Gradient Accumulation failing

I am trying to implement a train loop with mixed precision and gradient accumulation but when I do the second step it gives me this error:

Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.

This is the code I am using:

# Loop over the dataset multiple times
print("Start Training ...")
while steps < cfg.train.trainer.max_steps:
    dt = time.time()

    print('Loading Batches ...')
    for i, data in enumerate(train_dl):
        lr, hr = data[0].to(device), data[1].to(device)

        with torch.cuda.amp.autocast():
            sr, lq = model(lr)
            loss_dict = compute_loss(loss_fn, loss_dict, sr, hr, lq)
            loss = loss_dict["Loss"] / num_grad_acc

        print("Loss:", loss_dict["Loss"].item())
        metrics_dict = compute_metric(metric, metrics_dict, sr, hr)

        print("Scaling Loss ...")

        if (i + 1) % num_grad_acc == 0:
            print("Updating Parameters at Step {} ...".format(i))

It seems you are reuing loss_dict recursively, so could you check if this is attaching to the previous computation graph?

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Yes that was exactly the problem! As my supervisor is saying “It just took a look at Patrick to understand the problem while we raged an entire afternoon to understand the problem, he’s a f***ing legend!”

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