paddlenlp.losses.rdrop 源代码

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import paddle
import paddle.nn as nn
import paddle.nn.functional as F

__all__ = ['RDropLoss']

[文档]class RDropLoss(nn.Layer): """ R-Drop Loss implementation For more information about R-drop please refer to this paper: Original implementation please refer to this code: Args: reduction(str, optional): Indicate how to average the loss, the candicates are ``'none'``,``'batchmean'``,``'mean'``,``'sum'``. If `reduction` is ``'mean'``, the reduced mean loss is returned; If `reduction` is ``'batchmean'``, the sum loss divided by batch size is returned; If `reduction` is ``'sum'``, the reduced sum loss is returned; If `reduction` is ``'none'``, no reduction will be applied. Defaults to ``'none'``. """ def __init__(self, reduction='none'): super(RDropLoss, self).__init__() if reduction not in ['sum', 'mean', 'none', 'batchmean']: raise ValueError( "'reduction' in 'RDropLoss' should be 'sum', 'mean' 'batchmean', or 'none', " "but received {}.".format(reduction)) self.reduction = reduction
[文档] def forward(self, p, q, pad_mask=None): """ Args: p(Tensor): the first forward logits of training examples. q(Tensor): the second forward logits of training examples. pad_mask(Tensor, optional): The Tensor containing the binary mask to index with, it's data type is bool. Returns: Tensor: Returns tensor `loss`, the rdrop loss of p and q. """ p_loss = F.kl_div( F.log_softmax( p, axis=-1), F.softmax( q, axis=-1), reduction=self.reduction) q_loss = F.kl_div( F.log_softmax( q, axis=-1), F.softmax( p, axis=-1), reduction=self.reduction) # pad_mask is for seq-level tasks if pad_mask is not None: p_loss = paddle.masked_select(p_loss, pad_mask) q_loss = paddle.masked_select(q_loss, pad_mask) # You can choose whether to use function "sum" and "mean" depending on your task p_loss = p_loss.sum() q_loss = q_loss.sum() loss = (p_loss + q_loss) / 2 return loss