* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
ghstack dependencies: #131777
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
I run into this a lot. I can imagine that it would look opaque to users,
so made it more friendly
Old error message: "ValueError: infer_schema(func): Return has unsupported type <class 'inspect._empty'>."
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129896
Approved by: https://github.com/yushangdi
Fixes [#129370](https://github.com/pytorch/pytorch/issues/129370)
Suggest correct a List type annotation when input is in Tuple type. To avoid confusion, we only suggest a type if the type is supported.
Example:
Tuple[int, int] -> List[int]
Tuple[Tensor, Tensor, Optional[Tensor]] -> List[Optional[Tensor]]
Tuple[int, ...] -> List[int]
ValueError: infer_schema(func): Parameter y has unsupported type typing.Tuple[torch.Tensor, torch.Tensor, typing.Optional[torch.Tensor]]. Tuple type annotation is not supported. Please try to use a List instead. For example, typing.List[typing.Optional[torch.Tensor]].
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129417
Approved by: https://github.com/zou3519
This PR:
- moves some of the dtype-string utilities into ScalarType.{h, cpp}
- adds a new utility to get a mapping from dtype name to the C++ dtype
- the perser now checks if the string is a dtype name; if it is then it
pulls the c++ dtype from the mapping.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129189
Approved by: https://github.com/albanD
ghstack dependencies: #129177, #129178, #129179
Fixes#105157
Bug source: `from __future__ import annotations` converts type annotation to strings to make forwards references easier. However, existing custom ops do not consider strings to be valid types.
Fix: We check if the argument and return type annotation is string type. If so, we try to use `eval` to convert it to a type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128809
Approved by: https://github.com/zou3519
