mirror of https://github.com/hpcaitech/ColossalAI
62 lines
1.5 KiB
Python
62 lines
1.5 KiB
Python
from abc import ABC, abstractmethod
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from typing import Callable, List, Tuple, Union
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import torch.nn as nn
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
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from torch.utils.data import DataLoader
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from colossalai.checkpoint_io import CheckpointIO
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from colossalai.interface import OptimizerWrapper
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__all__ = ['Plugin']
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class Plugin(ABC):
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@abstractmethod
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def supported_devices(self) -> List[str]:
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pass
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@abstractmethod
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def supported_precisions(self) -> List[str]:
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pass
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@abstractmethod
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def control_precision(self) -> bool:
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pass
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@abstractmethod
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def control_device(self) -> bool:
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pass
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@abstractmethod
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def support_no_sync(self) -> bool:
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pass
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@abstractmethod
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def configure(
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self,
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model: nn.Module,
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optimizer: Optimizer,
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criterion: Callable = None,
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dataloader: DataLoader = None,
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lr_scheduler: LRScheduler = None,
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) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]:
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# implement this method
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pass
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@abstractmethod
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def control_checkpoint_io(self) -> bool:
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"""
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Whether the plugin controls the checkpoint io
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"""
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pass
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@abstractmethod
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def get_checkpoint_io(self) -> CheckpointIO:
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"""
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Get checkpoint io object for this plugin, only invoked when control_checkpoint_io is True.
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"""
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pass
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