[chatgpt] update readme about checkpoint (#2792)

* [chatgpt] add save/load checkpoint sample code

* [chatgpt] add save/load checkpoint readme

* [chatgpt] refactor save/load checkpoint readme

applications/ChatGPT/README.md

@@ -34,26 +34,103 @@ Simplest usage:
 ```python
 from chatgpt.trainer import PPOTrainer
 from chatgpt.trainer.strategies import ColossalAIStrategy
+from chatgpt.nn import GPTActor, GPTCritic, RewardModel
+from copy import deepcopy
+from colossalai.nn.optimizer import HybridAdam
 
 strategy = ColossalAIStrategy()
 
 with strategy.model_init_context():
   # init your model here
-  actor = Actor()
+  # load pretrained gpt2
-  critic = Critic()
+  actor = GPTActor(pretrained='gpt2')
+  critic = GPTCritic()
+  initial_model = deepcopy(actor).cuda()
+  reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda()
 
-trainer = PPOTrainer(actor = actor, critic= critic, strategy, ...)
+actor_optim = HybridAdam(actor.parameters(), lr=5e-6)
+critic_optim = HybridAdam(critic.parameters(), lr=5e-6)
+
+# prepare models and optimizers
+(actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare(
+        (actor, actor_optim), (critic, critic_optim), reward_model, initial_model)
+
+# load saved model checkpoint after preparing
+strategy.load_model(actor, 'actor_checkpoint.pt', strict=False)
+# load saved optimizer checkpoint after preparing
+strategy.load_optimizer(actor_optim, 'actor_optim_checkpoint.pt')
+
+trainer = PPOTrainer(strategy,
+                     actor,
+                     critic,
+                     reward_model,
+                     initial_model,
+                     actor_optim,
+                     critic_optim,
+                     ...)
 
 trainer.fit(dataset, ...)
+
+# save model checkpoint after fitting on only rank0
+strategy.save_model(actor, 'actor_checkpoint.pt', only_rank0=True)
+# save optimizer checkpoint on all ranks
+strategy.save_optimizer(actor_optim, 'actor_optim_checkpoint.pt', only_rank0=False)
 ```
 
 For more details, see `examples/`.
 
 We also support training reward model with true-world data. See `examples/train_reward_model.py`.
 
+## FAQ
+
+### How to save/load checkpoint
+
+To load pretrained model, you can simply use huggingface pretrained models:
+
+```python
+# load OPT-350m pretrained model
+actor = OPTActor(pretrained='facebook/opt-350m')
+```
+
+To save model checkpoint:
+
+```python
+# save model checkpoint on only rank0
+strategy.save_model(actor, 'actor_checkpoint.pt', only_rank0=True)
+```
+
+This function must be called after `strategy.prepare()`.
+
+For DDP strategy, model weights are replicated on all ranks. And for ColossalAI strategy, model weights may be sharded, but all-gather will be applied before returning state dict. You can set `only_rank0=True` for both of them, which only saves checkpoint on rank0, to save disk space usage. The checkpoint is float32.
+
+To save optimizer checkpoint:
+
+```python
+# save optimizer checkpoint on all ranks
+strategy.save_optimizer(actor_optim, 'actor_optim_checkpoint.pt', only_rank0=False)
+```
+
+For DDP strategy, optimizer states are replicated on all ranks. You can set `only_rank0=True`. But for ColossalAI strategy, optimizer states are sharded over all ranks, and no all-gather will be applied. So for ColossalAI strategy, you can only set `only_rank0=False`. That is to say, each rank will save a cehckpoint. When loading, each rank should load the corresponding part.
+
+Note that different stategy may have different shapes of optimizer checkpoint.
+
+To load model checkpoint:
+
+```python
+# load saved model checkpoint after preparing
+strategy.load_model(actor, 'actor_checkpoint.pt', strict=False)
+```
+
+To load optimizer checkpoint:
+
+```python
+# load saved optimizer checkpoint after preparing
+strategy.load_optimizer(actor_optim, 'actor_optim_checkpoint.pt')
+```
+
 ## Todo
 
-- [x] implement PPO training
+- [x] implement PPO fine-tuning
 - [x] implement training reward model
 - [x] support LoRA
 - [ ] implement PPO-ptx fine-tuning
@@ -65,7 +142,7 @@ Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.c
 
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