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42 lines
1.6 KiB
42 lines
1.6 KiB
from colossalai.global_variables import tensor_parallel_env as env
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from colossalai.nn.layer.utils import get_tensor_parallel_mode
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from torch import nn
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from torch.nn.modules.loss import *
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from torch.nn.modules.loss import _Loss
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from .loss_1d import VocabParallelCrossEntropyLoss1D
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from .loss_2d import CrossEntropyLoss2D, VocabParallelCrossEntropyLoss2D
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from .loss_2p5d import CrossEntropyLoss2p5D, VocabParallelCrossEntropyLoss2p5D
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from .loss_3d import CrossEntropyLoss3D, VocabParallelCrossEntropyLoss3D
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from .loss_moe import MoeCrossEntropyLoss, MoeLoss
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_parallel_cross_entropy = {
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'2d': CrossEntropyLoss2D,
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'2.5d': CrossEntropyLoss2p5D,
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'3d': CrossEntropyLoss3D,
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}
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_vocab_parallel_cross_entropy = {
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'1d': VocabParallelCrossEntropyLoss1D,
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'2d': VocabParallelCrossEntropyLoss2D,
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'2.5d': VocabParallelCrossEntropyLoss2p5D,
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'3d': VocabParallelCrossEntropyLoss3D,
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}
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class CrossEntropyLoss(_Loss):
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def __init__(self, reduction: bool = True, *args, **kwargs):
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super().__init__()
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tensor_parallel = get_tensor_parallel_mode()
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if tensor_parallel is not None and env.vocab_parallel:
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self.loss = _vocab_parallel_cross_entropy[tensor_parallel](reduction=reduction, *args, **kwargs)
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elif tensor_parallel is None or tensor_parallel == '1d':
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reduction = 'mean' if reduction else 'none'
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self.loss = nn.CrossEntropyLoss(reduction=reduction, *args, **kwargs)
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else:
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self.loss = _parallel_cross_entropy[tensor_parallel](reduction=reduction, *args, **kwargs)
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def forward(self, *args):
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return self.loss(*args)
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