PyTorch uses the inverse scaling during training, to avoid the operation during inference, which would yield the same overall result, and is mentioned in the dropout paper in section 10:

Another way to achieve the same effect is to scale up the retained activations by multiplying
by 1/p at training time and not modifying the weights at test time.