pytorch

80b46ca3 - Null AutogradMeta optimization (#28610)

Null AutogradMeta optimization (#28610) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/28610 The basic idea is, in some cases where we stored a pointer to a full AutogradMeta object, instead store a nullptr. We let a nullptr represent a default-constructed AutogradMeta object, and simply populate it with a real AutogradMeta if there is ever a situation where we need to modify it. The primary technical contrivance in this diff is I have to use AutogradMetaFactory to lazily initialize the AutogradMeta, as it is not available in the dynamic library that TensorImpl is in. (I spent a while trying to put them in the same compilation unit, but gave up in the end as it pushed us over the Windows linking binary size limit. Eep.) Some other notes: - `set_autograd_meta` now unconditionally turns a tensor into a variable. I audited all call sites and observed there are no occurrences where nullptr is passed (after this patch, there are now!) - `copy_tensor_metadata` is updated to unconditionally preserve the VariableTensorId-ness of the destination tensor. I think this is the more correct semantics; we can't do the old semantics anymore. - There's a bunch of places in the API where we return const references to objects. This is pretty weird to me, but I didn't feel like cleaning it up. But sometimes I don't conveniently have something that's the right lifetime, so I introduced a number of singletons to handle this correctly. You might wonder why I'm doing the optimization before the variable-tensor dynamic merge. The reason is simple: this change is semantics preserving, while variable-tensor dynamic merge is not. So it is easier to get right, and prevents us from regressing performance if we do it the other way. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Test Plan: Imported from OSS Differential Revision: D18171162 Pulled By: ezyang fbshipit-source-id: 580df729e4d04881b2b9caa0f0c00785b3afbb92