def reduce_metrics(logging_outputs) -> None:
        """Aggregate logging outputs from data parallel training."""
        lm_loss_sum = sum(log.get("lm_loss", 0) for log in logging_outputs)
        sentence_loss_sum = sum(log.get("sentence_loss", 0) for log in logging_outputs)
        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
        nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
        agg_loss = sum(log.get("loss", 0) for log in logging_outputs)

        metrics.log_scalar(
            "loss",
            agg_loss / sample_size / math.log(2) if sample_size > 0 else 0.0,
            sample_size,
            round=3,
        )
        metrics.log_scalar(
            "lm_loss",
            lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0,
            ntokens,
            round=3,
        )
        metrics.log_scalar(
            "sentence_loss",
            sentence_loss_sum / nsentences / math.log(2) if nsentences > 0 else 0.0,
            nsentences,
            round=3,
        )
        metrics.log_scalar(
            "nll_loss",
            lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0,
            ntokens,
            round=3,
        )
Aggregate logging outputs from data parallel training.
structured_prompting/fairseq-version/fairseq/fairseq/criterions/legacy_masked_lm.py