mirror of https://github.com/hpcaitech/ColossalAI
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from typing import Iterable
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import torch
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from colossalai.engine import Engine
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from ._base_schedule import BaseSchedule
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from colossalai.utils import conditional_context
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class NonPipelineSchedule(BaseSchedule):
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"""A helper schedule class for no pipeline parallelism running environment.
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During one process, it loads a batch of dataset and feeds it to the model.
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After getting the output and calculating the loss, it will use :meth:`step`
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to update the parameters if it is in training mode.
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Args:
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batch_data_process_func (Callable, optional): The preprocessing function which receives a batch of data,
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and it will be executed in load_batch.
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"""
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def forward_backward_step(self,
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engine: Engine,
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data_iter: Iterable,
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forward_only: bool = False,
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return_loss: bool = True,
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return_output_label: bool = True):
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"""The process function that loads a batch of dataset and feeds it to the model.
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The returned labels and loss will None if :attr:`return_loss` is False.
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Args:
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engine (colossalai.engine.Engine): Colossalai engine for training and inference.
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data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader).
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forward_only (bool, optional):
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If True, the model is run for the forward pass, else back propagation will be executed.
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return_loss (bool, optional): Loss will be returned if True.
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return_output_label (bool, optional): Output and label will be returned if True.
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Returns:
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Tuple[:class:`torch.Tensor`]: A tuple of (output, label, loss), loss and label could be None.
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"""
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assert forward_only or return_loss, \
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"The argument 'return_loss' has to be True when 'forward_only' is False, but got False."
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data, label = self.load_batch(data_iter)
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# forward
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with conditional_context(torch.no_grad(), enable=forward_only):
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output = self._call_engine(engine, data)
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if return_loss:
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loss = self._call_engine_criterion(engine, output, label)
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if not forward_only:
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engine.backward(loss)
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if return_output_label:
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if return_loss:
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return output, label, loss
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else:
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return output, label, None
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else:
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if return_loss:
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return None, None, loss
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else:
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return None, None, None
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