14c2381207c5972359b2af450a233730ff877ee1,TTS/bin/train_wavegrad.py,,train,#,84

Before Change

        // backward pass with loss scaling
        optimizer.zero_grad()

        if amp is not None:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            loss.backward()

        if c.clip_grad > 0:
            grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
                                           c.clip_grad)
        optimizer.step()

After Change

            scaler.update()
        else:
            loss.backward()
            grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
                                           c.clip_grad)
            optimizer.step()

Instances