1082ba352c5f1d524b1fcba43ee611280b169224,fairseq/trainer.py,Trainer,valid_step,#Trainer#Any#,310

Before Change

        agg_logging_output = self.criterion.__class__.aggregate_logging_outputs(logging_outputs)

        // update loss meters for validation
        if "loss" in agg_logging_output:
            self.meters["valid_loss"].update(agg_logging_output["loss"], grad_denom)
        // criterions can optionally log the NLL loss too
        if "nll_loss" in agg_logging_output:
            self.meters["valid_nll_loss"].update(agg_logging_output["nll_loss"], ntokens)

        return agg_logging_output

After Change

        with torch.no_grad():
            sample = self._prepare_sample(sample)
            if sample is None:
                sample = self._prepare_sample(self._dummy_batch)
            _loss, sample_size, logging_output = self.task.get_loss(
                self.model, self.criterion, sample,
            )

        // gather logging outputs from all replicas
        if self.args.distributed_world_size > 1:
            logging_output, sample_size = zip(*distributed_utils.all_gather_list(

Instances