    def reduce_metrics(logging_outputs) -> None:
        """Aggregate logging outputs from data parallel training."""
        sample_size = utils.item(
            sum(log.get("sample_size", 0) for log in logging_outputs)
        )
        loss = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
        nll_loss = utils.item(sum(log.get("nll_loss", 0) for log in logging_outputs))

        metrics.log_scalar(
            "loss", loss / sample_size / math.log(2), sample_size, round=3
        )
        metrics.log_scalar(
            "nll_loss", nll_loss / sample_size / math.log(2), sample_size, round=3
        )
        metrics.log_derived(
            "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
        )

        for key in logging_outputs[0]:
            if key[-5:] == "-loss":
                val = sum(log.get(key, 0) for log in logging_outputs)
                metrics.log_scalar(
                    key[:-5],
                    val / sample_size / math.log(2) if sample_size > 0 else 0.0,
                    sample_size,
                    round=3,
                )
Aggregate logging outputs from data parallel training.
fairseq/criterions/nat_loss.py