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

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

        total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
        if total > 0:
            metrics.log_scalar("total", total)
            n_correct = utils.item(
                sum(log.get("n_correct", 0) for log in logging_outputs)
            )
            metrics.log_scalar("n_correct", n_correct)
            metrics.log_derived(
                "accuracy",
                lambda meters: round(
                    meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
                )
                if meters["total"].sum > 0
                else float("nan"),
            )
Aggregate logging outputs from data parallel training.
understand_icl/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py