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Make fmod work with zero divisors consistently #41948

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Make fmod work with zero divisors consistently #41948

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1 change: 1 addition & 0 deletions aten/src/ATen/native/cpu/BinaryOpsKernel.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -646,6 +646,7 @@ void fmod_scalar_kernel(TensorIterator& iter, Scalar divisor) {
if (isIntegralType(iter.dtype(), /*includeBool=*/ false)) {
AT_DISPATCH_INTEGRAL_TYPES(iter.dtype(), "fmod_scalar_cpu", [&]() {
const auto div = divisor.to<scalar_t>();
TORCH_CHECK(div != 0, "ZeroDivisionError");
cpu_kernel(iter, [=](scalar_t x) -> scalar_t {
return x % div;
});
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21 changes: 12 additions & 9 deletions test/test_torch.py
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Expand Up @@ -16919,17 +16919,27 @@ def test_rdiv(self, device, dtype):
self.assertEqual(y, z)

@onlyCPU
@dtypes(torch.float)
@dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False))
def test_fmod(self, device, dtype):
m1 = torch.Tensor(10, 10).uniform_(-10., 10.).to(dtype=dtype, device=device)
res1 = m1.clone()
q = 2.1
q = 3
res1[:, 3].fmod_(q)
res2 = m1.clone()
for i in range(m1.size(1)):
res2[i, 3] = math.fmod(res2[i, 3], q)
self.assertEqual(res1, res2)

zero = torch.zeros_like(m1)
if dtype in torch.testing.get_all_int_dtypes():
with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError"):
m1.fmod(0)
with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError"):
m1.fmod(zero)
else:
self.assertTrue(torch.all(m1.fmod(0).isnan()))
self.assertTrue(torch.all(m1.fmod(zero).isnan()))

@onlyCPU
@dtypes(torch.float, torch.long)
def test_remainder(self, device, dtype):
Expand Down Expand Up @@ -17698,13 +17708,6 @@ def test_div_zero(self, device, dtype):
with self.assertRaisesRegex(RuntimeError, 'ZeroDivisionError'):
a // b

@onlyCPU
@dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64)
def test_fmod_zero(self, device, dtype):
a = torch.tensor([1, 0], dtype=dtype, device=device)
with self.assertRaisesRegex(RuntimeError, 'ZeroDivisionError'):
a.fmod(a)

@onlyCPU
def test_cat_bad_input_sizes(self, device):
x = torch.randn(2, 1, device=device)
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