Add get_accessed_features function to ModelLayer class (#23036)
Summary:
We need a way to figure get a complete list fo features that are used in training a model. One way to do this is to make it possible to get the list of features used in each Model Layer. Then once the model is complete we can go through the layers and aggregate the features.
I've introduced a function to expose that information here, get_accessed_features, and implemented it in the FeatureSparseToDense layer to start with.
I've tried to include the minimum amount of information to make this useful, while making it easy to integrate into the variety of model layers. This is, for example, why AccessedFeatures does not contain feature_names which is not always present in a model layer. I debated whether or not to include feature_type, but I think that's useful enough, and easy enough to figure out in a model layer, that it's worth including.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23036
Test Plan:
Added a unit test to verify the behavior of get_accessed_features in FeatureSparseToDense.
aml_dper2-fblearner-flow-integration-tests failed due to a known issue D16355865
aml_dper3-fblearner-flow-integration-tests failed due to a known issue T47197113
I verified no tests in the integration tests failed to issues other than those known ones.
DPER2 canaries: https://fburl.com/fblearner/1217voga
Reviewed By: volkhin
Differential Revision: D16365380
Pulled By: kevinwilfong
fbshipit-source-id: 2dbb4d832628180336533f29f7d917cbad171950
