87a61ad92a9e0129e5c81c242f0ea96d77e6b0af,allennlp/training/metrics/mean_absolute_error.py,MeanAbsoluteError,__call__,#MeanAbsoluteError#Any#Any#Any#,21

Before Change

        absolute_errors = torch.abs(predictions - gold_labels)
        if mask is not None:
            absolute_errors *= mask
            self._total_count += torch.sum(mask)
        else:
            self._total_count += gold_labels.numel()
        self._absolute_error += torch.sum(absolute_errors)

        if is_distributed():
            _absolute_error = torch.tensor(self._absolute_error).to(device)
            _total_count = torch.tensor(self._total_count).to(device)
            dist.all_reduce(_absolute_error, op=dist.ReduceOp.SUM)
            dist.all_reduce(_total_count, op=dist.ReduceOp.SUM)
            self._absolute_error = _absolute_error.item()
            self._total_count = _total_count.item()

    def get_metric(self, reset: bool = False):
        

After Change

            total_count = torch.tensor(_total_count).to(device)
            dist.all_reduce(absolute_error, op=dist.ReduceOp.SUM)
            dist.all_reduce(total_count, op=dist.ReduceOp.SUM)
            _absolute_error = absolute_error.item()
            _total_count = total_count.item()

        self._absolute_error += _absolute_error
        self._total_count += _total_count

    def get_metric(self, reset: bool = False):
        

Instances