prefer using q/Q over l/L for portability
@@ -227,7 +227,7 @@ class TestCubReduction(unittest.TestCase):
@testing.for_contiguous_axes()
# sum supports less dtypes; don't test float16 as it's not as accurate?
- @testing.for_dtypes('lLfdFD')
+ @testing.for_dtypes('qQfdFD')
@testing.numpy_cupy_allclose(rtol=1E-5)
def test_cub_sum(self, xp, dtype, axis):
a = testing.shaped_random(self.shape, xp, dtype)
@@ -265,7 +265,7 @@ class TestCubReduction(unittest.TestCase):
return a.sum(axis=axis)
# sum supports less dtypes; don't test float16 as it's not as accurate?
- @testing.for_dtypes('lLfdFD')
+ @testing.for_dtypes('qQfdFD')
@testing.numpy_cupy_allclose(rtol=1E-5, contiguous_check=False)
def test_cub_sum_empty_axis(self, xp, dtype):
a = testing.shaped_random(self.shape, xp, dtype)
@@ -277,7 +277,7 @@ class TestCubReduction(unittest.TestCase):
@testing.for_contiguous_axes()
# prod supports less dtypes; don't test float16 as it's not as accurate?
- @testing.for_dtypes('lLfdFD')
+ @testing.for_dtypes('qQfdFD')
@testing.numpy_cupy_allclose(rtol=1E-5)
def test_cub_prod(self, xp, dtype, axis):
a = testing.shaped_random(self.shape, xp, dtype)
