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