torch.backends.cudnn.enabled = True
uses cudnn, while ... = False
disables it and falls back to the native PyTorch implementations.
cudnn.benchmark = True
could fail to find a fast kernel, but should fall back to a (slower but) working algorithm, which is not the case for your workload.
Could you update PyTorch to the latest stable version (1.5
) or the nightly binaries, as we’ve recently implemented another fallback mechanism, which should raise a proper error messge.