[chatgpt]Reward Model Training Process update

applications/ChatGPT/chatgpt/dataset/__init__.py

@@ -1,4 +1,4 @@
-from .reward_dataset import RewardDataset
+from .reward_dataset import RmStaticDataset, HhRlhfDataset
 from .utils import is_rank_0
 
-__all__ = ['RewardDataset', 'is_rank_0']
+__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0']

applications/ChatGPT/chatgpt/dataset/reward_dataset.py

@@ -5,25 +5,80 @@
 
 from .utils import is_rank_0
 
-
-class RewardDataset(Dataset):
+# Dahaos/rm-static
+class RmStaticDataset(Dataset):
     """
     Dataset for reward model
 
     Args:
         dataset: dataset for reward model
         tokenizer: tokenizer for reward model
         max_length: max length of input
+        special_token: special token at the end of sentence
     """
 
-    def __init__(self, dataset, tokenizer: Callable, max_length: int) -> None:
+    def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None:
         super().__init__()
         self.chosen = []
         self.reject = []
+        if special_token is None:
+            self.end_token = tokenizer.eos_token
+        else:
+            self.end_token = special_token
         for data in tqdm(dataset, disable=not is_rank_0()):
             prompt = data['prompt']
 
-            chosen = prompt + data['chosen'] + "<|endoftext|>"
+            chosen = prompt + data['chosen'] + self.end_token
+            chosen_token = tokenizer(chosen,
+                                     max_length=max_length,
+                                     padding="max_length",
+                                     truncation=True,
+                                     return_tensors="pt")
+            self.chosen.append({
+                "input_ids": chosen_token['input_ids'],
+                "attention_mask": chosen_token['attention_mask']
+            })
+
+            reject = prompt + data['rejected'] + self.end_token
+            reject_token = tokenizer(reject,
+                                     max_length=max_length,
+                                     padding="max_length",
+                                     truncation=True,
+                                     return_tensors="pt")
+            self.reject.append({
+                "input_ids": reject_token['input_ids'],
+                "attention_mask": reject_token['attention_mask']
+            })
+
+    def __len__(self):
+        length = len(self.chosen)
+        return length
+
+    def __getitem__(self, idx):
+        return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][
+            "input_ids"], self.reject[idx]["attention_mask"]
+
+# Anthropic/hh-rlhf
+class HhRlhfDataset(Dataset):
+    """
+    Dataset for reward model
+
+    Args:
+        dataset: dataset for reward model
+        tokenizer: tokenizer for reward model
+        max_length: max length of input
+        special_token: special token at the end of sentence
+    """
+    def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None:
+        super().__init__()
+        self.chosen = []
+        self.reject = []
+        if special_token is None:
+            self.end_token = tokenizer.eos_token
+        else:
+            self.end_token = special_token
+        for data in tqdm(dataset, disable=not is_rank_0()):
+            chosen = data['chosen'] + self.end_token
             chosen_token = tokenizer(chosen,
                                      max_length=max_length,
                                      padding="max_length",
@@ -34,7 +89,7 @@ def __init__(self, dataset, tokenizer: Callable, max_length: int) -> None:
                 "attention_mask": chosen_token['attention_mask']
             })
 
-            reject = prompt + data['rejected'] + "<|endoftext|>"
+            reject = data['rejected'] + self.end_token
             reject_token = tokenizer(reject,
                                      max_length=max_length,
                                      padding="max_length",

applications/ChatGPT/chatgpt/models/__init__.py

@@ -1,4 +1,4 @@
 from .base import Actor, Critic, RewardModel
-from .loss import PairWiseLoss, PolicyLoss, PPOPtxActorLoss, ValueLoss
+from .loss import PolicyLoss, PPOPtxActorLoss, ValueLoss, LogSigLoss, LogExpLoss
 
-__all__ = ['Actor', 'Critic', 'RewardModel', 'PolicyLoss', 'ValueLoss', 'PPOPtxActorLoss', 'PairWiseLoss']
+__all__ = ['Actor', 'Critic', 'RewardModel', 'PolicyLoss', 'ValueLoss', 'PPOPtxActorLoss', 'LogSigLoss', 'LogExpLoss']

applications/ChatGPT/chatgpt/models/bloom/bloom_rm.py

@@ -33,4 +33,5 @@ def __init__(self,
         if checkpoint:
             model.gradient_checkpointing_enable()
         value_head = nn.Linear(model.config.hidden_size, 1)
+        value_head.weight.data.normal_(mean=0.0, std=1/(model.config.hidden_size + 1))
         super().__init__(model, value_head, lora_rank, lora_train_bias)

applications/ChatGPT/chatgpt/models/gpt/gpt_rm.py

@@ -35,4 +35,5 @@ def __init__(self,
             model.gradient_checkpointing_enable()
 
         value_head = nn.Linear(model.config.n_embd, 1)
+        value_head.weight.data.normal_(mean=0.0, std=1/(model.config.n_embd + 1))
         super().__init__(model, value_head, lora_rank, lora_train_bias)

applications/ChatGPT/chatgpt/models/loss.py

@@ -93,13 +93,23 @@ def forward(self,
         return policy_loss + self.pretrain_coef * lm_loss
 
 
-class PairWiseLoss(nn.Module):
+class LogSigLoss(nn.Module):
     """
     Pairwise Loss for Reward Model
+    Details: https://arxiv.org/abs/2203.02155
     """
-
     def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor:
         probs = torch.sigmoid(chosen_reward - reject_reward)
         log_probs = torch.log(probs)
         loss = -log_probs.mean()
         return loss
+
+
+class LogExpLoss(nn.Module):
+    """
+    Pairwise Loss for Reward Model
+    Details: https://arxiv.org/abs/2204.05862
+    """
+    def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor:
+        loss = torch.log(1 + torch.exp(reject_reward - chosen_reward)).mean()
+        return loss

applications/ChatGPT/chatgpt/models/opt/opt_rm.py

@@ -34,4 +34,5 @@ def __init__(self,
             model.gradient_checkpointing_enable()
 
         value_head = nn.Linear(model.config.word_embed_proj_dim, 1)
+        value_head.weight.data.normal_(mean=0.0, std=1/(model.config.word_embed_proj_dim + 1))
         super().__init__(model, value_head, lora_rank, lora_train_bias)

applications/ChatGPT/chatgpt/trainer/rm.py

@@ -1,13 +1,12 @@
 from abc import ABC
-
+import pandas as pd
 import loralib as lora
 import torch
-from chatgpt.dataset import RewardDataset
-from chatgpt.models.loss import PairWiseLoss
-from torch.optim import Adam, Optimizer
-from torch.utils.data import DataLoader
+from datetime import datetime
+from torch.optim import Optimizer, lr_scheduler
+from torch.utils.data import DataLoader, Dataset
 from tqdm import tqdm
-
+ 
 from .strategies import Strategy
 from .utils import is_rank_0
 
@@ -20,74 +19,102 @@ class RewardModelTrainer(ABC):
         model (torch.nn.Module): the model to train
         strategy (Strategy): the strategy to use for training
         optim(Optimizer): the optimizer to use for training
-        train_dataset (RewardDataset): the dataset to use for training
-        eval_dataset (RewardDataset): the dataset to use for evaluation
+        loss_fn (callable): the loss function to use for training
+        train_dataset (Dataset): the dataset to use for training
+        valid_dataset (Dataset): the dataset to use for validation
+        eval_dataset (Dataset): the dataset to use for evaluation
         batch_size (int, defaults to 1): the batch size while training
         max_epochs (int, defaults to 2): the number of epochs to train
-        optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer
     """
 
     def __init__(
         self,
         model,
         strategy: Strategy,
         optim: Optimizer,
-        train_dataset: RewardDataset,
-        eval_dataset: RewardDataset,
+        loss_fn,
+        train_dataset: Dataset,
+        valid_dataset: Dataset,
+        eval_dataset: Dataset,
         batch_size: int = 1,
-        max_epochs: int = 2,
+        max_epochs: int = 1,
     ) -> None:
         super().__init__()
         self.strategy = strategy
         self.epochs = max_epochs
-        self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size)
-        self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size)
-
+        self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+        self.valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True)
+        self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size, shuffle=True)
+
         self.model = strategy.setup_model(model)
-        if "DDP" in str(self.strategy):
-            self.model = self.model.module
-        self.loss_fn = PairWiseLoss()
+        self.loss_fn = loss_fn
         self.optimizer = strategy.setup_optimizer(optim, self.model)
+        self.scheduler = lr_scheduler.CosineAnnealingLR(self.optimizer, self.train_dataloader.__len__()//100)
+
 
-    def fit(self, use_lora):
+    def eval_acc(self, dataloader):
+        dist = 0
+        on = 0
+        cnt = 0
+        self.model.eval()
+        with torch.no_grad():
+            for chosen_ids, c_mask, reject_ids, r_mask in dataloader:
+                chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device())
+                c_mask = c_mask.squeeze(1).to(torch.cuda.current_device())
+                reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device())
+                r_mask = r_mask.squeeze(1).to(torch.cuda.current_device())
+                chosen_reward = self.model(chosen_ids, attention_mask=c_mask)
+                reject_reward = self.model(reject_ids, attention_mask=r_mask)
+                for i in range(len(chosen_reward)):
+                    cnt += 1
+                    if chosen_reward[i] > reject_reward[i]:
+                        on += 1
+                dist += (chosen_reward - reject_reward).mean().item()
+            dist_mean = dist / len(dataloader)
+            acc = on / cnt
+        self.model.train()
+        return dist_mean, acc
+
+
+    def fit(self):
+        time = datetime.now()
         epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0())
         for epoch in range(self.epochs):
             step_bar = tqdm(range(self.train_dataloader.__len__()),
                             desc='Train step of epoch %d' % epoch,
                             disable=not is_rank_0())
             # train
             self.model.train()
+            cnt = 0
+            acc = 0
+            dist = 0
             for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader:
-                chosen_ids = chosen_ids.squeeze(1).cuda()
-                c_mask = c_mask.squeeze(1).cuda()
-                reject_ids = reject_ids.squeeze(1).cuda()
-                r_mask = r_mask.squeeze(1).cuda()
+                chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device())
+                c_mask = c_mask.squeeze(1).to(torch.cuda.current_device())
+                reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device())
+                r_mask = r_mask.squeeze(1).to(torch.cuda.current_device())
                 chosen_reward = self.model(chosen_ids, attention_mask=c_mask)
                 reject_reward = self.model(reject_ids, attention_mask=r_mask)
                 loss = self.loss_fn(chosen_reward, reject_reward)
                 self.strategy.backward(loss, self.model, self.optimizer)
                 self.strategy.optimizer_step(self.optimizer)
                 self.optimizer.zero_grad()
+                cnt += 1
+                if cnt == 100:
+                    self.scheduler.step()
+                    dist, acc = self.eval_acc(self.valid_dataloader)
+                    cnt = 0
+                    if is_rank_0():
+                        log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc'])
+                        log.to_csv('log_%s.csv' % time, mode='a', header=False, index=False)
                 step_bar.update()
-                step_bar.set_postfix({'loss': loss.item()})
-
+                step_bar.set_postfix({'dist': dist, 'acc': acc})
+                
             # eval
-            self.model.eval()
-            with torch.no_grad():
-                dist = 0
-                loss_sum = 0
-                for chosen_ids, c_mask, reject_ids, r_mask in self.eval_dataloader:
-                    chosen_ids = chosen_ids.squeeze(1).cuda()
-                    c_mask = c_mask.squeeze(1).cuda()
-                    reject_ids = reject_ids.squeeze(1).cuda()
-                    r_mask = r_mask.squeeze(1).cuda()
-                    chosen_reward = self.model(chosen_ids, attention_mask=c_mask)
-                    reject_reward = self.model(reject_ids, attention_mask=r_mask)
-                    dist += (chosen_reward - reject_reward).mean().item()
-                    loss = self.loss_fn(chosen_reward, reject_reward)
-                    loss_sum += loss.item()
-                dist_mean = dist / self.eval_dataloader.__len__()
-                loss_mean = loss_sum / self.eval_dataloader.__len__()
+            dist, acc = self.eval_acc(self.eval_dataloader)
+            if is_rank_0():
+                    log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc'])
+                    log.to_csv('log.csv', mode='a', header=False, index=False)
             epoch_bar.update()
-            step_bar.set_postfix({'loss': loss_mean, 'dist_mean': dist_mean})
+            step_bar.set_postfix({'dist': dist, 'acc': acc})
             step_bar.close()

applications/ChatGPT/examples/README.md

@@ -7,26 +7,42 @@ pip install -r requirements.txt
 ```
 
 ## Train the reward model (Stage 2)
-We use [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) as dataset to train our reward model. It is a dataset of chosen & rejected response of the same prompt.
-
-You can download the dataset from huggingface automatically.
-
 Use these code to train your reward model.
-
 ```shell
-# Naive reward model training
-python train_reward_model.py --pretrain <your model path> --model <your model type> --strategy naive
+# Take naive reward model training with opt-350m as example
+python train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy naive
 # use colossalai_zero2
-torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain <your model path> --model <your model type> --strategy colossalai_zero2
+torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "facebook/opt-350m" --model 'opt' --strategy colossalai_zero2
 ```
 
+### 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 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/2112.00861).
+
+### Experiment result
+Model performance in [Anthropics paper](https://arxiv.org/abs/2112.00861):
+
+<div align=center> <img width="512" alt="image" src="https://user-images.githubusercontent.com/70618399/225263321-8d64c3a8-6877-4cc8-9b61-0e1c52d3d94f.png">
+
+<div align=left>Our training & test result of bloom-560m for 1 epoch:
+
+<div align=center> <img width="512" alt="image" src="https://user-images.githubusercontent.com/70618399/225262950-a7f0a686-25de-44ec-98f2-11b83ea86674.png">
+
+<div align=left>
+
 ## Train with dummy prompt data (Stage 3)
 
-This script supports 3 strategies:
+This script supports 4 kinds of strategies:
 
 - naive
 - ddp
-- colossalai
+- colossalai_zero2
+- colossalai_gemini
 
 It uses random generated prompt data.
 
@@ -53,7 +69,7 @@ We use [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-cha
 
 You should download `prompts.csv` first.
 
-This script also supports 3 strategies.
+This script also supports 4 strategies.
 
 ```shell
 # display cli help
@@ -75,6 +91,9 @@ python inference.py --model_path <your actor model path> --model <your model typ
 python inference.py --model_path ./actor_checkpoint_prompts.pt --pretrain bigscience/bloom-560m --model bloom
 ```
 
+## Attention
+The examples is just a demo for testing our progress of RM and PPO training.
+
 
 #### data
 - [x] [rm-static](https://huggingface.co/datasets/Dahoas/rm-static)