pytorch

41e1ab07 - Introduce isTensorSubclassLike; add special cases to backwards formulas (#69534)

Introduce isTensorSubclassLike; add special cases to backwards formulas (#69534) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69534 Something is TensorSubclassLike if it is a Tensor subclass or if it has the same problems as Tensor subclasses. Today that just includes Tensor Subclasses and meta tensors but may include other things in the future. Some of our backwards formulas are incompatible with TensorSubclassLike objects. For example, calling .data_ptr() is a problem because many TensorSubclassLike objects don't have storage. Another problem is in-place operations: performing `regular_tensor.inplace_(tensor_subclass)` is a problem. This PR adds special cases to the backward formulas for torch.max and torch.clamp to handle this. The backward formulas for torch.max and torch.clamp are not dispatcher operations so they cannot be overridden and we hesitate to make them dispatcher operations for FC/BC concerns and performance overhead concerns. Furthermore, the old concept of "is this inplace operation vmap compatible?" can be subsumed by the general "is this inplace operation tensor-subclass compatible" question, so I replaced all instances of isInplaceVmapCompatible and replaced it with the isTensorSubclassLike checks. Test Plan - I tested the changes using functorch. - It's possible to write a test for these in core (one has to make a custom tensor subclass and then send it through the operation and then invoke autograd), but I wanted to push the work to doing some generic testing for backward formulas (https://github.com/pytorch/pytorch/issues/69530) instead of doing some one-off things now. Test Plan: Imported from OSS Reviewed By: mrshenli Differential Revision: D32967727 Pulled By: zou3519 fbshipit-source-id: 30fda1a7581da4c55179b7a3ca05069150bbe2dc