Before 1.0, I could call eval() on my model while setting
requires_grad = True on the individual Variables, so I could inspect the gradients while dropout was turned off for the whole model. When I try this now (by setting
requires_grad on the top level module), I get an error:
RuntimeError: cudnn RNN backward can only be called in training mode
is there still a way to pull this off?