Neither.
The problem is that modern dropout scales the outputs by 1/p. This would mean that you can only use 1/n, n integer . Also you don’t get autograd with long tensors. As such, the use-case seems too limited to include it in PyTorch given that torch.empty_like(a).bernoulli_(keep_prob) * a will give you an (unscaled) dropout for long with not much code.
Best regards
Thomas