e73fddf45377e8a3c0ea2e8281fae18f7b498dd6,fairseq/criterions/label_smoothed_cross_entropy.py,LabelSmoothedCrossEntropyCriterion,forward,#LabelSmoothedCrossEntropyCriterion#Any#Any#Any#,66

Before Change

        
        net_output = model(**sample["net_input"])
        lprobs = model.get_normalized_probs(net_output, log_probs=True)
        lprobs = lprobs.view(-1, lprobs.size(-1))
        target = sample["target"].view(-1)
        loss = LabelSmoothedNLLLoss.apply(lprobs, target, self.eps, self.padding_idx, None, reduce)
        nll_loss = F.nll_loss(lprobs, target, size_average=False, ignore_index=self.padding_idx, reduce=reduce)
        sample_size = sample["target"].size(0) if self.args.sentence_avg else sample["ntokens"]

After Change

        lprobs = model.get_normalized_probs(net_output, log_probs=True)
        target = sample["target"].unsqueeze(-1)
        non_pad_mask = target.ne(self.padding_idx)
        nll_loss = -lprobs.gather(dim=-1, index=target)[non_pad_mask]
        smooth_loss = -lprobs.sum(dim=-1, keepdim=True)[non_pad_mask]
        if reduce:
            nll_loss = nll_loss.sum()
            smooth_loss = smooth_loss.sum()
        eps_i = self.eps / lprobs.size(-1)
        loss = (1. - self.eps) * nll_loss + eps_i * smooth_loss

Instances