[Profiler] Hold weak reference to prevent TensorImpl address reuse during profiling. (#87244)
A recurring problem with assigning Tensor IDs is that we want to preserve identity when storage changes but we don't observe TensorImpl destruction so identity assignment is not robust to the ABA problem with respect to TensorImpl*. ~TensorImpl is far too hot to instrument; even adding a call to a no-op function in a different compilation unit increases overhead by tens of percent. (OSS builds do not have any sort of LTO.)
Fortunately there is a solution. A PyTorch Tensor is a `c10::intrusive_ptr<c10::TensorImpl>`, which in turn holds a storage. (Which is a `c10::intrusive_ptr<c10::StorageImpl>`) `c10::intrusive_ptr` has a `c10::weak_intrusive_ptr` class for taking non-owning references to the underlying object. The implementation involves both a strong refcount and weak refcount in `c10::intrusive_ptr`. If the strong refcount of an intrusive_ptr goes to zero and there are no weak references then everything is deleted. However if there is a weak reference then the intrusive_ptr calls `release_resources()` but not delete.
This has the effect of freeing the underlying resources (ensuring that program semantics are unchanged) but leaves behind an empty shell of an `intrusive_ptr` that the `weak_intrusive_ptr`s use to check status. And herein lies the solution: as long as we hold a weak reference to a TensorImpl we will block deletion and prevent the `TensorImpl*` from being reused.
This PR uses a `c10::weak_intrusive_ptr<c10::TensorImpl>` to store the address of profiled TensorImpls and then converts it to a raw pointer (or rather, a `TensorImplAddress`) during post processing when we no longer care about blocking address reuse.
Differential Revision: [D40492848](https://our.internmc.facebook.com/intern/diff/D40492848/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87244
Approved by: https://github.com/slgong-fb, https://github.com/albanD
