Test case where some inputs are Tensor Subclasses in CompositeCompiance (#74645)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74645
This PR adds tests for when only some inputs are Tensor Subclasses.
Why is this important to test?
==============================
Consider the following hypothetical out-of-place operation:
```
def my_add(x, y):
result = x.clone()
result.add_(y)
return result
```
You may expect this to work the same as torch.add. If x is not a Tensor
Subclass, but y is a Tensor subclass, then this returns us a regular
Tensor, NOT a Tensor subclass!
This is exactly the type of in-place operations that causes `vmap` to
fail and will be problematic for certain Tensor Subclasses in the future
so we're adding tests to make sure Composite pytorch operations don't do
this.
What exactly does this PR do?
=============================
Composite compliance now takes a sample input and produces a test case
where some of the sample inputs are Tensor Subclasses. It then sends
this through the original operation, once with Python Mode and one
without.
(Why once with Python Mode? Because we want to use it to detect the
pattern of "create a Tensor and call resize_ on it")
Finally, it repeats this process for all possiblities where the inputs
are Tensor subclasses. For example, if the sample input is (x, y), then
we test all four of the following cases:
- Subclass(x), y
- x, Subclass(y)
- Subclass(x), Subclass(y)
- x, y
Test Plan
=========
- run tests
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D35186862
Pulled By: zou3519
fbshipit-source-id: 102477507b56583463668db7523a6586d92b357d
(cherry picked from commit bfcb087244b0598abb270f7c26d472482f00b5e2)
