4294c4f6d72e6ac2b0315133adb26e83e99f0e62,fairseq/criterions/cross_entropy.py,CrossEntropyCriterion,forward,#CrossEntropyCriterion#Any#Any#Any#,22

Before Change

        3) logging outputs to display while training
        
        net_output = model(**sample["net_input"])
        lprobs = model.get_normalized_probs(net_output, log_probs=True)
        lprobs = lprobs.view(-1, lprobs.size(-1))
        target = model.get_targets(sample, net_output).view(-1)
        loss = F.nll_loss(lprobs, target, size_average=False, ignore_index=self.padding_idx,
                          reduce=reduce)
        sample_size = sample["target"].size(0) if self.args.sentence_avg else sample["ntokens"]
        logging_output = {
            "loss": utils.item(loss.data) if reduce else loss.data,
            "ntokens": sample["ntokens"],

After Change

        3) logging outputs to display while training
        
        net_output = model(**sample["net_input"])
        loss, _ = self.compute_loss(model, net_output, sample, reduce=reduce)
        sample_size = sample["target"].size(0) if self.args.sentence_avg else sample["ntokens"]
        logging_output = {
            "loss": utils.item(loss.data) if reduce else loss.data,

Instances