[chatgpt] Add saving ckpt callback for PPO (#2880)

* add checkpoint callback for chatgpt

* add save ckpt callbacks for ppo

---------

Co-authored-by: Fazzie-Maqianli <55798671+Fazziekey@users.noreply.github.com>

applications/ChatGPT/chatgpt/trainer/callbacks/__init__.py

@@ -1,4 +1,5 @@
 from .base import Callback
 from .performance_evaluator import PerformanceEvaluator
+from .save_checkpoint import SaveCheckpoint
 
-__all__ = ['Callback', 'PerformanceEvaluator']
+__all__ = ['Callback', 'PerformanceEvaluator', 'SaveCheckpoint']

applications/ChatGPT/chatgpt/trainer/callbacks/save_checkpoint.py

@@ -0,0 +1,75 @@
+import os
+
+import torch.distributed as dist
+from chatgpt.trainer.strategies import ColossalAIStrategy, Strategy
+from chatgpt.trainer.utils import is_rank_0
+from torch import nn
+from torch.optim import Optimizer
+
+from .base import Callback
+
+
+class SaveCheckpoint(Callback):
+    """
+        The callback for saving checkpoint for chatgpt.
+
+        Only support saving actor and critic model.
+        A typical architecture of the saved checkpoint would be:
+            - checkpoint
+                - episode_x
+                    - actor.pt
+                    - actor-optim-rank-0.pt
+                    - actor-optim-rank-1.pt
+                    - critic.pt
+                    - critic-optim-rank-0.pt
+                    - critic-optim-rank-1.pt
+                - ...
+
+    Args:
+        path(str): the base path you want to save checkpoint, the checkpoint would be saved at `path/checkpoint`
+        interval(int): the interval episode of saving checkpoint
+        strategy(Strategy): the strategy used to train
+        actor(nn.Module): the actor model
+        critic(nn.Module): the critic model
+        actor_optim(Optimizer): the optimizer of actor
+        critic_optim(Optimizer): the optimizer of critic
+
+    """
+
+    def __init__(self,
+                 path: str,
+                 interval: int,
+                 strategy: Strategy,
+                 actor: nn.Module = None,
+                 critic: nn.Module = None,
+                 actor_optim: Optimizer = None,
+                 critic_optim: Optimizer = None) -> None:
+        super().__init__()
+        self.path = os.path.join(path, 'checkpoint')
+        self.interval = interval
+        self.strategy = strategy
+        self.model_dict = {'actor': [actor, actor_optim], 'critic': [critic, critic_optim]}
+
+    def on_episode_end(self, episode: int) -> None:
+        if (episode + 1) % self.interval != 0:
+            return
+        base_path = os.path.join(self.path, f'episode_{episode}')
+        if not os.path.exists(base_path):
+            os.makedirs(base_path)
+
+        for model in self.model_dict.keys():
+
+            # save model
+            if self.model_dict[model][0] is None:
+                # saving only optimizer states is meaningless, so it would be skipped
+                continue
+            model_path = os.path.join(base_path, f'{model}.pt')
+            self.strategy.save_model(model=self.model_dict[model][0], path=model_path, only_rank0=True)
+
+            # save optimizer
+            if self.model_dict[model][1] is None:
+                continue
+            only_rank0 = not isinstance(self.strategy, ColossalAIStrategy)
+            rank = 0 if is_rank_0() else dist.get_rank()
+            optim_path = os.path.join(base_path, f'{model}-optim-rank-{rank}.pt')
+            self.strategy.save_optimizer(optimizer=self.model_dict[model][1], path=optim_path, only_rank0=only_rank0)

applications/ChatGPT/examples/train_dummy.py

@@ -4,6 +4,7 @@ from copy import deepcopy
 import torch
 from chatgpt.nn import BLOOMActor, BLOOMCritic, GPTActor, GPTCritic, OPTActor, OPTCritic, RewardModel
 from chatgpt.trainer import PPOTrainer
+from chatgpt.trainer.callbacks import SaveCheckpoint
 from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
 from torch.optim import Adam
 from transformers import AutoTokenizer, BloomTokenizerFast
@@ -71,26 +72,38 @@ def main(args):
     (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare(
         (actor, actor_optim), (critic, critic_optim), reward_model, initial_model)
 
+    callbacks = []
+    if args.save_ckpt_path:
+        ckpt_callback = SaveCheckpoint(
+            args.save_ckpt_path,
+            args.save_ckpt_interval,
+            strategy,
+            actor,
+            critic,
+            actor_optim,
+            critic_optim,
+        )
+        callbacks.append(ckpt_callback)
+
     # configure trainer
-    trainer = PPOTrainer(
-        strategy,
-        actor,
-        critic,
-        reward_model,
-        initial_model,
-        actor_optim,
-        critic_optim,
-        max_epochs=args.max_epochs,
-        train_batch_size=args.train_batch_size,
-        experience_batch_size=args.experience_batch_size,
-        tokenizer=preprocess_batch,
-        max_length=128,
-        do_sample=True,
-        temperature=1.0,
-        top_k=50,
-        pad_token_id=tokenizer.pad_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-    )
+
+    trainer = PPOTrainer(strategy,
+                         actor,
+                         critic,
+                         reward_model,
+                         initial_model,
+                         actor_optim,
+                         critic_optim,
+                         max_epochs=args.max_epochs,
+                         train_batch_size=args.train_batch_size,
+                         tokenizer=preprocess_batch,
+                         max_length=128,
+                         do_sample=True,
+                         temperature=1.0,
+                         top_k=50,
+                         pad_token_id=tokenizer.pad_token_id,
+                         eos_token_id=tokenizer.eos_token_id,
+                         callbacks=callbacks)
 
     random_prompts = torch.randint(tokenizer.vocab_size, (1000, 64), device=torch.cuda.current_device())
     trainer.fit(random_prompts,
@@ -120,5 +133,10 @@ if __name__ == '__main__':
     parser.add_argument('--train_batch_size', type=int, default=8)
     parser.add_argument('--experience_batch_size', type=int, default=8)
     parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank")
+    parser.add_argument('--save_ckpt_path',
+                        type=str,
+                        default=None,
+                        help="path to save checkpoint, None means not to save")
+    parser.add_argument('--save_ckpt_interval', type=int, default=1, help="the interval of episode to save checkpoint")
     args = parser.parse_args()
     main(args)