Change C++ frontend to take optional<Tensor> arguments (#41947)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41947
Previously, if an op took an optional `Tensor?` argument, the C++ frontend (i.e. `at::op()` and `Tensor::op()`)
were generated to take `Tensor`. A previous PR (https://github.com/pytorch/pytorch/pull/41610) changed the kernels
to be written with `c10::optional<Tensor>` instead of `Tensor`, but that did not touch the C++ frontend yet.
This PR changes the C++ frontend API to take `c10::optional<Tensor>` instead of `Tensor` as well.
This should be mostly bc conserving. Since `Tensor` implicitly converts to `c10::optional<Tensor>`, any old code
calling an op with a `Tensor` would still work. There are likely corner cases that get broken though.
For example, C++ only ever does *one* implicit conversion. So if you call an op with a non-tensor object
that gets implicitly converted to a `Tensor`, then that previously worked since the API took a `Tensor` and
C++ allows one implicit conversion. Now it wouldn't work anymore because it would require two implicit conversions
(to `Tensor` and then to `c10::optional<Tensor>`) and C++ doesn't do that.
The main reasons for doing this are
- Make the C++ API more sane. Those arguments are optional and that should be visible from the signature.
- Allow easier integration for XLA and Autocast. Those backends generate code to wrap operators and forward
operator arguments to calls to at::op(). After https://github.com/pytorch/pytorch/pull/41610, there was
a mismatch because they had to implement operators with `optional<Tensor>` but call `at::op()` with `Tensor`,
so they had to manually convert between those. After this PR, they can just forward the `optional<Tensor>`
in their call to `at::op()`.
ghstack-source-id: 108873705
Test Plan: unit tests
Reviewed By: bhosmer
Differential Revision: D22704832
fbshipit-source-id: f4c00d457b178fbc124be9e884a538a3653aae1f
