Fix binary op name inference between unnamed and named tensors. (#24921)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24921
Let `unnamed = torch.randn(1, 1, 1)` and `named = torch.randn(1, 1,
names=('N', 'C'))`.
Previously, there was a bug where `unnamed + named` would error out.
This happened because `unify_from_right(unnamed.opt_names(),
named.opt_names())` would return `named.names()`, which was propagated
to the output tensor. However, the output tensor has dim 3, but
`names.names()` only has 2 elements, so the code would throw an error.
The solution implemented in this PR is to stop trying to do premature
optimization. If all inputs to an operation doesn't have names, then
don't run name inference. However, if any inputs do, then materialize
the names and run name inference.
It's possible to make this more efficient for the case where some inputs
are named and some aren't, but we should benchmark these cases
and determine if it is necessary for it to be more efficient.
Test Plan: - new tests [namedtensor ci]
Differential Revision: D16930710
Pulled By: zou3519
fbshipit-source-id: 0de73c803c8b0f9a1c2d80684b9a47cccba91cbc
