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unify broadcast_shapes functions and avoid duplicates (#160251)

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Pull Request resolved: https://github.com/pytorch/pytorch/pull/160251
Approved by: https://github.com/jingsh, https://github.com/ColinPeppler
ghstack dependencies: #160250
This commit is contained in:
Laith Sakka
2025-08-15 13:58:19 -07:00
committed by PyTorch MergeBot
parent c03809e8a5
commit 65dc4df74d
3 changed files with 29 additions and 65 deletions
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9
test/test_view_ops.py
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@ -1656,7 +1656,7 @@ class TestOldViewOps(TestCase):
inputs_with_neg_vals = [[1, 1, -12], [-1, 1], [-11]]
for integral_inputs_with_neg_vals in inputs_with_neg_vals:
with self.assertRaisesRegex(
RuntimeError, "Trying to create tensor with negative dimension"
ValueError, "Attempting to broadcast a dimension with negative length!"
):
torch.broadcast_shapes(*integral_inputs_with_neg_vals)
@ -1664,20 +1664,21 @@ class TestOldViewOps(TestCase):
for error_input in integral_inputs_error_case:
with self.assertRaisesRegex(
RuntimeError,
"Shape mismatch: objects cannot be broadcast to a single shape",
".*expected shape should be broadcastable to*",
):
torch.broadcast_shapes(*error_input)
negative_inputs = [(-1,), (1, -12), (4, -11), (-4, 1), (1, 1, -2)]
for s0 in negative_inputs:
with self.assertRaisesRegex(
RuntimeError, "Trying to create tensor with negative dimension"
ValueError, "Attempting to broadcast a dimension with negative length!"
):
torch.broadcast_shapes(s0)
for s1 in negative_inputs:
with self.assertRaisesRegex(
RuntimeError, "Trying to create tensor with negative dimension"
ValueError,
"Attempting to broadcast a dimension with negative length!",
):
torch.broadcast_shapes(s0, s1)