8bafae2ee7044529543768eec63d8460d894f5c6,fairseq/multiprocessing_trainer.py,MultiprocessingTrainer,_async_train_step,#MultiprocessingTrainer#Any#Any#Any#,154

Before Change

        loss = 0
        if self._sample is not None:
            self._sample = self.criterion.prepare(self.model, self._sample)
            net_output = self.model(**self._sample["net_input"])
            loss_ = self.criterion(net_output, self._sample)
            if grad_denom is not None:
                loss_ /= grad_denom
            loss_.backward()
            loss = loss_.data[0]

        // flatten grads into a contiguous block of memory
        if self.flat_grads is None:

After Change

        loss_dict = {}
        if self._sample is not None:
            loss_dict = self.criterion(self.model, self._sample, grad_denom)
            loss_dict["loss"].backward()
            loss = loss_dict["loss"].data[0]

        // flatten grads into a contiguous block of memory
        if self.flat_grads is None:

Instances