Why is convolution in cuDNN non-deterministic?

The PyTorch documentary says, when using cuDNN as backend for a convolution, one has to set two options to make the implementation deterministic. The options are torch.backends.cudnn.deterministic = True and torch.backends.cudnn.benchmark = False. Is this because of the way weights are initialized?