Speed up `Tensor::has_names` for unnamed tensors (#31436)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31436
Tensor::has_names is slower than it should be for unnamed tensors
because of the following:
- it always tries to access the TLS for NamesMode. Unnamed tensors don't
need to peek at NamesMode to determine if they have names or not.
- There is some virtual function being called because TensorImpl is in
c10 and NamedTensorMeta is in libtorch.
This PR short-circuits Tensor::has_names for unnamed tensors by
checking if the underlying TensorImpl hold a pointer to NamedTensorMeta
or not. If the NamedTensorMeta is nullptr; then the tensor is definitely
unnamed.
Benchmarks:
- I have a dedicated benchmarking machine where I isolate a single CPU
and make sure it runs at a fixed frequency.
- I benchmarked torch.add, which calls `tensor::has_names` three times.
- The TL;DR is that torch.add between size-1 unnamed tensors gets sped up
~200ns after this change which is a 9% improvement.
- Before, on my machine:
https://gist.github.com/zou3519/dfd648a1941d584711d850754e0694bc
- After on my machine:
https://gist.github.com/zou3519/e78f0d8980b43d0d9c3e3e78ecd0d4d5
Test Plan: - run tests
Differential Revision: D19166510
Pulled By: zou3519
fbshipit-source-id: 1888a4e92d29152a5e3b778a95e531087e532f53
