def aggregate_logging_outputs(logging_outputs):
        """Aggregate logging outputs from data parallel training."""
        loss_sum = sum(log.get("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_output = {
            "loss": loss_sum / sample_size / math.log(2),
            "ntokens": ntokens,
            "nsentences": nsentences,
            "sample_size": sample_size,
        }

        ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs)
        nqueries = sum(log.get("nqueries", 0) for log in logging_outputs)
        if nqueries > 0:
            agg_output["accuracy"] = ncorrect / float(nqueries)

        return agg_output
Aggregate logging outputs from data parallel training.
structured_prompting/fairseq-version/fairseq/examples/roberta/wsc/wsc_criterion.py