mirror of https://github.com/hpcaitech/ColossalAI
56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
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from typing import Any
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from coati.replay_buffer import ReplayBuffer
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader
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from .base import Strategy
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class NaiveStrategy(Strategy):
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"""
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Strategy for single GPU. No parallelism is used.
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"""
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def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None:
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loss.backward()
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def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None:
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optimizer.step()
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def setup_distributed(self) -> None:
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pass
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def setup_model(self, model: nn.Module) -> nn.Module:
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return model
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def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer:
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return optimizer
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def setup_dataloader(self, replay_buffer: ReplayBuffer, pin_memory: bool = False) -> DataLoader:
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return DataLoader(replay_buffer,
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batch_size=replay_buffer.sample_batch_size,
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shuffle=True,
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drop_last=True,
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pin_memory=pin_memory,
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collate_fn=replay_buffer.collate_fn)
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def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None:
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unwrapped_model = self._unwrap_model(model)
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torch.save(unwrapped_model.state_dict(), path)
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def load_model(self, model: nn.Module, path: str, map_location: Any = None, strict: bool = True) -> None:
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unwrapped_model = self._unwrap_model(model)
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state_dict = torch.load(path, map_location=map_location)
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unwrapped_model.load_state_dict(state_dict, strict=strict)
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def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None:
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torch.save(optimizer.state_dict(), path)
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def load_optimizer(self, optimizer: Optimizer, path: str, map_location: Any = None) -> None:
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state_dict = torch.load(path, map_location=map_location)
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optimizer.load_state_dict(state_dict)
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