[pytorch] prune based on custom importance scores (#48378)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48378
This commit adds support for accepting custom importance scores to use for pruning mask computation, rather than only using the parameter.
This is useful if one wants to prune based on scores from different technique such as activations, gradients, weighted scoring of parameters, etc.
An alternative to the above approach would be pass the custom mask to the already available interface. However, the ability to accept importance scores is easier it can leverage the mask computation logic that has already been baked in.
In addition, the commit also makes some minor lint fixes.
Test Plan:
* Unit tests
* Circle CI
Differential Revision: D24997355
fbshipit-source-id: 30797897977b57d3e3bc197987da20e88febb1fa
