Ignore `nn::Functional` submodules in `nn::Module` serialization (#19740)
Summary:
Currently, the Python API doesn't serialize layers that don't have weights (such as `nn.ReLU` and `nn.MaxPool2d`e.g. in https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py#L80-L81). If one saves a model that contains weight-less layers in Python and tries to load it into C++, the C++ module loading code (`torch::load(...)`) will throw an error complaining that the expected layers are not found in the serialized file (e.g. https://github.com/pytorch/vision/pull/728#issuecomment-480974175). This PR solves the problem by ignoring layers that are not serializable (which currently only include `nn::Functional`) in the C++ module serialization code (`torch::save(...)` and `torch::load(...)`), and the user is expected to use `nn::Functional` to wrap the weight-less layers so that they can be ignored when serializing / deserializing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19740
Differential Revision: D15100575
Pulled By: yf225
fbshipit-source-id: 956481a2355d1de45341585abedda05e35d2ee8b
