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.