Hey,
Yes we added a note to the doc about this: https://pytorch.org/docs/master/autograd.html#torch.autograd.backward 13
The short answer is:

If possible, use autograd.grad, it will avoir this, issues with accumulating unrelated gradients in the .grad field if you forget to .zero_grad and avoid computing un-needed gradients if you only want them for the input.
If you cannot, replace model.zero_grad() after the backward that create_graph=True by

for p in model.parameters():
    p.grad = None

This will be as fast but will properly remove the leak