Tag PyObject on TensorImpl per torchdeploy interpreter (#57985)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57985
Fixes https://github.com/pytorch/pytorch/issues/57756
This PR introduces a new `pyobj_interpreter_` field on TensorImpl which tracks what Python interpreter (if any) owns the TensorImpl. This makes it illegal to bind a TensorImpl from multiple Python interpreters, and means that we can now directly store PyObject pointer on TensorImpl even in the presence of multiple Python interpreters, as is the case in torchdeploy. This is a necessary step for PyObject preservation, which cannot be easily implemented when there are multiple Python interpreters.
Although the PR is not that long, there is a very subtle portion of the implementation devoted to ensuring that the tagging process is thread safe, since multiple threads can concurrently try to tag a PyObject. Check Note [Python interpreter tag] and Note [Memory ordering on Python interpreter tag] for detailed discussion of how this is handled. You will have to check this code carefully in code review; I did not torture test the multithreaded paths in any meaningful way.
In a follow up PR, I will pack the interpreter and PyObject fields into single atomic word on 64-bit.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: wconstab
Differential Revision: D28390242
Pulled By: ezyang
fbshipit-source-id: a6d9b244ee6b9c7209e1ed185e336297848e3017
