Initial version of general convolution_backward (#65219)
Summary:
Towards [convolution consolidation](https://fb.quip.com/tpDsAYtO15PO).
Introduces the general `convolution_backward` function that uses the factored-out backend routing logic from the forward function.
Some notes:
* `finput` is now recomputed in the backward pass for the slow 2d / 3d kernels instead of being saved from the forward pass. The logic for is based on the forward computation and is present in `compute_finput2d` / `compute_finput3d` functions in `ConvUtils.h`.
* Using structured kernels for `convolution_backward` requires extra copying since the backend-specific backward functions return tensors. Porting to structured is left as future work.
* The tests that check the routing logic have been renamed from `test_conv_backend_selection` -> `test_conv_backend` and now also include gradcheck validation using an `autograd.Function` hooking up `convolution` to `convolution_backward`. This was done to ensure that gradcheck passes for the same set of inputs / backends.
The forward pass routing is done as shown in this flowchart (probably need to download it for it to be readable since it's ridiculous):
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65219
Reviewed By: mruberry
Differential Revision: D32611368
Pulled By: jbschlosser
fbshipit-source-id: 26d759b7c908ab8f19ecce627acea7bd3d5f59ba
