mirror of https://github.com/hpcaitech/ColossalAI
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
import torch.nn as nn
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from torch.nn.modules.loss import _Loss
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from colossalai.moe.manager import MOE_MANAGER
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class MoeCrossEntropyLoss(_Loss):
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r"""torch.nn.CrossEntropyLoss added with auxiliary loss.
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Args:
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input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
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target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
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aux_weight (float, optional): Weight of auxiliary loss in total loss.Defaults 0.01.
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The ``args`` and ``kwargs`` should include parameters below:
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::
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weight (Tensor, optional)
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size_average (bool, optional)
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ignore_index (int, optional)
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reduce (bool, optional)
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reduction (str, optional)
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label_smoothing (float, optional)
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More details about ``args``, ``kwargs`` and ``torch.nn.functional.cross_entropy`` could be found in
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`Cross_entropy <https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html#torch.nn.functional.cross_entropy>`_.
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"""
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def __init__(self, aux_weight: float = 0.01, *args, **kwargs):
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super().__init__()
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self.loss = nn.CrossEntropyLoss(*args, **kwargs)
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self.aux_weight = aux_weight
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def forward(self, *args):
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"""
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The ``args`` should at least include parameters below:
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::
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input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
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target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
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More details about ``args``, ``kwargs`` and ``torch.nn.functional.cross_entropy`` could be found in
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`Cross_entropy <https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html#torch.nn.functional.cross_entropy>`_.
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"""
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main_loss = self.loss(*args)
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aux_loss = MOE_MANAGER.get_loss()
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return main_loss + self.aux_weight * aux_loss
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class MoeLoss(_Loss):
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"""A wrapper class for any loss module to add with auxiliary loss.
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Args:
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aux_weight (float): Weight of auxiliary loss in total loss.
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loss_fn (``Callable``): Loss function.
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args (list): Args in loss function.
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kwargs (dict): Kwargs in loss function
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"""
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def __init__(self, aux_weight: float, loss_fn, *args, **kwargs):
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super().__init__()
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self.loss_fn = loss_fn(*args, **kwargs)
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self.aux_weight = aux_weight
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def forward(self, *args, **kwargs):
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"""
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The ``args`` and ``kwargs`` should at least include parameters below:
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::
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input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
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target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
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Note:
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The ``args`` and ``kwargs`` may include different parameters varying with different loss function.
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"""
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main_loss = self.loss_fn(*args, **kwargs)
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aux_loss = MOE_MANAGER.get_loss()
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return main_loss + self.aux_weight * aux_loss
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