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[BE] Format uncategorized Python files with ruff format (#132576)

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Remove patterns `**`, `test/**`, and `torch/**` in `tools/linter/adapters/pyfmt_linter.py` and run `lintrunner`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132576
Approved by: https://github.com/ezyang, https://github.com/Skylion007
ghstack dependencies: #132574
This commit is contained in:
Xuehai Pan
2024-08-04 22:11:05 +08:00
committed by PyTorch MergeBot
parent c35061c542
commit 4226ed1585
58 changed files with 520 additions and 1428 deletions
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39
test/test_indexing.py
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@ -689,9 +689,7 @@ class TestIndexing(TestCase):
[[0, 2, 3], slice(None), [1, 3, 4]],
# [...]
# less dim, ellipsis
[
[0, 2],
],
[[0, 2]],
[[0, 2], slice(None)],
[[0, 2], Ellipsis],
[[0, 2], slice(None), Ellipsis],
@ -776,9 +774,7 @@ class TestIndexing(TestCase):
[[0], [1, 2, 4], slice(None)],
[[0], [1, 2, 4], Ellipsis],
[[0], [1, 2, 4], Ellipsis, slice(None)],
[
[1],
],
[[1]],
[[0, 2, 1], [3], [4]],
[[0, 2, 1], [3], [4], slice(None)],
[[0, 2, 1], [3], [4], Ellipsis],
@ -951,21 +947,10 @@ class TestIndexing(TestCase):
# and verifies consistency with CPU result
t = torch.zeros((5, 2))
t_dev = t.to(device)
indices = [
torch.tensor([0, 1, 2, 3]),
torch.tensor(
[
1,
]
),
]
indices = [torch.tensor([0, 1, 2, 3]), torch.tensor([1])]
indices_dev = [i.to(device) for i in indices]
values0d = torch.tensor(1.0)
values1d = torch.tensor(
[
1.0,
]
)
values1d = torch.tensor([1.0])
out_cuda = t_dev.index_put_(indices_dev, values0d.to(device), accumulate=True)
out_cpu = t.index_put_(indices, values0d, accumulate=True)
@ -979,21 +964,13 @@ class TestIndexing(TestCase):
t_dev = t.to(device)
indices = [
torch.tensor(
[
0,
]
),
torch.tensor([0]),
torch.arange(3)[:, None],
torch.arange(2)[None, :],
]
indices_dev = [i.to(device) for i in indices]
values1d = torch.tensor([-1.0, -2.0])
values2d = torch.tensor(
[
[-1.0, -2.0],
]
)
values2d = torch.tensor([[-1.0, -2.0]])
out_cuda = t_dev.index_put_(indices_dev, values1d.to(device), accumulate=True)
out_cpu = t.index_put_(indices, values1d, accumulate=True)
@ -1012,9 +989,7 @@ class TestIndexing(TestCase):
self.assertTrue(not t1.is_contiguous())
self.assertTrue(not t2.is_contiguous())
indices = [
torch.tensor([0, 1]),
]
indices = [torch.tensor([0, 1])]
indices_dev = [i.to(device) for i in indices]
value = torch.randn(2, 2)
out_cuda = t1.index_put_(indices_dev, value.to(device), accumulate=True)