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fix numpy compatibility for 2d small list indices (#154806)

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Will fix #119548 and linked issues once we switch from warning to the new behavior,
but for now, given how much this syntax was used in our test suite, we suspect a silent change will be disruptive.
We will change the behavior after 2.8 branch is cut.
Numpy behavior was changed at least in numpy 1.24 (more than 2 years ago)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/154806
Approved by: https://github.com/cyyever, https://github.com/Skylion007, https://github.com/albanD
This commit is contained in:
Natalia Gimelshein
2025-06-04 01:58:52 +00:00
committed by PyTorch MergeBot
parent e2760544fa
commit 34e3930401
13 changed files with 244 additions and 197 deletions
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44
test/test_autograd.py
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@ -3028,8 +3028,8 @@ class TestAutograd(TestCase):
check_index(x, y, ([1, 2, 3], [0]))
check_index(x, y, ([1, 2], [2, 1]))
check_index(x, y, ([[1, 2], [3, 0]], [[0, 1], [2, 3]]))
check_index(x, y, ([slice(None), [2, 3]]))
check_index(x, y, ([[2, 3], slice(None)]))
check_index(x, y, ((slice(None), [2, 3])))
check_index(x, y, (([2, 3], slice(None))))
# advanced indexing, with less dim, or ellipsis
check_index(x, y, ([0]))
@ -3061,8 +3061,8 @@ class TestAutograd(TestCase):
# advanced indexing, with a tensor wrapped in a variable
z = torch.LongTensor([0, 1])
zv = Variable(z, requires_grad=False)
seq = [z, Ellipsis]
seqv = [zv, Ellipsis]
seq = (z, Ellipsis)
seqv = (zv, Ellipsis)
if y.grad is not None:
with torch.no_grad():
@ -3086,7 +3086,7 @@ class TestAutograd(TestCase):
x = torch.arange(1.0, 17).view(4, 4)
y = Variable(x, requires_grad=True)
idx = [[1, 1, 3, 2, 1, 2], [0]]
idx = ([1, 1, 3, 2, 1, 2], [0])
y[idx].sum().backward()
expected_grad = torch.zeros(4, 4)
for i in idx[0]:
@ -3097,7 +3097,7 @@ class TestAutograd(TestCase):
x = torch.arange(1.0, 17).view(4, 4)
y = Variable(x, requires_grad=True)
idx = [[[1, 2], [0, 0]], [[0, 1], [1, 1]]]
idx = ([[1, 2], [0, 0]], [[0, 1], [1, 1]])
y[idx].sum().backward()
expected_grad = torch.tensor(
[
@ -3112,7 +3112,7 @@ class TestAutograd(TestCase):
x = torch.arange(1.0, 65).view(4, 4, 4)
y = Variable(x, requires_grad=True)
idx = [[1, 1, 1], slice(None), slice(None)]
idx = ([1, 1, 1], slice(None), slice(None))
y[idx].sum().backward()
expected_grad = torch.empty(4, 4, 4).zero_()
expected_grad[1].fill_(3)
@ -3541,32 +3541,32 @@ class TestAutograd(TestCase):
self._test_setitem((5, 5), 1)
self._test_setitem((5,), 1)
self._test_setitem((1,), 0)
self._test_setitem((10,), [[0, 4, 2]])
self._test_setitem((5, 5), [[0, 4], [2, 2]])
self._test_setitem((5, 5, 5), [slice(None), slice(None), [1, 3]])
self._test_setitem((5, 5, 5), [slice(None), [1, 3], slice(None)])
self._test_setitem((5, 5, 5), [[1, 3], slice(None), slice(None)])
self._test_setitem((5, 5, 5), [slice(None), [2, 4], [1, 3]])
self._test_setitem((5, 5, 5), [[1, 3], [2, 4], slice(None)])
self._test_setitem((10,), ([0, 4, 2]))
self._test_setitem((5, 5), ([0, 4], [2, 2]))
self._test_setitem((5, 5, 5), (slice(None), slice(None), [1, 3]))
self._test_setitem((5, 5, 5), (slice(None), [1, 3], slice(None)))
self._test_setitem((5, 5, 5), ([1, 3], slice(None), slice(None)))
self._test_setitem((5, 5, 5), (slice(None), [2, 4], [1, 3]))
self._test_setitem((5, 5, 5), ([1, 3], [2, 4], slice(None)))
self._test_setitem_tensor((5, 5), 3)
self._test_setitem_tensor((5, 5), [[0, 1], [1, 0]])
self._test_setitem_tensor((5, 5), ([0, 1], [1, 0]))
self._test_setitem_tensor((5,), 3)
self._test_setitem_tensor(
(5,), Variable(torch.LongTensor([3]), requires_grad=False).sum()
)
self._test_setitem_tensor((5,), [[0, 1, 2, 3]])
self._test_setitem_tensor((5, 5, 5), [slice(None), slice(None), [1, 3]])
self._test_setitem_tensor((5, 5, 5), [slice(None), [1, 3], slice(None)])
self._test_setitem_tensor((5, 5, 5), [[1, 3], slice(None), slice(None)])
self._test_setitem_tensor((5, 5, 5), [slice(None), [2, 4], [1, 3]])
self._test_setitem_tensor((5, 5, 5), [[1, 3], [2, 4], slice(None)])
self._test_setitem_tensor((5, 5, 5), (slice(None), slice(None), [1, 3]))
self._test_setitem_tensor((5, 5, 5), (slice(None), [1, 3], slice(None)))
self._test_setitem_tensor((5, 5, 5), ([1, 3], slice(None), slice(None)))
self._test_setitem_tensor((5, 5, 5), (slice(None), [2, 4], [1, 3]))
self._test_setitem_tensor((5, 5, 5), ([1, 3], [2, 4], slice(None)))
self._test_setitem_tensor(
(5, 5, 5),
[
(
Variable(torch.LongTensor([1, 3]), requires_grad=False),
[2, 4],
slice(None),
],
),
)
def test_setitem_mask(self):