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55 lines
2.3 KiB
55 lines
2.3 KiB
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import torch.nn as nn
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from torch.nn.modules.loss import _Loss
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from torch.optim import Optimizer
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from colossalai.context import Config
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from .amp_type import AMP_TYPE
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from .apex_amp import convert_to_apex_amp
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from .naive_amp import convert_to_naive_amp
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from .torch_amp import convert_to_torch_amp
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__all__ = ['convert_to_amp', 'convert_to_naive_amp', 'convert_to_apex_amp', 'convert_to_torch_amp', 'AMP_TYPE']
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def convert_to_amp(model: nn.Module, optimizer: Optimizer, criterion: _Loss, mode: AMP_TYPE, amp_config: Config = None):
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"""A helper function to wrap training components with Torch AMP modules.
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Args:
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param model (:class:`torch.nn.Module`): your model object.
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optimizer (:class:`torch.optim.Optimizer`): your optimizer object.
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criterion (:class:`torch.nn.modules.loss._Loss`): your loss function object.
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mode (:class:`colossalai.amp.AMP_TYPE`): amp mode.
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amp_config (Union[:class:`colossalai.context.Config`, dict]): configuration for different amp modes.
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Returns:
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A tuple (model, optimizer, criterion).
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Note:
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``amp_config`` may vary from different mode you choose. You should check the corresponding amp mode
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for more details about ``amp_config``.
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For ``apex_amp``, please check
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`apex_amp config <https://nvidia.github.io/apex/amp.html?highlight=apex%20amp>`_.
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For ``naive_amp``, please check
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`naive_amp config <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/amp/naive_amp/_fp16_optimizer.py#L42>`_.
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For ``torch_amp``, please check
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`torch_amp config <https://github.com/pytorch/pytorch/blob/master/torch/cuda/amp/grad_scaler.py#L97>`_.
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"""
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assert isinstance(mode, AMP_TYPE), \
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f'expected the argument mode be AMP_TYPE, but got {type(mode)}'
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if amp_config is None:
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amp_config = Config()
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if mode == AMP_TYPE.TORCH:
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model, optimizer, criterion = convert_to_torch_amp(model, optimizer, criterion, amp_config)
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elif mode == AMP_TYPE.APEX:
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model, optimizer = convert_to_apex_amp(model, optimizer, amp_config)
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elif mode == AMP_TYPE.NAIVE:
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model, optimizer = convert_to_naive_amp(model, optimizer, amp_config)
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return model, optimizer, criterion
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