    def forward(self, model, sample, reduce=True):
        """Compute the loss for the given sample.

        Returns a tuple with three elements:
        1) the loss
        2) the sample size, which is used as the denominator for the gradient
        3) logging outputs to display while training
        """
        masked_tokens = sample["target"].ne(self.padding_idx)
        sample_size = masked_tokens.int().sum()

        # Rare: when all tokens are masked, project all tokens.
        # We use torch.where to avoid device-to-host transfers,
        # except on CPU where torch.where is not well supported
        # (see github.com/pytorch/pytorch/issues/26247).
        if self.tpu:
            masked_tokens = None  # always project all tokens on TPU
        elif masked_tokens.device == torch.device("cpu"):
            if not masked_tokens.any():
                masked_tokens = None
        else:
            masked_tokens = torch.where(
                masked_tokens.any(),
                masked_tokens,
                masked_tokens.new([True]),
            )

        logits = model(**sample["net_input"], masked_tokens=masked_tokens)[0]
        targets = model.get_targets(sample, [logits])
        if masked_tokens is not None:
            targets = targets[masked_tokens]

        loss = modules.cross_entropy(
            logits.view(-1, logits.size(-1)),
            targets.view(-1),
            reduction="sum",
            ignore_index=self.padding_idx,
        )

        logging_output = {
            "loss": loss if self.tpu else loss.data,
            "ntokens": sample["ntokens"],
            "nsentences": sample["nsentences"],
            "sample_size": sample_size,
        }
        return loss, sample_size, logging_output
Compute the loss for the given sample.

Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
fairseq/criterions/masked_lm.py