + num_labels=16) +BATCH, N_HEADS, N_CTX, D_HEAD = 4, 8, 4096, 64 +model_func = lambda: transformers.LlamaForSequenceClassification(MODEL_CONFIG) + +# vary seq length for fixed head and batch=4 +configs = [ + triton.testing.Benchmark(x_names=['N_CTX'], + x_vals=[2**i for i in range(8, 13)], + line_arg='provider', + line_vals=['org_model', 'shard_model'], + line_names=['org_model', 'shard_model'], + styles=[('red', '-'), ('blue', '-')], + ylabel='ms', + plot_name=f'lama_for_sequence_classification-batch-{BATCH}', + args={ + 'BATCH': BATCH, + 'dtype': torch.float16, + 'model_func': model_func + }) +] + + +def train(model, data): + output = model(**data) + loss = output.logits.mean() + loss.backward() + + +@triton.testing.perf_report(configs) +def bench_shardformer(BATCH, N_CTX, provider, model_func, dtype=torch.float32, device="cuda"): + warmup = 10 + rep = 100 + # prepare data + data = data_gen_for_sequence_classification(BATCH, N_CTX) + data = {k: v.cuda() for k, v in data.items()} + model = model_func().to(device) + model.train() + if provider == "org_model": + fn = lambda: train(model, data) + ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep) + return ms + if provider == "shard_model": + shard_config = ShardConfig(enable_fused_normalization=True, enable_tensor_parallelism=True) + shard_former = ShardFormer(shard_config=shard_config) + sharded_model = shard_former.optimize(model).cuda() + fn = lambda: train(sharded_model, data) + ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep) + return ms + + +# start benchmark, command: +# torchrun --standalone --nproc_per_node=2 performance_benchmark.py +if __name__ == "__main__": + colossalai.launch_from_torch({}) + bench_shardformer.run(save_path='.', print_data=dist.get_rank() == 0) diff --git a/colossalai/shardformer/modeling/bert.py b/colossalai/shardformer/modeling/bert.py index 1b3c14d9d1c9..b9d4b5fda7af 100644 --- a/colossalai/shardformer/modeling/bert.py +++ b/colossalai/shardformer/modeling/bert.py @@ -1,5 +1,6 @@ +import math import warnings -from typing import Any, Dict, List, Optional, Tuple +from typing import Dict, List, Optional, Tuple import torch from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss @@ -962,3 +963,138 @@ def bert_for_question_answering_forward( else: hidden_states = outputs.get('hidden_states') return {'hidden_states': hidden_states} + + +def get_bert_flash_attention_forward(): + + try: + from xformers.ops import memory_efficient_attention as me_attention + except: + raise ImportError("Error: xformers module is not installed. Please install it to use flash attention.") + from transformers.models.bert.modeling_bert import BertAttention + + def forward( + self: BertAttention, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + use_cache = past_key_value is not None + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + final_attention_mask = None + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + query_length, key_length = query_layer.shape[2], key_layer.shape[2] + if use_cache: + position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(-1, 1) + else: + position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + final_attention_mask = relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + final_attention_mask = relative_position_scores_query + relative_position_scores_key + + scale = 1 / math.sqrt(self.attention_head_size) + if attention_mask is not None: + if final_attention_mask != None: + final_attention_mask = final_attention_mask * scale + attention_mask + else: + final_attention_mask = attention_mask + batch_size, src_len = query_layer.size()[0], query_layer.size()[2] + tgt_len = key_layer.size()[2] + final_attention_mask = final_attention_mask.expand(batch_size, self.num_attention_heads, src_len, tgt_len) + + query_layer = query_layer.permute(0, 2, 1, 3).contiguous() + key_layer = key_layer.permute(0, 2, 1, 3).contiguous() + value_layer = value_layer.permute(0, 2, 1, 3).contiguous() + + context_layer = me_attention(query_layer, + key_layer, + value_layer, + attn_bias=final_attention_mask, + p=self.dropout.p, + scale=scale) + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, None) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + return forward + + +def get_jit_fused_bert_self_output_forward(): + + from transformers.models.bert.modeling_bert import BertSelfOutput + + def forward(self: BertSelfOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + return forward + + +def get_jit_fused_bert_output_forward(): + + from transformers.models.bert.modeling_bert import BertOutput + + def forward(self: BertOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + return forward diff --git a/colossalai/shardformer/modeling/blip2.py b/colossalai/shardformer/modeling/blip2.py index b7945423ae83..c5c6b14ba993 100644 --- a/colossalai/shardformer/modeling/blip2.py +++ b/colossalai/shardformer/modeling/blip2.py @@ -1,3 +1,4 @@ +import math from typing import Optional, Tuple, Union import torch @@ -58,3 +59,62 @@ def forward( return outputs return forward + + +def get_blip2_flash_attention_forward(): + + from transformers.models.blip_2.modeling_blip_2 import Blip2Attention + + from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + + def forward( + self: Blip2Attention, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + bsz, tgt_len, embed_dim = hidden_states.size() + mixed_qkv = self.qkv(hidden_states) + mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, -1).permute(2, 0, 1, 3, 4) + query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2] + + attention = ColoAttention(embed_dim=self.embed_dim, + num_heads=self.num_heads, + dropout=self.dropout.p, + scale=self.scale) + context_layer = attention(query_states, key_states, value_states) + + output = self.projection(context_layer) + outputs = (output, None) + + return outputs + + return forward + + +def get_jit_fused_blip2_QFormer_self_output_forward(): + + from transformers.models.blip_2.modeling_blip_2 import Blip2QFormerSelfOutput + + def forward(self: Blip2QFormerSelfOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + return forward + + +def get_jit_fused_blip2_QFormer_output_forward(): + + from transformers.models.blip_2.modeling_blip_2 import Blip2QFormerOutput + + def forward(self: Blip2QFormerOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + return forward diff --git a/colossalai/shardformer/modeling/bloom.py b/colossalai/shardformer/modeling/bloom.py index 76948fc70439..57c45bc6adfa 100644 --- a/colossalai/shardformer/modeling/bloom.py +++ b/colossalai/shardformer/modeling/bloom.py @@ -5,6 +5,7 @@ import torch.distributed as dist from torch.distributed import ProcessGroup from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from torch.nn import functional as F from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, @@ -675,3 +676,223 @@ def bloom_for_question_answering_forward( else: hidden_states = outputs.get('hidden_states') return {'hidden_states': hidden_states} + + +def get_bloom_flash_attention_forward(enabel_jit_fused=False): + + try: + from xformers.ops import memory_efficient_attention as me_attention + except: + raise ImportError("Error: xformers module is not installed. Please install it to use flash attention.") + from transformers.models.bloom.modeling_bloom import BloomAttention + + def forward( + self: BloomAttention, + hidden_states: torch.Tensor, + residual: torch.Tensor, + alibi: torch.Tensor, + attention_mask: torch.Tensor, + layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + head_mask: Optional[torch.Tensor] = None, + use_cache: bool = False, + output_attentions: bool = False, + ): + + fused_qkv = self.query_key_value(hidden_states) + (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) + batch_size, tgt_len, _ = hidden_states.size() + assert tgt_len % 4 == 0, "Flash Attention Error: The sequence length should be a multiple of 4." + + _, kv_length, _, _ = key_layer.size() + + proj_shape = (batch_size, tgt_len, self.num_heads, self.head_dim) + query_layer = query_layer.contiguous().view(*proj_shape) + key_layer = key_layer.contiguous().view(*proj_shape) + value_layer = value_layer.contiguous().view(*proj_shape) + + if layer_past is not None: + past_key, past_value = layer_past + # concatenate along seq_length dimension: + # - key: [batch_size * self.num_heads, head_dim, kv_length] + # - value: [batch_size * self.num_heads, kv_length, head_dim] + key_layer = torch.cat((past_key, key_layer), dim=1) + value_layer = torch.cat((past_value, value_layer), dim=1) + + if use_cache is True: + present = (key_layer, value_layer) + else: + present = None + + tgt_len = key_layer.size()[1] + + attention_numerical_mask = torch.zeros((batch_size, self.num_heads, tgt_len, kv_length), + dtype=torch.float32, + device=query_layer.device, + requires_grad=True) + attention_numerical_mask = attention_numerical_mask + alibi.view(batch_size, self.num_heads, 1, + kv_length) * self.beta + attention_numerical_mask = torch.masked_fill(attention_numerical_mask, attention_mask, + torch.finfo(torch.float32).min) + + context_layer = me_attention(query_layer, + key_layer, + value_layer, + attn_bias=attention_numerical_mask, + scale=self.inv_norm_factor, + p=self.attention_dropout.p) + context_layer = context_layer.reshape(-1, kv_length, self.hidden_size) + if self.pretraining_tp > 1 and self.slow_but_exact: + slices = self.hidden_size / self.pretraining_tp + output_tensor = torch.zeros_like(context_layer) + for i in range(self.pretraining_tp): + output_tensor = output_tensor + F.linear( + context_layer[:, :, int(i * slices):int((i + 1) * slices)], + self.dense.weight[:, int(i * slices):int((i + 1) * slices)], + ) + else: + output_tensor = self.dense(context_layer) + + # TODO to replace with the bias_dropout_add function in jit + output_tensor = self.dropout_add(output_tensor, residual, self.hidden_dropout, self.training) + outputs = (output_tensor, present, None) + + return outputs + + return forward + + +def get_jit_fused_bloom_attention_forward(): + + from transformers.models.bloom.modeling_bloom import BloomAttention + + def forward( + self: BloomAttention, + hidden_states: torch.Tensor, + residual: torch.Tensor, + alibi: torch.Tensor, + attention_mask: torch.Tensor, + layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + head_mask: Optional[torch.Tensor] = None, + use_cache: bool = False, + output_attentions: bool = False, + ): + fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] + + # 3 x [batch_size, seq_length, num_heads, head_dim] + (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) + + batch_size, q_length, _, _ = query_layer.shape + + query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim) + key_layer = key_layer.permute(0, 2, 3, 1).reshape(batch_size * self.num_heads, self.head_dim, q_length) + value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim) + if layer_past is not None: + past_key, past_value = layer_past + # concatenate along seq_length dimension: + # - key: [batch_size * self.num_heads, head_dim, kv_length] + # - value: [batch_size * self.num_heads, kv_length, head_dim] + key_layer = torch.cat((past_key, key_layer), dim=2) + value_layer = torch.cat((past_value, value_layer), dim=1) + + _, _, kv_length = key_layer.shape + + if use_cache is True: + present = (key_layer, value_layer) + else: + present = None + + # [batch_size * num_heads, q_length, kv_length] + # we use `torch.Tensor.baddbmm` instead of `torch.baddbmm` as the latter isn't supported by TorchScript v1.11 + matmul_result = alibi.baddbmm( + batch1=query_layer, + batch2=key_layer, + beta=self.beta, + alpha=self.inv_norm_factor, + ) + + # change view to [batch_size, num_heads, q_length, kv_length] + attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length) + + # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length] + input_dtype = attention_scores.dtype + # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38` + if input_dtype == torch.float16: + attention_scores = attention_scores.to(torch.float) + attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min) + attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(input_dtype) + + # [batch_size, num_heads, q_length, kv_length] + attention_probs = self.attention_dropout(attention_probs) + + if head_mask is not None: + attention_probs = attention_probs * head_mask + + # change view [batch_size x num_heads, q_length, kv_length] + attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length) + + # matmul: [batch_size * num_heads, q_length, head_dim] + context_layer = torch.bmm(attention_probs_reshaped, value_layer) + + # change view [batch_size, num_heads, q_length, head_dim] + context_layer = self._merge_heads(context_layer) + + # aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232 + if self.pretraining_tp > 1 and self.slow_but_exact: + slices = self.hidden_size / self.pretraining_tp + output_tensor = torch.zeros_like(context_layer) + for i in range(self.pretraining_tp): + output_tensor = output_tensor + F.linear( + context_layer[:, :, int(i * slices):int((i + 1) * slices)], + self.dense.weight[:, int(i * slices):int((i + 1) * slices)], + ) + else: + output_tensor = self.dense(context_layer) + + output_tensor = self.dropout_add(output_tensor, residual, self.hidden_dropout, self.training) + + outputs = (output_tensor, present) + if output_attentions: + outputs += (attention_probs,) + + return outputs + + return forward + + +def get_jit_fused_bloom_mlp_forward(): + + from transformers.models.bloom.modeling_bloom import BloomMLP + + def forward(self: BloomMLP, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor: + hidden_states = self.gelu_impl(self.dense_h_to_4h(hidden_states)) + + if self.pretraining_tp > 1 and self.slow_but_exact: + intermediate_output = torch.zeros_like(residual) + slices = self.dense_4h_to_h.weight.shape[-1] / self.pretraining_tp + for i in range(self.pretraining_tp): + intermediate_output = intermediate_output + F.linear( + hidden_states[:, :, int(i * slices):int((i + 1) * slices)], + self.dense_4h_to_h.weight[:, int(i * slices):int((i + 1) * slices)], + ) + else: + intermediate_output = self.dense_4h_to_h(hidden_states) + output = self.dropout_add(intermediate_output, residual, self.hidden_dropout, self.training) + return output + + return forward + + +def get_jit_fused_bloom_gelu_forward(): + + from transformers.models.bloom.modeling_bloom import BloomGelu + + from colossalai.kernel.jit.bias_gelu import GeLUFunction as JitGeLUFunction + + def forward(self: BloomGelu, x: torch.Tensor) -> torch.Tensor: + bias = torch.zeros_like(x) + if self.training: + return JitGeLUFunction.apply(x, bias) + else: + return self.bloom_gelu_forward(x, bias) + + return forward diff --git a/colossalai/shardformer/modeling/chatglm.py b/colossalai/shardformer/modeling/chatglm.py index 0bb8bdc58218..3d453c3bd6db 100644 --- a/colossalai/shardformer/modeling/chatglm.py +++ b/colossalai/shardformer/modeling/chatglm.py @@ -17,6 +17,116 @@ ) +def get_flash_core_attention_forward(): + + from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + + from .chatglm2_6b.modeling_chatglm import CoreAttention + + def forward(self: CoreAttention, query_layer, key_layer, value_layer, attention_mask): + pytorch_major_version = int(torch.__version__.split(".")[0]) + if pytorch_major_version >= 2: + query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, + key_layer, + value_layer, + is_causal=True) + else: + if attention_mask is not None: + attention_mask = ~attention_mask + context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, + attention_mask) + context_layer = context_layer.permute(2, 0, 1, 3) + new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + context_layer = context_layer.reshape(*new_context_layer_shape) + else: + # Raw attention scores + query_layer = query_layer.permute(1, 0, 2, 3).contiguous() + key_layer = key_layer.permute(1, 0, 2, 3).contiguous() + value_layer = value_layer.permute(1, 0, 2, 3).contiguous() + + scale = 1.0 / self.norm_factor + if self.coeff is not None: + scale = scale * self.coeff + + flash_attention_mask = None + attn_mask_type = None + if attention_mask is None: + attn_mask_type = AttnMaskType.causal + else: + flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous() + attn_mask_type = AttnMaskType.paddedcausal + + attention = ColoAttention(embed_dim=self.hidden_size_per_partition, + num_heads=self.num_attention_heads_per_partition, + dropout=self.attention_dropout.p, + scale=scale) + context_layer = attention(query_layer, + key_layer, + value_layer, + attn_mask=flash_attention_mask, + attn_mask_type=attn_mask_type) + + context_layer = context_layer.permute(1, 0, -1).contiguous() + + return context_layer + + return forward + + +def get_jit_fused_glm_block_forward(): + + from .chatglm2_6b.modeling_chatglm import GLMBlock + + def forward( + self: GLMBlock, + hidden_states, + attention_mask, + rotary_pos_emb, + kv_cache=None, + use_cache=True, + ): + # hidden_states: [s, b, h] + # Layer norm at the beginning of the transformer layer. + layernorm_output = self.input_layernorm(hidden_states) + # Self attention. + attention_output, kv_cache = self.self_attention( + layernorm_output, + attention_mask, + rotary_pos_emb, + kv_cache=kv_cache, + use_cache=use_cache, + ) + + # Residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = hidden_states + + layernorm_input = self.dropout_add(attention_output, residual, self.hidden_dropout, self.training) + + # Layer norm post the self attention. + layernorm_output = self.post_attention_layernorm(layernorm_input) + + # MLP. + mlp_output = self.mlp(layernorm_output) + + # Second residual connection. + if self.apply_residual_connection_post_layernorm: + residual = layernorm_output + else: + residual = layernorm_input + + output = self.dropout_add(mlp_output, residual, self.hidden_dropout, self.training) + + return output, kv_cache + + return forward + + + class ChatGLMPipelineForwards: ''' This class serves as a micro library for ChatGLM model forwards under pipeline parallelism. diff --git a/colossalai/shardformer/modeling/gpt2.py b/colossalai/shardformer/modeling/gpt2.py index dc5a81dc912b..e02581fbaa9b 100644 --- a/colossalai/shardformer/modeling/gpt2.py +++ b/colossalai/shardformer/modeling/gpt2.py @@ -668,3 +668,88 @@ def gpt2_for_sequence_classification_forward( hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +def get_gpt2_flash_attention_forward(): + + from transformers.models.gpt2.modeling_gpt2 import GPT2Attention + + from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + + def split_heads(tensor, num_heads, attn_head_size): + """ + Splits hidden_size dim into attn_head_size and num_heads + """ + new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) + tensor = tensor.view(new_shape) + return tensor + + def forward( + self: GPT2Attention, + hidden_states: Optional[Tuple[torch.FloatTensor]], + layer_past: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = False, + output_attentions: Optional[bool] = False, + ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: + _, tgt_len, _ = hidden_states.size() + assert tgt_len % 4 == 0, "Flash Attention Error: The sequence length should be a multiple of 4." + + if encoder_hidden_states is not None: + if not hasattr(self, "q_attn"): + raise ValueError( + "If class is used as cross attention, the weights `q_attn` have to be defined. " + "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`.") + + query = self.q_attn(hidden_states) + key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) + attention_mask = encoder_attention_mask + else: + query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2) + + query = split_heads(query, self.num_heads, self.head_dim) + key = split_heads(key, self.num_heads, self.head_dim) + value = split_heads(value, self.num_heads, self.head_dim) + + if layer_past is not None: + past_key, past_value = layer_past + key = torch.cat((past_key, key), dim=1) + value = torch.cat((past_value, value), dim=1) + + if use_cache is True: + present = (key, value) + else: + present = None + + if not self.is_cross_attention: + attn_mask_type = AttnMaskType.causal + flash_attention_mask = None + if attention_mask != None: + if attn_mask_type == AttnMaskType.causal: + attn_mask_type == AttnMaskType.paddedcausal + else: + attn_mask_type = AttnMaskType.padding + flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous() + + scale = value.size(-1)**-0.5 + if self.scale_attn_by_inverse_layer_idx: + scale = scale * (1 / float(self.layer_idx + 1)) + + # use coloattention + attention = ColoAttention(embed_dim=self.embed_dim, + num_heads=self.num_heads, + dropout=self.attn_dropout.p, + scale=scale) + + attn_output = attention(query, key, value, attn_mask=flash_attention_mask, attn_mask_type=attn_mask_type) + + attn_output = self.c_proj(attn_output) + attn_output = self.resid_dropout(attn_output) + outputs = (attn_output, present, None) + + return outputs + + return forward diff --git a/colossalai/shardformer/modeling/jit.py b/colossalai/shardformer/modeling/jit.py new file mode 100644 index 000000000000..6434348ef823 --- /dev/null +++ b/colossalai/shardformer/modeling/jit.py @@ -0,0 +1,34 @@ +import torch + + +def get_dropout_add_func(): + + from transformers.models.bloom.modeling_bloom import dropout_add + + def self_dropout_add(self, x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor: + return dropout_add(x, residual, prob, training) + + return self_dropout_add + + +def get_jit_fused_dropout_add_func(): + + from colossalai.kernel.jit import bias_dropout_add_fused_inference, bias_dropout_add_fused_train + + def self_dropout_add(self, x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor: + bias = torch.zeros_like(x) + if training: + return bias_dropout_add_fused_train(x, bias, residual, prob) + return bias_dropout_add_fused_inference(x, bias, residual, prob) + + return self_dropout_add + + +def get_jit_fused_gelu_forward_func(): + + from colossalai.kernel.jit.bias_gelu import bias_gelu + + def bloom_gelu_forward(x: torch.Tensor, bias: torch.Tensor) -> torch.Tensor: + return bias_gelu(bias, x) + + return bloom_gelu_forward diff --git a/colossalai/shardformer/modeling/llama.py b/colossalai/shardformer/modeling/llama.py index e1ed5f64665c..9d6335503b36 100644 --- a/colossalai/shardformer/modeling/llama.py +++ b/colossalai/shardformer/modeling/llama.py @@ -1,4 +1,4 @@ -from typing import Callable, List, Optional +from typing import Callable, List, Optional, Tuple import torch from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss @@ -386,3 +386,67 @@ def llama_for_sequence_classification_forward( else: hidden_states = transformer_outputs.get('hidden_states') return {'hidden_states': hidden_states} + + +def get_llama_flash_attention_forward(): + + from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb + + from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + + def forward( + self: LlamaAttention, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + assert q_len % 4 == 0, "Flash Attention Error: The sequence length should be a multiple of 4." + + query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + me_input_shape = (bsz, q_len, self.num_heads, self.head_dim) + query_states = query_states.transpose(1, 2).contiguous().view(*me_input_shape) + key_states = key_states.transpose(1, 2).contiguous().view(*me_input_shape) + value_states = value_states.transpose(1, 2).contiguous().view(*me_input_shape) + + flash_attention_mask = None + attn_mask_type = AttnMaskType.causal + if attention_mask != None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}") + flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous() + attn_mask_type = AttnMaskType.paddedcausal + + attention = ColoAttention(embed_dim=self.hidden_size, num_heads=self.num_heads) + attn_output = attention(query_states, + key_states, + value_states, + attn_mask=flash_attention_mask, + attn_mask_type=attn_mask_type) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + return forward diff --git a/colossalai/shardformer/modeling/opt.py b/colossalai/shardformer/modeling/opt.py new file mode 100644 index 000000000000..299dfb5562f3 --- /dev/null +++ b/colossalai/shardformer/modeling/opt.py @@ -0,0 +1,174 @@ +from typing import Optional, Tuple + +import torch +from torch import nn + + +def get_opt_flash_attention_forward(): + + from transformers.models.opt.modeling_opt import OPTAttention + + from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + + def forward( + self: OPTAttention, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + bsz, tgt_len, _ = hidden_states.size() + assert tgt_len % 4 == 0, "Flash Attention Error: The sequence length should be a multiple of 4." + + attention_input_shape = (bsz, -1, self.num_heads, self.head_dim) + # get query proj + query_states = self.q_proj(hidden_states).view(*attention_input_shape) + # get key, value proj + if is_cross_attention and past_key_value is not None: + # reuse k, v, cross_attentions + key_states = past_key_value[0].transpose(1, 2).contiguous().view(*attention_input_shape) + value_states = past_key_value[1].transpose(1, 2).contiguous().view(*attention_input_shape) + elif is_cross_attention: + # cross_attentions + key_states = self.k_proj(key_value_states).view(*attention_input_shape) + value_states = self.v_proj(key_value_states).view(*attention_input_shape) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self.k_proj(hidden_states).view(*attention_input_shape) + value_states = self.v_proj(hidden_states).view(*attention_input_shape) + key_states = torch.cat([past_key_value[0], key_states], dim=1) + value_states = torch.cat([past_key_value[1], value_states], dim=1) + else: + # self_attention + key_states = self.k_proj(hidden_states).view(*attention_input_shape) + value_states = self.v_proj(hidden_states).view(*attention_input_shape) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + src_len = key_states.size(1) + if layer_head_mask != None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError(f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}") + + flash_attention_mask = None + attn_mask_type = AttnMaskType.causal + if attention_mask != None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}") + flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous() + attn_mask_type = AttnMaskType.paddedcausal + + attention = ColoAttention(embed_dim=self.embed_dim, + num_heads=self.num_heads, + dropout=self.dropout, + scale=self.scaling) + attn_output = attention(query_states, + key_states, + value_states, + attn_mask=flash_attention_mask, + attn_mask_type=attn_mask_type) + + attn_output = self.out_proj(attn_output) + return attn_output, None, past_key_value + + return forward + + +def get_jit_fused_opt_decoder_layer_forward(): + + from transformers.models.opt.modeling_opt import OPTDecoderLayer + + def forward( + self: OPTDecoderLayer, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention + if self.do_layer_norm_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + + hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training) + + # 350m applies layer norm AFTER attention + if not self.do_layer_norm_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Fully Connected + hidden_states_shape = hidden_states.shape + hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) + residual = hidden_states + + # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention + if self.do_layer_norm_before: + hidden_states = self.final_layer_norm(hidden_states) + + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + + hidden_states = self.fc2(hidden_states) + + hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training).view(hidden_states_shape) + + # 350m applies layer norm AFTER attention + if not self.do_layer_norm_before: + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + return forward diff --git a/colossalai/shardformer/modeling/sam.py b/colossalai/shardformer/modeling/sam.py index 63ebfe89d5fa..c40c02ec411a 100644 --- a/colossalai/shardformer/modeling/sam.py +++ b/colossalai/shardformer/modeling/sam.py @@ -1,4 +1,9 @@ +import math +from typing import Tuple + import torch +import torch.nn.functional as F +from torch import Tensor def forward_fn(): @@ -37,3 +42,162 @@ def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch return outputs return forward + + +def get_sam_flash_attention_forward(): + + from transformers.models.sam.modeling_sam import SamAttention + try: + from xformers.ops import memory_efficient_attention as me_attention + except: + raise ImportError("Error: xformers module is not installed. Please install it to use flash attention.") + + def _separate_heads(hidden_states: Tensor, num_attention_heads: int) -> Tensor: + batch, point_batch_size, n_tokens, channel = hidden_states.shape + c_per_head = channel // num_attention_heads + hidden_states = hidden_states.reshape(batch * point_batch_size, n_tokens, num_attention_heads, c_per_head) + return hidden_states + + def _recombine_heads(hidden_states: Tensor, point_batch_size: int) -> Tensor: + batch, n_tokens, n_heads, c_per_head = hidden_states.shape + return hidden_states.reshape(batch // point_batch_size, point_batch_size, n_tokens, n_heads * c_per_head) + + def forward(self: SamAttention, + query: Tensor, + key: Tensor, + value: Tensor, + attention_similarity: Tensor = None) -> Tensor: + # Input projections + query = self.q_proj(query) + key = self.k_proj(key) + value = self.v_proj(value) + + point_batch_size = query.shape[1] + # Separate into heads + query = _separate_heads(query, self.num_attention_heads) + key = _separate_heads(key, self.num_attention_heads) + value = _separate_heads(value, self.num_attention_heads) + + # SamAttention + _, _, _, c_per_head = query.shape + bias = None + if attention_similarity is not None: + bias = attention_similarity + + scale = 1.0 / math.sqrt(c_per_head) + out = me_attention(query, key, value, attn_bias=bias, scale=scale) + + out = _recombine_heads(out, point_batch_size) + out = self.out_proj(out) + + return out + + return forward + + +def get_sam_vision_flash_attention_forward(): + + from transformers.models.sam.modeling_sam import SamVisionAttention + try: + from xformers.ops import memory_efficient_attention as me_attention + except: + raise ImportError("Error: xformers module is not installed. Please install it to use flash attention.") + + def add_decomposed_rel_pos( + query: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: Tuple[int, int], + k_size: Tuple[int, int], + ) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py + + Args: + attn (`torch.Tensor`): + attention map. + query (`torch.Tensor`): + query q in the attention layer with shape (batch_size, query_height * query_width, channel). + rel_pos_h (`torch.Tensor`): + relative position embeddings (Lh, channel) for height axis. + rel_pos_w (`torch.Tensor`): + relative position embeddings (Lw, channel) for width axis. + q_size (tuple): + spatial sequence size of query q with (query_height, query_width). + k_size (tuple): + spatial sequence size of key k with (key_height, key_width). + + Returns: + attn (`torch.Tensor`): + attention map with added relative positional embeddings. + """ + + query_height, query_width = q_size + key_height, key_width = k_size + relative_position_height = get_rel_pos(query_height, key_height, rel_pos_h) + relative_position_width = get_rel_pos(query_width, key_width, rel_pos_w) + + batch_size, _, nHead, dim = query.shape + reshaped_query = query.transpose(1, 2).reshape(batch_size * nHead, query_height, query_width, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height) + rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width) + rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + rel_pos = rel_pos.reshape(batch_size, nHead, query_height * query_width, key_height * key_width) + return rel_pos + + def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + + Args: + q_size (int): + size of the query. + k_size (int): + size of key k. + rel_pos (`torch.Tensor`): + relative position embeddings (L, channel). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + def forward(self: SamVisionAttention, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, batch_size, nHead, height * width, channel) + qkv = (self.qkv(hidden_states).reshape(batch_size, height * width, 3, self.num_attention_heads, + -1).permute(2, 0, 1, 3, 4)) + + query, key, value = qkv.reshape(3, batch_size, height * width, self.num_attention_heads, -1).unbind(0) + + rel_pos = None + if self.use_rel_pos: + rel_pos = add_decomposed_rel_pos(query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)) + + attn_output = me_attention(query, key, value, attn_bias=rel_pos, p=self.dropout, scale=self.scale) + + attn_output = attn_output.reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + + outputs = (attn_output, None) + + return outputs + + return forward diff --git a/colossalai/shardformer/modeling/t5.py b/colossalai/shardformer/modeling/t5.py index 7eb4d17928d6..0b3486e87c7e 100644 --- a/colossalai/shardformer/modeling/t5.py +++ b/colossalai/shardformer/modeling/t5.py @@ -587,3 +587,209 @@ def t5_encoder_model_forward( decoder_starting_stage=decoder_starting_stage) return outputs + + +def get_t5_flash_attention_forward(): + + try: + from xformers.ops import memory_efficient_attention as me_attention + except: + raise ImportError("Error: xformers module is not installed. Please install it to use flash attention.") + from transformers.models.t5.modeling_t5 import T5Attention + + def forward( + self: T5Attention, + hidden_states: torch.Tensor, + mask: Optional[torch.Tensor] = None, + key_value_states: Optional[torch.Tensor] = None, + position_bias: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + layer_head_mask: Optional[torch.Tensor] = None, + query_length: Optional[int] = None, + use_cache: bool = False, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + """ + Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). + """ + # Input is (batch_size, seq_length, dim) + # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) + # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head) + batch_size, seq_length = hidden_states.shape[:2] + + real_seq_length = seq_length + + if past_key_value is not None: + if len(past_key_value) != 2: + raise ValueError( + f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states" + ) + real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length + + key_length = real_seq_length if key_value_states is None else key_value_states.shape[1] + + def shape(states): + """projection""" + return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim) + + def unshape(states): + """reshape""" + return states.view(batch_size, -1, self.inner_dim) + + def project(hidden_states, proj_layer, key_value_states, past_key_value): + """projects hidden states correctly to key/query states""" + if key_value_states is None: + # self-attn + # (batch_size, n_heads, seq_length, dim_per_head) + hidden_states = shape(proj_layer(hidden_states)) + elif past_key_value is None: + # cross-attn + # (batch_size, n_heads, seq_length, dim_per_head) + hidden_states = shape(proj_layer(key_value_states)) + + if past_key_value is not None: + if key_value_states is None: + # self-attn + # (batch_size, n_heads, key_length, dim_per_head) + hidden_states = torch.cat([past_key_value, hidden_states], dim=1) + elif past_key_value.shape[1] != key_value_states.shape[1]: + # checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + # cross-attn + # (batch_size, n_heads, seq_length, dim_per_head) + hidden_states = shape(proj_layer(key_value_states)) + else: + # cross-attn + hidden_states = past_key_value + return hidden_states + + # get query states + query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, seq_length, dim_per_head) + + # get key/value states + key_states = project(hidden_states, self.k, key_value_states, + past_key_value[0] if past_key_value is not None else None) + value_states = project(hidden_states, self.v, key_value_states, + past_key_value[1] if past_key_value is not None else None) + + if position_bias is None: + if not self.has_relative_attention_bias: + position_bias = torch.zeros((1, self.n_heads, real_seq_length, key_length), + device=query_states.device, + dtype=query_states.dtype) + if self.gradient_checkpointing and self.training: + position_bias.requires_grad = True + else: + position_bias = self.compute_bias(real_seq_length, key_length, device=query_states.device) + + # if key and values are already calculated + # we want only the last query position bias + if past_key_value is not None: + position_bias = position_bias[:, :, -hidden_states.size(1):, :] + + if mask is not None: + position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length) + + if self.pruned_heads: + mask = torch.ones(position_bias.shape[1]) + mask[list(self.pruned_heads)] = 0 + position_bias_masked = position_bias[:, mask.bool()] + else: + position_bias_masked = position_bias + + position_bias_masked = position_bias_masked.contiguous() + attn_output = me_attention(query_states, + key_states, + value_states, + attn_bias=position_bias_masked, + p=self.dropout, + scale=1.0) + attn_output = unshape(attn_output) + attn_output = self.o(attn_output) + + present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None + + outputs = (attn_output,) + (present_key_value_state,) + (position_bias,) + + return outputs + + return forward + + +def get_jit_fused_T5_layer_ff_forward(): + + from transformers.models.t5.modeling_t5 import T5LayerFF + + def forward(self: T5LayerFF, hidden_states: torch.Tensor) -> torch.Tensor: + forwarded_states = self.layer_norm(hidden_states) + forwarded_states = self.DenseReluDense(forwarded_states) + hidden_states = self.dropout_add(forwarded_states, hidden_states, self.dropout.p, self.dropout.training) + return hidden_states + + return forward + + +def get_T5_layer_self_attention_forward(): + + from transformers.models.t5.modeling_t5 import T5LayerSelfAttention + + def forward( + self: T5LayerSelfAttention, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_bias: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + use_cache: bool = False, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + normed_hidden_states = self.layer_norm(hidden_states) + attention_output = self.SelfAttention( + normed_hidden_states, + mask=attention_mask, + position_bias=position_bias, + layer_head_mask=layer_head_mask, + past_key_value=past_key_value, + use_cache=use_cache, + output_attentions=output_attentions, + ) + hidden_states = self.dropout_add(attention_output[0], hidden_states, self.dropout.p, self.dropout.training) + outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them + return outputs + + return forward + + +def get_T5_layer_cross_attention_forward(): + + from transformers.models.t5.modeling_t5 import T5LayerCrossAttention + + def forward( + self: T5LayerCrossAttention, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_bias: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + use_cache: bool = False, + query_length: Optional[int] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + normed_hidden_states = self.layer_norm(hidden_states) + attention_output = self.EncDecAttention( + normed_hidden_states, + mask=attention_mask, + key_value_states=key_value_states, + position_bias=position_bias, + layer_head_mask=layer_head_mask, + past_key_value=past_key_value, + use_cache=use_cache, + query_length=query_length, + output_attentions=output_attentions, + ) + layer_output = self.dropout_add(attention_output[0], hidden_states, self.dropout.p, self.dropout.training) + outputs = (layer_output,) + attention_output[1:] # add attentions if we output them + return outputs + + return forward diff --git a/colossalai/shardformer/modeling/vit.py b/colossalai/shardformer/modeling/vit.py index f28c13ad0aa2..22c4dd998cac 100644 --- a/colossalai/shardformer/modeling/vit.py +++ b/colossalai/shardformer/modeling/vit.py @@ -1,4 +1,5 @@ import logging +import math from typing import Dict, List, Optional, Set, Tuple, Union import torch @@ -335,3 +336,51 @@ def pp_forward( ) return pp_forward + + +def get_vit_flash_self_attention_forward(): + + from transformers.models.vit.modeling_vit import ViTSelfAttention + + from colossalai.kernel.cuda_native.flash_attention import ColoAttention + + def transpose_for_scores(x: torch.Tensor, num_attention_heads, attention_head_size) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (num_attention_heads, attention_head_size) + x = x.view(new_x_shape) + return x + + def forward(self: ViTSelfAttention, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + key_layer = transpose_for_scores(self.key(hidden_states), self.num_attention_heads, self.attention_head_size) + value_layer = transpose_for_scores(self.value(hidden_states), self.num_attention_heads, + self.attention_head_size) + query_layer = transpose_for_scores(mixed_query_layer, self.num_attention_heads, self.attention_head_size) + + scale = 1.0 / math.sqrt(self.attention_head_size) + attention = ColoAttention(embed_dim=self.all_head_size, + num_heads=self.num_attention_heads, + dropout=self.dropout.p, + scale=scale) + context_layer = attention(query_layer, key_layer, value_layer) + + outputs = (context_layer,) + + return outputs + + return forward + + +def get_jit_fused_vit_output_forward(): + + from transformers.models.vit.modeling_vit import ViTOutput + + def forward(self: ViTOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training) + return hidden_states + + return forward diff --git a/colossalai/shardformer/modeling/whisper.py b/colossalai/shardformer/modeling/whisper.py new file mode 100644 index 000000000000..6bc387ac8974 --- /dev/null +++ b/colossalai/shardformer/modeling/whisper.py @@ -0,0 +1,249 @@ +from typing import Optional, Tuple + +import torch +from torch import nn + + +def get_whisper_flash_attention_forward(): + + from transformers.models.whisper.modeling_whisper import WhisperAttention + + from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + + def shape(tensor: torch.Tensor, seq_len: int, bsz: int, num_heads: int, head_dim: int): + return tensor.view(bsz, seq_len, num_heads, head_dim).contiguous() + + def forward( + self: WhisperAttention, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if (is_cross_attention and past_key_value is not None + and past_key_value[0].shape[1] == key_value_states.shape[1]): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = shape(self.k_proj(key_value_states), -1, bsz, self.num_heads, self.head_dim) + value_states = shape(self.v_proj(key_value_states), -1, bsz, self.num_heads, self.head_dim) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = shape(self.k_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim) + value_states = shape(self.v_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim) + key_states = torch.cat([past_key_value[0], key_states], dim=1) + value_states = torch.cat([past_key_value[1], value_states], dim=1) + else: + # self_attention + key_states = shape(self.k_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim) + value_states = shape(self.v_proj(hidden_states), -1, bsz, self.num_heads, self.head_dim) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + # get query proj + query_states = shape(self.q_proj(hidden_states), tgt_len, bsz, self.num_heads, self.head_dim) + + src_len = key_states.size(1) + if layer_head_mask is not None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError(f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}") + + attn_type = None + flash_attention_mask = None + + if self.is_decoder: + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool).contiguous()) + attn_type = AttnMaskType.paddedcausal + + attention = ColoAttention(embed_dim=self.embed_dim, + num_heads=self.num_heads, + dropout=self.dropout, + scale=self.scaling) + attn_output = attention(query_states, + key_states, + value_states, + attn_mask=flash_attention_mask, + attn_mask_type=attn_type) + + attn_output = self.out_proj(attn_output) + + return attn_output, None, past_key_value + + return forward + + +def get_jit_fused_whisper_encoder_layer_forward(): + + from transformers.models.whisper.modeling_whisper import WhisperEncoderLayer + + def forward( + self: WhisperEncoderLayer, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + layer_head_mask: torch.Tensor, + output_attentions: bool = False, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, attn_weights, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training) + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training) + + if hidden_states.dtype == torch.float16 and (torch.isinf(hidden_states).any() + or torch.isnan(hidden_states).any()): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + return forward + + +def get_jit_fused_whisper_decoder_layer_forward(): + + from transformers.models.whisper.modeling_whisper import WhisperDecoderLayer + + def forward( + self: WhisperDecoderLayer, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + cross_attn_layer_head_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = True, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of + size `(decoder_attention_heads,)`. + past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + # add present self-attn cache to positions 1,2 of present_key_value tuple + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=self_attn_past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training) + + # Cross-Attention Block + cross_attn_present_key_value = None + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + layer_head_mask=cross_attn_layer_head_mask, + past_key_value=cross_attn_past_key_value, + output_attentions=output_attentions, + ) + hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training) + + # add cross-attn to positions 3,4 of present_key_value tuple + present_key_value = present_key_value + cross_attn_present_key_value + + # Fully Connected + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = self.dropout_add(hidden_states, residual, self.dropout, self.training) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + return forward diff --git a/colossalai/shardformer/policies/bert.py b/colossalai/shardformer/policies/bert.py index 6f86de232fad..ace9ada3904f 100644 --- a/colossalai/shardformer/policies/bert.py +++ b/colossalai/shardformer/policies/bert.py @@ -7,7 +7,14 @@ import colossalai.shardformer.layer as col_nn -from ..modeling.bert import BertPipelineForwards +from .._utils import getattr_, setattr_ +from ..modeling.bert import ( + BertPipelineForwards, + get_bert_flash_attention_forward, + get_jit_fused_bert_output_forward, + get_jit_fused_bert_self_output_forward, +) +from ..modeling.jit import get_jit_fused_dropout_add_func from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -37,7 +44,13 @@ def preprocess(self): return self.model def module_policy(self): - from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer + from transformers.models.bert.modeling_bert import ( + BertEmbeddings, + BertLayer, + BertOutput, + BertSelfAttention, + BertSelfOutput, + ) policy = {} @@ -126,6 +139,23 @@ def module_policy(self): policy=policy, target_key=BertEmbeddings) + # use flash attention + if self.shard_config.enable_flash_attention: + policy[BertSelfAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_bert_flash_attention_forward(), + }) + + # use jit operator + if self.shard_config.enable_jit_fused: + policy[BertSelfOutput] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_bert_self_output_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + policy[BertOutput] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_bert_output_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + return policy def add_lm_head_policy(self, base_policy): diff --git a/colossalai/shardformer/policies/blip2.py b/colossalai/shardformer/policies/blip2.py index a244d70b56f5..50356302e93e 100644 --- a/colossalai/shardformer/policies/blip2.py +++ b/colossalai/shardformer/policies/blip2.py @@ -3,7 +3,13 @@ import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ -from ..modeling.blip2 import forward_fn +from ..modeling.blip2 import ( + forward_fn, + get_blip2_flash_attention_forward, + get_jit_fused_blip2_QFormer_output_forward, + get_jit_fused_blip2_QFormer_self_output_forward, +) +from ..modeling.jit import get_jit_fused_dropout_add_func from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['BlipPolicy', 'BlipModelPolicy'] @@ -33,6 +39,8 @@ def module_policy(self): Blip2EncoderLayer, Blip2QFormerLayer, Blip2QFormerModel, + Blip2QFormerOutput, + Blip2QFormerSelfOutput, Blip2VisionModel, ) from transformers.models.opt.modeling_opt import OPTDecoderLayer, OPTForCausalLM @@ -275,6 +283,24 @@ def module_policy(self): policy=policy, target_key=OPTDecoderLayer) + # use flash attention + if self.shard_config.enable_flash_attention: + policy[Blip2Attention] = ModulePolicyDescription(method_replacement={ + 'forward': get_blip2_flash_attention_forward(), + }) + + # use jit operator + if self.shard_config.enable_jit_fused: + policy[Blip2QFormerSelfOutput] = ModulePolicyDescription( + method_replacement={ + 'forward': get_jit_fused_blip2_QFormer_self_output_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + policy[Blip2QFormerOutput] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_blip2_QFormer_output_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + return policy def postprocess(self): diff --git a/colossalai/shardformer/policies/bloom.py b/colossalai/shardformer/policies/bloom.py index 15bae2f4a959..b35764db3870 100644 --- a/colossalai/shardformer/policies/bloom.py +++ b/colossalai/shardformer/policies/bloom.py @@ -7,7 +7,16 @@ import colossalai.shardformer.layer as col_nn -from ..modeling.bloom import BloomPipelineForwards, build_bloom_alibi_tensor_fn +from .._utils import getattr_, setattr_ +from ..modeling.bloom import ( + BloomPipelineForwards, + build_bloom_alibi_tensor_fn, + get_bloom_flash_attention_forward, + get_jit_fused_bloom_attention_forward, + get_jit_fused_bloom_gelu_forward, + get_jit_fused_bloom_mlp_forward, +) +from ..modeling.jit import get_dropout_add_func, get_jit_fused_dropout_add_func, get_jit_fused_gelu_forward_func from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -30,7 +39,7 @@ def preprocess(self): return self.model def module_policy(self): - from transformers.models.bloom.modeling_bloom import BloomBlock, BloomModel + from transformers.models.bloom.modeling_bloom import BloomAttention, BloomBlock, BloomGelu, BloomMLP, BloomModel policy = {} @@ -107,6 +116,27 @@ def module_policy(self): policy=policy, target_key=BloomBlock) + if self.shard_config.enable_flash_attention: + policy[BloomAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_bloom_flash_attention_forward(), + 'dropout_add': get_dropout_add_func() + }) + + # enable jit fused operator + if self.shard_config.enable_jit_fused: + policy[BloomAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_bloom_attention_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + policy[BloomMLP] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_bloom_mlp_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + policy[BloomGelu] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_bloom_gelu_forward(), + 'bloom_gelu_forward': get_jit_fused_gelu_forward_func(), + }) + return policy def postprocess(self): diff --git a/colossalai/shardformer/policies/chatglm.py b/colossalai/shardformer/policies/chatglm.py index 9cc651caddc1..e6b458936637 100644 --- a/colossalai/shardformer/policies/chatglm.py +++ b/colossalai/shardformer/policies/chatglm.py @@ -15,6 +15,8 @@ GLMBlock, ) +from ..modeling.chatglm import get_flash_core_attention_forward, get_jit_fused_glm_block_forward +from ..modeling.jit import get_jit_fused_dropout_add_func from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['ChatGLMPolicy', 'ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy'] @@ -35,12 +37,11 @@ def preprocess(self): new_vocab_size = vocab_size + world_size - vocab_size % world_size self.model.resize_token_embeddings(new_vocab_size) - return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock + from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMModel, CoreAttention, GLMBlock policy = {} @@ -121,6 +122,19 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: policy=policy, target_key=ChatGLMModel) + # use flash attention + if self.shard_config.enable_flash_attention: + policy[CoreAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_flash_core_attention_forward(), + }) + + # use jit fused operator + if self.shard_config.enable_jit_fused: + policy[GLMBlock] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_glm_block_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + return policy def postprocess(self): @@ -192,7 +206,6 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: return [] - class ChatGLMForConditionalGenerationPolicy(ChatGLMModelPolicy): def module_policy(self): @@ -213,4 +226,3 @@ def get_held_layers(self) -> List[nn.Module]: def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared params in ChatGLMForConditionalGenerationModel.""" return [] - diff --git a/colossalai/shardformer/policies/gpt2.py b/colossalai/shardformer/policies/gpt2.py index 6d734b063036..20e5fa372c8f 100644 --- a/colossalai/shardformer/policies/gpt2.py +++ b/colossalai/shardformer/policies/gpt2.py @@ -5,7 +5,8 @@ import colossalai.shardformer.layer as col_nn -from ..modeling.gpt2 import GPT2PipelineForwards +from .._utils import getattr_, setattr_ +from ..modeling.gpt2 import GPT2PipelineForwards, get_gpt2_flash_attention_forward from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -33,7 +34,7 @@ def preprocess(self): return self.model def module_policy(self): - from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model + from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2Block, GPT2Model policy = {} @@ -53,42 +54,42 @@ def module_policy(self): "attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, }, - sub_module_replacement=[ - SubModuleReplacementDescription( - suffix="attn.c_attn", - target_module=col_nn.GPT2FusedLinearConv1D_Col, - kwargs={ - "n_fused": 3, - }, - ), - SubModuleReplacementDescription( - suffix="attn.c_proj", - target_module=col_nn.GPT2FusedLinearConv1D_Row, - ), - SubModuleReplacementDescription( - suffix="mlp.c_fc", - target_module=col_nn.GPT2FusedLinearConv1D_Col, - kwargs={ - "n_fused": 1, - }, - ), - SubModuleReplacementDescription( - suffix="mlp.c_proj", - target_module=col_nn.GPT2FusedLinearConv1D_Row, - ), - SubModuleReplacementDescription( - suffix="attn.attn_dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="attn.resid_dropout", - target_module=col_nn.DropoutForParallelInput, - ), - SubModuleReplacementDescription( - suffix="mlp.dropout", - target_module=col_nn.DropoutForParallelInput, - ), - ]) + sub_module_replacement=[ + SubModuleReplacementDescription( + suffix="attn.c_attn", + target_module=col_nn.GPT2FusedLinearConv1D_Col, + kwargs={ + "n_fused": 3, + }, + ), + SubModuleReplacementDescription( + suffix="attn.c_proj", + target_module=col_nn.GPT2FusedLinearConv1D_Row, + ), + SubModuleReplacementDescription( + suffix="mlp.c_fc", + target_module=col_nn.GPT2FusedLinearConv1D_Col, + kwargs={ + "n_fused": 1, + }, + ), + SubModuleReplacementDescription( + suffix="mlp.c_proj", + target_module=col_nn.GPT2FusedLinearConv1D_Row, + ), + SubModuleReplacementDescription( + suffix="attn.attn_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="attn.resid_dropout", + target_module=col_nn.DropoutForParallelInput, + ), + SubModuleReplacementDescription( + suffix="mlp.dropout", + target_module=col_nn.DropoutForParallelInput, + ), + ]) # optimization configuration if self.shard_config.enable_fused_normalization: @@ -96,8 +97,8 @@ def module_policy(self): suffix="ln_f", target_module=col_nn.FusedLayerNorm, ), - policy=policy, - target_key=GPT2Model) + policy=policy, + target_key=GPT2Model) self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( @@ -112,8 +113,13 @@ def module_policy(self): target_module=col_nn.FusedLayerNorm, ignore_if_not_exist=True) ], - policy=policy, - target_key=GPT2Block) + policy=policy, + target_key=GPT2Block) + + if self.shard_config.enable_flash_attention: + policy[GPT2Attention] = ModulePolicyDescription(method_replacement={ + 'forward': get_gpt2_flash_attention_forward(), + }) return policy def postprocess(self): diff --git a/colossalai/shardformer/policies/llama.py b/colossalai/shardformer/policies/llama.py index 5988366ed57b..5ee95f3be8fa 100644 --- a/colossalai/shardformer/policies/llama.py +++ b/colossalai/shardformer/policies/llama.py @@ -7,7 +7,7 @@ from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D -from ..modeling.llama import LlamaPipelineForwards +from ..modeling.llama import LlamaPipelineForwards, get_llama_flash_attention_forward from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy'] @@ -31,7 +31,7 @@ def preprocess(self): return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel + from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel policy = {} @@ -104,6 +104,11 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: policy=policy, target_key=LlamaModel) + if self.shard_config.enable_flash_attention: + policy[LlamaAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_llama_flash_attention_forward(), + }) + return policy def postprocess(self): diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index 6fc3a2d31f4d..88ecd8565091 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -25,6 +25,8 @@ from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D from .._utils import getattr_, setattr_ +from ..modeling.jit import get_jit_fused_dropout_add_func +from ..modeling.opt import get_jit_fused_opt_decoder_layer_forward, get_opt_flash_attention_forward from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -114,6 +116,19 @@ def module_policy(self): policy=policy, target_key=OPTDecoderLayer) + # use flash attention + if self.shard_config.enable_flash_attention: + policy[OPTAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_opt_flash_attention_forward(), + }) + + # use jit fused operator + if self.shard_config.enable_jit_fused: + policy[OPTDecoderLayer] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_opt_decoder_layer_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + return policy def postprocess(self): @@ -189,13 +204,11 @@ def module_policy(self): from transformers.models.opt.modeling_opt import OPTForCausalLM policy = super().module_policy() - if self.shard_config.enable_tensor_parallelism: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)), policy=policy, target_key=OPTForCausalLM) - if self.pipeline_stage_manager: self.set_pipeline_forward(model_cls=OPTForCausalLM, new_forward=OPTPipelineForwards.opt_for_causal_lm_forward, diff --git a/colossalai/shardformer/policies/sam.py b/colossalai/shardformer/policies/sam.py index ca20fff715f2..b1eba0432b49 100644 --- a/colossalai/shardformer/policies/sam.py +++ b/colossalai/shardformer/policies/sam.py @@ -3,7 +3,7 @@ import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ -from ..modeling.sam import forward_fn +from ..modeling.sam import forward_fn, get_sam_flash_attention_forward, get_sam_vision_flash_attention_forward from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ['SamPolicy', 'SamModelPolicy'] @@ -19,6 +19,7 @@ def preprocess(self): def module_policy(self): from transformers.models.sam.modeling_sam import ( + SamAttention, SamFeedForward, SamTwoWayAttentionBlock, SamTwoWayTransformer, @@ -196,6 +197,15 @@ def module_policy(self): policy=policy, target_key=SamTwoWayTransformer) + # use flash attention + if self.shard_config.enable_flash_attention: + policy[SamAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_sam_flash_attention_forward(), + }) + policy[SamVisionAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_sam_vision_flash_attention_forward(), + }) + return policy def postprocess(self): diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 0ee18d6c4940..5e78ae9093fa 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -14,7 +14,14 @@ from colossalai.shardformer.policies.base_policy import ModulePolicyDescription from .._utils import getattr_, setattr_ -from ..modeling.t5 import T5PipelineForwards +from ..modeling.jit import get_jit_fused_dropout_add_func +from ..modeling.t5 import ( + T5PipelineForwards, + get_jit_fused_T5_layer_ff_forward, + get_t5_flash_attention_forward, + get_T5_layer_cross_attention_forward, + get_T5_layer_self_attention_forward, +) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["distribute_t5_layers", "T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"] @@ -168,6 +175,27 @@ def module_policy(self): suffix="final_layer_norm", target_module=FusedRMSNorm), policy=policy, target_key=T5Stack) + + # use flash attention + if self.shard_config.enable_flash_attention: + policy[T5Attention] = ModulePolicyDescription(method_replacement={ + 'forward': get_t5_flash_attention_forward(), + }) + + # use jit operator + if self.shard_config.enable_jit_fused: + policy[T5LayerFF] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_T5_layer_ff_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + policy[T5LayerSelfAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_T5_layer_self_attention_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + policy[T5LayerCrossAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_T5_layer_cross_attention_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) return policy def postprocess(self): diff --git a/colossalai/shardformer/policies/vit.py b/colossalai/shardformer/policies/vit.py index 1feb11ffcf24..26fcb6e77d35 100644 --- a/colossalai/shardformer/policies/vit.py +++ b/colossalai/shardformer/policies/vit.py @@ -3,11 +3,15 @@ import torch.nn as nn import colossalai.shardformer.layer as col_nn +from colossalai.shardformer.layer import DropoutForReplicatedInput, Linear1D_Col +from ..modeling.jit import get_jit_fused_dropout_add_func from ..modeling.vit import ( ViTForImageClassification_pipeline_forward, ViTForMaskedImageModeling_pipeline_forward, ViTModel_pipeline_forward, + get_jit_fused_vit_output_forward, + get_vit_flash_self_attention_forward, ) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -23,7 +27,8 @@ def preprocess(self): return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: - from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer, ViTModel + + from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer, ViTModel, ViTOutput, ViTSelfAttention policy = {} @@ -33,7 +38,7 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", - target_module=col_nn.DropoutForReplicatedInput, + target_module=DropoutForReplicatedInput, ) ]) @@ -83,8 +88,18 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: ), ]) - return policy - + # use flash attention + if self.shard_config.enable_flash_attention: + policy[ViTSelfAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_vit_flash_self_attention_forward(), + }) + + # use jit fused operator + if self.shard_config.enable_jit_fused: + policy[ViTOutput] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_vit_output_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) return policy def new_model_class(self): @@ -167,7 +182,7 @@ def module_policy(self): ViTForImageClassification: ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( - suffix="classifier", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)) + suffix="classifier", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) ]) } policy.update(new_item) diff --git a/colossalai/shardformer/policies/whisper.py b/colossalai/shardformer/policies/whisper.py index 2f3565bdaa96..2ac7a49fd27b 100644 --- a/colossalai/shardformer/policies/whisper.py +++ b/colossalai/shardformer/policies/whisper.py @@ -3,6 +3,12 @@ import colossalai.shardformer.layer as col_nn from .._utils import getattr_, setattr_ +from ..modeling.jit import get_jit_fused_dropout_add_func +from ..modeling.whisper import ( + get_jit_fused_whisper_decoder_layer_forward, + get_jit_fused_whisper_encoder_layer_forward, + get_whisper_flash_attention_forward, +) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -30,6 +36,7 @@ def preprocess(self): def module_policy(self): from transformers.models.whisper.modeling_whisper import ( + WhisperAttention, WhisperDecoder, WhisperDecoderLayer, WhisperEncoder, @@ -181,6 +188,24 @@ def module_policy(self): ], policy=policy, target_key=WhisperDecoder) + + # enable flash attention + if self.shard_config.enable_flash_attention: + policy[WhisperAttention] = ModulePolicyDescription(method_replacement={ + 'forward': get_whisper_flash_attention_forward(), + }) + + # use jit fused operator + if self.shard_config.enable_jit_fused: + policy[WhisperEncoderLayer] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_whisper_encoder_layer_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + policy[WhisperDecoderLayer] = ModulePolicyDescription(method_replacement={ + 'forward': get_jit_fused_whisper_decoder_layer_forward(), + 'dropout_add': get_jit_fused_dropout_add_func(), + }) + return policy def add_lm_head_policy(self, base_policy): diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index 75fad4eb7431..ec6e0cd0d4be 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -26,6 +26,8 @@ class ShardConfig: enable_tensor_parallelism: bool = True enable_fused_normalization: bool = False enable_all_optimization: bool = False + enable_flash_attention: bool = False + enable_jit_fused: bool = False # TODO: add support for tensor parallel # pipeline_parallel_size: int @@ -44,7 +46,6 @@ def __post_init__(self): else: # get the parallel size self._tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group) - # turn on all optimization if all_optimization is set to True if self.enable_all_optimization: self._turn_on_all_optimization() @@ -55,3 +56,5 @@ def _turn_on_all_optimization(self): """ # you can add all the optimization flag here self.enable_fused_normalization = True + self.enable_flash_attention = True + self.enable_jit_fused = True diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index 2dae645f7eb9..510af5f3c7ff 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -18,3 +18,5 @@ SentencePiece ninja flash_attn>=2.0 datasets +ninja +flash-attn diff --git a/tests/kit/model_zoo/transformers/bert.py b/tests/kit/model_zoo/transformers/bert.py index d17b8fda425a..9834f5425027 100644 --- a/tests/kit/model_zoo/transformers/bert.py +++ b/tests/kit/model_zoo/transformers/bert.py @@ -20,7 +20,7 @@ def data_gen(): # token_type_ids = tokenized_input['token_type_ids'] input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]], dtype=torch.int64) token_type_ids = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64) - attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 0]], dtype=torch.int64) return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask) @@ -69,19 +69,21 @@ def data_gen_for_mcq(): # data['labels'] = torch.tensor([0], dtype=torch.int64) input_ids = torch.tensor([[[ 101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591, - 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102 + 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102 ], [ 101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096, - 2218, 1999, 1996, 2192, 1012, 102, 0 + 2218, 1999, 1996, 2192, 1012, 102, 0, 0 ]]]) token_type_ids = torch.tensor( - [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]]) + [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, + 0]]]) attention_mask = torch.tensor( - [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], - [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]]) + [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, + 0]]]) labels = torch.tensor([0], dtype=torch.int64) return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels) diff --git a/tests/kit/model_zoo/transformers/blip2.py b/tests/kit/model_zoo/transformers/blip2.py index 7338f740be7f..984a6ffa920d 100644 --- a/tests/kit/model_zoo/transformers/blip2.py +++ b/tests/kit/model_zoo/transformers/blip2.py @@ -38,6 +38,7 @@ def data_gen(): loss_fn_blip2_model = lambda x: x.loss config = transformers.Blip2Config() +config.vision_config.patch_size = 14 config.text_config.num_hidden_layers = 1 config.qformer_config.num_hidden_layers = 1 config.vision_config.num_hidden_layers = 1 diff --git a/tests/kit/model_zoo/transformers/bloom.py b/tests/kit/model_zoo/transformers/bloom.py index 5d195db2c68d..177edbef8935 100644 --- a/tests/kit/model_zoo/transformers/bloom.py +++ b/tests/kit/model_zoo/transformers/bloom.py @@ -16,8 +16,8 @@ def data_gen(): # tokenized_input = tokenizer(input, return_tensors='pt') # input_ids = tokenized_input['input_ids'] # attention_mask = tokenized_input['attention_mask'] - input_ids = torch.tensor([[59414, 15, 2670, 35433, 632, 207595]], dtype=torch.int64) - attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]], dtype=torch.int64) + input_ids = torch.tensor([[59414, 15, 2670, 35433, 632, 207595, 632, 207595]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) return dict(input_ids=input_ids, attention_mask=attention_mask) @@ -33,7 +33,7 @@ def data_gen_for_token_classification(): # token classification data gen # `labels` is the type not the token id for token classification, 0 or 1 data = data_gen() - data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 0]], dtype=torch.int64) + data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64) return data @@ -53,8 +53,8 @@ def data_gen_for_question_answering(): # inputs = tokenizer(question, text, return_tensors="pt") input_ids = torch.tensor( - [[57647, 1620, 23967, 620, 107373, 34, 91514, 620, 107373, 1620, 267, 35378, 48946, 18161]], dtype=torch.int64) - attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) + [[57647, 1620, 23967, 620, 107373, 34, 91514, 620, 107373, 1620, 267, 35378, 48946, 18161, 48946, 18161]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) start_positions = torch.tensor([1], dtype=torch.int64) end_positions = torch.tensor([10], dtype=torch.int64) return dict(input_ids=input_ids, diff --git a/tests/kit/model_zoo/transformers/chatglm.py b/tests/kit/model_zoo/transformers/chatglm.py index 056c910a8dfe..90bb70bc7f79 100644 --- a/tests/kit/model_zoo/transformers/chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm.py @@ -6,7 +6,6 @@ from ..registry import ModelAttribute, model_zoo - # ================================ # Register single-sentence ChatGLM # ================================ diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py deleted file mode 100644 index 3e78732be2da..000000000000 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/configuration_chatglm.py +++ /dev/null @@ -1,58 +0,0 @@ -from transformers import PretrainedConfig - - -class ChatGLMConfig(PretrainedConfig): - model_type = "chatglm" - - def __init__(self, - num_layers=28, - padded_vocab_size=65024, - hidden_size=4096, - ffn_hidden_size=13696, - kv_channels=128, - num_attention_heads=32, - seq_length=2048, - hidden_dropout=0.0, - attention_dropout=0.0, - layernorm_epsilon=1e-5, - rmsnorm=True, - apply_residual_connection_post_layernorm=False, - post_layer_norm=True, - add_bias_linear=False, - add_qkv_bias=False, - bias_dropout_fusion=True, - multi_query_attention=False, - multi_query_group_num=1, - apply_query_key_layer_scaling=True, - attention_softmax_in_fp32=True, - fp32_residual_connection=False, - quantization_bit=0, - pre_seq_len=None, - prefix_projection=False, - **kwargs): - self.num_layers = num_layers - self.vocab_size = padded_vocab_size - self.padded_vocab_size = padded_vocab_size - self.hidden_size = hidden_size - self.ffn_hidden_size = ffn_hidden_size - self.kv_channels = kv_channels - self.num_attention_heads = num_attention_heads - self.seq_length = seq_length - self.hidden_dropout = hidden_dropout - self.attention_dropout = attention_dropout - self.layernorm_epsilon = layernorm_epsilon - self.rmsnorm = rmsnorm - self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm - self.post_layer_norm = post_layer_norm - self.add_bias_linear = add_bias_linear - self.add_qkv_bias = add_qkv_bias - self.bias_dropout_fusion = bias_dropout_fusion - self.multi_query_attention = multi_query_attention - self.multi_query_group_num = multi_query_group_num - self.apply_query_key_layer_scaling = apply_query_key_layer_scaling - self.attention_softmax_in_fp32 = attention_softmax_in_fp32 - self.fp32_residual_connection = fp32_residual_connection - self.quantization_bit = quantization_bit - self.pre_seq_len = pre_seq_len - self.prefix_projection = prefix_projection - super().__init__(**kwargs) diff --git a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py b/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py deleted file mode 100644 index bae6d425878d..000000000000 --- a/tests/kit/model_zoo/transformers/chatglm2_6b/modeling_chatglm.py +++ /dev/null @@ -1,1372 +0,0 @@ -""" -The ChatGLM2-6B License - -1. Definitions - -“Licensor” means the ChatGLM2-6B Model Team that distributes its Software. - -“Software” means the ChatGLM2-6B model parameters made available under this license. - -2. License Grant - -Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license to use the Software solely for your non-commercial research purposes. - -The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. - -3. Restriction - -You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any commercial, military, or illegal purposes. - -You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings. - -4. Disclaimer - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - -5. Limitation of Liability - -EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. - -6. Dispute Resolution - -This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing. - -Note that the license is subject to update to a more comprehensive version. For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com. -""" -""" PyTorch ChatGLM model. """ - -import copy -import math -import re -import sys -import warnings -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -import torch -import torch.nn.functional as F -import torch.utils.checkpoint -from torch import nn -from torch.nn import CrossEntropyLoss, LayerNorm -from torch.nn.utils import skip_init -from transformers.generation.logits_process import LogitsProcessor -from transformers.generation.utils import GenerationConfig, LogitsProcessorList, ModelOutput, StoppingCriteriaList -from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast -from transformers.modeling_utils import PreTrainedModel -from transformers.utils import logging - -from .configuration_chatglm import ChatGLMConfig - -# flags required to enable jit fusion kernels - -if sys.platform != "darwin": - torch._C._jit_set_profiling_mode(False) - torch._C._jit_set_profiling_executor(False) - torch._C._jit_override_can_fuse_on_cpu(True) - torch._C._jit_override_can_fuse_on_gpu(True) - -logger = logging.get_logger(__name__) - -_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B" -_CONFIG_FOR_DOC = "ChatGLM6BConfig" - -CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "THUDM/chatglm2-6b", - # See all ChatGLM models at https://huggingface.co/models?filter=chatglm -] - - -def default_init(cls, *args, **kwargs): - return cls(*args, **kwargs) - - -class InvalidScoreLogitsProcessor(LogitsProcessor): - - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: - if torch.isnan(scores).any() or torch.isinf(scores).any(): - scores.zero_() - scores[..., 5] = 5e4 - return scores - - -class PrefixEncoder(torch.nn.Module): - """ - The torch.nn model to encode the prefix - Input shape: (batch-size, prefix-length) - Output shape: (batch-size, prefix-length, 2*layers*hidden) - """ - - def __init__(self, config: ChatGLMConfig): - super().__init__() - self.prefix_projection = config.prefix_projection - if self.prefix_projection: - # Use a two-layer MLP to encode the prefix - kv_size = (config.num_layers * config.kv_channels * config.multi_query_group_num * 2) - self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) - self.trans = torch.nn.Sequential( - torch.nn.Linear(kv_size, config.hidden_size), - torch.nn.Tanh(), - torch.nn.Linear(config.hidden_size, kv_size), - ) - else: - self.embedding = torch.nn.Embedding( - config.pre_seq_len, - config.num_layers * config.kv_channels * config.multi_query_group_num * 2, - ) - - def forward(self, prefix: torch.Tensor): - if self.prefix_projection: - prefix_tokens = self.embedding(prefix) - past_key_values = self.trans(prefix_tokens) - else: - past_key_values = self.embedding(prefix) - return past_key_values - - -def split_tensor_along_last_dim( - tensor: torch.Tensor, - num_partitions: int, - contiguous_split_chunks: bool = False, -) -> List[torch.Tensor]: - """Split a tensor along its last dimension. - - Arguments: - tensor: input tensor. - num_partitions: number of partitions to split the tensor - contiguous_split_chunks: If True, make each chunk contiguous - in memory. - - Returns: - A list of Tensors - """ - # Get the size and dimension. - last_dim = tensor.dim() - 1 - last_dim_size = tensor.size()[last_dim] // num_partitions - # Split. - tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) - # Note: torch.split does not create contiguous tensors by default. - if contiguous_split_chunks: - return tuple(chunk.contiguous() for chunk in tensor_list) - - return tensor_list - - -class RotaryEmbedding(nn.Module): - - def __init__(self, dim, original_impl=False, device=None, dtype=None): - super().__init__() - inv_freq = 1.0 / (10000**(torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) - self.register_buffer("inv_freq", inv_freq) - self.dim = dim - self.original_impl = original_impl - - def forward_impl( - self, - seq_len: int, - n_elem: int, - dtype: torch.dtype, - device: torch.device, - base: int = 10000, - ): - """Enhanced Transformer with Rotary Position Embedding. - - Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ - transformers/rope/__init__.py. MIT License: - https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. - """ - # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ - theta = 1.0 / (base**(torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) - - # Create position indexes `[0, 1, ..., seq_len - 1]` - seq_idx = torch.arange(seq_len, dtype=dtype, device=device) - - # Calculate the product of position index and $\theta_i$ - idx_theta = torch.outer(seq_idx, theta).float() - - cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) - - # this is to mimic the behaviour of complex32, else we will get different results - if dtype in (torch.float16, torch.bfloat16, torch.int8): - cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() - return cache - - def forward(self, max_seq_len, offset=0): - return self.forward_impl( - max_seq_len, - self.dim, - dtype=self.inv_freq.dtype, - device=self.inv_freq.device, - ) - - -@torch.jit.script -def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: - # x: [sq, b, np, hn] - sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) - rot_dim = rope_cache.shape[-2] * 2 - x, x_pass = x[..., :rot_dim], x[..., rot_dim:] - # truncate to support variable sizes - rope_cache = rope_cache[:sq] - xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) - rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) - x_out2 = torch.stack( - [ - xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], - xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], - ], - -1, - ) - x_out2 = x_out2.flatten(3) - return torch.cat((x_out2, x_pass), dim=-1) - - -class RMSNorm(torch.nn.Module): - - def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): - super().__init__() - self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) - self.eps = eps - - def forward(self, hidden_states: torch.Tensor): - input_dtype = hidden_states.dtype - variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt(variance + self.eps) - - return (self.weight * hidden_states).to(input_dtype) - - -class CoreAttention(torch.nn.Module): - - def __init__(self, config: ChatGLMConfig, layer_number): - super(CoreAttention, self).__init__() - - self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling - self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 - if self.apply_query_key_layer_scaling: - self.attention_softmax_in_fp32 = True - self.layer_number = max(1, layer_number) - - projection_size = config.kv_channels * config.num_attention_heads - - # Per attention head and per partition values. - self.hidden_size_per_partition = projection_size - self.hidden_size_per_attention_head = (projection_size // config.num_attention_heads) - self.num_attention_heads_per_partition = config.num_attention_heads - - coeff = None - self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) - if self.apply_query_key_layer_scaling: - coeff = self.layer_number - self.norm_factor *= coeff - self.coeff = coeff - - self.attention_dropout = torch.nn.Dropout(config.attention_dropout) - - def forward(self, query_layer, key_layer, value_layer, attention_mask): - pytorch_major_version = int(torch.__version__.split(".")[0]) - if pytorch_major_version >= 2: - query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] - if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: - context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, - key_layer, - value_layer, - is_causal=True) - else: - if attention_mask is not None: - attention_mask = ~attention_mask - context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, - attention_mask) - context_layer = context_layer.permute(2, 0, 1, 3) - new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) - context_layer = context_layer.reshape(*new_context_layer_shape) - else: - # Raw attention scores - - # [b, np, sq, sk] - output_size = ( - query_layer.size(1), - query_layer.size(2), - query_layer.size(0), - key_layer.size(0), - ) - - # [sq, b, np, hn] -> [sq, b * np, hn] - query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) - # [sk, b, np, hn] -> [sk, b * np, hn] - key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) - - # preallocting input tensor: [b * np, sq, sk] - matmul_input_buffer = torch.empty( - output_size[0] * output_size[1], - output_size[2], - output_size[3], - dtype=query_layer.dtype, - device=query_layer.device, - ) - - # Raw attention scores. [b * np, sq, sk] - matmul_result = torch.baddbmm( - matmul_input_buffer, - query_layer.transpose(0, 1), # [b * np, sq, hn] - key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] - beta=0.0, - alpha=(1.0 / self.norm_factor), - ) - - # change view to [b, np, sq, sk] - attention_scores = matmul_result.view(*output_size) - - # =========================== - # Attention probs and dropout - # =========================== - - # attention scores and attention mask [b, np, sq, sk] - if self.attention_softmax_in_fp32: - attention_scores = attention_scores.float() - if self.coeff is not None: - attention_scores = attention_scores * self.coeff - if (attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]): - attention_mask = torch.ones( - output_size[0], - 1, - output_size[2], - output_size[3], - device=attention_scores.device, - dtype=torch.bool, - ) - attention_mask.tril_() - attention_mask = ~attention_mask - if attention_mask is not None: - attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) - attention_probs = F.softmax(attention_scores, dim=-1) - attention_probs = attention_probs.type_as(value_layer) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs = self.attention_dropout(attention_probs) - # ========================= - # Context layer. [sq, b, hp] - # ========================= - - # value_layer -> context layer. - # [sk, b, np, hn] --> [b, np, sq, hn] - - # context layer shape: [b, np, sq, hn] - output_size = ( - value_layer.size(1), - value_layer.size(2), - query_layer.size(0), - value_layer.size(3), - ) - # change view [sk, b * np, hn] - value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) - # change view [b * np, sq, sk] - attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) - # matmul: [b * np, sq, hn] - context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) - # change view [b, np, sq, hn] - context_layer = context_layer.view(*output_size) - # [b, np, sq, hn] --> [sq, b, np, hn] - context_layer = context_layer.permute(2, 0, 1, 3).contiguous() - # [sq, b, np, hn] --> [sq, b, hp] - new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) - context_layer = context_layer.view(*new_context_layer_shape) - - return context_layer - - -class SelfAttention(torch.nn.Module): - """Parallel self-attention layer abstract class. - - Self-attention layer takes input with size [s, b, h] - and returns output of the same size. - """ - - def __init__(self, config: ChatGLMConfig, layer_number, device=None): - super(SelfAttention, self).__init__() - self.layer_number = max(1, layer_number) - - self.projection_size = config.kv_channels * config.num_attention_heads - # Per attention head and per partition values. - self.hidden_size_per_attention_head = (self.projection_size // config.num_attention_heads) - self.num_attention_heads_per_partition = config.num_attention_heads - - self.multi_query_attention = config.multi_query_attention - self.qkv_hidden_size = 3 * self.projection_size - if self.multi_query_attention: - self.num_multi_query_groups_per_partition = config.multi_query_group_num - self.qkv_hidden_size = (self.projection_size + - 2 * self.hidden_size_per_attention_head * config.multi_query_group_num) - self.query_key_value = nn.Linear( - config.hidden_size, - self.qkv_hidden_size, - bias=config.add_bias_linear or config.add_qkv_bias, - device=device, - **_config_to_kwargs(config), - ) - - self.core_attention = CoreAttention(config, self.layer_number) - - # Output. - self.dense = nn.Linear( - self.projection_size, - config.hidden_size, - bias=config.add_bias_linear, - device=device, - **_config_to_kwargs(config), - ) - - def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): - if self.multi_query_attention: - num_attention_heads = self.num_multi_query_groups_per_partition - else: - num_attention_heads = self.num_attention_heads_per_partition - return torch.empty( - inference_max_sequence_len, - batch_size, - num_attention_heads, - self.hidden_size_per_attention_head, - dtype=dtype, - device=device, - ) - - def forward( - self, - hidden_states, - attention_mask, - rotary_pos_emb, - kv_cache=None, - use_cache=True, - ): - # hidden_states: [sq, b, h] - - # ================================================= - # Pre-allocate memory for key-values for inference. - # ================================================= - # ===================== - # Query, Key, and Value - # ===================== - - # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] - mixed_x_layer = self.query_key_value(hidden_states) - - if self.multi_query_attention: - (query_layer, key_layer, value_layer) = mixed_x_layer.split( - [ - self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, - self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, - self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, - ], - dim=-1, - ) - query_layer = query_layer.view(query_layer.size()[:-1] + ( - self.num_attention_heads_per_partition, - self.hidden_size_per_attention_head, - )) - key_layer = key_layer.view(key_layer.size()[:-1] + ( - self.num_multi_query_groups_per_partition, - self.hidden_size_per_attention_head, - )) - value_layer = value_layer.view(value_layer.size()[:-1] + ( - self.num_multi_query_groups_per_partition, - self.hidden_size_per_attention_head, - )) - else: - new_tensor_shape = mixed_x_layer.size()[:-1] + ( - self.num_attention_heads_per_partition, - 3 * self.hidden_size_per_attention_head, - ) - mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) - # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] - (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) - - # apply relative positional encoding (rotary embedding) - if rotary_pos_emb is not None: - query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) - key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) - - # adjust key and value for inference - if kv_cache is not None: - cache_k, cache_v = kv_cache - key_layer = torch.cat((cache_k, key_layer), dim=0) - value_layer = torch.cat((cache_v, value_layer), dim=0) - if use_cache: - kv_cache = (key_layer, value_layer) - else: - kv_cache = None - - if self.multi_query_attention: - key_layer = key_layer.unsqueeze(-2) - key_layer = key_layer.expand( - -1, - -1, - -1, - self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, - -1, - ) - key_layer = key_layer.contiguous().view(key_layer.size()[:2] + ( - self.num_attention_heads_per_partition, - self.hidden_size_per_attention_head, - )) - value_layer = value_layer.unsqueeze(-2) - value_layer = value_layer.expand( - -1, - -1, - -1, - self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, - -1, - ) - value_layer = value_layer.contiguous().view(value_layer.size()[:2] + ( - self.num_attention_heads_per_partition, - self.hidden_size_per_attention_head, - )) - - # ================================== - # core attention computation - # ================================== - - context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) - - # ================= - # Output. [sq, b, h] - # ================= - - output = self.dense(context_layer) - - return output, kv_cache - - -def _config_to_kwargs(args): - common_kwargs = { - "dtype": args.torch_dtype, - } - return common_kwargs - - -class MLP(torch.nn.Module): - """MLP. - - MLP will take the input with h hidden state, project it to 4*h - hidden dimension, perform nonlinear transformation, and project the - state back into h hidden dimension. - """ - - def __init__(self, config: ChatGLMConfig, device=None): - super(MLP, self).__init__() - - self.add_bias = config.add_bias_linear - - # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf - self.dense_h_to_4h = nn.Linear( - config.hidden_size, - config.ffn_hidden_size * 2, - bias=self.add_bias, - device=device, - **_config_to_kwargs(config), - ) - - def swiglu(x): - x = torch.chunk(x, 2, dim=-1) - return F.silu(x[0]) * x[1] - - self.activation_func = swiglu - - # Project back to h. - self.dense_4h_to_h = nn.Linear( - config.ffn_hidden_size, - config.hidden_size, - bias=self.add_bias, - device=device, - **_config_to_kwargs(config), - ) - - def forward(self, hidden_states): - # [s, b, 4hp] - intermediate_parallel = self.dense_h_to_4h(hidden_states) - intermediate_parallel = self.activation_func(intermediate_parallel) - # [s, b, h] - output = self.dense_4h_to_h(intermediate_parallel) - return output - - -class GLMBlock(torch.nn.Module): - """A single transformer layer. - - Transformer layer takes input with size [s, b, h] and returns an - output of the same size. - """ - - def __init__(self, config: ChatGLMConfig, layer_number, device=None): - super(GLMBlock, self).__init__() - self.layer_number = layer_number - - self.apply_residual_connection_post_layernorm = (config.apply_residual_connection_post_layernorm) - - self.fp32_residual_connection = config.fp32_residual_connection - - LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm - # Layernorm on the input data. - self.input_layernorm = LayerNormFunc( - config.hidden_size, - eps=config.layernorm_epsilon, - device=device, - dtype=config.torch_dtype, - ) - - # Self attention. - self.self_attention = SelfAttention(config, layer_number, device=device) - self.hidden_dropout = config.hidden_dropout - - # Layernorm on the attention output - self.post_attention_layernorm = LayerNormFunc( - config.hidden_size, - eps=config.layernorm_epsilon, - device=device, - dtype=config.torch_dtype, - ) - - # MLP - self.mlp = MLP(config, device=device) - - def forward( - self, - hidden_states, - attention_mask, - rotary_pos_emb, - kv_cache=None, - use_cache=True, - ): - # hidden_states: [s, b, h] - - # Layer norm at the beginning of the transformer layer. - layernorm_output = self.input_layernorm(hidden_states) - # Self attention. - attention_output, kv_cache = self.self_attention( - layernorm_output, - attention_mask, - rotary_pos_emb, - kv_cache=kv_cache, - use_cache=use_cache, - ) - - # Residual connection. - if self.apply_residual_connection_post_layernorm: - residual = layernorm_output - else: - residual = hidden_states - - layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) - layernorm_input = residual + layernorm_input - - # Layer norm post the self attention. - layernorm_output = self.post_attention_layernorm(layernorm_input) - - # MLP. - mlp_output = self.mlp(layernorm_output) - - # Second residual connection. - if self.apply_residual_connection_post_layernorm: - residual = layernorm_output - else: - residual = layernorm_input - - output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) - output = residual + output - - return output, kv_cache - - -class GLMTransformer(torch.nn.Module): - """Transformer class.""" - - def __init__(self, config: ChatGLMConfig, device=None): - super(GLMTransformer, self).__init__() - - self.fp32_residual_connection = config.fp32_residual_connection - self.post_layer_norm = config.post_layer_norm - - # Number of layers. - self.num_layers = config.num_layers - - # Transformer layers. - def build_layer(layer_number): - return GLMBlock(config, layer_number, device=device) - - self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) - - if self.post_layer_norm: - LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm - # Final layer norm before output. - self.final_layernorm = LayerNormFunc( - config.hidden_size, - eps=config.layernorm_epsilon, - device=device, - dtype=config.torch_dtype, - ) - - self.gradient_checkpointing = False - - def _get_layer(self, layer_number): - return self.layers[layer_number] - - def forward( - self, - hidden_states, - attention_mask, - rotary_pos_emb, - kv_caches=None, - use_cache: Optional[bool] = True, - output_hidden_states: Optional[bool] = False, - ): - if not kv_caches: - kv_caches = [None for _ in range(self.num_layers)] - presents = () if use_cache else None - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") - use_cache = False - - all_self_attentions = None - all_hidden_states = () if output_hidden_states else None - for index in range(self.num_layers): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer = self._get_layer(index) - if self.gradient_checkpointing and self.training: - layer_ret = torch.utils.checkpoint.checkpoint( - layer, - hidden_states, - attention_mask, - rotary_pos_emb, - kv_caches[index], - use_cache, - ) - else: - layer_ret = layer( - hidden_states, - attention_mask, - rotary_pos_emb, - kv_cache=kv_caches[index], - use_cache=use_cache, - ) - hidden_states, kv_cache = layer_ret - if use_cache: - presents = presents + (kv_cache,) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - # Final layer norm. - if self.post_layer_norm: - hidden_states = self.final_layernorm(hidden_states) - - return hidden_states, presents, all_hidden_states, all_self_attentions - - -class ChatGLMPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and - a simple interface for downloading and loading pretrained models. - """ - - is_parallelizable = False - supports_gradient_checkpointing = True - config_class = ChatGLMConfig - base_model_prefix = "transformer" - _no_split_modules = ["GLMBlock"] - - def _init_weights(self, module: nn.Module): - """Initialize the weights.""" - return - - def get_masks(self, input_ids, past_key_values, padding_mask=None): - batch_size, seq_length = input_ids.shape - full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) - full_attention_mask.tril_() - past_length = 0 - if past_key_values: - past_length = past_key_values[0][0].shape[0] - if past_length: - full_attention_mask = torch.cat( - ( - torch.ones(batch_size, seq_length, past_length, device=input_ids.device), - full_attention_mask, - ), - dim=-1, - ) - if padding_mask is not None: - full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) - if not past_length and padding_mask is not None: - full_attention_mask -= padding_mask.unsqueeze(-1) - 1 - full_attention_mask = (full_attention_mask < 0.5).bool() - full_attention_mask.unsqueeze_(1) - return full_attention_mask - - def get_position_ids(self, input_ids, device): - batch_size, seq_length = input_ids.shape - position_ids = (torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)) - return position_ids - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, GLMTransformer): - module.gradient_checkpointing = value - - -class Embedding(torch.nn.Module): - """Language model embeddings.""" - - def __init__(self, config: ChatGLMConfig, device=None): - super(Embedding, self).__init__() - - self.hidden_size = config.hidden_size - # Word embeddings (parallel). - self.word_embeddings = nn.Embedding( - config.padded_vocab_size, - self.hidden_size, - dtype=config.torch_dtype, - device=device, - ) - self.fp32_residual_connection = config.fp32_residual_connection - - def forward(self, input_ids): - # Embeddings. - words_embeddings = self.word_embeddings(input_ids) - embeddings = words_embeddings - # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. - embeddings = embeddings.transpose(0, 1).contiguous() - # If the input flag for fp32 residual connection is set, convert for float. - if self.fp32_residual_connection: - embeddings = embeddings.float() - return embeddings - - -class ChatGLMModel(ChatGLMPreTrainedModel): - - def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): - super().__init__(config) - if empty_init: - init_method = skip_init - else: - init_method = default_init - init_kwargs = {} - if device is not None: - init_kwargs["device"] = device - self.embedding = init_method(Embedding, config, **init_kwargs) - self.num_layers = config.num_layers - self.multi_query_group_num = config.multi_query_group_num - self.kv_channels = config.kv_channels - - # Rotary positional embeddings - self.seq_length = config.seq_length - rotary_dim = (config.hidden_size // - config.num_attention_heads if config.kv_channels is None else config.kv_channels) - - self.rotary_pos_emb = RotaryEmbedding( - rotary_dim // 2, - original_impl=config.original_rope, - device=device, - dtype=config.torch_dtype, - ) - self.encoder = init_method(GLMTransformer, config, **init_kwargs) - self.output_layer = init_method( - nn.Linear, - config.hidden_size, - config.padded_vocab_size, - bias=False, - dtype=config.torch_dtype, - **init_kwargs, - ) - self.pre_seq_len = config.pre_seq_len - self.prefix_projection = config.prefix_projection - if self.pre_seq_len is not None: - for param in self.parameters(): - param.requires_grad = False - self.prefix_tokens = torch.arange(self.pre_seq_len).long() - self.prefix_encoder = PrefixEncoder(config) - self.dropout = torch.nn.Dropout(0.1) - - def get_input_embeddings(self): - return self.embedding.word_embeddings - - def get_prompt(self, batch_size, device, dtype=torch.half): - prefix_tokens = (self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)) - past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) - past_key_values = past_key_values.view( - batch_size, - self.pre_seq_len, - self.num_layers * 2, - self.multi_query_group_num, - self.kv_channels, - ) - # seq_len, b, nh, hidden_size - past_key_values = self.dropout(past_key_values) - past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) - return past_key_values - - def forward( - self, - input_ids, - position_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.BoolTensor] = None, - full_attention_mask: Optional[torch.BoolTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, - inputs_embeds: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ): - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = (return_dict if return_dict is not None else self.config.use_return_dict) - - batch_size, seq_length = input_ids.shape - - if inputs_embeds is None: - inputs_embeds = self.embedding(input_ids) - - if self.pre_seq_len is not None: - if past_key_values is None: - past_key_values = self.get_prompt( - batch_size=batch_size, - device=input_ids.device, - dtype=inputs_embeds.dtype, - ) - if attention_mask is not None: - attention_mask = torch.cat( - [ - attention_mask.new_ones((batch_size, self.pre_seq_len)), - attention_mask, - ], - dim=-1, - ) - - if full_attention_mask is None: - if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): - full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) - - # Rotary positional embeddings - rotary_pos_emb = self.rotary_pos_emb(self.seq_length) - if position_ids is not None: - rotary_pos_emb = rotary_pos_emb[position_ids] - else: - rotary_pos_emb = rotary_pos_emb[None, :seq_length] - rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() - - # Run encoder. - hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( - inputs_embeds, - full_attention_mask, - rotary_pos_emb=rotary_pos_emb, - kv_caches=past_key_values, - use_cache=use_cache, - output_hidden_states=output_hidden_states, - ) - - if not return_dict: - return tuple(v for v in [ - hidden_states, - presents, - all_hidden_states, - all_self_attentions, - ] if v is not None) - - return BaseModelOutputWithPast( - last_hidden_state=hidden_states, - past_key_values=presents, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - ) - - def quantize(self, weight_bit_width: int): - from .quantization import quantize - - quantize(self.encoder, weight_bit_width) - return self - - -class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): - - def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): - super().__init__(config) - - self.max_sequence_length = config.max_length - self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) - self.config = config - self.quantized = False - - if self.config.quantization_bit: - self.quantize(self.config.quantization_bit, empty_init=True) - - def _update_model_kwargs_for_generation( - self, - outputs: ModelOutput, - model_kwargs: Dict[str, Any], - is_encoder_decoder: bool = False, - standardize_cache_format: bool = False, - ) -> Dict[str, Any]: - # update past_key_values - model_kwargs["past_key_values"] = self._extract_past_from_model_output( - outputs, standardize_cache_format=standardize_cache_format) - - # update attention mask - if "attention_mask" in model_kwargs: - attention_mask = model_kwargs["attention_mask"] - model_kwargs["attention_mask"] = torch.cat( - [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], - dim=-1, - ) - - # update position ids - if "position_ids" in model_kwargs: - position_ids = model_kwargs["position_ids"] - new_position_id = position_ids[..., -1:].clone() - new_position_id += 1 - model_kwargs["position_ids"] = torch.cat([position_ids, new_position_id], dim=-1) - - model_kwargs["is_first_forward"] = False - return model_kwargs - - def prepare_inputs_for_generation( - self, - input_ids: torch.LongTensor, - past_key_values: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - is_first_forward: bool = True, - **kwargs, - ) -> dict: - # only last token for input_ids if past is not None - if position_ids is None: - position_ids = self.get_position_ids(input_ids, device=input_ids.device) - if not is_first_forward: - position_ids = position_ids[..., -1:] - input_ids = input_ids[:, -1:] - return { - "input_ids": input_ids, - "past_key_values": past_key_values, - "position_ids": position_ids, - "attention_mask": attention_mask, - "return_last_logit": True, - } - - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[Tuple[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - return_last_logit: Optional[bool] = False, - ): - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = (return_dict if return_dict is not None else self.config.use_return_dict) - - transformer_outputs = self.transformer( - input_ids=input_ids, - position_ids=position_ids, - attention_mask=attention_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - hidden_states = transformer_outputs[0] - if return_last_logit: - hidden_states = hidden_states[-1:] - lm_logits = self.transformer.output_layer(hidden_states) - lm_logits = lm_logits.transpose(0, 1).contiguous() - - loss = None - if labels is not None: - lm_logits = lm_logits.to(torch.float32) - - # Shift so that tokens < n predict n - shift_logits = lm_logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss(ignore_index=-100) - loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) - - lm_logits = lm_logits.to(hidden_states.dtype) - loss = loss.to(hidden_states.dtype) - - if not return_dict: - output = (lm_logits,) + transformer_outputs[1:] - return ((loss,) + output) if loss is not None else output - - return CausalLMOutputWithPast( - loss=loss, - logits=lm_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - - @staticmethod - def _reorder_cache(past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], - beam_idx: torch.LongTensor) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: - """ - This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or - [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct - beam_idx at every generation step. - - Output shares the same memory storage as `past`. - """ - return tuple(( - layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), - layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), - ) for layer_past in past) - - def process_response(self, response): - response = response.strip() - response = response.replace("[[训练时间]]", "2023年") - return response - - def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): - prompt = tokenizer.build_prompt(query, history=history) - inputs = tokenizer([prompt], return_tensors="pt") - inputs = inputs.to(self.device) - return inputs - - def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): - if history: - prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) - input_ids = tokenizer.encode(prompt, add_special_tokens=False) - input_ids = input_ids[1:] - inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False) - else: - prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) - inputs = tokenizer([prompt], return_tensors="pt") - inputs = inputs.to(self.device) - return inputs - - @torch.no_grad() - def chat( - self, - tokenizer, - query: str, - history: List[Tuple[str, str]] = None, - max_length: int = 8192, - num_beams=1, - do_sample=True, - top_p=0.8, - temperature=0.8, - logits_processor=None, - **kwargs, - ): - if history is None: - history = [] - if logits_processor is None: - logits_processor = LogitsProcessorList() - logits_processor.append(InvalidScoreLogitsProcessor()) - gen_kwargs = { - "max_length": max_length, - "num_beams": num_beams, - "do_sample": do_sample, - "top_p": top_p, - "temperature": temperature, - "logits_processor": logits_processor, - **kwargs, - } - inputs = self.build_inputs(tokenizer, query, history=history) - outputs = self.generate(**inputs, **gen_kwargs) - outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] - response = tokenizer.decode(outputs) - response = self.process_response(response) - history = history + [(query, response)] - return response, history - - @torch.no_grad() - def stream_chat( - self, - tokenizer, - query: str, - history: List[Tuple[str, str]] = None, - past_key_values=None, - max_length: int = 8192, - do_sample=True, - top_p=0.8, - temperature=0.8, - logits_processor=None, - return_past_key_values=False, - **kwargs, - ): - if history is None: - history = [] - if logits_processor is None: - logits_processor = LogitsProcessorList() - logits_processor.append(InvalidScoreLogitsProcessor()) - gen_kwargs = { - "max_length": max_length, - "do_sample": do_sample, - "top_p": top_p, - "temperature": temperature, - "logits_processor": logits_processor, - **kwargs, - } - if past_key_values is None and not return_past_key_values: - inputs = self.build_inputs(tokenizer, query, history=history) - else: - inputs = self.build_stream_inputs(tokenizer, query, history=history) - if past_key_values is not None: - past_length = past_key_values[0][0].shape[0] - if self.transformer.pre_seq_len is not None: - past_length -= self.transformer.pre_seq_len - inputs.position_ids += past_length - attention_mask = inputs.attention_mask - attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) - inputs["attention_mask"] = attention_mask - for outputs in self.stream_generate( - **inputs, - past_key_values=past_key_values, - return_past_key_values=return_past_key_values, - **gen_kwargs, - ): - if return_past_key_values: - outputs, past_key_values = outputs - outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] - response = tokenizer.decode(outputs) - if response and response[-1] != "�": - response = self.process_response(response) - new_history = history + [(query, response)] - if return_past_key_values: - yield response, new_history, past_key_values - else: - yield response, new_history - - @torch.no_grad() - def stream_generate( - self, - input_ids, - generation_config: Optional[GenerationConfig] = None, - logits_processor: Optional[LogitsProcessorList] = None, - stopping_criteria: Optional[StoppingCriteriaList] = None, - prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None, - return_past_key_values=False, - **kwargs, - ): - batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] - - if generation_config is None: - generation_config = self.generation_config - generation_config = copy.deepcopy(generation_config) - model_kwargs = generation_config.update(**kwargs) - bos_token_id, eos_token_id = ( - generation_config.bos_token_id, - generation_config.eos_token_id, - ) - - if isinstance(eos_token_id, int): - eos_token_id = [eos_token_id] - - has_default_max_length = (kwargs.get("max_length") is None and generation_config.max_length is not None) - if has_default_max_length and generation_config.max_new_tokens is None: - warnings.warn( - f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " - "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" - " recommend using `max_new_tokens` to control the maximum length of the generation.", - UserWarning, - ) - elif generation_config.max_new_tokens is not None: - generation_config.max_length = (generation_config.max_new_tokens + input_ids_seq_length) - if not has_default_max_length: - logger.warn( - f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" - f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " - "Please refer to the documentation for more information. " - "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", - UserWarning, - ) - - if input_ids_seq_length >= generation_config.max_length: - input_ids_string = ("decoder_input_ids" if self.config.is_encoder_decoder else "input_ids") - logger.warning(f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" - f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" - " increasing `max_new_tokens`.") - - # 2. Set generation parameters if not already defined - logits_processor = (logits_processor if logits_processor is not None else LogitsProcessorList()) - stopping_criteria = (stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()) - - logits_processor = self._get_logits_processor( - generation_config=generation_config, - input_ids_seq_length=input_ids_seq_length, - encoder_input_ids=input_ids, - prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, - logits_processor=logits_processor, - ) - - stopping_criteria = self._get_stopping_criteria(generation_config=generation_config, - stopping_criteria=stopping_criteria) - logits_warper = self._get_logits_warper(generation_config) - - unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) - scores = None - while True: - model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) - # forward pass to get next token - outputs = self( - **model_inputs, - return_dict=True, - output_attentions=False, - output_hidden_states=False, - ) - - next_token_logits = outputs.logits[:, -1, :] - - # pre-process distribution - next_token_scores = logits_processor(input_ids, next_token_logits) - next_token_scores = logits_warper(input_ids, next_token_scores) - - # sample - probs = nn.functional.softmax(next_token_scores, dim=-1) - if generation_config.do_sample: - next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) - else: - next_tokens = torch.argmax(probs, dim=-1) - - # update generated ids, model inputs, and length for next step - input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) - model_kwargs = self._update_model_kwargs_for_generation(outputs, - model_kwargs, - is_encoder_decoder=self.config.is_encoder_decoder) - unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) - if return_past_key_values: - yield input_ids, outputs.past_key_values - else: - yield input_ids - # stop when each sentence is finished, or if we exceed the maximum length - if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): - break - - def quantize(self, bits: int, empty_init=False, device=None, **kwargs): - if bits == 0: - return - - from .quantization import quantize - - if self.quantized: - logger.info("Already quantized.") - return self - - self.quantized = True - - self.config.quantization_bit = bits - - self.transformer.encoder = quantize( - self.transformer.encoder, - bits, - empty_init=empty_init, - device=device, - **kwargs, - ) - return self diff --git a/tests/kit/model_zoo/transformers/gpt.py b/tests/kit/model_zoo/transformers/gpt.py index 73c210221e61..5c3eb4438bc8 100644 --- a/tests/kit/model_zoo/transformers/gpt.py +++ b/tests/kit/model_zoo/transformers/gpt.py @@ -18,8 +18,8 @@ def data_gen(): # tokenized_input = tokenizer(input, return_tensors='pt') # input_ids = tokenized_input['input_ids'] # attention_mask = tokenized_input['attention_mask'] - input_ids = torch.tensor([[15496, 11, 616, 3290, 318, 13779]], dtype=torch.int64) - attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]], dtype=torch.int64) + input_ids = torch.tensor([[15496, 11, 616, 3290, 318, 13779, 318, 13779]], dtype=torch.int64) + attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) return dict(input_ids=input_ids, attention_mask=attention_mask) @@ -46,7 +46,7 @@ def data_gen_for_token_classification(): # token classification data gen # `labels` is the type not the token id for token classification, 0 or 1 data = data_gen() - data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 1]], dtype=torch.int64) + data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 1]], dtype=torch.int64) return data diff --git a/tests/kit/model_zoo/transformers/t5.py b/tests/kit/model_zoo/transformers/t5.py index 689db2c40abb..435cb6f46937 100644 --- a/tests/kit/model_zoo/transformers/t5.py +++ b/tests/kit/model_zoo/transformers/t5.py @@ -16,8 +16,9 @@ def data_gen_for_encoder_only(): # config = T5Config(decoder_start_token_id=0) # tokenizer = T5Tokenizer.from_pretrained("t5-small") # input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids - input_ids = torch.Tensor([[13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 1]]).long() - return dict(input_ids=input_ids) + input_ids = torch.Tensor([[13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 1, 12]]).long() + attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]).long() + return dict(input_ids=input_ids, attention_mask=attention_mask) def data_gen_for_conditional_generation(): @@ -25,17 +26,16 @@ def data_gen_for_conditional_generation(): # # labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids data = data_gen_for_encoder_only() - labels = torch.Tensor([[644, 4598, 229, 19250, 5, 1]]).long() + labels = torch.Tensor([[644, 4598, 229, 19250, 5, 1, 644, 4598, 229, 19250, 5, 1]]).long() data['labels'] = labels return data def data_gen_for_t5_model(): # decoder_inputs_ids is obtained with the following code - # # decoder_input_ids = model._shift_right(input_ids) data = data_gen_for_encoder_only() - decoder_input_ids = torch.Tensor([[0, 13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5]]).long() + decoder_input_ids = torch.Tensor([[0, 13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 5]]).long() data['decoder_input_ids'] = decoder_input_ids return data diff --git a/tests/kit/model_zoo/transformers/whisper.py b/tests/kit/model_zoo/transformers/whisper.py index 40c96a5777ab..f7cdc052aaf0 100644 --- a/tests/kit/model_zoo/transformers/whisper.py +++ b/tests/kit/model_zoo/transformers/whisper.py @@ -76,14 +76,14 @@ def data_gen_for_audio_classification(): loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) -model_zoo.register(name='transformers_whisperForConditionalGeneration', +model_zoo.register(name='transformers_whisper_for_conditional_generation', model_fn=lambda: transformers.WhisperForConditionalGeneration(config), data_gen_fn=data_gen_for_conditional_generation, output_transform_fn=output_transform_fn, loss_fn=loss_fn_attr, model_attribute=ModelAttribute(has_control_flow=True)) -model_zoo.register(name='transformers_whisperWhisperForAudioClassification', +model_zoo.register(name='transformers_whisper_for_audio_classification', model_fn=lambda: transformers.WhisperForAudioClassification(config), data_gen_fn=data_gen_for_audio_classification, output_transform_fn=output_transform_fn, diff --git a/tests/test_booster/test_plugin/test_gemini_plugin.py b/tests/test_booster/test_plugin/test_gemini_plugin.py index a06b2c963bfe..fee153baf1ac 100644 --- a/tests/test_booster/test_plugin/test_gemini_plugin.py +++ b/tests/test_booster/test_plugin/test_gemini_plugin.py @@ -93,7 +93,7 @@ def check_gemini_plugin(init_method: str = 'none', early_stop: bool = True): 'transformers_vit_for_image_classification', 'transformers_chatglm', 'transformers_chatglm_for_conditional_generation', 'transformers_blip2', 'transformers_blip2_conditional_gerneration', 'transformers_sam', 'transformers_whisper', - 'transformers_whisperForConditionalGeneration', 'transformers_whisperWhisperForAudioClassification' + 'transformers_whisper_for_conditional_generation', 'transformers_whisper_for_audio_classification' ]: continue diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index 0e5cb8144ef3..98cdc5a4b95b 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -21,7 +21,13 @@ from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor -def build_model(model_fn, enable_fused_normalization=True, enable_tensor_parallelism=True, use_lazy_init: bool = False): +def build_model(model_fn, + enable_fused_normalization=True, + enable_tensor_parallelism=True, + enable_flash_attention=False, + enable_jit_fused=False, + use_lazy_init: bool = False): + # create new model ctx = LazyInitContext() if use_lazy_init else nullcontext() with ctx: # create new model @@ -31,7 +37,10 @@ def build_model(model_fn, enable_fused_normalization=True, enable_tensor_paralle ctx.materialize(org_model) # shard model shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, - enable_tensor_parallelism=enable_tensor_parallelism) + enable_tensor_parallelism=enable_tensor_parallelism, + enable_flash_attention=enable_flash_attention, + enable_jit_fused=enable_jit_fused) + model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) sharded_model, shared_params = shard_former.optimize(model_copy) return org_model.cuda(), sharded_model.cuda() diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index 1d42f1c4703e..afc1507e8b24 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -46,14 +46,17 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo check_grad(bert, sharded_bert, row_layer_for_check, atol=1e-7, rtol=1e-3, dim=1, verbose=False) -@parameterize('enable_fused_normalization', [False, True]) -@parameterize('enable_tensor_parallelism', [False, True]) +@parameterize('enable_fused_normalization', [True, False]) +@parameterize('enable_tensor_parallelism', [True, False]) +@parameterize('enable_flash_attention', [True, False]) +@parameterize('enable_jit_fused', [True, False]) @parameterize('use_lazy_init', [False, True]) -def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): +def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused, + use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - use_lazy_init) + enable_flash_attention, enable_jit_fused, use_lazy_init) check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) diff --git a/tests/test_shardformer/test_model/test_shard_blip2.py b/tests/test_shardformer/test_model/test_shard_blip2.py index cb9725f4de7f..cd034d0c139a 100644 --- a/tests/test_shardformer/test_model/test_shard_blip2.py +++ b/tests/test_shardformer/test_model/test_shard_blip2.py @@ -47,10 +47,13 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_blip2_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('enable_flash_attention', [True, False]) +@parameterize('enable_jit_fused', [True, False]) +def run_blip2_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused): sub_model_zoo = model_zoo.get_sub_registry('transformers_blip2') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + enable_flash_attention, enable_jit_fused) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index c13596fe8db3..e11bcf92ea3c 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -44,13 +44,15 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) +@parameterize('enable_flash_attention', [True, False]) +@parameterize('enable_jit_fused', [True, False]) @parameterize('use_lazy_init', [False, True]) -def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): +def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused, + use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - use_lazy_init) - check_state_dict(org_model, sharded_model, name=name) + enable_flash_attention, enable_jit_fused, use_lazy_init) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index 005223fb8ae4..c455a99d26ce 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -72,7 +72,9 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('enable_flash_attention', [True, False]) +@parameterize('enable_jit_fused', [True, False]) +def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused): sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): # create new model @@ -80,7 +82,9 @@ def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism): # shard model shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, - enable_tensor_parallelism=enable_tensor_parallelism) + enable_tensor_parallelism=enable_tensor_parallelism, + enable_flash_attention=enable_flash_attention, + enable_jit_fused=enable_jit_fused) model_copy = copy.deepcopy(org_model) shard_former = ShardFormer(shard_config=shard_config) if name == "transformers_chatglm": diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index cebb40bd16fe..f7213d8c50b4 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -68,7 +68,6 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, torch.cuda.empty_cache() - @parameterize('test_config', [{ 'tp_size': 1, 'pp_size': 2, diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index 2cfc172c8df6..ead14ab111e6 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -49,12 +49,13 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) +@parameterize('enable_flash_attention', [True, False]) @parameterize('use_lazy_init', [False, True]) -def run_gpt2_llama(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): +def run_gpt2_llama(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, use_lazy_init): sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - use_lazy_init) + enable_flash_attention, use_lazy_init) check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index 4684bacb4788..99a278d4303a 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -42,18 +42,21 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check grad col_layer_for_check = ['decoder.layers[0].self_attn.q_proj', 'decoder.embed_tokens'] row_layer_for_check = ['decoder.layers[0].self_attn.out_proj'] - check_grad(opt_model, shard_opt_model, col_layer_for_check, atol=1e-7, rtol=1e-3, dim=0, verbose=False) - check_grad(opt_model, shard_opt_model, row_layer_for_check, atol=1e-7, rtol=1e-3, dim=1, verbose=False) + check_grad(opt_model, shard_opt_model, col_layer_for_check, atol=1e-6, rtol=1e-3, dim=0, verbose=False) + check_grad(opt_model, shard_opt_model, row_layer_for_check, atol=1e-6, rtol=1e-3, dim=1, verbose=False) +@parameterize('use_lazy_init', [False, True]) @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('use_lazy_init', [False, True]) -def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): +@parameterize('enable_flash_attention', [True, False]) +@parameterize('enable_jit_fused', [True, False]) +def run_opt_test(use_lazy_init, enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, + enable_jit_fused): sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - use_lazy_init) + enable_flash_attention, enable_jit_fused, use_lazy_init) check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() @@ -62,7 +65,7 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_ def check_OPTModel(rank, world_size, port): disable_existing_loggers() colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_t5_test() + run_opt_test() @pytest.mark.dist diff --git a/tests/test_shardformer/test_model/test_shard_sam.py b/tests/test_shardformer/test_model/test_shard_sam.py index e7748cfd189d..616104cd7828 100644 --- a/tests/test_shardformer/test_model/test_shard_sam.py +++ b/tests/test_shardformer/test_model/test_shard_sam.py @@ -41,10 +41,12 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_sam_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('enable_flash_attention', [True, False]) +def run_sam_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention): sub_model_zoo = model_zoo.get_sub_registry('transformers_sam') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + enable_flash_attention) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 024c5016b0c1..22f04c879879 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -33,8 +33,8 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check grad col_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.q', 'shared'] row_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.relative_attention_bias'] - check_grad(org_model, sharded_model, col_layer_for_check, atol=1e-7, rtol=1e-5, dim=0, verbose=False) - check_grad(org_model, sharded_model, row_layer_for_check, atol=1e-7, rtol=1e-5, dim=1, verbose=False) + check_grad(org_model, sharded_model, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False) + check_grad(org_model, sharded_model, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False) # check weights are tied if hasattr(org_model, 'lm_head'): @@ -45,11 +45,14 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) @parameterize('use_lazy_init', [False, True]) -def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): +@parameterize('enable_flash_attention', [True, False]) +@parameterize('enable_jit_fused', [True, False]) +def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init, enable_flash_attention, + enable_jit_fused): sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - use_lazy_init) + enable_flash_attention, enable_jit_fused, use_lazy_init) check_state_dict(org_model, sharded_model, name=name) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index 7833ab70275d..d179c8a8ee32 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -20,7 +20,9 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo # check forward org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + assert_hf_output_close(org_output, shard_output, atol=1e-3, rtol=1e-3) + # do backward org_loss.backward() shard_loss.backward() @@ -45,10 +47,13 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_vit_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('enable_flash_attention', [True, False]) +@parameterize('enable_jit_fused', [True, False]) +def run_vit_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused): sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism) + org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, + enable_flash_attention, enable_jit_fused) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_whisper.py b/tests/test_shardformer/test_model/test_shard_whisper.py index a271bbdf1223..9b38ae07b1d6 100644 --- a/tests/test_shardformer/test_model/test_shard_whisper.py +++ b/tests/test_shardformer/test_model/test_shard_whisper.py @@ -48,12 +48,16 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo @parameterize('enable_fused_normalization', [True, False]) @parameterize('enable_tensor_parallelism', [True, False]) -def run_whisper_test(enable_fused_normalization, enable_tensor_parallelism): +@parameterize('enable_flash_attention', [True, False]) +@parameterize('enable_jit_fused', [True, False]) +def run_whisper_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused): sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper') for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): org_model, sharded_model = build_model(model_fn, enable_fused_normalization=enable_fused_normalization, - enable_tensor_parallelism=enable_tensor_parallelism) + enable_tensor_parallelism=enable_tensor_parallelism, + enable_flash_attention=enable_flash_attention, + enable_jit_fused=enable_jit_fused) check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) torch.cuda.empty_cache() diff --git a/tests/test_utils/test_flash_attention.py b/tests/test_utils/test_flash_attention.py index e1c7446f40db..28369d4c9fdb 100644 --- a/tests/test_utils/test_flash_attention.py +++ b/tests/test_utils/test_flash_attention.py @@ -167,4 +167,4 @@ def test_cross_attention(proj_shape, dtype, dropout): torch.allclose(y, out_ref, atol=1e-18), f"{(y - out_ref).abs().max()}" torch.allclose(grad_q, grad_ref_q, atol=1e-7), f"{(grad_q - grad_ref_q).abs().max()}" torch.allclose(grad_k, grad_ref_k, atol=1e-7), f"{(grad_k - grad_ref_k).abs().max()}" - torch.allclose(grad_v, grad_ref_v, atol=1e-7), f"{(grad_v - grad_ref_v).abs().max()}" \ No newline at end of file + torch.allclose(grad_v, grad_ref_v, atol=1e-7), f"{(grad_v - grad_ref_v).abs().max()}" From ed4c4484880b733894e6088e681f7cca32afe0b4 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Tue, 8 Aug 2023 17:46:44 +0800 Subject: [PATCH 72/84] [pipeline] rewrite t5 tests & support multi-tensor transmitting in pipeline (#4388) * fix remaining t5 bugs/rewrite t5 tests * fix multi-tensor communication in pipeline * rearrange test_config * fix keyerror in sync_shared_params * fix get_held_layers & Randomnizer, complete t5 tests * erase printing * fix get_held_layers through modifying _release_unheld_layers * fix _get_recursive_held_layers bug --- .../booster/plugin/hybrid_parallel_plugin.py | 6 +- colossalai/pipeline/p2p.py | 6 +- colossalai/pipeline/schedule/_utils.py | 2 +- colossalai/pipeline/schedule/one_f_one_b.py | 11 +- colossalai/shardformer/layer/utils.py | 7 + colossalai/shardformer/modeling/t5.py | 95 +++++------ colossalai/shardformer/policies/t5.py | 51 ++---- colossalai/shardformer/shard/sharder.py | 16 +- .../test_model/test_shard_gpt2.py | 7 +- .../test_model/test_shard_t5.py | 150 ++++++++++++------ .../test_model/test_shard_t5_pipeline.py | 101 ------------ 11 files changed, 201 insertions(+), 251 deletions(-) delete mode 100644 tests/test_shardformer/test_model/test_shard_t5_pipeline.py diff --git a/colossalai/booster/plugin/hybrid_parallel_plugin.py b/colossalai/booster/plugin/hybrid_parallel_plugin.py index a22bdb7199bb..42942aaeb89d 100644 --- a/colossalai/booster/plugin/hybrid_parallel_plugin.py +++ b/colossalai/booster/plugin/hybrid_parallel_plugin.py @@ -50,8 +50,10 @@ def __init__(self, module: Module, precision: str, shard_config: ShardConfig, dp def sync_shared_params(self): for shared_param, group in zip(self.shared_params, self.shared_param_process_groups): - param = shared_param[self.stage_manager.stage] - dist.all_reduce(param.grad, group=group) + if self.stage_manager.stage in shared_param: + param = shared_param[self.stage_manager.stage] + dist.all_reduce(param.grad, group=group) + dist.barrier() def no_sync(self) -> Iterator[None]: # no sync grads across data parallel diff --git a/colossalai/pipeline/p2p.py b/colossalai/pipeline/p2p.py index f741b8363f13..af7a00b5c720 100644 --- a/colossalai/pipeline/p2p.py +++ b/colossalai/pipeline/p2p.py @@ -3,6 +3,7 @@ import io import pickle +import re from typing import Any, List, Optional, Union import torch @@ -31,7 +32,10 @@ def _cuda_safe_tensor_to_object(tensor: torch.Tensor, tensor_size: torch.Size) - if b'cuda' in buf: buf_array = bytearray(buf) device_index = torch.cuda.current_device() - buf_array[buf_array.find(b'cuda') + 5] = 48 + device_index + # There might be more than one output tensors during forward + for cuda_str in re.finditer(b'cuda', buf_array): + pos = cuda_str.start() + buf_array[pos + 5] = 48 + device_index buf = bytes(buf_array) io_bytes = io.BytesIO(buf) diff --git a/colossalai/pipeline/schedule/_utils.py b/colossalai/pipeline/schedule/_utils.py index 045c86e40e63..3ed9239272f1 100644 --- a/colossalai/pipeline/schedule/_utils.py +++ b/colossalai/pipeline/schedule/_utils.py @@ -86,7 +86,7 @@ def retain_grad(x: Any) -> None: Args: x (Any): Object to be called. """ - if isinstance(x, torch.Tensor): + if isinstance(x, torch.Tensor) and x.requires_grad: x.retain_grad() diff --git a/colossalai/pipeline/schedule/one_f_one_b.py b/colossalai/pipeline/schedule/one_f_one_b.py index d907d53edcde..ade3cf456fe3 100644 --- a/colossalai/pipeline/schedule/one_f_one_b.py +++ b/colossalai/pipeline/schedule/one_f_one_b.py @@ -107,8 +107,15 @@ def backward_step(self, optimizer: OptimizerWrapper, input_obj: Optional[dict], if output_obj_grad is None: optimizer.backward(output_obj) else: - for k, grad in output_obj_grad.items(): - optimizer.backward_by_grad(output_obj[k], grad) + if "backward_tensor_keys" not in output_obj: + for k, grad in output_obj_grad.items(): + optimizer.backward_by_grad(output_obj[k], grad) + else: + for k, grad in output_obj_grad.items(): + output_obj[k].grad = grad + for k in output_obj["backward_tensor_keys"]: + tensor_to_backward = output_obj[k] + optimizer.backward_by_grad(tensor_to_backward, tensor_to_backward.grad) # Collect the grad of the input_obj. input_obj_grad = None diff --git a/colossalai/shardformer/layer/utils.py b/colossalai/shardformer/layer/utils.py index f2ac6563c46f..09cb7bfe1407 100644 --- a/colossalai/shardformer/layer/utils.py +++ b/colossalai/shardformer/layer/utils.py @@ -122,6 +122,13 @@ def increment_index(): """ Randomizer._INDEX += 1 + @staticmethod + def reset_index(): + """ + Reset the index to zero. + """ + Randomizer._INDEX = 0 + @staticmethod def is_randomizer_index_synchronized(process_group: ProcessGroup = None): """ diff --git a/colossalai/shardformer/modeling/t5.py b/colossalai/shardformer/modeling/t5.py index 0b3486e87c7e..d622da452366 100644 --- a/colossalai/shardformer/modeling/t5.py +++ b/colossalai/shardformer/modeling/t5.py @@ -238,7 +238,8 @@ def custom_forward(*inputs): return { 'hidden_states': hidden_states, 'position_bias': position_bias, - 'encoder_decoder_position_bias': encoder_decoder_position_bias + 'encoder_decoder_position_bias': encoder_decoder_position_bias, + 'backward_tensor_keys': ['hidden_states'] } @staticmethod @@ -261,8 +262,10 @@ def t5_model_forward( return_dict: Optional[bool] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, position_bias: Optional[torch.Tensor] = None, encoder_decoder_position_bias: Optional[torch.Tensor] = None, + backward_tensor_keys: Optional[List[str]] = None, stage_index: Optional[List[int]] = None, decoder_starting_stage: Optional[int] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]: @@ -303,7 +306,6 @@ def t5_model_forward( decoder_head_mask = head_mask in_decoder = stage_manager.stage >= decoder_starting_stage - # Stage is in encoder, directly return the output of t5_stack_forward if not in_decoder: encoder_outputs = T5PipelineForwards.t5_stack_forward( @@ -323,25 +325,18 @@ def t5_model_forward( decoder_starting_stage=decoder_starting_stage) if stage_manager.stage == decoder_starting_stage - 1: # last stage of encoder - return {'encoder_outputs': encoder_outputs} + return {'encoder_hidden_states': encoder_outputs[0]} else: return encoder_outputs at_last_decoder_stage = stage_manager.is_last_stage() at_first_decoder_stage = stage_manager.stage == decoder_starting_stage - if encoder_outputs is None: - raise ValueError("Non-empty encoder_outputs should be passed in at decoder stages.") - - encoder_hidden_states = encoder_outputs[0] - if return_dict and not isinstance(encoder_outputs, BaseModelOutput): - encoder_outputs = BaseModelOutput( - last_hidden_state=encoder_outputs[0], - hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, - attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, - ) + if encoder_outputs is not None: + encoder_hidden_states = encoder_outputs[0] + elif encoder_hidden_states is None: + raise ValueError("Non-empty encoder_hidden_states should be passed in at decoder stages.") - # Stage is in decoder, we assume that the outputs of last stage of encoder will be passed in. if not at_first_decoder_stage and hidden_states is None: raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.") @@ -360,6 +355,7 @@ def t5_model_forward( output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + stage_manager=stage_manager, hidden_states=hidden_states, position_bias=position_bias, encoder_decoder_position_bias=encoder_decoder_position_bias, @@ -368,22 +364,19 @@ def t5_model_forward( # Directly return outputs of overloaded T5Stack forward if not at last stage. if not at_last_decoder_stage: - decoder_outputs['encoder_outputs'] = encoder_outputs # encoder_outputs should be passed to the next stage + # encoder_hidden_states should be passed to the next stage + decoder_outputs['encoder_hidden_states'] = encoder_hidden_states return decoder_outputs if not return_dict: - return decoder_outputs + encoder_outputs - - return Seq2SeqModelOutput( - last_hidden_state=decoder_outputs.last_hidden_state, - past_key_values=decoder_outputs.past_key_values, - decoder_hidden_states=decoder_outputs.hidden_states, - decoder_attentions=decoder_outputs.attentions, - cross_attentions=decoder_outputs.cross_attentions, - encoder_last_hidden_state=encoder_outputs.last_hidden_state, - encoder_hidden_states=encoder_outputs.hidden_states, - encoder_attentions=encoder_outputs.attentions, - ) + return decoder_outputs + encoder_hidden_states + else: + return Seq2SeqModelOutput(last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_hidden_states) @staticmethod def t5_for_conditional_generation_forward( @@ -406,8 +399,10 @@ def t5_for_conditional_generation_forward( return_dict: Optional[bool] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, position_bias: Optional[torch.Tensor] = None, encoder_decoder_position_bias: Optional[torch.Tensor] = None, + backward_tensor_keys: Optional[List[str]] = None, stage_index: Optional[List[int]] = None, decoder_starting_stage: Optional[int] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: @@ -468,28 +463,25 @@ def t5_for_conditional_generation_forward( decoder_starting_stage=decoder_starting_stage) if stage_manager.stage == decoder_starting_stage - 1: # last stage of encoder - return {'encoder_outputs': encoder_outputs} + return {'encoder_hidden_states': encoder_outputs[0]} else: return encoder_outputs at_last_decoder_stage = stage_manager.is_last_stage() at_first_decoder_stage = stage_manager.stage == decoder_starting_stage - if encoder_outputs is None: - raise ValueError("Non-empty encoder_outputs should be passed in at decoder stages.") + if encoder_outputs is not None: + encoder_hidden_states = encoder_outputs[0] + elif encoder_hidden_states is None: + raise ValueError("Non-empty encoder_hidden_states should be passed in at decoder stages.") - encoder_hidden_states = encoder_outputs[0] - if return_dict and not isinstance(encoder_outputs, BaseModelOutput): - encoder_outputs = BaseModelOutput( - last_hidden_state=encoder_outputs[0], - hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, - attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, - ) - - # Stage is in decoder, we assume that the outputs of last stage of encoder will be passed in. if not at_first_decoder_stage and hidden_states is None: raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.") + if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: + # get decoder inputs from shifting lm labels to the right + decoder_input_ids = self._shift_right(labels) + # Decode decoder_outputs = T5PipelineForwards.t5_stack_forward( self.decoder, @@ -505,6 +497,7 @@ def t5_for_conditional_generation_forward( output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + stage_manager=stage_manager, hidden_states=hidden_states, position_bias=position_bias, encoder_decoder_position_bias=encoder_decoder_position_bias, @@ -513,7 +506,8 @@ def t5_for_conditional_generation_forward( # Directly return outputs of overloaded T5Stack forward if not at last stage. if not at_last_decoder_stage: - decoder_outputs['encoder_outputs'] = encoder_outputs # encoder_outputs should be passed to the next stage + # encoder_hidden_states should be passed to the next stage + decoder_outputs['encoder_hidden_states'] = encoder_hidden_states return decoder_outputs sequence_output = decoder_outputs[0] @@ -533,20 +527,16 @@ def t5_for_conditional_generation_forward( loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) if not return_dict: - output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs + output = (lm_logits,) + decoder_outputs[1:] + encoder_hidden_states return ((loss,) + output) if loss is not None else output - return Seq2SeqLMOutput( - loss=loss, - logits=lm_logits, - past_key_values=decoder_outputs.past_key_values, - decoder_hidden_states=decoder_outputs.hidden_states, - decoder_attentions=decoder_outputs.attentions, - cross_attentions=decoder_outputs.cross_attentions, - encoder_last_hidden_state=encoder_outputs.last_hidden_state, - encoder_hidden_states=encoder_outputs.hidden_states, - encoder_attentions=encoder_outputs.attentions, - ) + return Seq2SeqLMOutput(loss=loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_hidden_states) @staticmethod def t5_encoder_model_forward( @@ -562,6 +552,7 @@ def t5_encoder_model_forward( hidden_states: Optional[torch.FloatTensor] = None, position_bias: Optional[torch.Tensor] = None, encoder_decoder_position_bias: Optional[torch.Tensor] = None, + backward_tensor_keys: Optional[List[str]] = None, stage_index: Optional[List[int]] = None, decoder_starting_stage: Optional[int] = None, ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]: diff --git a/colossalai/shardformer/policies/t5.py b/colossalai/shardformer/policies/t5.py index 5e78ae9093fa..2ef52c214c6b 100644 --- a/colossalai/shardformer/policies/t5.py +++ b/colossalai/shardformer/policies/t5.py @@ -260,7 +260,7 @@ def get_held_layers(self) -> List[nn.Module]: model = self.model encoder = self.model.encoder - decoder = self.model.__dict__.get('decoder', None) + decoder = getattr(self.model, 'decoder', None) num_encoder_layers = len(encoder.block) num_decoder_layers = len(decoder.block) if decoder else 0 @@ -300,7 +300,7 @@ def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, poli stage_manager = self.pipeline_stage_manager encoder = self.model.encoder - decoder = self.model.__dict__.get('decoder', None) + decoder = getattr(self.model, 'decoder', None) num_encoder_layers = len(encoder.block) num_decoder_layers = len(decoder.block) if decoder else 0 @@ -355,15 +355,6 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: return [{0: module.shared.weight, decoder_starting_stage: module.decoder.embed_tokens.weight}] return [] - def postprocess(self): - if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: - binding_map = {"shared.weight": ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]} - for k, v in binding_map.items(): - src = getattr_(self.model, k) - for dst in v: - setattr_(self.model, dst, src) - return self.model - class T5ForConditionalGenerationPolicy(T5BasePolicy): @@ -409,28 +400,21 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: stage_manager.num_stages) shared_params = [] + shared_embedding = {} if id(module.decoder.embed_tokens.weight) == id(module.shared.weight): - shared_params.append({ - 0: module.shared.weight, - decoder_starting_stage: module.decoder.embed_tokens.weight - }) + shared_embedding[0] = module.shared.weight + shared_embedding[decoder_starting_stage] = module.decoder.embed_tokens.weight + if id(module.lm_head.weight) == id(module.shared.weight): - shared_params.append({0: module.shared.weight, stage_manager.num_stages - 1: module.lm_head.weight}) - return shared_params - return [] + shared_embedding[0] = module.shared.weight + shared_embedding[stage_manager.num_stages - 1] = module.lm_head.weight - def postprocess(self): - super().postprocess() - if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: - binding_map = { - "shared.weight": ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] - } - for k, v in binding_map.items(): - src = getattr_(self.model, k) - for dst in v: - setattr_(self.model, dst, src) + if len(shared_embedding) > 0: + shared_params.append(shared_embedding) - return self.model + return shared_params + + return [] class T5EncoderPolicy(T5BasePolicy): @@ -462,12 +446,3 @@ def get_held_layers(self) -> List[nn.Module]: def get_shared_params(self) -> List[Dict[int, Tensor]]: return [] - - def postprocess(self): - if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: - binding_map = {"shared.weight": ["encoder.embed_tokens.weight"]} - for k, v in binding_map.items(): - src = getattr_(self.model, k) - for dst in v: - setattr_(self.model, dst, src) - return self.model diff --git a/colossalai/shardformer/shard/sharder.py b/colossalai/shardformer/shard/sharder.py index ae8cd8c6e553..0ed745a1fc4a 100644 --- a/colossalai/shardformer/shard/sharder.py +++ b/colossalai/shardformer/shard/sharder.py @@ -198,6 +198,20 @@ def _replace_sub_module( setattr_(org_layer, suffix, replace_layer) + def _get_recursive_held_layers(self, held_layers: Optional[List[nn.Module]]) -> Optional[List[nn.Module]]: + + def collect_sub_modules(module: nn.Module): + if module is None: + return + recursive_held_layers.append(module) + for name, child in module.named_children(): + collect_sub_modules(child) + + recursive_held_layers = [] + for module in held_layers: + collect_sub_modules(module) + return recursive_held_layers + def _release_unheld_layers(self) -> Optional[Set[nn.Module]]: r""" Release the unheld layers in the model @@ -205,7 +219,7 @@ def _release_unheld_layers(self) -> Optional[Set[nn.Module]]: if self.shard_config and self.shard_config.pipeline_stage_manager: held_layers = self.policy.get_held_layers() set_tensors_to_none(self.model, exclude=set(held_layers)) - return set(held_layers) + return set(self._get_recursive_held_layers(held_layers)) return None def _materialize(self) -> None: diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index f7213d8c50b4..1882bf7822cc 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -68,16 +68,17 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, torch.cuda.empty_cache() + @parameterize('test_config', [{ - 'tp_size': 1, + 'tp_size': 2, 'pp_size': 2, 'num_microbatches': 4, + 'enable_fused_normalization': True, 'use_lazy_init': True }, { - 'tp_size': 2, + 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': False, 'use_lazy_init': False }, { 'tp_size': 4, diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index 22f04c879879..d807ffa06296 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -1,60 +1,110 @@ -import os - import pytest import torch import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) +from colossalai.shardformer.layer.utils import Randomizer +from colossalai.tensor.d_tensor.api import clear_layout_converter +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward - - -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # check forward - # the value "past_key_values" is sharded, so we ignore - org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, - output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], atol=1e-5) - - # do backward - org_loss.backward() - shard_loss.backward() - - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" - - # check grad - col_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.q', 'shared'] - row_layer_for_check = ['encoder.block[0].layer[0].SelfAttention.relative_attention_bias'] - check_grad(org_model, sharded_model, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False) - check_grad(org_model, sharded_model, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False) - - # check weights are tied - if hasattr(org_model, 'lm_head'): - assert org_model.shared.weight.data.data_ptr() == org_model.lm_head.weight.data.data_ptr() - assert sharded_model.shared.weight.data.data_ptr() == sharded_model.lm_head.weight.data.data_ptr() - - -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('use_lazy_init', [False, True]) -@parameterize('enable_flash_attention', [True, False]) -@parameterize('enable_jit_fused', [True, False]) -def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init, enable_flash_attention, - enable_jit_fused): +from tests.test_shardformer.test_model._utils import ( + build_model_from_hybrid_plugin, + check_grad, + check_loss, + check_output_hidden_state, + check_weight, + run_forward_backward_with_hybrid_plugin, +) + + +def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): + + org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ + build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) + + org_loss, org_output, sharded_loss, sharded_output = \ + run_forward_backward_with_hybrid_plugin( + org_model, + sharded_model, + sharded_optimizer, + data_gen_fn, + output_transform_fn, + criterion, + booster) + + stage_manager = booster.plugin.stage_manager + tp_group = booster.plugin.tp_group + + # check last hidden state & loss + if stage_manager is None or stage_manager.is_last_stage(): + + if org_model.__class__.__name__ != 'T5ForConditionalGeneration': + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + + check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3) + + # unwrap model + t5 = org_model + sharded_t5 = sharded_model.unwrap() + + row_layer_for_check = ['shared', 'encoder.block[0].layer[0].SelfAttention.q'] + + # check weights and gradients + if stage_manager is None or stage_manager.is_first_stage(): + check_grad(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-5, rtol=1e-3, dim=0) + + # check weights after optimizer.step() + org_optimizer.step() + sharded_optimizer.step() + if stage_manager is None or stage_manager.is_first_stage(): + check_weight(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False) + + torch.cuda.empty_cache() + + +@parameterize('test_config', [{ + 'tp_size': 2, + 'pp_size': 2, + 'num_microbatches': 2, + 'enable_fused_normalization': True, + 'use_lazy_init': True +}, { + 'tp_size': 1, + 'pp_size': 2, + 'num_microbatches': 4, + 'use_lazy_init': False +}, { + 'tp_size': 4, + 'pp_size': 1, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}, { + 'tp_size': 1, + 'pp_size': 4, + 'num_microbatches': 4, + 'use_lazy_init': False +}]) +@clear_cache_before_run() +def run_t5_test(test_config): + + # TODO: add plugin_config for TP+DP after supporting & debugging it + # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} + + # TODO: add test_config for flash attention & jit operator after supporting + sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') + test_config['precision'] = 'float' # Do not use fp16/bf16 in testing + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - enable_flash_attention, enable_jit_fused, use_lazy_init) - check_state_dict(org_model, sharded_model, name=name) - check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + + # skip 4-stage pp test for t5_encoder + if test_config['pp_size'] > 2 and name == 'transformers_t5_encoder_model': + continue + + check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) + + clear_layout_converter() + Randomizer.reset_index() torch.cuda.empty_cache() @@ -68,7 +118,7 @@ def check_t5(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_t5(): - spawn(check_t5, 2) + spawn(check_t5, 4) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_t5_pipeline.py b/tests/test_shardformer/test_model/test_shard_t5_pipeline.py deleted file mode 100644 index 7f3a5f2ea40b..000000000000 --- a/tests/test_shardformer/test_model/test_shard_t5_pipeline.py +++ /dev/null @@ -1,101 +0,0 @@ -import pytest -import torch - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.t5 import T5BasePolicy -from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_pipeline_model - - -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # TODO: add tests for forward/backward later - pass - - -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('enable_fused_normalization', [False]) -@parameterize('use_lazy_init', [False]) -#TODO: merge this into test_shard_t5.py -def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - - sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') - for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): - - inputs = data_gen_fn() - inputs = {k: v.cuda() for k, v in inputs.items()} - input_ids = inputs['input_ids'] - - _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - - batch_size, seq_len = input_ids.shape - hidden_size = sharded_model.config.d_model - num_heads = sharded_model.config.num_heads - hidden_state_shape = (batch_size, seq_len, hidden_size) - position_bias_shape = (batch_size, num_heads, seq_len, seq_len) - - num_encoder_layers = len(sharded_model.encoder.block) - decoder = sharded_model.__dict__.get('decoder', None) - num_decoder_layers = len(decoder.block) if decoder else 0 - - _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(num_encoder_layers, num_decoder_layers, PP_SIZE) - stage = stage_manager.stage - at_first_stage = (stage == 0) or (stage == decoder_starting_stage) - at_last_stage = (stage == decoder_starting_stage - 1) or (stage == stage_manager.num_stages - 1) - in_decoder = stage >= decoder_starting_stage - - if not at_first_stage: - # change inputs if not the first stage - hidden_states = torch.zeros(*hidden_state_shape).cuda() - position_bias = torch.zeros(*position_bias_shape).cuda() - encoder_decoder_position_bias = torch.zeros(*position_bias_shape).cuda() - inputs['input_ids'] = None - inputs['hidden_states'] = hidden_states - inputs['position_bias'] = position_bias - inputs['encoder_decoder_position_bias'] = encoder_decoder_position_bias - if in_decoder: - encoder_output_states = torch.zeros(*hidden_state_shape).cuda() - inputs['encoder_outputs'] = (encoder_output_states,) - - sharded_model.train() - output = sharded_model(**inputs) - if at_last_stage: - if name == 'transformers_t5_for_conditional_generation' and in_decoder: - assert output.loss is not None - else: - if name != 'transformers_t5_encoder_model' and not in_decoder: - output = output['encoder_outputs'] - assert output[0].shape == hidden_state_shape - else: - assert output['hidden_states'].shape == hidden_state_shape - # position_bias information should be passed in T5 - assert output['position_bias'].shape == position_bias_shape - if in_decoder: - assert output['encoder_decoder_position_bias'].shape == position_bias_shape - - torch.cuda.empty_cache() - - -def check_t5(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_t5_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_t5(): - spawn(check_t5, 4) - - -if __name__ == "__main__": - test_t5() From 7a3dfd0c645fba51a02eb3c6ac88b4f09160ea7d Mon Sep 17 00:00:00 2001 From: flybird1111 <1829166702@qq.com> Date: Wed, 9 Aug 2023 14:32:19 +0800 Subject: [PATCH 73/84] [shardformer] update shardformer to use flash attention 2 (#4392) * cherry-pick flash attention 2 cherry-pick flash attention 2 * [shardformer] update shardformer to use flash attention 2 [shardformer] update shardformer to use flash attention 2, fix [shardformer] update shardformer to use flash attention 2, fix [shardformer] update shardformer to use flash attention 2, fix --- colossalai/kernel/cuda_native/__init__.py | 5 +++-- colossalai/shardformer/modeling/blip2.py | 2 +- colossalai/shardformer/modeling/chatglm.py | 3 +-- colossalai/shardformer/modeling/gpt2.py | 2 +- colossalai/shardformer/modeling/llama.py | 2 +- colossalai/shardformer/modeling/opt.py | 2 +- colossalai/shardformer/modeling/vit.py | 2 +- colossalai/shardformer/modeling/whisper.py | 2 +- tests/test_utils/test_flash_attention.py | 1 - 9 files changed, 10 insertions(+), 11 deletions(-) diff --git a/colossalai/kernel/cuda_native/__init__.py b/colossalai/kernel/cuda_native/__init__.py index 4910717b5723..e0136d86e561 100644 --- a/colossalai/kernel/cuda_native/__init__.py +++ b/colossalai/kernel/cuda_native/__init__.py @@ -1,8 +1,9 @@ from .layer_norm import MixedFusedLayerNorm as LayerNorm from .mha.mha import ColoAttention from .multihead_attention import MultiHeadAttention -from .scaled_softmax import FusedScaleMaskSoftmax, ScaledUpperTriangMaskedSoftmax +from .scaled_softmax import AttnMaskType, FusedScaleMaskSoftmax, ScaledUpperTriangMaskedSoftmax __all__ = [ - 'LayerNorm', 'MultiHeadAttention', 'FusedScaleMaskSoftmax', 'ScaledUpperTriangMaskedSoftmax', 'ColoAttention' + 'LayerNorm', 'MultiHeadAttention', 'FusedScaleMaskSoftmax', 'ScaledUpperTriangMaskedSoftmax', 'ColoAttention', + 'AttnMaskType' ] diff --git a/colossalai/shardformer/modeling/blip2.py b/colossalai/shardformer/modeling/blip2.py index c5c6b14ba993..69730fd3d254 100644 --- a/colossalai/shardformer/modeling/blip2.py +++ b/colossalai/shardformer/modeling/blip2.py @@ -65,7 +65,7 @@ def get_blip2_flash_attention_forward(): from transformers.models.blip_2.modeling_blip_2 import Blip2Attention - from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + from colossalai.kernel.cuda_native import ColoAttention def forward( self: Blip2Attention, diff --git a/colossalai/shardformer/modeling/chatglm.py b/colossalai/shardformer/modeling/chatglm.py index 3d453c3bd6db..a95966c3b99e 100644 --- a/colossalai/shardformer/modeling/chatglm.py +++ b/colossalai/shardformer/modeling/chatglm.py @@ -19,7 +19,7 @@ def get_flash_core_attention_forward(): - from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention from .chatglm2_6b.modeling_chatglm import CoreAttention @@ -126,7 +126,6 @@ def forward( return forward - class ChatGLMPipelineForwards: ''' This class serves as a micro library for ChatGLM model forwards under pipeline parallelism. diff --git a/colossalai/shardformer/modeling/gpt2.py b/colossalai/shardformer/modeling/gpt2.py index e02581fbaa9b..a12a9796fa8a 100644 --- a/colossalai/shardformer/modeling/gpt2.py +++ b/colossalai/shardformer/modeling/gpt2.py @@ -674,7 +674,7 @@ def get_gpt2_flash_attention_forward(): from transformers.models.gpt2.modeling_gpt2 import GPT2Attention - from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention def split_heads(tensor, num_heads, attn_head_size): """ diff --git a/colossalai/shardformer/modeling/llama.py b/colossalai/shardformer/modeling/llama.py index 9d6335503b36..2f54daac586a 100644 --- a/colossalai/shardformer/modeling/llama.py +++ b/colossalai/shardformer/modeling/llama.py @@ -392,7 +392,7 @@ def get_llama_flash_attention_forward(): from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb - from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention def forward( self: LlamaAttention, diff --git a/colossalai/shardformer/modeling/opt.py b/colossalai/shardformer/modeling/opt.py index 299dfb5562f3..bdf141816737 100644 --- a/colossalai/shardformer/modeling/opt.py +++ b/colossalai/shardformer/modeling/opt.py @@ -8,7 +8,7 @@ def get_opt_flash_attention_forward(): from transformers.models.opt.modeling_opt import OPTAttention - from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention def forward( self: OPTAttention, diff --git a/colossalai/shardformer/modeling/vit.py b/colossalai/shardformer/modeling/vit.py index 22c4dd998cac..eb0ea4c7502b 100644 --- a/colossalai/shardformer/modeling/vit.py +++ b/colossalai/shardformer/modeling/vit.py @@ -342,7 +342,7 @@ def get_vit_flash_self_attention_forward(): from transformers.models.vit.modeling_vit import ViTSelfAttention - from colossalai.kernel.cuda_native.flash_attention import ColoAttention + from colossalai.kernel.cuda_native import ColoAttention def transpose_for_scores(x: torch.Tensor, num_attention_heads, attention_head_size) -> torch.Tensor: new_x_shape = x.size()[:-1] + (num_attention_heads, attention_head_size) diff --git a/colossalai/shardformer/modeling/whisper.py b/colossalai/shardformer/modeling/whisper.py index 6bc387ac8974..0a16c6f788da 100644 --- a/colossalai/shardformer/modeling/whisper.py +++ b/colossalai/shardformer/modeling/whisper.py @@ -8,7 +8,7 @@ def get_whisper_flash_attention_forward(): from transformers.models.whisper.modeling_whisper import WhisperAttention - from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention + from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention def shape(tensor: torch.Tensor, seq_len: int, bsz: int, num_heads: int, head_dim: int): return tensor.view(bsz, seq_len, num_heads, head_dim).contiguous() diff --git a/tests/test_utils/test_flash_attention.py b/tests/test_utils/test_flash_attention.py index 28369d4c9fdb..f775710c40c2 100644 --- a/tests/test_utils/test_flash_attention.py +++ b/tests/test_utils/test_flash_attention.py @@ -13,7 +13,6 @@ from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType DTYPE = [torch.float16, torch.bfloat16, torch.float32] -FLASH_DTYPE = [torch.float16, torch.bfloat16] def attention_ref(q, k, v, attn_mask=None, causal=False): From d2cd48e0bec01a4192a5248ea312e108544066b9 Mon Sep 17 00:00:00 2001 From: flybird1111 <1829166702@qq.com> Date: Thu, 10 Aug 2023 13:59:30 +0800 Subject: [PATCH 74/84] [shardformer] test all optimizations (#4399) [shardformer] test all optimizations [shardformer] test all optimizations [shardformer] test all optimizations --- .../booster/plugin/hybrid_parallel_plugin.py | 11 +++- requirements/requirements-test.txt | 2 +- tests/test_shardformer/test_model/_utils.py | 16 ++--- .../test_model/test_shard_gpt2.py | 59 ++++++++++++------- 4 files changed, 59 insertions(+), 29 deletions(-) diff --git a/colossalai/booster/plugin/hybrid_parallel_plugin.py b/colossalai/booster/plugin/hybrid_parallel_plugin.py index 42942aaeb89d..28a19af0ce91 100644 --- a/colossalai/booster/plugin/hybrid_parallel_plugin.py +++ b/colossalai/booster/plugin/hybrid_parallel_plugin.py @@ -148,7 +148,10 @@ def __init__( precision: str = 'fp16', zero_stage: int = 0, cpu_offload: bool = False, + enable_all_optimization: bool = False, enable_fused_normalization: bool = False, + enable_flash_attention: bool = False, + enable_jit_fused: bool = False, num_microbatches: Optional[int] = None, initial_scale: float = 2**16, min_scale: float = 1, @@ -171,7 +174,10 @@ def __init__( self.precision = precision self.zero_stage = zero_stage self.cpu_offload = cpu_offload + self.enable_all_optimization = enable_all_optimization self.enable_fused_normalization = enable_fused_normalization + self.enable_flash_attention = enable_flash_attention + self.enable_jit_fused = enable_jit_fused self.pg_mesh = ProcessGroupMesh(self.dp_size, self.pp_size, self.tp_size) self.stage_manager = None self.schedule = None @@ -186,7 +192,10 @@ def __init__( self.shard_config = ShardConfig(tensor_parallel_process_group=self.tp_group, pipeline_stage_manager=self.stage_manager, enable_tensor_parallelism=self.tp_size > 1, - enable_fused_normalization=self.enable_fused_normalization) + enable_all_optimization=self.enable_all_optimization, + enable_fused_normalization=self.enable_fused_normalization, + enable_flash_attention=self.enable_flash_attention, + enable_jit_fused=self.enable_jit_fused) self.amp_config = dict( initial_scale=initial_scale, growth_factor=growth_factor, diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index 510af5f3c7ff..a37d00326a08 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -19,4 +19,4 @@ ninja flash_attn>=2.0 datasets ninja -flash-attn +flash-attn>=2.0 diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index 98cdc5a4b95b..cce21809d829 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -1,6 +1,5 @@ import copy from contextlib import nullcontext -from typing import Optional from typing import Any, Callable, Dict, List, Optional import torch @@ -16,8 +15,8 @@ from colossalai.lazy import LazyInitContext from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer import ShardConfig, ShardFormer -from colossalai.shardformer.policies.auto_policy import Policy from colossalai.shardformer._utils import getattr_ +from colossalai.shardformer.policies.auto_policy import Policy from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor @@ -156,10 +155,12 @@ def _criterion(outputs, inputs): else: data = {k: v.cuda() for k, v in data.items()} sharded_output = sharded_model(**data) + sharded_loss = criterion(sharded_output) - sharded_loss.backward() + sharded_optimizer.backward(sharded_loss) org_model.train() + data = {k: v.cuda() for k, v in data.items()} org_output = org_model(**data) org_loss = criterion(org_output) org_loss.backward() @@ -181,12 +182,12 @@ def check_output_hidden_state(org_output: Tensor, if stage_manager and stage_manager.is_last_stage(): sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']], dim=0) - assert torch.allclose(org_hidden_state, sharded_hidden_state, atol=atol, rtol=rtol), \ + assert torch.allclose(org_hidden_state.float(), sharded_hidden_state.float(), atol=atol, rtol=rtol), \ f"shard model's output hidden state is not equal to origin model's last hidden state\n{org_hidden_state}\n{sharded_hidden_state}" def check_loss(org_loss: Tensor, sharded_loss: Tensor, atol: float = 1e-5, rtol: float = 1e-3): - assert torch.allclose(org_loss, sharded_loss, atol=atol, rtol=rtol), \ + assert torch.allclose(org_loss.float(), sharded_loss.float(), atol=atol, rtol=rtol), \ f"shard model loss is not equal to origin model loss\n{org_loss}\n{sharded_loss}" @@ -213,7 +214,7 @@ def check_weight(org_model: Module, if verbose and dist.get_rank() == 0: print(f"'{suffix}' weight: {org_weight}, {sharded_weight}") - assert torch.allclose(org_weight, sharded_weight, atol=atol, rtol=rtol), \ + assert torch.allclose(org_weight.float(), sharded_weight.float(), atol=atol, rtol=rtol), \ f"shard model weight is not equal to origin model weight\n{org_weight}\n{sharded_weight}" @@ -244,6 +245,7 @@ def check_grad(org_model: Module, if verbose and dist.get_rank() == 0: print(f"'{suffix}' grad: {org_grad}, {shard_grad}") + assert torch.allclose( - org_grad, shard_grad, rtol=rtol, atol=atol + org_grad.float(), shard_grad.float(), rtol=rtol, atol=atol ), f"error attribute '{suffix}', orgin model grad is not equal to shard model grad\n{org_grad}\n{shard_grad}" diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 1882bf7822cc..3ac8fa26d860 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -3,6 +3,7 @@ from torch import distributed as dist import colossalai +from colossalai.booster.plugin.hybrid_parallel_plugin import HybridParallelModule from colossalai.logging import disable_existing_loggers from colossalai.tensor.d_tensor.api import clear_layout_converter from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn @@ -38,33 +39,49 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if org_model.__class__.__name__ == 'GPT2Model': - check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) - check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3) + # check loss + check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) + + def unwrap(module): + if isinstance(module, HybridParallelModule): + module = module.unwrap() + if module.__class__.__name__ == 'GPT2Model': + return module + return module.transformer # unwrap model - if org_model.__class__.__name__ == 'GPT2Model': - gpt2 = org_model - sharded_gpt2 = sharded_model.unwrap() - else: - gpt2 = org_model.transformer - sharded_gpt2 = sharded_model.unwrap().transformer + gpt2 = unwrap(org_model) + sharded_gpt2 = unwrap(sharded_model) col_layer_for_check = ['h[0].mlp.c_fc'] row_layer_for_check = ['wte', 'h[0].mlp.c_proj'] # check grad + if test_config['precision'] == 'fp32': + atol, rtol = 1e-4, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if stage_manager is None or stage_manager.is_first_stage(): - check_grad(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False) - check_grad(gpt2, sharded_gpt2, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False) + check_grad(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False) + check_grad(gpt2, sharded_gpt2, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False) # check weights after optimizer.step() org_optimizer.step() sharded_optimizer.step() + if test_config['precision'] == 'fp32': + atol, rtol = 5e-3, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if stage_manager is None or stage_manager.is_first_stage(): - check_weight(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=5e-3, rtol=1e-3, dim=1, verbose=False) + check_weight(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False) torch.cuda.empty_cache() @@ -73,29 +90,31 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, 'tp_size': 2, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': True, - 'use_lazy_init': True + 'enable_all_optimization': True, + 'use_lazy_init': True, + 'precision': 'fp32', }, { 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 4, 'pp_size': 1, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp32', }]) @clear_cache_before_run() def run_gpt2_test(test_config): # TODO: add test_config for TP+DP after supporting & debugging it - # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} - - # TODO: add test_config for flash attention & jit operator after supporting + # TODO: check and debug TP+AMP sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') - test_config['precision'] = 'float' # Do not use fp16/bf16 in testing for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) From 7596e9ae08e32a386d11e896b08c9e15fd120c0b Mon Sep 17 00:00:00 2001 From: Jianghai <72591262+CjhHa1@users.noreply.github.com> Date: Fri, 11 Aug 2023 10:32:53 +0800 Subject: [PATCH 75/84] [pipeline] rewrite bert tests and fix some bugs (#4409) * add pipeline policy and bert forward to be done * add bertmodel pipeline forward and make tests * add Bert_Policy and test for policy * update formatting * update formatting * update the code * fix bugs * fix name confilt * add bloom model and policy ,revise the base class of policy * revise * revision * add bert_for_pretraining * add bert_for_pretraining forward and policy * fix typos * cancel warning * change the imediate output to default dict * change the default output of get_shared_params * rewrite bert test * rewrite bert test * fix some bugs * del pipeline tests * del pipeline tests * del useless print * del useless print * rewrite data repeats --- tests/kit/model_zoo/transformers/bert.py | 3 +- tests/test_shardformer/test_model/_utils.py | 8 +- .../test_model/test_shard_bert.py | 129 +++++++++++------- .../test_model/test_shard_bert_pipeline.py | 107 --------------- 4 files changed, 88 insertions(+), 159 deletions(-) delete mode 100644 tests/test_shardformer/test_model/test_shard_bert_pipeline.py diff --git a/tests/kit/model_zoo/transformers/bert.py b/tests/kit/model_zoo/transformers/bert.py index 9834f5425027..52158596bcf8 100644 --- a/tests/kit/model_zoo/transformers/bert.py +++ b/tests/kit/model_zoo/transformers/bert.py @@ -104,7 +104,8 @@ def data_gen_for_qa(): output_transform_fn = lambda x: x # define loss funciton -loss_fn_for_bert_model = lambda x: x.pooler_output.sum() +loss_fn_for_bert_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state + )) loss_fn = lambda x: x.loss config = transformers.BertConfig(hidden_size=128, diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index cce21809d829..c9da9d32e554 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -131,6 +131,8 @@ def build_model_from_hybrid_plugin(model_fn: Callable, loss_fn: Callable, test_c def run_forward_backward_with_hybrid_plugin(org_model: Module, sharded_model: Module, sharded_optimizer: Optimizer, data_gen_fn: Callable, output_transform_fn: Callable, criterion: Callable, booster: Booster): + org_model.cuda() + sharded_model.cuda() def _criterion(outputs, inputs): outputs = output_transform_fn(outputs) @@ -141,7 +143,8 @@ def _criterion(outputs, inputs): sharded_model.train() if booster.plugin.stage_manager is not None: data = { - k: v.to('cuda').repeat(4, 1) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v + k: v.to('cuda').repeat(*([4] + [1] * + (v.dim() - 1))) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items() } data_iter = iter([data]) @@ -162,6 +165,7 @@ def _criterion(outputs, inputs): org_model.train() data = {k: v.cuda() for k, v in data.items()} org_output = org_model(**data) + org_loss = criterion(org_output) org_loss.backward() @@ -226,7 +230,6 @@ def check_grad(org_model: Module, atol: float = 1e-5, rtol: float = 1e-3, verbose: bool = False): - for suffix in layer_suffix: org_grad = getattr_(org_model, suffix).weight.grad shard_grad = getattr_(sharded_model, suffix).weight.grad @@ -242,7 +245,6 @@ def check_grad(org_model: Module, # embedding may be resized when using tensor parallel if shard_grad.shape[0] > org_grad.shape[0]: shard_grad = shard_grad[:org_grad.shape[0], :] - if verbose and dist.get_rank() == 0: print(f"'{suffix}' grad: {org_grad}, {shard_grad}") diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index afc1507e8b24..fdbcd014e1b8 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -1,65 +1,98 @@ import pytest import torch +from torch import distributed as dist import colossalai -from colossalai.cluster import ProcessGroupMesh from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.auto_policy import get_autopolicy -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) +from colossalai.shardformer.layer.utils import Randomizer +from colossalai.tensor.d_tensor.api import clear_layout_converter +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import ( + build_model_from_hybrid_plugin, + check_grad, + check_loss, + check_output_hidden_state, + check_weight, + run_forward_backward_with_hybrid_plugin, +) -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # unwarp model +def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): + + org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ + build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) + + org_loss, org_output, sharded_loss, sharded_output = \ + run_forward_backward_with_hybrid_plugin( + org_model, + sharded_model, + sharded_optimizer, + data_gen_fn, + output_transform_fn, + criterion, + booster) + stage_manager = booster.plugin.stage_manager + tp_group = booster.plugin.tp_group + # check last hidden state & loss + if stage_manager is None or stage_manager.is_last_stage(): + if org_model.__class__.__name__ == 'BertModel': + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + + check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3) + # unwrap model if org_model.__class__.__name__ == 'BertModel': bert = org_model - sharded_bert = sharded_model + sharded_bert = sharded_model.unwrap() else: bert = org_model.bert - sharded_bert = sharded_model.bert - - # check forward - org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, - output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output) - - # do backward - org_loss.backward() - shard_loss.backward() - - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" - - # check grad - col_layer_for_check = ['encoder.layer[0].attention.self.query', 'embeddings.word_embeddings'] - row_layer_for_check = ['encoder.layer[0].attention.output.dense'] - check_grad(bert, sharded_bert, col_layer_for_check, atol=1e-7, rtol=1e-3, dim=0, verbose=False) - check_grad(bert, sharded_bert, row_layer_for_check, atol=1e-7, rtol=1e-3, dim=1, verbose=False) - - -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('enable_flash_attention', [True, False]) -@parameterize('enable_jit_fused', [True, False]) -@parameterize('use_lazy_init', [False, True]) -def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused, - use_lazy_init): + sharded_bert = sharded_model.unwrap().bert + + col_layer_for_check = ['encoder.layer[0].output.dense'] + row_layer_for_check = ['embeddings.word_embeddings', 'encoder.layer[0].intermediate.dense'] + + if stage_manager is None or stage_manager.is_first_stage(): + #check_weight(bert.embeddings.word_embeddings, sharded_bert.embeddings.word_embeddings, tp_group, atol=1e-5, rtol=1e-3) + #check_weight(bert.encoder.layer[0].attention.self.query, sharded_bert.encoder.layer[0].attention.self.query, tp_group, atol=5e-3, rtol=1e-3) + check_grad(bert, sharded_bert, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False) + check_grad(bert, sharded_bert, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False) + + # check weights after optimizer.step() + org_optimizer.step() + sharded_optimizer.step() + if stage_manager is None or stage_manager.is_first_stage(): + check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=5e-3, rtol=1e-3, dim=1, verbose=False) + + torch.cuda.empty_cache() + + +@parameterize('test_config', [{ + 'tp_size': 1, + 'pp_size': 2, + 'num_microbatches': 4, + 'use_lazy_init': True +}, { + 'tp_size': 2, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': False, + 'use_lazy_init': False +}, { + 'tp_size': 4, + 'pp_size': 1, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}]) +def run_bert_test(test_config): + sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') + test_config['precision'] = 'float' + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - enable_flash_attention, enable_jit_fused, use_lazy_init) - check_state_dict(org_model, sharded_model, name=name) - check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) + clear_layout_converter() + Randomizer.reset_index() torch.cuda.empty_cache() @@ -73,7 +106,7 @@ def check_bert(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_bert(): - spawn(check_bert, 2) + spawn(check_bert, 4) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_bert_pipeline.py b/tests/test_shardformer/test_model/test_shard_bert_pipeline.py deleted file mode 100644 index 3170b58a1175..000000000000 --- a/tests/test_shardformer/test_model/test_shard_bert_pipeline.py +++ /dev/null @@ -1,107 +0,0 @@ -import pytest -import torch - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.auto_policy import get_autopolicy -from colossalai.shardformer.shard import ShardConfig -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward - - -def check_bert_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager): - stage_manager = stage_manager - policy = get_autopolicy(model) - policy.set_model(model) - model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) - policy.set_shard_config(model_config) - layers = policy.get_held_layers() - if stage_manager.is_first_stage(): - assert len(layers) == 1 + 1 - else: - if name == "transformers_bert": - assert len(layers) == 1 + 1 - elif name in [ - "transformers_bert_for_sequence_classification", "transformers_bert_for_token_classification", - "transformers_bert_for_mcq" - ]: - assert len(layers) == 1 + 3 - else: - assert len(layers) == 1 + 2 - - -def check_bert_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager): - if name == 'transformers_bert_for_mcq': - x = torch.randint(0, 1000, (2, 3, 3)).cuda() - attention_mask = torch.ones_like(x).cuda() - if stage_manager.stage == 0: - output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) - assert output['hidden_states'].shape == (6, 3, 128) - else: - hidden_states = torch.randint(0, 1000, (6, 3, 128)).to(torch.float32).cuda() - output = sharded_model(input_ids=x, - hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager) - assert output[0].shape == (2, 3) - else: - x = torch.randint(0, 1000, (2, 3)).cuda() - # one batch, 2 single sentences, each sentence has 3 tokens - hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x).cuda() - output = sharded_model(input_ids=x, attention_mask=attention_mask, stage_manager=stage_manager) - assert output['hidden_states'].shape == (2, 3, 128) - else: - attention_mask = torch.ones((2, 3)).cuda() - output = sharded_model(hidden_states=hidden_states, - attention_mask=attention_mask, - stage_manager=stage_manager) - assert output[0].shape[0] == 2 - - -@parameterize('enable_fused_normalization', [False]) -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('use_lazy_init', [False]) -#TODO: merge this into test_shard_bert -def run_bert_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - PP_DIM = 0 - PP_SIZE = 2 - pg_mesh = ProcessGroupMesh(PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - - sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') - for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - check_bert_model_policy(name, org_model, stage_manager) - check_bert_model_pipeline_forward(name, sharded_model, stage_manager) - - torch.cuda.empty_cache() - - -def check_bert(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_bert_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_bert(): - spawn(check_bert, 2) - - -if __name__ == "__main__": - test_bert() From 21e0a42fd17d91307f68a8ebb5e0acf492e39430 Mon Sep 17 00:00:00 2001 From: flybird11111 <1829166702@qq.com> Date: Fri, 11 Aug 2023 11:44:23 +0800 Subject: [PATCH 76/84] [shardformer]fix, test gpt2 for AMP+TP (#4403) * [shardformer] gpt2 tests fix [shardformer] test all optimizations (#4399) [shardformer] test all optimizations [shardformer] test all optimizations [shardformer] test all optimizations [shardformer] gpt2 tests fix * [shardformer] gpt2 tests fix --- tests/test_shardformer/test_model/_utils.py | 8 +++----- tests/test_shardformer/test_model/test_shard_gpt2.py | 8 +++----- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index c9da9d32e554..c51df07f6c11 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -210,7 +210,7 @@ def check_weight(org_model: Module, if is_distributed_tensor(sharded_weight) or is_customized_distributed_tensor(sharded_weight): sharded_weight_list = [ - torch.zeros([*sharded_weight.shape]).to('cuda') for _ in range(dist.get_world_size(tp_group)) + torch.zeros_like(sharded_weight).to('cuda') for _ in range(dist.get_world_size(tp_group)) ] dist.all_gather(sharded_weight_list, sharded_weight, tp_group) sharded_weight = torch.cat(sharded_weight_list, dim=dim) @@ -219,7 +219,7 @@ def check_weight(org_model: Module, print(f"'{suffix}' weight: {org_weight}, {sharded_weight}") assert torch.allclose(org_weight.float(), sharded_weight.float(), atol=atol, rtol=rtol), \ - f"shard model weight is not equal to origin model weight\n{org_weight}\n{sharded_weight}" + f"shard model weight {suffix} is not equal to origin model weight\n{org_weight}\n{sharded_weight}" def check_grad(org_model: Module, @@ -236,9 +236,7 @@ def check_grad(org_model: Module, shard_weight = getattr_(sharded_model, suffix).weight if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [ - torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(dist.get_world_size(tp_group)) - ] + shard_grad_list = [torch.zeros_like(shard_grad).to('cuda') for _ in range(dist.get_world_size(tp_group))] dist.all_gather(shard_grad_list, shard_grad, tp_group) shard_grad = torch.cat(shard_grad_list, dim=dim) diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 3ac8fa26d860..274cfaa39ad1 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -23,7 +23,6 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) - org_loss, org_output, sharded_loss, sharded_output = \ run_forward_backward_with_hybrid_plugin( org_model, @@ -47,7 +46,6 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, if org_model.__class__.__name__ == 'GPT2Model': check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) - # check loss check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) def unwrap(module): @@ -92,13 +90,14 @@ def unwrap(module): 'num_microbatches': 4, 'enable_all_optimization': True, 'use_lazy_init': True, - 'precision': 'fp32', + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, 'enable_all_optimization': True, - 'use_lazy_init': False, + 'use_lazy_init': True, 'precision': 'fp16', 'initial_scale': 1, }, { @@ -112,7 +111,6 @@ def unwrap(module): def run_gpt2_test(test_config): # TODO: add test_config for TP+DP after supporting & debugging it - # TODO: check and debug TP+AMP sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') From 7711bd524a47cc533b49d8e1c35087928e76e94b Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Fri, 11 Aug 2023 15:43:23 +0800 Subject: [PATCH 77/84] [shardformer] rewrite tests for opt/bloom/llama/vit/chatglm (#4395) * rewrite opt tests * rewrite llama tests * rewrite bloom & vit tests * rewrite chatglm tests * fix LinearCol for classfiers * add judge for other tp layers, fix lazy init in util --- colossalai/shardformer/layer/linear.py | 16 + .../shardformer/layer/qkv_fused_linear.py | 16 + colossalai/shardformer/modeling/opt.py | 497 +++++++++++++- .../shardformer/policies/auto_policy.py | 6 + colossalai/shardformer/policies/opt.py | 618 +----------------- tests/kit/model_zoo/transformers/bloom.py | 8 +- tests/kit/model_zoo/transformers/chatglm.py | 19 +- tests/kit/model_zoo/transformers/vit.py | 6 +- tests/test_shardformer/test_model/_utils.py | 35 +- .../test_model/test_shard_bloom.py | 118 ++-- .../test_model/test_shard_bloom_pipeline.py | 90 --- .../test_model/test_shard_chatglm.py | 179 ++--- .../test_model/test_shard_chatglm_pipeline.py | 86 --- .../test_model/test_shard_llama.py | 144 ++-- .../test_model/test_shard_llama_pipeline.py | 89 --- .../test_model/test_shard_opt.py | 145 ++-- .../test_model/test_shard_opt_pipeline.py | 70 -- .../test_model/test_shard_vit.py | 137 +++- .../test_model/test_shard_vit_pipeline.py | 74 --- 19 files changed, 1072 insertions(+), 1281 deletions(-) delete mode 100644 tests/test_shardformer/test_model/test_shard_bloom_pipeline.py delete mode 100644 tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py delete mode 100644 tests/test_shardformer/test_model/test_shard_llama_pipeline.py delete mode 100644 tests/test_shardformer/test_model/test_shard_opt_pipeline.py delete mode 100644 tests/test_shardformer/test_model/test_shard_vit_pipeline.py diff --git a/colossalai/shardformer/layer/linear.py b/colossalai/shardformer/layer/linear.py index bb36854bd772..d59b68ce4480 100644 --- a/colossalai/shardformer/layer/linear.py +++ b/colossalai/shardformer/layer/linear.py @@ -143,6 +143,14 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] + tp_size = dist.get_world_size(process_group) + if out_features < tp_size: + return module + + if out_features % tp_size != 0: + raise ValueError( + f"The size of out_features:{out_features} is not integer multiples of tensor parallel size: {tp_size}!") + linear_1d = Linear1D_Col(in_features=in_features, out_features=out_features, bias=bias, @@ -293,6 +301,14 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] + tp_size = dist.get_world_size(process_group) + if in_features < tp_size: + return module + + if in_features % tp_size != 0: + raise ValueError( + f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!") + linear_1d = Linear1D_Row(in_features=in_features, out_features=out_features, bias=bias, diff --git a/colossalai/shardformer/layer/qkv_fused_linear.py b/colossalai/shardformer/layer/qkv_fused_linear.py index 42417f8bcc43..df942d43ee2d 100644 --- a/colossalai/shardformer/layer/qkv_fused_linear.py +++ b/colossalai/shardformer/layer/qkv_fused_linear.py @@ -265,6 +265,14 @@ def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, Lis f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] + tp_size = dist.get_world_size(process_group) + if out_features < tp_size: + return module + + if out_features % tp_size != 0: + raise ValueError( + f"The size of out_features:{out_features} is not integer multiples of tensor parallel size: {tp_size}!") + linear_1d = GPT2FusedLinearConv1D_Col(in_features=in_features, out_features=out_features, bias=bias, @@ -420,6 +428,14 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis f'Expected only one process group, got {len(process_group)}.' process_group = process_group[0] + tp_size = dist.get_world_size(process_group) + if in_features < tp_size: + return module + + if in_features % tp_size != 0: + raise ValueError( + f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!") + linear_1d = GPT2FusedLinearConv1D_Row(in_features=in_features, out_features=out_features, bias=bias, diff --git a/colossalai/shardformer/modeling/opt.py b/colossalai/shardformer/modeling/opt.py index bdf141816737..9afdfff4d71d 100644 --- a/colossalai/shardformer/modeling/opt.py +++ b/colossalai/shardformer/modeling/opt.py @@ -1,7 +1,500 @@ -from typing import Optional, Tuple +import random +from typing import List, Optional, Tuple, Union import torch -from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, +) +from transformers.models.opt.modeling_opt import ( + OPTForCausalLM, + OPTForQuestionAnswering, + OPTForSequenceClassification, + OPTModel, +) +from transformers.utils import logging + +from colossalai.pipeline.stage_manager import PipelineStageManager + + +class OPTPipelineForwards: + ''' + This class serves as a micro library for forward function substitution of OPT models + under pipeline setting. + ''' + + @staticmethod + def _prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, device, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + from transformers.models.opt.modeling_opt import _make_causal_mask + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + _dtype, + device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = OPTPipelineForwards._expand_mask(attention_mask, _dtype, + tgt_len=input_shape[-1]).to(device) + combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + + combined_attention_mask) + + return combined_attention_mask + + @staticmethod + def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + @staticmethod + def opt_model_forward( + self: OPTModel, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + ''' + This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward + ''' + + from transformers.modeling_outputs import BaseModelOutputWithPast + from transformers.utils import logging + logger = logging.get_logger(__name__) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + decoder = self.decoder + if stage_manager.is_first_stage(): + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + batch_size, seq_length = input_shape + + if inputs_embeds is None: + inputs_embeds = decoder.embed_tokens(input_ids) + + if decoder.project_in is not None: + inputs_embeds = decoder.project_in(inputs_embeds) + device = input_ids.device if input_ids is not None else inputs_embeds.device + _dtype = inputs_embeds.dtype + + else: + if hidden_states is None: + raise ValueError("hidden_states shouln't be None for intermediate stages.") + input_shape = hidden_states.size()[:-1] + batch_size, seq_length = input_shape[0], input_shape[1] + device = hidden_states.device + _dtype = hidden_states.dtype + + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + # required mask seq length can be calculated via length of past + mask_seq_length = past_key_values_length + seq_length + # embed positions + if attention_mask is None: + attention_mask = torch.ones(batch_size, mask_seq_length, device=device) + elif attention_mask.shape[1] != mask_seq_length: + raise ValueError( + f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be " + f"{mask_seq_length} (sum of the lengths of current and past inputs)") + + causal_attention_mask = OPTPipelineForwards._prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, + device, past_key_values_length) + + if stage_manager.is_first_stage(): + pos_embeds = decoder.embed_positions(attention_mask, past_key_values_length) + hidden_states = inputs_embeds + pos_embeds + + if decoder.gradient_checkpointing and decoder.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") + use_cache = False + + # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + if past_key_values: + logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') + past_key_values = None + if output_attentions: + logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') + output_attentions = False + if output_hidden_states: + logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') + output_hidden_states = False + if use_cache: + logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + # check if head_mask has a correct number of layers specified if desired + for attn_mask, mask_name in zip([head_mask], ["head_mask"]): + if attn_mask is not None: + if attn_mask.size()[0] != (len(decoder.layers)): + raise ValueError( + f"The `{mask_name}` should be specified for {len(decoder.layers)} layers, but it is for" + f" {head_mask.size()[0]}.") + + start_idx, end_idx = stage_index[0], stage_index[1] + + torch.cuda.set_device(device) + + for idx in range(start_idx, end_idx): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + decoder_layer = decoder.layers[idx] + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + dropout_probability = random.uniform(0, 1) + if decoder.training and (dropout_probability < decoder.layerdrop): + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if decoder.gradient_checkpointing and decoder.training: + + def create_custom_forward(module): + + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + causal_attention_mask, + head_mask[idx] if head_mask is not None else None, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if stage_manager.is_last_stage(): + if decoder.final_layer_norm is not None: + hidden_states = decoder.final_layer_norm(hidden_states) + if decoder.project_out is not None: + hidden_states = decoder.project_out(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + + if stage_manager.is_last_stage(): + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + else: + return {'hidden_states': hidden_states} + + @staticmethod + def opt_for_causal_lm_forward( + self: OPTForCausalLM, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForCausalLM.forward. + Please refer to original code of transformers for more details. + """ + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else self.config.output_hidden_states) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = OPTPipelineForwards.opt_model_forward( + self.model, + input_ids=input_ids, + attention_mask=attention_mask, + head_mask=head_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index, + ) + if stage_manager.is_last_stage(): + logits = self.lm_head(outputs[0]).contiguous() + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + else: + hidden_states = outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def opt_for_sequence_classification_forward( + self: OPTForSequenceClassification, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForSequenceClassification.forward. + Please refer to original code of transformers for more details. + """ + + logger = logging.get_logger(__name__) + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + batch_size = input_ids.shape[0] if input_ids is not None else hidden_states.shape[0] + + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + logger.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`") + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} + + @staticmethod + def opt_for_question_answering_forward( + self: OPTForQuestionAnswering, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + stage_manager: Optional[PipelineStageManager] = None, + hidden_states: Optional[torch.FloatTensor] = None, + stage_index: Optional[List[int]] = None, + ) -> Union[Tuple, QuestionAnsweringModelOutput]: + r""" + This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForQuestionAnswering.forward. + Please refer to original code of transformers for more details. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model, + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + stage_manager=stage_manager, + hidden_states=hidden_states, + stage_index=stage_index) + if stage_manager.is_last_stage(): + hidden_states = transformer_outputs[0] + + logits = self.qa_outputs(hidden_states) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + transformer_outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + else: + hidden_states = transformer_outputs.get('hidden_states') + return {'hidden_states': hidden_states} def get_opt_flash_attention_forward(): diff --git a/colossalai/shardformer/policies/auto_policy.py b/colossalai/shardformer/policies/auto_policy.py index 2a041af19be8..eec339c02872 100644 --- a/colossalai/shardformer/policies/auto_policy.py +++ b/colossalai/shardformer/policies/auto_policy.py @@ -122,6 +122,12 @@ class PolicyLocation: PolicyLocation(file_name="blip2", class_name="Blip2ModelPolicy"), "transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGeneration": PolicyLocation(file_name="blip2", class_name="Blip2ForConditionalGenerationPolicy"), + + # ChatGLM + "colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm.ChatGLMModel": + PolicyLocation(file_name="chatglm", class_name="ChatGLMModelPolicy"), + "colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm.ChatGLMForConditionalGeneration": + PolicyLocation(file_name="chatglm", class_name="ChatGLMForConditionalGenerationPolicy"), } diff --git a/colossalai/shardformer/policies/opt.py b/colossalai/shardformer/policies/opt.py index 88ecd8565091..ba6036bd0658 100644 --- a/colossalai/shardformer/policies/opt.py +++ b/colossalai/shardformer/policies/opt.py @@ -1,32 +1,14 @@ -import logging -import random from functools import partial -from types import MethodType -from typing import Callable, Dict, List, Optional, Tuple, Union +from typing import Callable, Dict, List -import torch import torch.nn as nn from torch import Tensor, nn -from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss -from transformers.modeling_outputs import ( - BaseModelOutputWithPast, - CausalLMOutputWithPast, - QuestionAnsweringModelOutput, - SequenceClassifierOutputWithPast, -) -from transformers.models.opt.modeling_opt import ( - OPTForCausalLM, - OPTForQuestionAnswering, - OPTForSequenceClassification, - OPTModel, -) - -from colossalai.pipeline.stage_manager import PipelineStageManager + from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D -from .._utils import getattr_, setattr_ +from .._utils import getattr_ from ..modeling.jit import get_jit_fused_dropout_add_func -from ..modeling.opt import get_jit_fused_opt_decoder_layer_forward, get_opt_flash_attention_forward +from ..modeling.opt import OPTPipelineForwards, get_jit_fused_opt_decoder_layer_forward, get_opt_flash_attention_forward from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ @@ -228,6 +210,7 @@ def get_shared_params(self) -> List[Dict[int, Tensor]]: num_stages = self.pipeline_stage_manager.num_stages if id(opt_model.model.decoder.embed_tokens.weight) == id(opt_model.lm_head.weight): return [{0: opt_model.model.decoder.embed_tokens.weight, num_stages - 1: opt_model.lm_head.weight}] + return [] def postprocess(self): if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: @@ -295,594 +278,3 @@ def get_held_layers(self) -> List[nn.Module]: def get_shared_params(self) -> List[Dict[int, Tensor]]: "no shared params in OPTForSequenceClassification" return [] - - -class OPTPipelineForwards: - ''' - This class serves as a micro library for forward function substitution of OPT models - under pipeline setting. - ''' - - @staticmethod - def _prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, device, past_key_values_length): - # create causal mask - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - from transformers.models.opt.modeling_opt import _make_causal_mask - combined_attention_mask = None - if input_shape[-1] > 1: - combined_attention_mask = _make_causal_mask( - input_shape, - _dtype, - device, - past_key_values_length=past_key_values_length, - ) - - if attention_mask is not None: - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - expanded_attn_mask = OPTPipelineForwards._expand_mask(attention_mask, _dtype, - tgt_len=input_shape[-1]).to(device) - combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + - combined_attention_mask) - - return combined_attention_mask - - @staticmethod - def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): - """ - Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. - """ - bsz, src_len = mask.size() - tgt_len = tgt_len if tgt_len is not None else src_len - - expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) - - inverted_mask = 1.0 - expanded_mask - - return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) - - @staticmethod - def opt_model_forward( - self: OPTModel, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, - ) -> Union[Tuple, BaseModelOutputWithPast]: - ''' - This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward - ''' - - from transformers.modeling_outputs import BaseModelOutputWithPast - from transformers.utils import logging - logger = logging.get_logger(__name__) - - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - decoder = self.decoder - if stage_manager.is_first_stage(): - # retrieve input_ids and inputs_embeds - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - input_ids = input_ids.view(-1, input_shape[-1]) - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") - - batch_size, seq_length = input_shape - - if inputs_embeds is None: - inputs_embeds = decoder.embed_tokens(input_ids) - - if decoder.project_in is not None: - inputs_embeds = decoder.project_in(inputs_embeds) - device = input_ids.device if input_ids is not None else inputs_embeds.device - _dtype = inputs_embeds.dtype - - else: - if hidden_states is None: - raise ValueError("hidden_states shouln't be None for intermediate stages.") - input_shape = hidden_states.size()[:-1] - batch_size, seq_length = input_shape[0], input_shape[1] - device = hidden_states.device - _dtype = hidden_states.dtype - - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - # required mask seq length can be calculated via length of past - mask_seq_length = past_key_values_length + seq_length - # embed positions - if attention_mask is None: - attention_mask = torch.ones(batch_size, mask_seq_length, device=device) - elif attention_mask.shape[1] != mask_seq_length: - raise ValueError( - f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be " - f"{mask_seq_length} (sum of the lengths of current and past inputs)") - - causal_attention_mask = OPTPipelineForwards._prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, - device, past_key_values_length) - - if stage_manager.is_first_stage(): - pos_embeds = decoder.embed_positions(attention_mask, past_key_values_length) - hidden_states = inputs_embeds + pos_embeds - - if decoder.gradient_checkpointing and decoder.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") - use_cache = False - - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. - if past_key_values: - logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') - past_key_values = None - if output_attentions: - logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') - output_attentions = False - if output_hidden_states: - logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') - output_hidden_states = False - if use_cache: - logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') - use_cache = False - - # decoder layers - all_hidden_states = () if output_hidden_states else None - all_self_attns = () if output_attentions else None - next_decoder_cache = () if use_cache else None - - # check if head_mask has a correct number of layers specified if desired - for attn_mask, mask_name in zip([head_mask], ["head_mask"]): - if attn_mask is not None: - if attn_mask.size()[0] != (len(decoder.layers)): - raise ValueError( - f"The `{mask_name}` should be specified for {len(decoder.layers)} layers, but it is for" - f" {head_mask.size()[0]}.") - - start_idx, end_idx = stage_index[0], stage_index[1] - - torch.cuda.set_device(device) - - for idx in range(start_idx, end_idx): - # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) - decoder_layer = decoder.layers[idx] - - if output_hidden_states: - all_hidden_states += (hidden_states,) - - dropout_probability = random.uniform(0, 1) - if decoder.training and (dropout_probability < decoder.layerdrop): - continue - - past_key_value = past_key_values[idx] if past_key_values is not None else None - - if decoder.gradient_checkpointing and decoder.training: - - def create_custom_forward(module): - - def custom_forward(*inputs): - # None for past_key_value - return module(*inputs, output_attentions, None) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(decoder_layer), - hidden_states, - causal_attention_mask, - head_mask[idx] if head_mask is not None else None, - None, - ) - else: - layer_outputs = decoder_layer( - hidden_states, - attention_mask=causal_attention_mask, - layer_head_mask=(head_mask[idx] if head_mask is not None else None), - past_key_value=past_key_value, - output_attentions=output_attentions, - use_cache=use_cache, - ) - - hidden_states = layer_outputs[0] - - if use_cache: - next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) - - if output_attentions: - all_self_attns += (layer_outputs[1],) - - if stage_manager.is_last_stage(): - if decoder.final_layer_norm is not None: - hidden_states = decoder.final_layer_norm(hidden_states) - if decoder.project_out is not None: - hidden_states = decoder.project_out(hidden_states) - - # add hidden states from the last decoder layer - if output_hidden_states: - all_hidden_states += (hidden_states,) - - next_cache = next_decoder_cache if use_cache else None - - if stage_manager.is_last_stage(): - if not return_dict: - return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) - - return BaseModelOutputWithPast( - last_hidden_state=hidden_states, - past_key_values=next_cache, - hidden_states=all_hidden_states, - attentions=all_self_attns, - ) - else: - return {'hidden_states': hidden_states} - - @staticmethod - def opt_for_causal_lm_forward( - self: OPTForCausalLM, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, - ) -> Union[Tuple, CausalLMOutputWithPast]: - r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you - provide it. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): - Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of - shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of - shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional - tensors are only required when the model is used as a decoder in a Sequence to Sequence model. - - Contains pre-computed hidden-states (key and values in the self-attention blocks and in the - cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those - that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of - all `decoder_input_ids` of shape `(batch_size, sequence_length)`. - inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. - This is useful if you want more control over how to convert `input_ids` indices into associated vectors - than the model's internal embedding lookup matrix. - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., - config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored - (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding - (see `past_key_values`). - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors - for more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, OPTForCausalLM - - >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m") - >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - - >>> prompt = "Hey, are you consciours? Can you talk to me?" - >>> inputs = tokenizer(prompt, return_tensors="pt") - - >>> # Generate - >>> generate_ids = model.generate(inputs.input_ids, max_length=30) - >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] - "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." - ```""" - from transformers.modeling_outputs import CausalLMOutputWithPast - - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = (output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) - outputs = OPTPipelineForwards.opt_model_forward( - self.model, - input_ids=input_ids, - attention_mask=attention_mask, - head_mask=head_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index, - ) - if stage_manager.is_last_stage(): - logits = self.lm_head(outputs[0]).contiguous() - loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(logits.device) - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) - if not return_dict: - output = (logits,) + outputs[1:] - return (loss,) + output if loss is not None else output - - return CausalLMOutputWithPast( - loss=loss, - logits=logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - else: - hidden_states = outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - @staticmethod - def opt_for_sequence_classification_forward( - self: OPTForSequenceClassification, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, - ) -> Union[Tuple, SequenceClassifierOutputWithPast]: - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - """ - from transformers.modeling_outputs import SequenceClassifierOutputWithPast - from transformers.utils import logging - logger = logging.get_logger(__name__) - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index) - - if stage_manager.is_last_stage(): - hidden_states = transformer_outputs[0] - logits = self.score(hidden_states) - - batch_size = input_ids.shape[0] if input_ids is not None else hidden_states.shape[0] - - if self.config.pad_token_id is None: - sequence_lengths = -1 - else: - if input_ids is not None: - sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) - else: - sequence_lengths = -1 - logger.warning( - f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " - "unexpected if using padding tokens in conjunction with `inputs_embeds.`") - - pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] - - loss = None - if labels is not None: - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - loss_fct = MSELoss() - if self.num_labels == 1: - loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) - else: - loss = loss_fct(pooled_logits, labels) - elif self.config.problem_type == "single_label_classification": - loss_fct = CrossEntropyLoss() - loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) - elif self.config.problem_type == "multi_label_classification": - loss_fct = BCEWithLogitsLoss() - loss = loss_fct(pooled_logits, labels) - - if not return_dict: - output = (pooled_logits,) + transformer_outputs[1:] - return ((loss,) + output) if loss is not None else output - - return SequenceClassifierOutputWithPast( - loss=loss, - logits=pooled_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - else: - hidden_states = transformer_outputs.get('hidden_states') - return {'hidden_states': hidden_states} - - @staticmethod - def opt_for_question_answering_forward( - self: OPTForQuestionAnswering, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - start_positions: Optional[torch.LongTensor] = None, - end_positions: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - stage_manager: Optional[PipelineStageManager] = None, - hidden_states: Optional[torch.FloatTensor] = None, - stage_index: Optional[List[int]] = None, - ) -> Union[Tuple, QuestionAnsweringModelOutput]: - r""" - start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the start of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the end of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, OPTForQuestionAnswering - >>> import torch - - >>> torch.manual_seed(4) # doctest: +IGNORE_RESULT - >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - - >>> # note: we are loading a OPTForQuestionAnswering from the hub here, - >>> # so the head will be randomly initialized, hence the predictions will be random - >>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m") - - >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" - - >>> inputs = tokenizer(question, text, return_tensors="pt") - >>> with torch.no_grad(): - ... outputs = model(**inputs) - - >>> answer_start_index = outputs.start_logits.argmax() - >>> answer_end_index = outputs.end_logits.argmax() - - >>> answer_offset = len(tokenizer(question)[0]) - - >>> predict_answer_tokens = inputs.input_ids[ - ... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1 - ... ] - >>> predicted = tokenizer.decode(predict_answer_tokens) - >>> predicted - ' a nice puppet' - ```""" - from transformers.modeling_outputs import QuestionAnsweringModelOutput - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model, - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - stage_manager=stage_manager, - hidden_states=hidden_states, - stage_index=stage_index) - if stage_manager.is_last_stage(): - hidden_states = transformer_outputs[0] - - logits = self.qa_outputs(hidden_states) - start_logits, end_logits = logits.split(1, dim=-1) - start_logits = start_logits.squeeze(-1).contiguous() - end_logits = end_logits.squeeze(-1).contiguous() - - total_loss = None - if start_positions is not None and end_positions is not None: - # If we are on multi-GPU, split add a dimension - if len(start_positions.size()) > 1: - start_positions = start_positions.squeeze(-1) - if len(end_positions.size()) > 1: - end_positions = end_positions.squeeze(-1) - # sometimes the start/end positions are outside our model inputs, we ignore these terms - ignored_index = start_logits.size(1) - start_positions = start_positions.clamp(0, ignored_index) - end_positions = end_positions.clamp(0, ignored_index) - - loss_fct = CrossEntropyLoss(ignore_index=ignored_index) - start_loss = loss_fct(start_logits, start_positions) - end_loss = loss_fct(end_logits, end_positions) - total_loss = (start_loss + end_loss) / 2 - - if not return_dict: - output = (start_logits, end_logits) + transformer_outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output - - return QuestionAnsweringModelOutput( - loss=total_loss, - start_logits=start_logits, - end_logits=end_logits, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - else: - hidden_states = transformer_outputs.get('hidden_states') - return {'hidden_states': hidden_states} diff --git a/tests/kit/model_zoo/transformers/bloom.py b/tests/kit/model_zoo/transformers/bloom.py index 177edbef8935..2d9c882089cb 100644 --- a/tests/kit/model_zoo/transformers/bloom.py +++ b/tests/kit/model_zoo/transformers/bloom.py @@ -53,7 +53,8 @@ def data_gen_for_question_answering(): # inputs = tokenizer(question, text, return_tensors="pt") input_ids = torch.tensor( - [[57647, 1620, 23967, 620, 107373, 34, 91514, 620, 107373, 1620, 267, 35378, 48946, 18161, 48946, 18161]], dtype=torch.int64) + [[57647, 1620, 23967, 620, 107373, 34, 91514, 620, 107373, 1620, 267, 35378, 48946, 18161, 48946, 18161]], + dtype=torch.int64) attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64) start_positions = torch.tensor([1], dtype=torch.int64) end_positions = torch.tensor([10], dtype=torch.int64) @@ -73,12 +74,13 @@ def data_gen_for_question_answering(): loss_fn_for_classification = lambda x: x.loss loss_fn_for_question_answering = lambda x: x.loss -config = transformers.BloomConfig(n_layer=1, +config = transformers.BloomConfig(n_layer=2, n_head=4, vocab_size=250880, hidden_dropout=0, attention_dropout=0, - hidden_size=64) + hidden_size=64, + pad_token_id=50256) # register the following models model_zoo.register(name='transformers_bloom', diff --git a/tests/kit/model_zoo/transformers/chatglm.py b/tests/kit/model_zoo/transformers/chatglm.py index 90bb70bc7f79..c6473ee2a025 100644 --- a/tests/kit/model_zoo/transformers/chatglm.py +++ b/tests/kit/model_zoo/transformers/chatglm.py @@ -17,14 +17,24 @@ def data_gen(): return dict(input_ids=input_ids, attention_mask=attention_mask) +def data_gen_for_conditional_generation(): + # token classification data gen + # `labels` is the type not the token id for token classification, 0 or 1 + data = data_gen() + labels = data['input_ids'].clone() + data['labels'] = labels + return data + + # define output transform function output_transform_fn = lambda x: x # define loss function -loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.sum() -loss_fn = lambda x: x.logits.sum() +loss_fn_for_chatglm_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, + torch.ones_like(x.last_hidden_state)) +loss_fn = lambda x: x.loss -config = ChatGLMConfig(num_layers=1, +config = ChatGLMConfig(num_layers=2, padded_vocab_size=65024, hidden_size=64, num_attention_heads=8, @@ -33,7 +43,6 @@ def data_gen(): use_cache=True, torch_dtype=torch.float32) - model_zoo.register(name='transformers_chatglm', model_fn=lambda: ChatGLMModel(config, empty_init=False), data_gen_fn=data_gen, @@ -43,7 +52,7 @@ def data_gen(): model_zoo.register(name="transformers_chatglm_for_conditional_generation", model_fn=lambda: ChatGLMForConditionalGeneration(config, empty_init=False), - data_gen_fn=data_gen, + data_gen_fn=data_gen_for_conditional_generation, output_transform_fn=output_transform_fn, loss_fn=loss_fn, model_attribute=ModelAttribute(has_control_flow=True)) diff --git a/tests/kit/model_zoo/transformers/vit.py b/tests/kit/model_zoo/transformers/vit.py index 93a8d6c615d7..a84b8d31c284 100644 --- a/tests/kit/model_zoo/transformers/vit.py +++ b/tests/kit/model_zoo/transformers/vit.py @@ -7,11 +7,7 @@ # Register single-sentence VIT # =============================== -config = transformers.ViTConfig( - num_hidden_layers=4, - # hidden_size=128, - # intermediate_size=256, - num_attention_heads=4) +config = transformers.ViTConfig(num_hidden_layers=4, hidden_size=128, intermediate_size=256, num_attention_heads=4) # define data gen function diff --git a/tests/test_shardformer/test_model/_utils.py b/tests/test_shardformer/test_model/_utils.py index c51df07f6c11..921af2a8b1d0 100644 --- a/tests/test_shardformer/test_model/_utils.py +++ b/tests/test_shardformer/test_model/_utils.py @@ -104,27 +104,22 @@ def build_model_from_hybrid_plugin(model_fn: Callable, loss_fn: Callable, test_c if 'use_lazy_init' in test_config: use_lazy_init = test_config.pop('use_lazy_init') - if use_lazy_init: - ctx = LazyInitContext() - else: - ctx = nullcontext() - - plugin = HybridParallelPlugin(**test_config) - booster = Booster(plugin=plugin) - + ctx = LazyInitContext() if use_lazy_init else nullcontext() with ctx: - org_model = model_fn().cuda() + org_model = model_fn() sharded_model = copy.deepcopy(org_model) - if use_lazy_init: - org_model = ctx.materialize(org_model) + ctx.materialize(org_model) + org_model = org_model.cuda() org_optimizer = Adam(org_model.parameters(), lr=1e-3) sharded_optimizer = Adam(sharded_model.parameters(), lr=1e-3) criterion = loss_fn - sharded_model, sharded_optimizer, criterion, _, _ = booster.boost(sharded_model, sharded_optimizer, criterion) + plugin = HybridParallelPlugin(**test_config) + booster = Booster(plugin=plugin) + sharded_model, sharded_optimizer, criterion, _, _ = booster.boost(sharded_model, sharded_optimizer, criterion) return org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster @@ -142,11 +137,12 @@ def _criterion(outputs, inputs): data = data_gen_fn() sharded_model.train() if booster.plugin.stage_manager is not None: - data = { - k: v.to('cuda').repeat(*([4] + [1] * - (v.dim() - 1))) if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v - for k, v in data.items() - } + for k, v in data.items(): + if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__: + new_shape = [1] * v.dim() + new_shape[0] = 4 + data[k] = v.to('cuda').repeat(*new_shape) + data_iter = iter([data]) sharded_output = booster.execute_pipeline(data_iter, sharded_model, @@ -176,7 +172,8 @@ def check_output_hidden_state(org_output: Tensor, sharded_output: Tensor, stage_manager: Optional[PipelineStageManager] = None, atol: float = 1e-5, - rtol: float = 1e-3): + rtol: float = 1e-3, + dim: int = 0): org_hidden_state = org_output.last_hidden_state @@ -184,7 +181,7 @@ def check_output_hidden_state(org_output: Tensor, sharded_hidden_state = sharded_output.last_hidden_state if stage_manager and stage_manager.is_last_stage(): - sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']], dim=0) + sharded_hidden_state = torch.cat([output.last_hidden_state for output in sharded_output['outputs']], dim=dim) assert torch.allclose(org_hidden_state.float(), sharded_hidden_state.float(), atol=atol, rtol=rtol), \ f"shard model's output hidden state is not equal to origin model's last hidden state\n{org_hidden_state}\n{sharded_hidden_state}" diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index e11bcf92ea3c..d5a4ce083e2b 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -3,57 +3,101 @@ import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) +from colossalai.tensor.d_tensor.api import clear_layout_converter +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import ( + build_model_from_hybrid_plugin, + check_grad, + check_loss, + check_output_hidden_state, + check_weight, + run_forward_backward_with_hybrid_plugin, +) + + +def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): + org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ + build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # check forward - org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, - output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) + org_loss, org_output, sharded_loss, sharded_output = \ + run_forward_backward_with_hybrid_plugin( + org_model, + sharded_model, + sharded_optimizer, + data_gen_fn, + output_transform_fn, + criterion, + booster) - # do backward - org_loss.backward() - shard_loss.backward() + stage_manager = booster.plugin.stage_manager + tp_group = booster.plugin.tp_group - assert torch.allclose(org_loss, shard_loss, - atol=1e-6), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + # check last hidden state & loss + if stage_manager is None or stage_manager.is_last_stage(): + + if org_model.__class__.__name__ == 'BloomModel': + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + + check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) # unwrap model if org_model.__class__.__name__ == 'BloomModel': bloom = org_model - sharded_bloom = sharded_model + sharded_bloom = sharded_model.unwrap() else: bloom = org_model.transformer - sharded_bloom = sharded_model.transformer + sharded_bloom = sharded_model.unwrap().transformer # check grad - col_layer_for_check = ['h[0].self_attention.query_key_value'] - row_layer_for_check = ['h[0].self_attention.dense'] - check_grad(bloom, sharded_bloom, col_layer_for_check, atol=1e-6, rtol=1e-5, dim=0, verbose=False) - check_grad(bloom, sharded_bloom, row_layer_for_check, atol=1e-6, rtol=1e-5, dim=1, verbose=False) - - -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('enable_flash_attention', [True, False]) -@parameterize('enable_jit_fused', [True, False]) -@parameterize('use_lazy_init', [False, True]) -def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused, - use_lazy_init): + row_layer_for_check = ['h[0].self_attention.query_key_value', 'word_embeddings'] + col_layer_for_check = ['h[0].self_attention.dense'] + if stage_manager is None or stage_manager.is_first_stage(): + check_grad(bloom, sharded_bloom, row_layer_for_check, tp_group, atol=1e-6, rtol=1e-5, dim=0, verbose=False) + check_grad(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=1e-6, rtol=1e-5, dim=1, verbose=False) + + # check weights after optimizer.step() + org_optimizer.step() + sharded_optimizer.step() + if stage_manager is None or stage_manager.is_first_stage(): + check_weight(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False) + + torch.cuda.empty_cache() + + +@parameterize('test_config', [{ + 'tp_size': 2, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': True, + 'use_lazy_init': True +}, { + 'tp_size': 1, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': False, + 'use_lazy_init': False +}, { + 'tp_size': 4, + 'pp_size': 1, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}]) +def run_bloom_test(test_config): + + # TODO: add test_config for TP+DP after supporting & debugging it + # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} + + # TODO: add test_config for flash attention & jit operator after supporting + sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') + test_config['precision'] = 'float' # Do not use fp16/bf16 in testing + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - enable_flash_attention, enable_jit_fused, use_lazy_init) - check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) + + clear_layout_converter() torch.cuda.empty_cache() @@ -67,7 +111,7 @@ def check_bloom(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_bloom(): - spawn(check_bloom, 2) + spawn(check_bloom, 4) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py b/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py deleted file mode 100644 index 6695e8a687bd..000000000000 --- a/tests/test_shardformer/test_model/test_shard_bloom_pipeline.py +++ /dev/null @@ -1,90 +0,0 @@ -import pytest -import torch - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.auto_policy import get_autopolicy -from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.shard import ShardConfig -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward - - -def check_bloom_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager): - policy = get_autopolicy(model) - policy.set_model(model) - model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) - policy.set_shard_config(model_config) - layers = policy.get_held_layers() - if stage_manager.is_first_stage(): - assert len(layers) == 0 + 2 - else: - if name == 'transformers_bloom': - assert len(layers) == 1 + 1 - elif name == 'transformers_bloom_for_token_classification': - assert len(layers) == 1 + 3 - else: - assert len(layers) == 1 + 2 - - -def check_bloom_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager): - if stage_manager.stage == 0: - x = torch.randint(0, 1000, (1, 3)).cuda() - attention_mask = torch.ones_like(x).cuda() - output = sharded_model(input_ids=x, attention_mask=attention_mask) - assert output['hidden_states'].shape == (1, 3, 64) - else: - attention_mask = torch.ones((1, 3)).cuda() - hidden_states = torch.randint(0, 1000, (1, 3, 64)).to(torch.float32).cuda() - output = sharded_model( - hidden_states=hidden_states, - attention_mask=attention_mask, - ) - assert output[0].shape[0] == 1 - - -@parameterize('enable_fused_normalization', [False]) -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('use_lazy_init', [False]) -#TODO: merge this into test_shard_bloom -def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - PP_DIM = 0 - PP_SIZE = 2 - pg_mesh = ProcessGroupMesh(PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - - sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') - for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - check_bloom_model_policy(name, org_model, stage_manager) - check_bloom_model_pipeline_forward(name, sharded_model, stage_manager) - - torch.cuda.empty_cache() - - -def check_bloom(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_bloom_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_bloom(): - spawn(check_bloom, 2) - - -if __name__ == "__main__": - test_bloom() diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index c455a99d26ce..69e63ffc854e 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -1,99 +1,126 @@ -import copy -import os - import pytest import torch +from torch import distributed as dist import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.shardformer import ShardConfig, ShardFormer -from colossalai.shardformer.policies.chatglm import ChatGLMForConditionalGenerationPolicy, ChatGLMModelPolicy -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) +from colossalai.tensor.d_tensor.api import clear_layout_converter +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, run_forward +from tests.test_shardformer.test_model._utils import ( + build_model_from_hybrid_plugin, + check_grad, + check_loss, + check_output_hidden_state, + check_weight, + run_forward_backward_with_hybrid_plugin, +) + + +def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): + + org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ + build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) + + org_loss, org_output, sharded_loss, sharded_output = \ + run_forward_backward_with_hybrid_plugin( + org_model, + sharded_model, + sharded_optimizer, + data_gen_fn, + output_transform_fn, + criterion, + booster) + stage_manager = booster.plugin.stage_manager + tp_group = booster.plugin.tp_group -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # check forward - org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, - output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values']) - # do backward - org_loss.backward() - shard_loss.backward() + # check last hidden state & loss + if stage_manager is None or stage_manager.is_last_stage(): - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + if org_model.__class__.__name__ == 'ChatGLMModel': + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3, dim=1) + + check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) # unwrap model if org_model.__class__.__name__ == 'ChatGLMModel': chatglm_model = org_model - shard_chatglm_model = sharded_model + shard_chatglm_model = sharded_model.unwrap() else: chatglm_model = org_model.transformer - shard_chatglm_model = sharded_model.transformer - - # check attention grad - org_grad = chatglm_model.encoder.layers[0].self_attention.query_key_value.weight.grad - shard_grad = shard_chatglm_model.encoder.layers[0].self_attention.query_key_value.weight.grad - shard_weight = shard_chatglm_model.encoder.layers[0].self_attention.query_key_value.weight + shard_chatglm_model = sharded_model.unwrap().transformer + + # check grad + row_layer_for_check = ['encoder.layers[0].self_attention.query_key_value', 'embedding.word_embeddings'] + col_layer_for_check = ['encoder.layers[0].self_attention.dense'] + if stage_manager is None or stage_manager.is_first_stage(): + check_grad(chatglm_model, + shard_chatglm_model, + row_layer_for_check, + tp_group, + atol=1e-6, + rtol=1e-3, + dim=0, + verbose=False) + + check_grad(chatglm_model, + shard_chatglm_model, + col_layer_for_check, + tp_group, + atol=1e-6, + rtol=1e-3, + dim=1, + verbose=False) + + # check weights after optimizer.step() + org_optimizer.step() + sharded_optimizer.step() + if stage_manager is None or stage_manager.is_first_stage(): + check_weight(chatglm_model, + shard_chatglm_model, + col_layer_for_check, + tp_group, + atol=1e-4, + rtol=1e-3, + dim=1, + verbose=False) - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)] - shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}" - - # check embedding weights - org_grad = chatglm_model.embedding.word_embeddings.weight.grad - shard_grad = shard_chatglm_model.embedding.word_embeddings.weight.grad - shard_weight = shard_chatglm_model.embedding.word_embeddings.weight - - if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight): - shard_grad_list = [torch.zeros_like(shard_grad) for _ in range(2)] - torch.distributed.all_gather(shard_grad_list, shard_grad) - all_shard_grad = torch.cat(shard_grad_list, dim=0) - else: - all_shard_grad = shard_grad + torch.cuda.empty_cache() - assert torch.allclose(org_grad, all_shard_grad, - atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}" +@parameterize('test_config', [{ + 'tp_size': 2, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': True, + 'use_lazy_init': True +}, { + 'tp_size': 1, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': False, + 'use_lazy_init': False +}, { + 'tp_size': 4, + 'pp_size': 1, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}]) +def run_chatglm_test(test_config): + + # TODO: add test_config for TP+DP after supporting & debugging it + # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} + + # TODO: add test_config for flash attention & jit operator after supporting -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('enable_flash_attention', [True, False]) -@parameterize('enable_jit_fused', [True, False]) -def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused): sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm') + test_config['precision'] = 'float' # Do not use fp16/bf16 in testing + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - # create new model - org_model = model_fn().cuda() - - # shard model - shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, - enable_tensor_parallelism=enable_tensor_parallelism, - enable_flash_attention=enable_flash_attention, - enable_jit_fused=enable_jit_fused) - model_copy = copy.deepcopy(org_model) - shard_former = ShardFormer(shard_config=shard_config) - if name == "transformers_chatglm": - sharded_model, _ = shard_former.optimize(model_copy, ChatGLMModelPolicy()) - else: - sharded_model, _ = shard_former.optimize(model_copy, ChatGLMForConditionalGenerationPolicy()) - sharded_model = sharded_model.cuda() - - check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) + + clear_layout_converter() torch.cuda.empty_cache() @@ -107,7 +134,7 @@ def check_chatglm(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_chatglm(): - spawn(check_chatglm, 2) + spawn(check_chatglm, 4) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py b/tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py deleted file mode 100644 index ee474ac7be3b..000000000000 --- a/tests/test_shardformer/test_model/test_shard_chatglm_pipeline.py +++ /dev/null @@ -1,86 +0,0 @@ -import copy -import os - -import pytest -import torch - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.chatglm import ChatGLMForConditionalGenerationPolicy, ChatGLMModelPolicy -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward - - -@parameterize('enable_fused_normalization', [False]) -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('use_lazy_init', [False]) -def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm') - for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - # create new model for test - inputs = data_gen_fn() - inputs = {k: v.cuda() for k, v in inputs.items()} - input_ids = inputs['input_ids'] - hidden_size = 64 - batch_size, seq_len = input_ids.shape - hidden_state_shape = (seq_len, batch_size, hidden_size) - if name == "transformers_chatglm": - _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init, ChatGLMModelPolicy()) - if stage_manager.is_last_stage(): - hidden_states = torch.randn(*hidden_state_shape).cuda() - inputs['input_ids'] = None - inputs['hidden_states'] = hidden_states - outputs = sharded_model(**inputs) - if stage_manager.is_last_stage(): - assert outputs[0].shape == hidden_state_shape - - else: - assert outputs['hidden_states'].shape == hidden_state_shape - - if name == "transformers_chatglm_for_conditional_generation": - _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init, - ChatGLMForConditionalGenerationPolicy()) - if stage_manager.is_last_stage(): - hidden_states = torch.randn(*hidden_state_shape).cuda() - inputs['input_ids'] = None - inputs['hidden_states'] = hidden_states - outputs = sharded_model(**inputs) - if stage_manager.is_last_stage(): - assert outputs[0].shape == (batch_size, seq_len, 65024) - else: - assert outputs['hidden_states'].shape == hidden_state_shape - - torch.cuda.empty_cache() - - -def check_chatglm(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_chatglm_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_chatglm(): - spawn(check_chatglm, 4) - - -if __name__ == "__main__": - test_chatglm() diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index ead14ab111e6..c5f8d22f18c9 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -2,69 +2,139 @@ import pytest import torch +from torch import distributed as dist import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) +from colossalai.tensor.d_tensor.api import clear_layout_converter +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import ( + build_model_from_hybrid_plugin, + check_grad, + check_loss, + check_output_hidden_state, + check_weight, + run_forward_backward_with_hybrid_plugin, +) os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, - output_transform_fn, loss_fn) +def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): + + org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ + build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) + + org_loss, org_output, sharded_loss, sharded_output = \ + run_forward_backward_with_hybrid_plugin( + org_model, + sharded_model, + sharded_optimizer, + data_gen_fn, + output_transform_fn, + criterion, + booster) - # forward check - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-5) + stage_manager = booster.plugin.stage_manager + tp_group = booster.plugin.tp_group - # run backward - org_loss.backward() - shard_loss.backward() + # check last hidden state & loss + if stage_manager is None or stage_manager.is_last_stage(): - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + if org_model.__class__.__name__ == 'LlamaModel': + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + + check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) # unwrap model - if hasattr(org_model, 'model'): - llama_model = org_model.model - shard_llama_model = sharded_model.model - else: + if org_model.__class__.__name__ == 'LlamaModel': llama_model = org_model - shard_llama_model = sharded_model + shard_llama_model = sharded_model.unwrap() + else: + llama_model = org_model.model + shard_llama_model = sharded_model.unwrap().model # check grad - col_layer_for_check = ['layers[0].self_attn.q_proj', 'embed_tokens'] - row_layer_for_check = ['layers[0].self_attn.o_proj'] - check_grad(llama_model, shard_llama_model, col_layer_for_check, atol=1e-6, rtol=1e-4, dim=0, verbose=False) - check_grad(llama_model, shard_llama_model, row_layer_for_check, atol=1e-6, rtol=1e-4, dim=1, verbose=False) + row_layer_for_check = ['layers[0].self_attn.q_proj', 'embed_tokens'] + col_layer_for_check = ['layers[0].self_attn.o_proj'] + if stage_manager is None or stage_manager.is_first_stage(): + check_grad(llama_model, + shard_llama_model, + row_layer_for_check, + tp_group, + atol=1e-6, + rtol=1e-4, + dim=0, + verbose=False) + check_grad(llama_model, + shard_llama_model, + col_layer_for_check, + tp_group, + atol=1e-6, + rtol=1e-4, + dim=1, + verbose=False) + + # check weights after optimizer.step() + org_optimizer.step() + sharded_optimizer.step() + if stage_manager is None or stage_manager.is_first_stage(): + check_weight(llama_model, + shard_llama_model, + col_layer_for_check, + tp_group, + atol=1e-4, + rtol=1e-3, + dim=1, + verbose=False) + + torch.cuda.empty_cache() -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('enable_flash_attention', [True, False]) -@parameterize('use_lazy_init', [False, True]) -def run_gpt2_llama(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, use_lazy_init): +@parameterize('test_config', [{ + 'tp_size': 2, + 'pp_size': 2, + 'num_microbatches': 2, + 'enable_fused_normalization': True, + 'use_lazy_init': True +}, { + 'tp_size': 1, + 'pp_size': 2, + 'num_microbatches': 4, + 'use_lazy_init': False +}, { + 'tp_size': 4, + 'pp_size': 1, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}, { + 'tp_size': 1, + 'pp_size': 4, + 'num_microbatches': 4, + 'use_lazy_init': False +}]) +def run_llama_test(test_config): + + # TODO: add test_config for TP+DP after supporting & debugging it + # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} + + # TODO: add test_config for flash attention & jit operator after supporting + sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') + test_config['precision'] = 'float' # Do not use fp16/bf16 in testing + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - enable_flash_attention, use_lazy_init) - check_state_dict(org_model, sharded_model, name=name) - check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) + + clear_layout_converter() torch.cuda.empty_cache() def check_llama(rank, world_size, port): disable_existing_loggers() colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_gpt2_llama() + run_llama_test() @pytest.mark.dist diff --git a/tests/test_shardformer/test_model/test_shard_llama_pipeline.py b/tests/test_shardformer/test_model/test_shard_llama_pipeline.py deleted file mode 100644 index 6f1f0cb34508..000000000000 --- a/tests/test_shardformer/test_model/test_shard_llama_pipeline.py +++ /dev/null @@ -1,89 +0,0 @@ -import pytest -import torch - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer.policies.auto_policy import get_autopolicy -from colossalai.shardformer.policies.base_policy import Policy -from colossalai.shardformer.shard import ShardConfig -from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward - - -def check_llama_model_policy(name, model: torch.nn.Module, stage_manager: PipelineStageManager): - policy = get_autopolicy(model) - policy.set_model(model) - model_config = ShardConfig(pipeline_stage_manager=stage_manager, enable_tensor_parallelism=False) - policy.set_shard_config(model_config) - layers = policy.get_held_layers() - if stage_manager.is_first_stage(): - assert len(layers) == 2 + 1 - else: - if name == "transformers_llama": - assert len(layers) == 2 + 1 - else: - assert len(layers) == 2 + 2 - - -def check_llama_model_pipeline_forward(name, sharded_model, stage_manager: PipelineStageManager): - x = torch.randint(0, 1000, (2, 3)).cuda() - if stage_manager.stage == 0: - attention_mask = torch.ones_like(x).cuda() - output = sharded_model(input_ids=x, attention_mask=attention_mask) - assert output['hidden_states'].shape == (2, 3, 128) - else: - hidden_states = torch.randint(0, 1000, (2, 3, 128)).to(torch.float32).cuda() - attention_mask = torch.ones((2, 3)).cuda() - output = sharded_model( - hidden_states=hidden_states, - attention_mask=attention_mask, - ) - assert output[0] is not None - - -@parameterize('enable_fused_normalization', [False]) -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('use_lazy_init', [False]) -#TODO: merge this into test_shard_llama -def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - PP_DIM = 0 - PP_SIZE = 2 - pg_mesh = ProcessGroupMesh(PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - - sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') - - for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - check_llama_model_policy(name, org_model, stage_manager) - check_llama_model_pipeline_forward(name, sharded_model, stage_manager) - - torch.cuda.empty_cache() - - -def check_llama(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_llama_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_llama(): - spawn(check_llama, 2) - - -if __name__ == "__main__": - test_llama() diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index 99a278d4303a..d8fa8104bb07 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -1,64 +1,129 @@ -import copy import os import pytest import torch +from torch import distributed as dist import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) +from colossalai.tensor.d_tensor.api import clear_layout_converter +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_grad, check_state_dict, run_forward +from tests.test_shardformer.test_model._utils import ( + build_model_from_hybrid_plugin, + check_grad, + check_loss, + check_output_hidden_state, + check_weight, + run_forward_backward_with_hybrid_plugin, +) os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true' -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, - output_transform_fn, loss_fn) - assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-5) +def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): + + org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ + build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) + + org_loss, org_output, sharded_loss, sharded_output = \ + run_forward_backward_with_hybrid_plugin( + org_model, + sharded_model, + sharded_optimizer, + data_gen_fn, + output_transform_fn, + criterion, + booster) + + stage_manager = booster.plugin.stage_manager + tp_group = booster.plugin.tp_group - # run backward - org_loss.backward() - shard_loss.backward() + # check last hidden state & loss + if stage_manager is None or stage_manager.is_last_stage(): - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + if org_model.__class__.__name__ == 'OPTModel': + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + + check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) # unwrap model - if hasattr(org_model, 'model'): - opt_model = org_model.model - shard_opt_model = sharded_model.model - else: + if org_model.__class__.__name__ == 'OPTModel': opt_model = org_model - shard_opt_model = sharded_model + shard_opt_model = sharded_model.unwrap() + else: + opt_model = org_model.model + shard_opt_model = sharded_model.unwrap().model # check grad - col_layer_for_check = ['decoder.layers[0].self_attn.q_proj', 'decoder.embed_tokens'] - row_layer_for_check = ['decoder.layers[0].self_attn.out_proj'] - check_grad(opt_model, shard_opt_model, col_layer_for_check, atol=1e-6, rtol=1e-3, dim=0, verbose=False) - check_grad(opt_model, shard_opt_model, row_layer_for_check, atol=1e-6, rtol=1e-3, dim=1, verbose=False) - - -@parameterize('use_lazy_init', [False, True]) -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('enable_flash_attention', [True, False]) -@parameterize('enable_jit_fused', [True, False]) -def run_opt_test(use_lazy_init, enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, - enable_jit_fused): + row_layer_for_check = ['decoder.layers[0].self_attn.q_proj', 'decoder.embed_tokens'] + col_layer_for_check = ['decoder.layers[0].self_attn.out_proj'] + if stage_manager is None or stage_manager.is_first_stage(): + check_grad(opt_model, + shard_opt_model, + row_layer_for_check, + tp_group, + atol=1e-6, + rtol=1e-3, + dim=0, + verbose=False) + check_grad(opt_model, + shard_opt_model, + col_layer_for_check, + tp_group, + atol=1e-6, + rtol=1e-3, + dim=1, + verbose=False) + + # check weights after optimizer.step() + org_optimizer.step() + sharded_optimizer.step() + if stage_manager is None or stage_manager.is_first_stage(): + check_weight(opt_model, + shard_opt_model, + col_layer_for_check, + tp_group, + atol=1e-3, + rtol=1e-3, + dim=1, + verbose=False) + + torch.cuda.empty_cache() + + +@parameterize('test_config', [{ + 'tp_size': 2, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': True, + 'use_lazy_init': True +}, { + 'tp_size': 1, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': False, + 'use_lazy_init': False +}, { + 'tp_size': 4, + 'pp_size': 1, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}]) +def run_opt_test(test_config): + + # TODO: add test_config for TP+DP after supporting & debugging it + # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} + + # TODO: add test_config for flash attention & jit operator after supporting + sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') + test_config['precision'] = 'float' # Do not use fp16/bf16 in testing + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - enable_flash_attention, enable_jit_fused, use_lazy_init) - check_state_dict(org_model, sharded_model, name=name) - check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) + + clear_layout_converter() torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_opt_pipeline.py b/tests/test_shardformer/test_model/test_shard_opt_pipeline.py deleted file mode 100644 index 0684418d0a8d..000000000000 --- a/tests/test_shardformer/test_model/test_shard_opt_pipeline.py +++ /dev/null @@ -1,70 +0,0 @@ -import pytest -import torch - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_pipeline_model - - -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # TODO: add tests for forward/backward later - pass - - -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('enable_fused_normalization', [False]) -@parameterize('use_lazy_init', [False]) -#TODO: merge this into test_shard_opt -def run_opt_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - - sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') - for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): - inputs = data_gen_fn() - inputs = {k: v.cuda() for k, v in inputs.items()} - input_ids, _ = inputs['input_ids'], inputs['attention_mask'] - batch_size, seq_len = input_ids.shape - hidden_size = 128 - hidden_state_shape = (batch_size, seq_len, hidden_size) - - if not stage_manager.is_first_stage(): - # change inputs if not the first stage - - hidden_states = torch.zeros(*hidden_state_shape).cuda() - inputs['input_ids'] = None - inputs['hidden_states'] = hidden_states - - _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - sharded_model.train() - - output = sharded_model(**inputs) - if stage_manager.is_last_stage(): - assert output[0] is not None - else: - assert output['hidden_states'].shape == hidden_state_shape - torch.cuda.empty_cache() - - -def check_opt(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_opt_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_opt(): - spawn(check_opt, 4) - - -if __name__ == "__main__": - test_opt() diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index d179c8a8ee32..8a78d7c2b8ce 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -1,60 +1,127 @@ -import os - import pytest import torch import colossalai from colossalai.logging import disable_existing_loggers -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) +from colossalai.shardformer.layer.utils import Randomizer +from colossalai.tensor.d_tensor.api import clear_layout_converter +from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, check_grad, run_forward +from tests.test_shardformer.test_model._utils import ( + build_model_from_hybrid_plugin, + check_grad, + check_loss, + check_output_hidden_state, + check_weight, + run_forward_backward_with_hybrid_plugin, +) + + +def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config): + + org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = \ + build_model_from_hybrid_plugin(model_fn, loss_fn, test_config) + org_loss, org_output, sharded_loss, sharded_output = \ + run_forward_backward_with_hybrid_plugin( + org_model, + sharded_model, + sharded_optimizer, + data_gen_fn, + output_transform_fn, + criterion, + booster) -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # check forward - org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn, - output_transform_fn, loss_fn) + stage_manager = booster.plugin.stage_manager + tp_group = booster.plugin.tp_group - assert_hf_output_close(org_output, shard_output, atol=1e-3, rtol=1e-3) + # check last hidden state & loss + if stage_manager is None or stage_manager.is_last_stage(): - # do backward - org_loss.backward() - shard_loss.backward() + if org_model.__class__.__name__ == 'ViTModel': + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) - assert torch.allclose(org_loss, shard_loss, - atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}" + check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) # unwrap model if org_model.__class__.__name__ == 'ViTModel': vit_model = org_model - shard_vit_model = sharded_model + shard_vit_model = sharded_model.unwrap() else: vit_model = org_model.vit - shard_vit_model = sharded_model.vit + shard_vit_model = sharded_model.unwrap().vit # check grad - col_layer_for_check = ['encoder.layer[0].attention.attention.query'] - row_layer_for_check = ['encoder.layer[0].attention.output.dense'] - check_grad(vit_model, shard_vit_model, col_layer_for_check, atol=1e-5, rtol=1e-3, dim=0, verbose=False) - check_grad(vit_model, shard_vit_model, row_layer_for_check, atol=1e-5, rtol=1e-3, dim=1, verbose=False) + row_layer_for_check = ['encoder.layer[0].attention.attention.query', 'embeddings.patch_embeddings.projection'] + col_layer_for_check = ['encoder.layer[0].attention.output.dense'] + if stage_manager is None or stage_manager.is_first_stage(): + check_grad(vit_model, + shard_vit_model, + row_layer_for_check, + tp_group, + atol=1e-5, + rtol=1e-3, + dim=0, + verbose=False) + check_grad(vit_model, + shard_vit_model, + col_layer_for_check, + tp_group, + atol=1e-5, + rtol=1e-3, + dim=1, + verbose=False) + + # check weights after optimizer.step() + org_optimizer.step() + sharded_optimizer.step() + if stage_manager is None or stage_manager.is_first_stage(): + check_weight(vit_model, + shard_vit_model, + col_layer_for_check, + tp_group, + atol=5e-3, + rtol=1e-3, + dim=1, + verbose=False) + torch.cuda.empty_cache() + + +@parameterize('test_config', [{ + 'tp_size': 2, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}, { + 'tp_size': 1, + 'pp_size': 2, + 'num_microbatches': 4, + 'enable_fused_normalization': False, + 'use_lazy_init': False +}, { + 'tp_size': 4, + 'pp_size': 1, + 'enable_fused_normalization': True, + 'use_lazy_init': False +}]) +def run_vit_test(test_config): + + # TODO: add test_config for TP+DP after supporting & debugging it + # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} + + # TODO: add test_config for flash attention & jit operator after supporting + # TODO: fix bug when settign lazy_init for Conv2D Layers in ViT models -@parameterize('enable_fused_normalization', [True, False]) -@parameterize('enable_tensor_parallelism', [True, False]) -@parameterize('enable_flash_attention', [True, False]) -@parameterize('enable_jit_fused', [True, False]) -def run_vit_test(enable_fused_normalization, enable_tensor_parallelism, enable_flash_attention, enable_jit_fused): sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') + test_config['precision'] = 'float' # Do not use fp16/bf16 in testing + for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism, - enable_flash_attention, enable_jit_fused) - check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn) + check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) + + clear_layout_converter() + Randomizer.reset_index() torch.cuda.empty_cache() @@ -68,7 +135,7 @@ def check_vit(rank, world_size, port): @rerun_if_address_is_in_use() @clear_cache_before_run() def test_vit(): - spawn(check_vit, 2) + spawn(check_vit, 4) if __name__ == "__main__": diff --git a/tests/test_shardformer/test_model/test_shard_vit_pipeline.py b/tests/test_shardformer/test_model/test_shard_vit_pipeline.py deleted file mode 100644 index 114992a2a2a5..000000000000 --- a/tests/test_shardformer/test_model/test_shard_vit_pipeline.py +++ /dev/null @@ -1,74 +0,0 @@ -import pytest -import torch - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_pipeline_model - - -def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn): - # TODO: add tests for forward/backward later - pass - - -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('enable_fused_normalization', [False]) -@parameterize('use_lazy_init', [False]) -#TODO: merge this into test_shard_vit -def run_vit_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - DP_DIM, PP_DIM = 0, 1 - DP_SIZE, PP_SIZE = 2, 2 - pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - - sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') - - for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items(): - - inputs = data_gen_fn() - inputs = {k: v.cuda() for k, v in inputs.items()} - pixel_values = inputs['pixel_values'] - batch_size = len(pixel_values) - hidden_size = 768 - hidden_state_shape = (batch_size, 197, hidden_size) - - if not stage_manager.is_first_stage(): - # change inputs if not the first stage - hidden_states = torch.randn(*hidden_state_shape).cuda() - # inputs['pixel_values'] = None - inputs['hidden_states'] = hidden_states - - _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization, - enable_tensor_parallelism, use_lazy_init) - sharded_model.train() - - output = sharded_model(**inputs) - if stage_manager.is_last_stage(): - if name != 'transformers_vit': - assert output.loss is not None - else: - assert output['hidden_states'].shape == hidden_state_shape, \ - f'hidden_states shape is not correct, output:{output["hidden_states"].shape} is not equal to hidden_state:{hidden_state_shape}' - - torch.cuda.empty_cache() - - -def check_vit(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_vit_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_vit(): - spawn(check_vit, 4) - - -if __name__ == "__main__": - test_vit() From 1edc9b5fb3f8a9c9ec5d71a62bb33914a0d5f0c4 Mon Sep 17 00:00:00 2001 From: flybird11111 <1829166702@qq.com> Date: Fri, 11 Aug 2023 16:40:06 +0800 Subject: [PATCH 78/84] [shardformer] update tests for all optimization (#4413) [shardformer] update tests for all optimization --- colossalai/shardformer/modeling/bert.py | 5 ++- tests/kit/model_zoo/transformers/bert.py | 29 +++++++++----- .../test_model/test_shard_bert.py | 39 +++++++++++++------ 3 files changed, 50 insertions(+), 23 deletions(-) diff --git a/colossalai/shardformer/modeling/bert.py b/colossalai/shardformer/modeling/bert.py index b9d4b5fda7af..eaafd67b8968 100644 --- a/colossalai/shardformer/modeling/bert.py +++ b/colossalai/shardformer/modeling/bert.py @@ -1048,9 +1048,12 @@ def forward( final_attention_mask = final_attention_mask * scale + attention_mask else: final_attention_mask = attention_mask + + if final_attention_mask is not None: batch_size, src_len = query_layer.size()[0], query_layer.size()[2] tgt_len = key_layer.size()[2] - final_attention_mask = final_attention_mask.expand(batch_size, self.num_attention_heads, src_len, tgt_len) + final_attention_mask = final_attention_mask.expand(batch_size, self.num_attention_heads, src_len, + tgt_len).contiguous() query_layer = query_layer.permute(0, 2, 1, 3).contiguous() key_layer = key_layer.permute(0, 2, 1, 3).contiguous() diff --git a/tests/kit/model_zoo/transformers/bert.py b/tests/kit/model_zoo/transformers/bert.py index 52158596bcf8..e16d3b269ba0 100644 --- a/tests/kit/model_zoo/transformers/bert.py +++ b/tests/kit/model_zoo/transformers/bert.py @@ -69,21 +69,30 @@ def data_gen_for_mcq(): # data['labels'] = torch.tensor([0], dtype=torch.int64) input_ids = torch.tensor([[[ 101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591, - 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102 + 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102, 5442, + 1012, 102, 102 ], [ 101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096, - 2218, 1999, 1996, 2192, 1012, 102, 0, 0 + 2218, 1999, 1996, 2192, 1012, 102, 0, 0, 1012, 102, 0, 0 ]]]) - token_type_ids = torch.tensor( - [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, - 0]]]) - attention_mask = torch.tensor( - [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], - [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, - 0]]]) + token_type_ids = torch.tensor([[[ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1 + ], + [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0 + ]]]) + attention_mask = torch.tensor([[[ + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1 + ], + [ + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0 + ]]]) labels = torch.tensor([0], dtype=torch.int64) return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels) diff --git a/tests/test_shardformer/test_model/test_shard_bert.py b/tests/test_shardformer/test_model/test_shard_bert.py index fdbcd014e1b8..0a24e46d28f2 100644 --- a/tests/test_shardformer/test_model/test_shard_bert.py +++ b/tests/test_shardformer/test_model/test_shard_bert.py @@ -36,10 +36,14 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, tp_group = booster.plugin.tp_group # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if org_model.__class__.__name__ == 'BertModel': - check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) - check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3) + check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) # unwrap model if org_model.__class__.__name__ == 'BertModel': bert = org_model @@ -51,17 +55,25 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, col_layer_for_check = ['encoder.layer[0].output.dense'] row_layer_for_check = ['embeddings.word_embeddings', 'encoder.layer[0].intermediate.dense'] + if test_config['precision'] == 'fp32': + atol, rtol = 1e-4, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if stage_manager is None or stage_manager.is_first_stage(): #check_weight(bert.embeddings.word_embeddings, sharded_bert.embeddings.word_embeddings, tp_group, atol=1e-5, rtol=1e-3) #check_weight(bert.encoder.layer[0].attention.self.query, sharded_bert.encoder.layer[0].attention.self.query, tp_group, atol=5e-3, rtol=1e-3) - check_grad(bert, sharded_bert, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False) - check_grad(bert, sharded_bert, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False) + check_grad(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False) + check_grad(bert, sharded_bert, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False) # check weights after optimizer.step() org_optimizer.step() sharded_optimizer.step() + if test_config['precision'] == 'fp32': + atol, rtol = 5e-3, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if stage_manager is None or stage_manager.is_first_stage(): - check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=5e-3, rtol=1e-3, dim=1, verbose=False) + check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False) torch.cuda.empty_cache() @@ -70,23 +82,26 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'use_lazy_init': True + 'use_lazy_init': True, + 'precision': 'fp32', }, { 'tp_size': 2, 'pp_size': 2, - 'num_microbatches': 4, - 'enable_fused_normalization': False, - 'use_lazy_init': False + 'num_microbatches': 2, + 'enable_all_optimization': True, + 'use_lazy_init': True, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 4, 'pp_size': 1, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp32', }]) def run_bert_test(test_config): sub_model_zoo = model_zoo.get_sub_registry('transformers_bert') - test_config['precision'] = 'float' for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) From 108e54a0b46bf7b103110842c303ebc26318efff Mon Sep 17 00:00:00 2001 From: flybird11111 <1829166702@qq.com> Date: Mon, 14 Aug 2023 15:49:13 +0800 Subject: [PATCH 79/84] [shardformer]update t5 tests for using all optimizations. (#4407) * [shardformer] gpt2 tests fix [shardformer] test all optimizations (#4399) [shardformer] test all optimizations [shardformer] test all optimizations [shardformer] test all optimizations [shardformer] gpt2 tests fix * [shardformer]update t5 to use all optimizations --- colossalai/shardformer/README.md | 2 +- tests/kit/model_zoo/transformers/t5.py | 8 ++-- .../test_model/test_shard_t5.py | 39 +++++++++++++------ 3 files changed, 33 insertions(+), 16 deletions(-) diff --git a/colossalai/shardformer/README.md b/colossalai/shardformer/README.md index 5d00e606dc94..7dc15f0a0635 100644 --- a/colossalai/shardformer/README.md +++ b/colossalai/shardformer/README.md @@ -30,7 +30,7 @@ ### Quick Start -The sample API usage is given below(If you enable the use of flash attention, please install xformers.): +The sample API usage is given below(If you enable the use of flash attention, please install `flash_attn`. In addition, xformers's `cutlass_op` provide a supplementary optimization, It requires that the sequence length be a multiple of 8.): ```python from colossalai.shardformer import ShardConfig, Shard diff --git a/tests/kit/model_zoo/transformers/t5.py b/tests/kit/model_zoo/transformers/t5.py index 435cb6f46937..175d48963480 100644 --- a/tests/kit/model_zoo/transformers/t5.py +++ b/tests/kit/model_zoo/transformers/t5.py @@ -16,8 +16,8 @@ def data_gen_for_encoder_only(): # config = T5Config(decoder_start_token_id=0) # tokenizer = T5Tokenizer.from_pretrained("t5-small") # input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids - input_ids = torch.Tensor([[13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 1, 12]]).long() - attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]).long() + input_ids = torch.Tensor([[13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 1, 12, 1627, 5, 1, 12]]).long() + attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]).long() return dict(input_ids=input_ids, attention_mask=attention_mask) @@ -26,7 +26,7 @@ def data_gen_for_conditional_generation(): # # labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids data = data_gen_for_encoder_only() - labels = torch.Tensor([[644, 4598, 229, 19250, 5, 1, 644, 4598, 229, 19250, 5, 1]]).long() + labels = torch.Tensor([[644, 4598, 229, 19250, 5, 1, 644, 4598, 229, 19250, 5, 1, 229, 19250, 5, 1]]).long() data['labels'] = labels return data @@ -35,7 +35,7 @@ def data_gen_for_t5_model(): # decoder_inputs_ids is obtained with the following code # decoder_input_ids = model._shift_right(input_ids) data = data_gen_for_encoder_only() - decoder_input_ids = torch.Tensor([[0, 13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 5]]).long() + decoder_input_ids = torch.Tensor([[0, 13959, 1566, 12, 2968, 10, 37, 629, 19, 1627, 5, 5, 19, 1627, 5, 5]]).long() data['decoder_input_ids'] = decoder_input_ids return data diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index d807ffa06296..fb065b42250b 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -37,11 +37,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if org_model.__class__.__name__ != 'T5ForConditionalGeneration': - check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) - check_loss(org_loss, sharded_loss, atol=1e-5, rtol=1e-3) + check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) # unwrap model t5 = org_model @@ -50,14 +54,22 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, row_layer_for_check = ['shared', 'encoder.block[0].layer[0].SelfAttention.q'] # check weights and gradients + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if stage_manager is None or stage_manager.is_first_stage(): - check_grad(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-5, rtol=1e-3, dim=0) + check_grad(t5, sharded_t5, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0) # check weights after optimizer.step() org_optimizer.step() sharded_optimizer.step() + if test_config['precision'] == 'fp32': + atol, rtol = 1e-4, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if stage_manager is None or stage_manager.is_first_stage(): - check_weight(t5, sharded_t5, row_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=0, verbose=False) + check_weight(t5, sharded_t5, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False) torch.cuda.empty_cache() @@ -66,23 +78,29 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, 'tp_size': 2, 'pp_size': 2, 'num_microbatches': 2, - 'enable_fused_normalization': True, - 'use_lazy_init': True + 'enable_all_optimization': True, + 'use_lazy_init': True, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'use_lazy_init': False + 'use_lazy_init': False, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 4, 'pp_size': 1, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp32', }, { 'tp_size': 1, 'pp_size': 4, 'num_microbatches': 4, - 'use_lazy_init': False + 'use_lazy_init': False, + 'precision': 'fp32', }]) @clear_cache_before_run() def run_t5_test(test_config): @@ -93,7 +111,6 @@ def run_t5_test(test_config): # TODO: add test_config for flash attention & jit operator after supporting sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') - test_config['precision'] = 'float' # Do not use fp16/bf16 in testing for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): From 328a791d100c6dffe84026092e92db012c6cf30c Mon Sep 17 00:00:00 2001 From: flybird11111 <1829166702@qq.com> Date: Mon, 14 Aug 2023 15:51:13 +0800 Subject: [PATCH 80/84] [shardformer] update bloom/llama/vit/chatglm tests (#4420) [shardformer] update bloom/llama/vit/chatglm tests [shardformer] update opt tests [shardformer] update opt tests [shardformer] update bloom/llama/vit/chatglm tests [shardformer] update bloom/llama/vit/chatglm tests [shardformer] update bloom/llama/vit/chatglm tests --- .../test_model/test_shard_bloom.py | 43 ++++++++++------ .../test_model/test_shard_chatglm.py | 48 ++++++++++------- .../test_model/test_shard_gpt2.py | 16 +++--- .../test_model/test_shard_llama.py | 49 +++++++++++------- .../test_model/test_shard_opt.py | 51 +++++++++++-------- .../test_model/test_shard_vit.py | 48 ++++++++++------- 6 files changed, 157 insertions(+), 98 deletions(-) diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index d5a4ce083e2b..145ccf97c388 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -36,11 +36,14 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): - + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if org_model.__class__.__name__ == 'BloomModel': - check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) - check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) + check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) # unwrap model if org_model.__class__.__name__ == 'BloomModel': @@ -54,14 +57,22 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, row_layer_for_check = ['h[0].self_attention.query_key_value', 'word_embeddings'] col_layer_for_check = ['h[0].self_attention.dense'] if stage_manager is None or stage_manager.is_first_stage(): - check_grad(bloom, sharded_bloom, row_layer_for_check, tp_group, atol=1e-6, rtol=1e-5, dim=0, verbose=False) - check_grad(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=1e-6, rtol=1e-5, dim=1, verbose=False) + if test_config['precision'] == 'fp32': + atol, rtol = 1e-6, 1e-5 + else: + atol, rtol = 5e-3, 5e-3 + check_grad(bloom, sharded_bloom, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False) + check_grad(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False) # check weights after optimizer.step() org_optimizer.step() sharded_optimizer.step() if stage_manager is None or stage_manager.is_first_stage(): - check_weight(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=1e-4, rtol=1e-3, dim=1, verbose=False) + if test_config['precision'] == 'fp32': + atol, rtol = 1e-4, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 + check_weight(bloom, sharded_bloom, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False) torch.cuda.empty_cache() @@ -70,29 +81,29 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, 'tp_size': 2, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': True, - 'use_lazy_init': True + 'enable_all_optimization': True, + 'use_lazy_init': True, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': False, - 'use_lazy_init': False + 'enable_all_optimization': False, + 'use_lazy_init': False, + 'precision': 'fp32', }, { 'tp_size': 4, 'pp_size': 1, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp32', }]) def run_bloom_test(test_config): # TODO: add test_config for TP+DP after supporting & debugging it - # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} - - # TODO: add test_config for flash attention & jit operator after supporting sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') - test_config['precision'] = 'float' # Do not use fp16/bf16 in testing for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index 69e63ffc854e..e9c74b300daa 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -37,11 +37,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if org_model.__class__.__name__ == 'ChatGLMModel': - check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3, dim=1) + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol, dim=1) - check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) + check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) # unwrap model if org_model.__class__.__name__ == 'ChatGLMModel': @@ -55,12 +59,16 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, row_layer_for_check = ['encoder.layers[0].self_attention.query_key_value', 'embedding.word_embeddings'] col_layer_for_check = ['encoder.layers[0].self_attention.dense'] if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-6, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 check_grad(chatglm_model, shard_chatglm_model, row_layer_for_check, tp_group, - atol=1e-6, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=0, verbose=False) @@ -68,8 +76,8 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, shard_chatglm_model, col_layer_for_check, tp_group, - atol=1e-6, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=1, verbose=False) @@ -77,12 +85,16 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, org_optimizer.step() sharded_optimizer.step() if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-4, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 check_weight(chatglm_model, shard_chatglm_model, col_layer_for_check, tp_group, - atol=1e-4, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=1, verbose=False) @@ -93,29 +105,29 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, 'tp_size': 2, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': True, - 'use_lazy_init': True + 'enable_all_optimization': True, + 'use_lazy_init': True, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': False, - 'use_lazy_init': False + 'enable_all_optimization': False, + 'use_lazy_init': False, + 'precision': 'fp32', }, { 'tp_size': 4, 'pp_size': 1, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp32', }]) def run_chatglm_test(test_config): # TODO: add test_config for TP+DP after supporting & debugging it - # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} - - # TODO: add test_config for flash attention & jit operator after supporting sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm') - test_config['precision'] = 'float' # Do not use fp16/bf16 in testing for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 274cfaa39ad1..8b7a6bf29c8b 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -63,22 +63,22 @@ def unwrap(module): row_layer_for_check = ['wte', 'h[0].mlp.c_proj'] # check grad - if test_config['precision'] == 'fp32': - atol, rtol = 1e-4, 1e-3 - else: - atol, rtol = 5e-3, 5e-3 if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-4, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 check_grad(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False) check_grad(gpt2, sharded_gpt2, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False) # check weights after optimizer.step() org_optimizer.step() sharded_optimizer.step() - if test_config['precision'] == 'fp32': - atol, rtol = 5e-3, 1e-3 - else: - atol, rtol = 5e-3, 5e-3 if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 5e-3, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 check_weight(gpt2, sharded_gpt2, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False) torch.cuda.empty_cache() diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index c5f8d22f18c9..fa4ee43e3114 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -41,11 +41,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if org_model.__class__.__name__ == 'LlamaModel': - check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) - check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) + check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) # unwrap model if org_model.__class__.__name__ == 'LlamaModel': @@ -59,20 +63,24 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, row_layer_for_check = ['layers[0].self_attn.q_proj', 'embed_tokens'] col_layer_for_check = ['layers[0].self_attn.o_proj'] if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-6, 1e-4 + else: + atol, rtol = 5e-3, 5e-3 check_grad(llama_model, shard_llama_model, row_layer_for_check, tp_group, - atol=1e-6, - rtol=1e-4, + atol=atol, + rtol=rtol, dim=0, verbose=False) check_grad(llama_model, shard_llama_model, col_layer_for_check, tp_group, - atol=1e-6, - rtol=1e-4, + atol=atol, + rtol=rtol, dim=1, verbose=False) @@ -80,12 +88,16 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, org_optimizer.step() sharded_optimizer.step() if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-4, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 check_weight(llama_model, shard_llama_model, col_layer_for_check, tp_group, - atol=1e-4, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=1, verbose=False) @@ -96,33 +108,34 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, 'tp_size': 2, 'pp_size': 2, 'num_microbatches': 2, - 'enable_fused_normalization': True, - 'use_lazy_init': True + 'enable_all_optimization': True, + 'use_lazy_init': True, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'use_lazy_init': False + 'use_lazy_init': False, + 'precision': 'fp32', }, { 'tp_size': 4, 'pp_size': 1, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp32', }, { 'tp_size': 1, 'pp_size': 4, 'num_microbatches': 4, - 'use_lazy_init': False + 'use_lazy_init': False, + 'precision': 'fp32', }]) def run_llama_test(test_config): # TODO: add test_config for TP+DP after supporting & debugging it - # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} - - # TODO: add test_config for flash attention & jit operator after supporting sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') - test_config['precision'] = 'float' # Do not use fp16/bf16 in testing for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index d8fa8104bb07..403c3e75f52c 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -41,11 +41,14 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): - + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if org_model.__class__.__name__ == 'OPTModel': - check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) - check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) + check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) # unwrap model if org_model.__class__.__name__ == 'OPTModel': @@ -56,23 +59,27 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, shard_opt_model = sharded_model.unwrap().model # check grad - row_layer_for_check = ['decoder.layers[0].self_attn.q_proj', 'decoder.embed_tokens'] + row_layer_for_check = ['decoder.layers[0].self_attn.q_proj', 'decoder.embed_tokens'] # 'decoder.embed_tokens' col_layer_for_check = ['decoder.layers[0].self_attn.out_proj'] if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-6, 1e-3 + else: + atol, rtol = 3e-2, 3e-2 check_grad(opt_model, shard_opt_model, row_layer_for_check, tp_group, - atol=1e-6, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=0, verbose=False) check_grad(opt_model, shard_opt_model, col_layer_for_check, tp_group, - atol=1e-6, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=1, verbose=False) @@ -80,12 +87,16 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, org_optimizer.step() sharded_optimizer.step() if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-3, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 check_weight(opt_model, shard_opt_model, col_layer_for_check, tp_group, - atol=1e-3, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=1, verbose=False) @@ -96,29 +107,29 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, 'tp_size': 2, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': True, - 'use_lazy_init': True + 'enable_all_optimization': True, + 'use_lazy_init': True, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': False, - 'use_lazy_init': False + 'enable_all_optimization': False, + 'use_lazy_init': False, + 'precision': 'fp32', }, { 'tp_size': 4, 'pp_size': 1, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp32', }]) def run_opt_test(test_config): # TODO: add test_config for TP+DP after supporting & debugging it - # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} - - # TODO: add test_config for flash attention & jit operator after supporting sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') - test_config['precision'] = 'float' # Do not use fp16/bf16 in testing for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index 8a78d7c2b8ce..919bceffc847 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -37,11 +37,15 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, # check last hidden state & loss if stage_manager is None or stage_manager.is_last_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 if org_model.__class__.__name__ == 'ViTModel': - check_output_hidden_state(org_output, sharded_output, stage_manager, atol=1e-5, rtol=1e-3) + check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol) - check_loss(org_loss, sharded_loss, atol=1e-6, rtol=1e-3) + check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol) # unwrap model if org_model.__class__.__name__ == 'ViTModel': @@ -55,20 +59,24 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, row_layer_for_check = ['encoder.layer[0].attention.attention.query', 'embeddings.patch_embeddings.projection'] col_layer_for_check = ['encoder.layer[0].attention.output.dense'] if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 1e-5, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 check_grad(vit_model, shard_vit_model, row_layer_for_check, tp_group, - atol=1e-5, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=0, verbose=False) check_grad(vit_model, shard_vit_model, col_layer_for_check, tp_group, - atol=1e-5, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=1, verbose=False) @@ -76,12 +84,16 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, org_optimizer.step() sharded_optimizer.step() if stage_manager is None or stage_manager.is_first_stage(): + if test_config['precision'] == 'fp32': + atol, rtol = 5e-3, 1e-3 + else: + atol, rtol = 5e-3, 5e-3 check_weight(vit_model, shard_vit_model, col_layer_for_check, tp_group, - atol=5e-3, - rtol=1e-3, + atol=atol, + rtol=rtol, dim=1, verbose=False) @@ -92,30 +104,30 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, 'tp_size': 2, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp16', + 'initial_scale': 1, }, { 'tp_size': 1, 'pp_size': 2, 'num_microbatches': 4, - 'enable_fused_normalization': False, - 'use_lazy_init': False + 'enable_all_optimization': False, + 'use_lazy_init': False, + 'precision': 'fp32', }, { 'tp_size': 4, 'pp_size': 1, - 'enable_fused_normalization': True, - 'use_lazy_init': False + 'enable_all_optimization': True, + 'use_lazy_init': False, + 'precision': 'fp32', }]) def run_vit_test(test_config): # TODO: add test_config for TP+DP after supporting & debugging it - # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} - - # TODO: add test_config for flash attention & jit operator after supporting # TODO: fix bug when settign lazy_init for Conv2D Layers in ViT models sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') - test_config['precision'] = 'float' # Do not use fp16/bf16 in testing for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config) From 172f7fa3cf54eaff7b281ea2a91d449177867622 Mon Sep 17 00:00:00 2001 From: Hongxin Liu Date: Mon, 14 Aug 2023 17:43:33 +0800 Subject: [PATCH 81/84] [misc] resolve code factor issues (#4433) --- colossalai/booster/booster.py | 2 +- colossalai/shardformer/layer/utils.py | 2 - colossalai/shardformer/modeling/bert.py | 8 +- colossalai/shardformer/modeling/bloom.py | 12 +- colossalai/shardformer/modeling/chatglm.py | 2 +- colossalai/shardformer/modeling/gpt2.py | 2 +- colossalai/shardformer/modeling/llama.py | 6 +- colossalai/shardformer/modeling/opt.py | 2 +- colossalai/shardformer/modeling/t5.py | 6 +- colossalai/shardformer/modeling/vit.py | 2 +- colossalai/shardformer/shard/shard_config.py | 1 - .../test_tracer/test_hf_model/test_hf_gpt.py | 2 +- .../test_model/test_pure_pipeline.py | 171 ------------------ .../test_model/test_shard_bloom.py | 2 +- .../test_model/test_shard_chatglm.py | 2 +- .../test_model/test_shard_gpt2.py | 2 +- .../test_model/test_shard_llama.py | 2 +- .../test_model/test_shard_opt.py | 2 +- .../test_model/test_shard_t5.py | 4 +- .../test_model/test_shard_vit.py | 4 +- 20 files changed, 31 insertions(+), 205 deletions(-) delete mode 100644 tests/test_shardformer/test_model/test_pure_pipeline.py diff --git a/colossalai/booster/booster.py b/colossalai/booster/booster.py index 8a28b1286cfa..adb8f62a5084 100644 --- a/colossalai/booster/booster.py +++ b/colossalai/booster/booster.py @@ -139,7 +139,7 @@ def backward(self, loss: torch.Tensor, optimizer: Optimizer) -> None: loss (torch.Tensor): The loss to be backpropagated. optimizer (Optimizer): The optimizer to be updated. """ - # TODO: implement this method with plugin + # TODO(frank lee): implement this method with plugin optimizer.backward(loss) def execute_pipeline(self, diff --git a/colossalai/shardformer/layer/utils.py b/colossalai/shardformer/layer/utils.py index 09cb7bfe1407..577bef076a7e 100644 --- a/colossalai/shardformer/layer/utils.py +++ b/colossalai/shardformer/layer/utils.py @@ -29,8 +29,6 @@ class Randomizer: _INDEX = 0 def __init__(self, seed: int): - # TODO: remove colossalai.context.random - self.seed = seed # Handle CUDA rng state diff --git a/colossalai/shardformer/modeling/bert.py b/colossalai/shardformer/modeling/bert.py index eaafd67b8968..5bd1c531cc68 100644 --- a/colossalai/shardformer/modeling/bert.py +++ b/colossalai/shardformer/modeling/bert.py @@ -57,7 +57,7 @@ def bert_model_forward( hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage stage_index: Optional[List[int]] = None, ): - # TODO: add explaination of the output here. + # TODO(jianghai): add explaination of the output here. r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if @@ -113,7 +113,7 @@ def bert_model_forward( batch_size, seq_length = input_shape device = hidden_states.device - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False @@ -272,7 +272,7 @@ def bert_for_pretraining_forward( logger = logging.get_logger(__name__) return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai) left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False @@ -534,7 +534,7 @@ def bert_for_next_sentence_prediction_forward( stage_index: Optional[List[int]] = None, **kwargs, ): - #-> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: + # -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair diff --git a/colossalai/shardformer/modeling/bloom.py b/colossalai/shardformer/modeling/bloom.py index 57c45bc6adfa..12276635ecfa 100644 --- a/colossalai/shardformer/modeling/bloom.py +++ b/colossalai/shardformer/modeling/bloom.py @@ -252,7 +252,7 @@ def custom_forward(*inputs): # Add last hidden state hidden_states = self.ln_f(hidden_states) - # TODO: deal with all_hidden_states, all_self_attentions, presents + # TODO(jianghai): deal with all_hidden_states, all_self_attentions, presents if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) @@ -307,7 +307,7 @@ def bloom_for_causal_lm_forward(self: BloomForCausalLM, raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False @@ -402,7 +402,7 @@ def bloom_for_sequence_classification_forward( return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False @@ -431,7 +431,7 @@ def bloom_for_sequence_classification_forward( all_cross_attentions = None if stage_manager.is_last_stage(): batch_size = hidden_states.shape[0] - #update batch size + # update batch size hidden_states = transformer_outputs[0] logits = self.score(hidden_states) @@ -525,7 +525,7 @@ def bloom_for_token_classification_forward( return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False @@ -611,7 +611,7 @@ def bloom_for_question_answering_forward( logger = logging.get_logger(__name__) return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False diff --git a/colossalai/shardformer/modeling/chatglm.py b/colossalai/shardformer/modeling/chatglm.py index a95966c3b99e..409e2e1f5497 100644 --- a/colossalai/shardformer/modeling/chatglm.py +++ b/colossalai/shardformer/modeling/chatglm.py @@ -152,7 +152,7 @@ def chatglm_model_forward( if output_hidden_states is not None else self.config.output_hidden_states) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None diff --git a/colossalai/shardformer/modeling/gpt2.py b/colossalai/shardformer/modeling/gpt2.py index a12a9796fa8a..47835d5d5468 100644 --- a/colossalai/shardformer/modeling/gpt2.py +++ b/colossalai/shardformer/modeling/gpt2.py @@ -57,7 +57,7 @@ def gpt2_model_forward( logger = logging.get_logger(__name__) # Preprocess passed in arguments - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None diff --git a/colossalai/shardformer/modeling/llama.py b/colossalai/shardformer/modeling/llama.py index 2f54daac586a..f1d2998bbee4 100644 --- a/colossalai/shardformer/modeling/llama.py +++ b/colossalai/shardformer/modeling/llama.py @@ -65,7 +65,7 @@ def llama_model_forward( seq_length_with_past = seq_length past_key_values_length = 0 - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False @@ -216,7 +216,7 @@ def llama_for_causal_lm_forward( if output_hidden_states is not None else self.config.output_hidden_states) return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False @@ -301,7 +301,7 @@ def llama_for_sequence_classification_forward( logger = logging.get_logger(__name__) return_dict = return_dict if return_dict is not None else self.config.use_return_dict - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False diff --git a/colossalai/shardformer/modeling/opt.py b/colossalai/shardformer/modeling/opt.py index 9afdfff4d71d..b4251f33b457 100644 --- a/colossalai/shardformer/modeling/opt.py +++ b/colossalai/shardformer/modeling/opt.py @@ -148,7 +148,7 @@ def opt_model_forward( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") use_cache = False - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None diff --git a/colossalai/shardformer/modeling/t5.py b/colossalai/shardformer/modeling/t5.py index d622da452366..9cc071f91dfc 100644 --- a/colossalai/shardformer/modeling/t5.py +++ b/colossalai/shardformer/modeling/t5.py @@ -50,7 +50,7 @@ def t5_stack_forward( logger = logging.get_logger(__name__) - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None @@ -285,7 +285,7 @@ def t5_model_forward( logger = logging.get_logger(__name__) - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None @@ -422,7 +422,7 @@ def t5_for_conditional_generation_forward( logger = logging.get_logger(__name__) - # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. + # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None diff --git a/colossalai/shardformer/modeling/vit.py b/colossalai/shardformer/modeling/vit.py index eb0ea4c7502b..9fc0b7488803 100644 --- a/colossalai/shardformer/modeling/vit.py +++ b/colossalai/shardformer/modeling/vit.py @@ -96,7 +96,7 @@ def pp_forward( if pixel_values is None: raise ValueError("You have to specify pixel_values") - # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) + # TODO(FoolPlayer): maybe have a cleaner way to cast the input (from `ImageProcessor` side?) expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype if pixel_values.dtype != expected_dtype: pixel_values = pixel_values.to(expected_dtype) diff --git a/colossalai/shardformer/shard/shard_config.py b/colossalai/shardformer/shard/shard_config.py index ec6e0cd0d4be..0c28f115d018 100644 --- a/colossalai/shardformer/shard/shard_config.py +++ b/colossalai/shardformer/shard/shard_config.py @@ -29,7 +29,6 @@ class ShardConfig: enable_flash_attention: bool = False enable_jit_fused: bool = False - # TODO: add support for tensor parallel # pipeline_parallel_size: int # data_parallel_size: int # tensor_parallel_mode: Literal['1d', '2d', '2.5d', '3d'] diff --git a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py index e29afe786c46..1cd3b90db917 100644 --- a/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py +++ b/tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py @@ -15,7 +15,7 @@ def test_gpt(): for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items(): model = model_fn() - # TODO: support the following models + # TODO(ver217): support the following models # 1. GPT2DoubleHeadsModel # as they are not supported, let's skip them if model.__class__.__name__ in ['GPT2DoubleHeadsModel', 'GPT2ForQuestionAnswering']: diff --git a/tests/test_shardformer/test_model/test_pure_pipeline.py b/tests/test_shardformer/test_model/test_pure_pipeline.py deleted file mode 100644 index 31e76ef5107c..000000000000 --- a/tests/test_shardformer/test_model/test_pure_pipeline.py +++ /dev/null @@ -1,171 +0,0 @@ -import copy -import random -from typing import Any, Callable, Iterator, List, Optional, Tuple - -import numpy as np -import pytest -import torch -import torch.distributed as dist -from torch.nn import Module -from torch.optim import Optimizer -from torch.optim.lr_scheduler import _LRScheduler as LRScheduler -from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler - -import colossalai -from colossalai.cluster import ProcessGroupMesh -from colossalai.interface import ModelWrapper, OptimizerWrapper -from colossalai.logging import disable_existing_loggers -from colossalai.pipeline.schedule import OneForwardOneBackwardSchedule -from colossalai.pipeline.stage_manager import PipelineStageManager -from colossalai.shardformer import ShardConfig, ShardFormer -from colossalai.testing import ( - assert_hf_output_close, - clear_cache_before_run, - parameterize, - rerun_if_address_is_in_use, - spawn, -) -from tests.kit.model_zoo import model_zoo -from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward - -DP_AXIS, PP_AXIS, TP_AXIS = 0, 1, 2 - - -class PipelineOptimizer(OptimizerWrapper): - - def __init__(self, optim: Optimizer, model: Module): - super().__init__(optim) - params = set(model.parameters()) - new_param_groups = [] - for group in optim.param_groups: - params = [p for p in group['params'] if p in params] - new_param_groups.append({**group, 'params': params}) - optim.__setstate__({'param_groups': new_param_groups}) - # TODO: support amp - - -class PipelinedModel(ModelWrapper): - - def __init__(self, module: Module, shard_config: ShardConfig, stage_manager: PipelineStageManager) -> None: - self.stage_manager = stage_manager - shardformer = ShardFormer(shard_config) - module, self.shared_params = shardformer.optimize(module) - self.shared_param_process_groups = [] - super().__init__(module) - - -def prepare_dataloader(dataset, batch_size, shuffle=False, seed=1024, drop_last=False, pin_memory=False, num_workers=0): - sampler = DistributedSampler( - dataset, - # rank=self.pg_mesh.coordinate(DP_AXIS), - shuffle=shuffle) - - # Deterministic dataloader - def seed_worker(worker_id): - worker_seed = seed - np.random.seed(worker_seed) - torch.manual_seed(worker_seed) - random.seed(worker_seed) - - return DataLoader( - dataset, - batch_size=batch_size, - sampler=sampler, - worker_init_fn=seed_worker, - drop_last=drop_last, - pin_memory=pin_memory, - num_workers=num_workers, - ) - - -def execute_pipeline( - data_iter: Iterator, - model: PipelinedModel, - criterion: Callable[[Any, Any], torch.Tensor], - optimizer: PipelineOptimizer, - return_loss: bool = True, - return_outputs: bool = False, - schedule: OneForwardOneBackwardSchedule = None, -) -> dict: - # return loss or outputs if needed - outputs = schedule.forward_backward_step(model, optimizer, data_iter, criterion, return_loss, return_outputs) - return outputs - - -class data_loader(): - - def __getitem__(self, x): - return torch.ones((4, 128), dtype=torch.int).cuda() * 10 - - -def loss(y, x): - return (y[0].float().mean() - x[0].float().mean()) - - -@parameterize('enable_fused_normalization', [False]) -@parameterize('enable_tensor_parallelism', [False]) -@parameterize('use_lazy_init', [False]) -def run_llama_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init): - PP_DIM = 0 - PP_SIZE = 2 - RANK_TO_COORDINATE = { - 0: (0, 0), - 1: (0, 1), - 2: (1, 0), - 3: (1, 1), - } - PP_RANKS_IN_GROUP = { - 0: [0, 1], - 1: [0, 1], - 2: [2, 3], - 3: [2, 3], - } - - pg_mesh = ProcessGroupMesh(PP_SIZE) - stage_manager = PipelineStageManager(pg_mesh, PP_DIM) - sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') - for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items(): - if name != 'transformers_llama': - continue - num_microbatches = 2 - org_model = model_fn().cuda() - data_iter = iter(data_loader()) - - model_copy = copy.deepcopy(org_model) - batch = next(data_iter) - with torch.no_grad(): - y = model_copy(batch) - org_loss = loss(y, batch) - optimizer = torch.optim.AdamW(org_model.parameters(), lr=1e-3) - schedule = OneForwardOneBackwardSchedule(num_microbatches, stage_manager) - shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization, - enable_tensor_parallelism=enable_tensor_parallelism, - pipeline_stage_manager=stage_manager) - pipelined_model = PipelinedModel(org_model, shard_config, stage_manager) - pp_optimizer = PipelineOptimizer(optimizer, pipelined_model) - results = execute_pipeline(data_iter, pipelined_model, loss, pp_optimizer, schedule=schedule) - - if stage_manager.is_last_stage(): - assert results['loss'] == org_loss - else: - assert results['loss'] is None - assert results['outputs'] is None - torch.cuda.empty_cache() - - -def check_llama(rank, world_size, port): - disable_existing_loggers() - colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') - run_llama_test() - - -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_llama(): - spawn(check_llama, 2) - - -if __name__ == "__main__": - test_llama() diff --git a/tests/test_shardformer/test_model/test_shard_bloom.py b/tests/test_shardformer/test_model/test_shard_bloom.py index 145ccf97c388..ed0d1d8e401d 100644 --- a/tests/test_shardformer/test_model/test_shard_bloom.py +++ b/tests/test_shardformer/test_model/test_shard_bloom.py @@ -101,7 +101,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, }]) def run_bloom_test(test_config): - # TODO: add test_config for TP+DP after supporting & debugging it + # TODO(baizhou): add test_config for TP+DP after supporting & debugging it sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom') diff --git a/tests/test_shardformer/test_model/test_shard_chatglm.py b/tests/test_shardformer/test_model/test_shard_chatglm.py index e9c74b300daa..bb77759048b3 100644 --- a/tests/test_shardformer/test_model/test_shard_chatglm.py +++ b/tests/test_shardformer/test_model/test_shard_chatglm.py @@ -125,7 +125,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, }]) def run_chatglm_test(test_config): - # TODO: add test_config for TP+DP after supporting & debugging it + # TODO(baizhou): add test_config for TP+DP after supporting & debugging it sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm') diff --git a/tests/test_shardformer/test_model/test_shard_gpt2.py b/tests/test_shardformer/test_model/test_shard_gpt2.py index 8b7a6bf29c8b..ca086bf12776 100644 --- a/tests/test_shardformer/test_model/test_shard_gpt2.py +++ b/tests/test_shardformer/test_model/test_shard_gpt2.py @@ -110,7 +110,7 @@ def unwrap(module): @clear_cache_before_run() def run_gpt2_test(test_config): - # TODO: add test_config for TP+DP after supporting & debugging it + # TODO(baizhou): add test_config for TP+DP after supporting & debugging it sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt') diff --git a/tests/test_shardformer/test_model/test_shard_llama.py b/tests/test_shardformer/test_model/test_shard_llama.py index fa4ee43e3114..30ebdfbe5cd9 100644 --- a/tests/test_shardformer/test_model/test_shard_llama.py +++ b/tests/test_shardformer/test_model/test_shard_llama.py @@ -133,7 +133,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, }]) def run_llama_test(test_config): - # TODO: add test_config for TP+DP after supporting & debugging it + # TODO(baizhou): add test_config for TP+DP after supporting & debugging it sub_model_zoo = model_zoo.get_sub_registry('transformers_llama') diff --git a/tests/test_shardformer/test_model/test_shard_opt.py b/tests/test_shardformer/test_model/test_shard_opt.py index 403c3e75f52c..8d1154d82638 100644 --- a/tests/test_shardformer/test_model/test_shard_opt.py +++ b/tests/test_shardformer/test_model/test_shard_opt.py @@ -127,7 +127,7 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, }]) def run_opt_test(test_config): - # TODO: add test_config for TP+DP after supporting & debugging it + # TODO(baizhou): add test_config for TP+DP after supporting & debugging it sub_model_zoo = model_zoo.get_sub_registry('transformers_opt') diff --git a/tests/test_shardformer/test_model/test_shard_t5.py b/tests/test_shardformer/test_model/test_shard_t5.py index fb065b42250b..066f7ee815b4 100644 --- a/tests/test_shardformer/test_model/test_shard_t5.py +++ b/tests/test_shardformer/test_model/test_shard_t5.py @@ -105,10 +105,10 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, @clear_cache_before_run() def run_t5_test(test_config): - # TODO: add plugin_config for TP+DP after supporting & debugging it + # TODO(baizhou): add plugin_config for TP+DP after supporting & debugging it # {'tp_size': 2, 'pp_size': 1, 'enable_fused_normalization': True} - # TODO: add test_config for flash attention & jit operator after supporting + # TODO(baizhou): add test_config for flash attention & jit operator after supporting sub_model_zoo = model_zoo.get_sub_registry('transformers_t5') diff --git a/tests/test_shardformer/test_model/test_shard_vit.py b/tests/test_shardformer/test_model/test_shard_vit.py index 919bceffc847..18df8ef555f2 100644 --- a/tests/test_shardformer/test_model/test_shard_vit.py +++ b/tests/test_shardformer/test_model/test_shard_vit.py @@ -124,8 +124,8 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, }]) def run_vit_test(test_config): - # TODO: add test_config for TP+DP after supporting & debugging it - # TODO: fix bug when settign lazy_init for Conv2D Layers in ViT models + # TODO(baizhou): add test_config for TP+DP after supporting & debugging it + # TODO(baizhou): fix bug when settign lazy_init for Conv2D Layers in ViT models sub_model_zoo = model_zoo.get_sub_registry('transformers_vit') From 922302263b8f04d135cb88792ddeb4a17383dc58 Mon Sep 17 00:00:00 2001 From: ver217 Date: Tue, 15 Aug 2023 17:07:29 +0800 Subject: [PATCH 82/84] [misc] update requirements --- requirements/requirements-test.txt | 4 +--- requirements/requirements.txt | 3 +-- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index a37d00326a08..ba5ea0936010 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -16,7 +16,5 @@ triton==2.0.0.dev20221202 requests==2.27.1 # downgrade to avoid huggingface error https://github.com/huggingface/transformers/issues/17611 SentencePiece ninja -flash_attn>=2.0 +flash_attn==2.0.5 datasets -ninja -flash-attn>=2.0 diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 65eecce2c34f..9aa5f2822e40 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -8,7 +8,6 @@ click fabric contexttimer ninja -torch>=1.11 +torch>=1.12 safetensors -flash_attn>=2.0 einops From 73a4144b9101f0be94424025f12fd8f9b67f1df8 Mon Sep 17 00:00:00 2001 From: ver217 Date: Tue, 15 Aug 2023 17:59:12 +0800 Subject: [PATCH 83/84] [shardformer] fix embedding --- colossalai/shardformer/layer/embedding.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/colossalai/shardformer/layer/embedding.py b/colossalai/shardformer/layer/embedding.py index f07a93bd6908..847ca175ad57 100644 --- a/colossalai/shardformer/layer/embedding.py +++ b/colossalai/shardformer/layer/embedding.py @@ -214,6 +214,9 @@ def __init__(self, self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition + # padding index + self.padding_idx = self._select_padding_idx(padding_idx) + # offset the seed with randomizer index and rank seed = torch.random.initial_seed() self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group) From 5d4efdf58fcdbe78dbce077189005d89d254aa2e Mon Sep 17 00:00:00 2001 From: ver217 Date: Tue, 15 Aug 2023 18:56:16 +0800 Subject: [PATCH 84/84] [shardformer] fix import --- colossalai/shardformer/layer/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/colossalai/shardformer/layer/__init__.py b/colossalai/shardformer/layer/__init__.py index 0c44e6621711..c4586d18b90c 100644 --- a/colossalai/shardformer/layer/__init__.py +++ b/colossalai/shardformer/layer/__init__.py @@ -3,10 +3,11 @@ from .linear import Linear1D_Col, Linear1D_Row from .loss import cross_entropy_1d from .normalization import FusedLayerNorm, FusedRMSNorm +from .parallel_module import ParallelModule from .qkv_fused_linear import FusedLinear1D_Col, GPT2FusedLinearConv1D_Col, GPT2FusedLinearConv1D_Row __all__ = [ "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", 'GPT2FusedLinearConv1D_Col', 'GPT2FusedLinearConv1D_Row', 'DropoutForParallelInput', 'DropoutForReplicatedInput', "cross_entropy_1d", - 'FusedLayerNorm', 'FusedRMSNorm', 'FusedLinear1D_Col' + 'FusedLayerNorm', 'FusedRMSNorm', 'FusedLinear1D_Col', 'ParallelModule' ]