Implement PReLU in a compositional way (#91238)
The PReLU implementation was all over the place. This lead to a number
of bugs like https://github.com/pytorch/pytorch/issues/68760. We fix it by:
- Keeping the weird broadcasting logic it has as a CompositeImplicit kernel that calls into a second kernel
- This second kernel is just a good-ol' pointwise kernel.
- We implement the derivative for the pointwise kernel via TI as well for speed.
- We implement the second derivative for the pointwise kernel and the forward AD derivatives compositionally
This fixes a number of issues:
- We don't perform copies any more when the inputs are not contiguous
- The derivatives are now correct
- We fix vmap and many other functorch-related issues.
- CPU and CUDA now share the relevant broadcasting logic
- The implementation is about 1/3 the length.
Fixes https://github.com/pytorch/pytorch/issues/68760
Fixes https://github.com/pytorch/pytorch/issues/89895
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91238
Approved by: https://github.com/kshitij12345, https://github.com/jbschlosser, https://github.com/albanD
