Preprocessing is transforming your data “inplace”, meaning you do it for purposes other than expanding your data. Augmentation is transforming your data to create more samples (usually to prevent overfitting).
For example, whitening or normalization would be preprocessing, while distortion or random crops would be augmentation.
As to where to do each, take a look here: https://pytorch.org/docs/stable/torchvision/transforms.html 22