I have a sequence to sequence model with nn.Transformer and in my training loop, I have:
        for i in range(x.size(1)):
            pred = model(x, tgt[:, 0:i + 1, :])
            loss = loss_fn(pred, ground_truth[:, 0:i + 1, :])
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
            cnt += 1
        return total_loss / cnt

Where x.size() is [batch, seq, features]. So my sequence increases in each iteration, but of course, this takes a LONG time Can this be done in parallel somehow?