[chatgpt] Support saving ckpt in examples (#2846)

* [chatgpt]fix train_rm bug with lora

* [chatgpt]support colossalai strategy to train rm

* fix pre-commit

* fix pre-commit 2

* [chatgpt]fix rm eval typo

* fix rm eval

* fix pre commit

* add support of saving ckpt in examples

* fix single-gpu save

applications/ChatGPT/examples/train_dummy.py

@@ -97,6 +97,13 @@ def main(args):
                 max_timesteps=args.max_timesteps,
                 update_timesteps=args.update_timesteps)
 
+    # save model checkpoint after fitting on only rank0
+    strategy.save_model(actor, 'actor_checkpoint_dummy.pt', only_rank0=True)
+    # save optimizer checkpoint on all ranks
+    strategy.save_optimizer(actor_optim,
+                            'actor_optim_checkpoint_dummy_%d.pt' % (torch.cuda.current_device()),
+                            only_rank0=False)
+
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()

applications/ChatGPT/examples/train_prompts.py

@@ -2,6 +2,7 @@
 from copy import deepcopy
 
 import pandas as pd
+import torch
 from chatgpt.nn import BLOOMActor, BLOOMCritic, GPTActor, GPTCritic, OPTActor, OPTCritic, RewardModel
 from chatgpt.trainer import PPOTrainer
 from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
@@ -95,6 +96,12 @@ def tokenize_fn(texts):
                 num_episodes=args.num_episodes,
                 max_timesteps=args.max_timesteps,
                 update_timesteps=args.update_timesteps)
+    # save model checkpoint after fitting on only rank0
+    strategy.save_model(actor, 'actor_checkpoint_prompts.pt', only_rank0=True)
+    # save optimizer checkpoint on all ranks
+    strategy.save_optimizer(actor_optim,
+                            'actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()),
+                            only_rank0=False)
 
 
 if __name__ == '__main__':