718677ebb044e27aaf1a30640c2f7ab6b8fa8509,fairseq/criterions/masked_lm.py,MaskedLmLoss,forward,#MaskedLmLoss#Any#Any#Any#,25

Before Change

            ignore_index=self.padding_idx,
        )

        sample_size = targets.ne(self.padding_idx).int().sum().item()

        logging_output = {
            "loss": utils.item(loss.data) if reduce else loss.data,
            "nll_loss": utils.item(loss.data) if reduce else loss.data,

After Change

        3) logging outputs to display while training
        
        // compute MLM loss
        masked_tokens = sample["target"].ne(self.padding_idx)
        logits = model(**sample["net_input"], masked_tokens=masked_tokens)[0]
        targets = model.get_targets(sample, [logits])
        targets = targets[masked_tokens]

        loss = F.nll_loss(
            F.log_softmax(
                logits.view(-1, logits.size(-1)),
                dim=-1,
                dtype=torch.float32,
            ),
            targets.view(-1),
            reduction="sum",
            ignore_index=self.padding_idx,
        )

        sample_size = masked_tokens.int().sum().item()

        logging_output = {
            "loss": utils.item(loss.data) if reduce else loss.data,
            "nll_loss": utils.item(loss.data) if reduce else loss.data,

Instances