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[BE] Enable C419 rule for any all shortcircuiting (#99890)

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Apparently https://github.com/pytorch/pytorch/pull/78142 made torch.JIT allow for simple generator expressions which allows us to enable rules that replace unnecessary list comprehensions with generators in any/all. This was originally part of #99280 but I split it off into this PR so that it can be easily reverted should anything break.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/99890
Approved by: https://github.com/justinchuby, https://github.com/kit1980, https://github.com/malfet
This commit is contained in:
Aaron Gokaslan
2023-04-25 15:02:13 +00:00
committed by PyTorch MergeBot
parent e43918b93a
commit e2a3817dfd
83 changed files with 175 additions and 203 deletions
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4
torch/utils/_foreach_utils.py
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@ -21,7 +21,7 @@ from torch.autograd.grad_mode import no_grad
def _group_tensors_by_device_and_dtype(tensorlistlist: List[List[Tensor]],
with_indices: Optional[bool] = False) -> \
Dict[Tuple[torch.device, torch.dtype], List[List[Union[Tensor, int]]]]:
assert all([not x or len(x) == len(tensorlistlist[0]) for x in tensorlistlist]), (
assert all(not x or len(x) == len(tensorlistlist[0]) for x in tensorlistlist), (
"all specified tensorlists must match in length")
per_device_and_dtype_tensors: Dict[Tuple[torch.device, torch.dtype], List[List[Union[Tensor, int]]]] = defaultdict(
lambda: [[] for _ in range(len(tensorlistlist) + (1 if with_indices else 0))])
@ -39,4 +39,4 @@ def _group_tensors_by_device_and_dtype(tensorlistlist: List[List[Tensor]],
def _has_foreach_support(tensors: List[Tensor], device: torch.device) -> bool:
if device.type not in ['cpu', 'cuda'] or torch.jit.is_scripting():
return False
return all([t is None or type(t) == torch.Tensor for t in tensors])
return all(t is None or type(t) == torch.Tensor for t in tensors)