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Enable all PIE rules on ruff (#165814)

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This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796  Enum contains duplicate value: {value}
PIE808  Unnecessary start argument in range
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
This commit is contained in:
Yuanyuan Chen
2025-10-18 06:40:12 +00:00
committed by PyTorch MergeBot
parent e595136187
commit c79dfdc655
91 changed files with 195 additions and 200 deletions
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14
test/test_mps.py
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@ -1900,7 +1900,7 @@ class TestMPS(TestCaseMPS):
res_cpu = torch.linalg.vector_norm(B_cpu, ord=3.5)
self.assertEqual(res_mps, res_cpu)
for dim in range(0, B_mps.dim()):
for dim in range(B_mps.dim()):
res_mps = torch.linalg.vector_norm(B_mps, ord=3.5, dim=dim)
res_cpu = torch.linalg.vector_norm(B_cpu, ord=3.5, dim=dim)
self.assertEqual(res_mps, res_cpu)
@ -2871,8 +2871,8 @@ class TestMPS(TestCaseMPS):
def test_contiguous_slice_2d(self):
def helper(shape):
for i in range(0, shape[0]):
for j in range(0, shape[1]):
for i in range(shape[0]):
for j in range(shape[1]):
t_mps = torch.randn(shape, device="mps")
t_cpu = t_mps.detach().clone().cpu()
@ -3432,12 +3432,12 @@ class TestMPS(TestCaseMPS):
elems = torch.arange(n_tensors * n_tensor_elems, dtype=torch.float32)
tensor_list = []
for i in range(0, n_tensors - 1):
for i in range(n_tensors - 1):
# create a list of contiguous view tensors (view tensor created by the slice op)
t = elems[n_tensor_elems * i : n_tensor_elems * (i + 1)]
tensor_list.append(t)
for i in range(0, n_tensors - 1):
for i in range(n_tensors - 1):
t = tensor_list[i].view(1, n_tensor_elems)
t_mps = t.to("mps")
self.assertEqual(t, t_mps.cpu(), f"i={i}")
@ -4942,7 +4942,7 @@ class TestMPS(TestCaseMPS):
x_mps = fn(torch.zeros(shape, device="mps"), dim=dim)
self.assertEqual(x_cpu, x_mps.cpu())
for fn in [torch.any, torch.all]:
for dim in range(0, 4):
for dim in range(4):
helper(fn, dim)
# 6D tensor reductions
@ -9750,7 +9750,7 @@ class TestGatherScatter(TestCaseMPS):
self.assertEqual(x_cpu, x_mps)
def test_cast_gather_scatter(self):
for _ in range(0, 50):
for _ in range(50):
input = np.random.randint(0, 255, size=(5, 5, 4), dtype=np.uint8)
with torch.no_grad():
s = torch.tensor(input, dtype=torch.uint8, device="mps").unsqueeze(0)