def reduce_metrics(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)
        symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs)

        metrics.log_scalar("symm_kl", symm_kl_sum / sample_size, sample_size, round=3)
        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)
        )
Aggregate logging outputs from data parallel training.
structured_prompting/fairseq-version/fairseq/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py