[chatgpt] startegy add prepare method (#2766)

* [chatgpt] startegy add prepare method

* [chatgpt] refactor examples

* [chatgpt] refactor strategy.prepare

* [chatgpt] support save/load checkpoint

* [chatgpt] fix unwrap actor

* [chatgpt] fix unwrap actor

applications/ChatGPT/benchmarks/benchmark_gpt_dummy.py

@@ -133,6 +133,9 @@ def main(args):
     tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
     tokenizer.pad_token = tokenizer.eos_token
 
+    (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare(
+        (actor, actor_optim), (critic, critic_optim), reward_model, initial_model)
+
     trainer = PPOTrainer(strategy,
                          actor,
                          critic,

applications/ChatGPT/benchmarks/benchmark_opt_lora_dummy.py

@@ -126,6 +126,9 @@ def main(args):
     tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m')
     tokenizer.pad_token = tokenizer.eos_token
 
+    (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare(
+        (actor, actor_optim), (critic, critic_optim), reward_model, initial_model)
+
     trainer = PPOTrainer(strategy,
                          actor,
                          critic,

applications/ChatGPT/chatgpt/trainer/ppo.py

@@ -60,11 +60,6 @@ class PPOTrainer(Trainer):
                  dataloader_pin_memory: bool = True,
                  callbacks: List[Callback] = [],
                  **generate_kwargs) -> None:
-        self._set_default_generate_kwargs(generate_kwargs, actor)
-        actor = Actor(strategy.setup_model(actor.model))
-        critic = strategy.setup_model(critic)
-        reward_model = strategy.setup_model(reward_model)
-        initial_model = Actor(strategy.setup_model(initial_model.model))
         experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef)
         replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload)
         super().__init__(strategy, experience_maker, replay_buffer, experience_batch_size, max_epochs, tokenizer,
@@ -75,8 +70,9 @@ class PPOTrainer(Trainer):
         self.actor_loss_fn = PolicyLoss(eps_clip)
         self.critic_loss_fn = ValueLoss(value_clip)
 
-        self.actor_optim = strategy.setup_optimizer(actor_optim, self.actor.model)
-        self.critic_optim = strategy.setup_optimizer(critic_optim, self.critic)
+        self.actor_optim = actor_optim
+        self.critic_optim = critic_optim
+        self._set_default_generate_kwargs(generate_kwargs, actor)
 
     def training_step(self, experience: Experience) -> Dict[str, float]:
         self.actor.train()
@@ -106,9 +102,10 @@ class PPOTrainer(Trainer):
         return {'actor_loss': actor_loss.item(), 'critic_loss': critic_loss.item()}
 
     def _set_default_generate_kwargs(self, generate_kwargs: dict, actor: Actor) -> None:
+        origin_model = self.strategy._unwrap_actor(actor)
         # use huggingface models method directly
-        if 'prepare_inputs_fn' not in generate_kwargs and hasattr(actor.model, 'prepare_inputs_for_generation'):
-            generate_kwargs['prepare_inputs_fn'] = actor.model.prepare_inputs_for_generation
+        if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'):
+            generate_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation
 
         if 'update_model_kwargs_fn' not in generate_kwargs:
             generate_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn

applications/ChatGPT/chatgpt/trainer/strategies/base.py

@@ -1,12 +1,17 @@
 from abc import ABC, abstractmethod
 from contextlib import nullcontext
+from typing import Any, List, Tuple, Union
 
 import torch
 import torch.nn as nn
-import torch.optim as optim
+from chatgpt.nn import Actor, Critic, RewardModel
 from chatgpt.replay_buffer import ReplayBuffer
+from torch.optim import Optimizer
 from torch.utils.data import DataLoader
 
+ModelOptimPair = Tuple[nn.Module, Optimizer]
+ModelOrModelOptimPair = Union[nn.Module, ModelOptimPair]
+
 
 class Strategy(ABC):
     """
@@ -18,11 +23,11 @@ class Strategy(ABC):
         self.setup_distributed()
 
     @abstractmethod
-    def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None:
+    def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: Optimizer, **kwargs) -> None:
         pass
 
     @abstractmethod
-    def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None:
+    def optimizer_step(self, optimizer: Optimizer, **kwargs) -> None:
         pass
 
     @abstractmethod
@@ -34,7 +39,7 @@ class Strategy(ABC):
         pass
 
     @abstractmethod
-    def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer:
+    def setup_optimizer(self, optimizer: Optimizer, model: nn.Module) -> Optimizer:
         pass
 
     @abstractmethod
@@ -43,3 +48,78 @@ class Strategy(ABC):
 
     def model_init_context(self):
         return nullcontext()
+
+    def prepare(
+        self, *models_or_model_optim_pairs: ModelOrModelOptimPair
+    ) -> Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]:
+        """Prepare models or model-optimizer-pairs based on each strategy.
+
+        Example::
+            >>> # when fine-tuning actor and critic
+            >>> (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare((actor, actor_optim), (critic, critic_optim), reward_model, initial_model)
+            >>> # or when training reward model
+            >>> (reward_model, reward_model_optim) = strategy.prepare((reward_model, reward_model_optim))
+            >>> # or just inference
+            >>> actor, critic = strategy.prepare(actor, critic)
+
+        Returns:
+            Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: Models or model-optimizer-pairs in the original order.
+        """
+
+        def prepare_model(model: nn.Module):
+            if isinstance(model, Actor):
+                return Actor(self.setup_model(self._unwrap_model(model)))
+            return self.setup_model(self._unwrap_model(model))
+
+        rets = []
+        for arg in models_or_model_optim_pairs:
+            if isinstance(arg, tuple):
+                assert len(arg) == 2, f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"'
+                model, optimizer = arg
+                model = prepare_model(model)
+                optimizer = self.setup_optimizer(optimizer, self._unwrap_model(model))
+                rets.append((model, optimizer))
+            elif isinstance(arg, nn.Module):
+                rets.append(prepare_model(arg))
+            else:
+                raise RuntimeError(f'Expect model or (model, optimizer) pair, got {type(arg)}')
+
+        if len(rets) == 1:
+            return rets[0]
+        return rets
+
+    @staticmethod
+    def _unwrap_model(model: nn.Module) -> nn.Module:
+        """Useful for saving state dict. As actor is wrapped by Actor class again in `prepare()`, we should unwrap it before saving.
+
+        Args:
+            model (nn.Module): an actor or a critic
+        """
+        if isinstance(model, Actor):
+            return model.model
+        return model
+
+    @staticmethod
+    def _unwrap_actor(actor: Actor) -> nn.Module:
+        """Get `actor.model` from a wrapped (by `prepare()`) actor. Useful for getting original huggingface model.
+
+        Args:
+            actor (Actor): a wrapped actor
+        """
+        return Strategy._unwrap_model(actor)
+
+    @abstractmethod
+    def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None:
+        pass
+
+    @abstractmethod
+    def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None:
+        pass
+
+    @abstractmethod
+    def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None:
+        pass
+
+    @abstractmethod
+    def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None:
+        pass

applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py

@@ -1,18 +1,21 @@
 import warnings
-from typing import Optional
+from typing import Optional, Union
 
 import torch
 import torch.distributed as dist
 import torch.nn as nn
 import torch.optim as optim
+from chatgpt.nn import Actor
+from torch.optim import Optimizer
 
 import colossalai
 from colossalai.nn.optimizer import CPUAdam, HybridAdam
-from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper
+from colossalai.nn.parallel import ZeroDDP, zero_model_wrapper, zero_optim_wrapper
 from colossalai.tensor import ProcessGroup, ShardSpec
 from colossalai.utils import get_current_device
 from colossalai.utils.model.colo_init_context import ColoInitContext
 
+from .base import Strategy
 from .ddp import DDPStrategy
 
 
@@ -129,3 +132,23 @@ class ColossalAIStrategy(DDPStrategy):
 
     def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None:
         optimizer.step()
+
+    @staticmethod
+    def _unwrap_actor(actor: Actor) -> nn.Module:
+        model: Union[nn.Module, ZeroDDP] = Strategy._unwrap_actor(actor)
+        if isinstance(model, ZeroDDP):
+            return model.module
+        return model
+
+    def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None:
+        unwrapped_model = self._unwrap_model(model)
+        state_dict = unwrapped_model.state_dict()
+        if only_rank0 and dist.get_rank() != 0:
+            return
+        torch.save(state_dict, path)
+
+    def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None:
+        if only_rank0:
+            raise RuntimeError(
+                f'Optimizer states are sharded when using ColossalAIStrategy. Only rank0 is not supported.')
+        torch.save(optimizer.state_dict(), path)

applications/ChatGPT/chatgpt/trainer/strategies/ddp.py

@@ -5,10 +5,13 @@ import numpy as np
 import torch
 import torch.distributed as dist
 import torch.nn as nn
+from chatgpt.nn import Actor
 from chatgpt.replay_buffer import ReplayBuffer
 from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
 from torch.utils.data import DataLoader, DistributedSampler
 
+from .base import Strategy
 from .naive import NaiveStrategy
 
 
@@ -57,3 +60,18 @@ class DDPStrategy(NaiveStrategy):
                           sampler=sampler,
                           pin_memory=pin_memory,
                           collate_fn=replay_buffer.collate_fn)
+
+    @staticmethod
+    def _unwrap_actor(actor: Actor) -> nn.Module:
+        model: DDP = Strategy._unwrap_actor(actor)
+        return model.module
+
+    def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None:
+        if only_rank0 and dist.get_rank() != 0:
+            return
+        super().save_model(model, path, only_rank0)
+
+    def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None:
+        if only_rank0 and dist.get_rank() != 0:
+            return
+        super().save_optimizer(optimizer, path, only_rank0)

applications/ChatGPT/chatgpt/trainer/strategies/naive.py

@@ -1,7 +1,10 @@
+from typing import Any
+
 import torch
 import torch.nn as nn
 import torch.optim as optim
 from chatgpt.replay_buffer import ReplayBuffer
+from torch.optim import Optimizer
 from torch.utils.data import DataLoader
 
 from .base import Strategy
@@ -34,3 +37,19 @@ class NaiveStrategy(Strategy):
                           drop_last=True,
                           pin_memory=pin_memory,
                           collate_fn=replay_buffer.collate_fn)
+
+    def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None:
+        unwrapped_model = self._unwrap_model(model)
+        torch.save(unwrapped_model.state_dict(), path)
+
+    def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None:
+        unwrapped_model = self._unwrap_model(model)
+        state_dict = torch.load(path, map_location=map_location)
+        unwrapped_model.load_state_dict(state_dict, strict=strict)
+
+    def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None:
+        torch.save(optimizer.state_dict(), path)
+
+    def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None:
+        state_dict = torch.load(path, map_location=map_location)
+        optimizer.load_state_dict(state_dict)

applications/ChatGPT/examples/train_dummy.py

@@ -68,6 +68,9 @@ def main(args):
     else:
         raise ValueError(f'Unsupported model "{args.model}"')
 
+    (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare(
+        (actor, actor_optim), (critic, critic_optim), reward_model, initial_model)
+
     # configure trainer
     trainer = PPOTrainer(
         strategy,

applications/ChatGPT/examples/train_prompts.py

@@ -68,6 +68,9 @@ def main(args):
         batch = tokenizer(texts, return_tensors='pt', max_length=96, padding=True, truncation=True)
         return {k: v.cuda() for k, v in batch.items()}
 
+    (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare(
+        (actor, actor_optim), (critic, critic_optim), reward_model, initial_model)
+
     # configure trainer
     trainer = PPOTrainer(
         strategy,