frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-11-04 16:04:58 +08:00
Code Issues Packages Projects Releases Wiki Activity

fix test_float_to_int_conversion_nonfinite for NumPy 2 (#138131)

Browse Source 
Related to #107302

We saw `test_float_to_int_conversion_nonfinite` failed as we upgrade to NumPy 2.

It is caused by the undefined behavior of `numpy` casting `inf`, `-inf` and `nan` from `np.float32` to other dtypes.
The test is using NumPy as reference for the ground truth. (see line 1013-1015)
However, these behaviors are undefined in NumPy.
If you do `np.array([float("inf")]).astype(np.uint8, casting="safe")`, it results in an error `TypeError: Cannot cast array data from dtype('float64') to dtype('uint8') according to the rule 'safe'`.
The undefined behaviors are always subject to change.

This PR address this issue by passing concrete values as the ground truth references.
In the future, even NumPy changes its behavior the test would still remain stable.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/138131
Approved by: https://github.com/drisspg
This commit is contained in:
Haifeng Jin
2024-11-14 04:19:15 +00:00
committed by PyTorch MergeBot
parent d32eac86f3
commit e6083016b3
1 changed files with 7 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
test/test_tensor_creation_ops.py
View File

@ -1081,11 +1081,17 @@ class TestTensorCreation(TestCase):
# NB: torch.uint16, torch.uint32, torch.uint64 excluded as this
# nondeterministically fails, warning "invalid value encountered in cast"
@onlyCPU
@unittest.skipIf(IS_MACOS, "Nonfinite conversion results on MacOS are different from others.")
@dtypes(torch.bool, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64)
def test_float_to_int_conversion_nonfinite(self, device, dtype):
vals = (float('-inf'), float('inf'), float('nan'))
refs = 0
if dtype == torch.bool:
refs = True
elif dtype in (torch.int32, torch.int64):
refs = torch.iinfo(dtype).min
self._float_to_int_conversion_helper(vals, device, dtype)
self._float_to_int_conversion_helper(vals, device, dtype, (refs, ) * 3)
@onlyNativeDeviceTypes
def test_complex_type_conversions(self, device):