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ColossalAI/colossalai/amp/__init__.py

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1.6 KiB

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from .amp_type import AMP_TYPE
from colossalai.context import Config
import torch.nn as nn
from torch.optim import Optimizer
from torch.nn.modules.loss import _Loss
from .torch_amp import convert_to_torch_amp
from .apex_amp import convert_to_apex_amp
from .naive_amp import convert_to_naive_amp
def convert_to_amp(model: nn.Module, optimizer: Optimizer, criterion: _Loss, mode: AMP_TYPE, amp_config: Config = None):
"""A helper function to wrap training components with Torch AMP modules
:param model: your model object
:type model: :class:`torch.nn.Module`
:param optimizer: your optimizer object
:type optimizer: :class:`torch.optim.Optimizer`
:param criterion: your loss function object
:type criterion: :class:`torch.nn.modules.loss._Loss`
:param mode: amp mode
:type mode: :class:`colossalai.amp.AMP_TYPE`
:param amp_config: configuration for different amp modes
:type amp_config: :class:`colossalai.context.Config` or dict
:return: (model, optimizer, criterion)
:rtype: Tuple
"""
assert isinstance(mode, AMP_TYPE), \
f'expected the argument mode be AMP_TYPE, but got {type(mode)}'
if amp_config is None:
amp_config = Config()
if mode == AMP_TYPE.TORCH:
model, optimizer, criterion = convert_to_torch_amp(model, optimizer, criterion, amp_config)
elif mode == AMP_TYPE.APEX:
model, optimizer = convert_to_apex_amp(model, optimizer, amp_config)
elif mode == AMP_TYPE.NAIVE:
model, optimizer = convert_to_naive_amp(model, optimizer, amp_config)
return model, optimizer, criterion