AOTAutograd: correctness fix when tracing custom autograd functions that alias inputs (#102992)
Fixes https://github.com/pytorch/pytorch/issues/102970. See the comment [here](https://github.com/pytorch/pytorch/issues/102970#issuecomment-1577223773) for details.
We normally treat "outputs that alias inputs" specially in AOTAutograd, by replaying the views at runtime, instead of baking them into the graph. For views that are part of custom autograd functions though, we can't do that view-replay, since it will clobber the backwards function that the user specified in their custom autograd.Function.
Right now in this PR, I distinguish between "aliased inputs that are normal views" vs. "aliased inputs that are views that came from an autograd.Function call" by checking the outputs `.grad_fn` field, to see if it inherits from our custom CBackward function class. Then I added a new `OutputType` enum value, that we effectively treat the "normal" way (the same way that we treat ordinary, non-aliased outputs). The new enum val is mostly for debugging - so we can print it and know that our graph had custom autograd.Function aliased outputs in it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102992
Approved by: https://github.com/ezyang, https://github.com/zou3519
