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36 lines
1.5 KiB
36 lines
1.5 KiB
from typing import Any, Callable, Iterable
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from torch import Tensor
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from torch.nn import Module
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from colossalai.interface import OptimizerWrapper
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from colossalai.pipeline.stage_manager import PipelineStageManager
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class PipelineSchedule:
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def __init__(self, stage_manager: PipelineStageManager) -> None:
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self.stage_manager = stage_manager
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def forward_backward_step(self,
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model: Module,
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optimizer: OptimizerWrapper,
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data_iter: Iterable,
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criterion: Callable[[Any, Any], Tensor],
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return_loss: bool = False,
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return_outputs: bool = False) -> dict:
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"""Forward and backward step for pipeline training.
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Args:
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model (Module): Model to be trained.
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optimizer (OptimizerWrapper): Optimizer to be used.
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data_iter (Iterable): Data iterator.
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criterion (Callable[[Any, Any], Tensor]): Criterion to be used. It should take two arguments: model outputs and inputs, and returns loss tensor.
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return_loss (bool, optional): Whether to return loss. Defaults to False. Whether to return loss.
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return_outputs (bool, optional): Whether to return model outputs. Defaults to False. Whether to return model outputs.
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Returns:
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dict: A dict with keys: 'loss' and 'outputs'.
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"""
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raise NotImplementedError
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