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test_complex.py
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test_complex.py
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# mypy: allow-untyped-defs
# Owner(s): ["module: complex"]
import torch
from torch.testing._internal.common_device_type import (
dtypes,
instantiate_device_type_tests,
onlyCPU,
)
from torch.testing._internal.common_dtype import complex_types
from torch.testing._internal.common_utils import run_tests, set_default_dtype, TestCase
devices = (torch.device("cpu"), torch.device("cuda:0"))
class TestComplexTensor(TestCase):
@dtypes(*complex_types())
def test_to_list(self, device, dtype):
# test that the complex float tensor has expected values and
# there's no garbage value in the resultant list
self.assertEqual(
torch.zeros((2, 2), device=device, dtype=dtype).tolist(),
[[0j, 0j], [0j, 0j]],
)
@dtypes(torch.float32, torch.float64, torch.float16)
def test_dtype_inference(self, device, dtype):
# issue: https://github.com/pytorch/pytorch/issues/36834
with set_default_dtype(dtype):
x = torch.tensor([3.0, 3.0 + 5.0j], device=device)
if dtype == torch.float16:
self.assertEqual(x.dtype, torch.chalf)
elif dtype == torch.float32:
self.assertEqual(x.dtype, torch.cfloat)
else:
self.assertEqual(x.dtype, torch.cdouble)
@dtypes(*complex_types())
def test_conj_copy(self, device, dtype):
# issue: https://github.com/pytorch/pytorch/issues/106051
x1 = torch.tensor([5 + 1j, 2 + 2j], device=device, dtype=dtype)
xc1 = torch.conj(x1)
x1.copy_(xc1)
self.assertEqual(x1, torch.tensor([5 - 1j, 2 - 2j], device=device, dtype=dtype))
@dtypes(*complex_types())
def test_all(self, device, dtype):
# issue: https://github.com/pytorch/pytorch/issues/120875
x = torch.tensor([1 + 2j, 3 - 4j, 5j, 6], device=device, dtype=dtype)
self.assertTrue(torch.all(x))
@dtypes(*complex_types())
def test_any(self, device, dtype):
# issue: https://github.com/pytorch/pytorch/issues/120875
x = torch.tensor(
[0, 0j, -0 + 0j, -0 - 0j, 0 + 0j, 0 - 0j], device=device, dtype=dtype
)
self.assertFalse(torch.any(x))
@onlyCPU
@dtypes(*complex_types())
def test_eq(self, device, dtype):
"Test eq on complex types"
nan = float("nan")
# Non-vectorized operations
for a, b in (
(
torch.tensor([-0.0610 - 2.1172j], device=device, dtype=dtype),
torch.tensor([-6.1278 - 8.5019j], device=device, dtype=dtype),
),
(
torch.tensor([-0.0610 - 2.1172j], device=device, dtype=dtype),
torch.tensor([-6.1278 - 2.1172j], device=device, dtype=dtype),
),
(
torch.tensor([-0.0610 - 2.1172j], device=device, dtype=dtype),
torch.tensor([-0.0610 - 8.5019j], device=device, dtype=dtype),
),
):
actual = torch.eq(a, b)
expected = torch.tensor([False], device=device, dtype=torch.bool)
self.assertEqual(
actual, expected, msg=f"\neq\nactual {actual}\nexpected {expected}"
)
actual = torch.eq(a, a)
expected = torch.tensor([True], device=device, dtype=torch.bool)
self.assertEqual(
actual, expected, msg=f"\neq\nactual {actual}\nexpected {expected}"
)
actual = torch.full_like(b, complex(2, 2))
torch.eq(a, b, out=actual)
expected = torch.tensor([complex(0)], device=device, dtype=dtype)
self.assertEqual(
actual, expected, msg=f"\neq(out)\nactual {actual}\nexpected {expected}"
)
actual = torch.full_like(b, complex(2, 2))
torch.eq(a, a, out=actual)
expected = torch.tensor([complex(1)], device=device, dtype=dtype)
self.assertEqual(
actual, expected, msg=f"\neq(out)\nactual {actual}\nexpected {expected}"
)
# Vectorized operations
for a, b in (
(
torch.tensor(
[
-0.0610 - 2.1172j,
5.1576 + 5.4775j,
complex(2.8871, nan),
-6.6545 - 3.7655j,
-2.7036 - 1.4470j,
0.3712 + 7.989j,
-0.0610 - 2.1172j,
5.1576 + 5.4775j,
complex(nan, -3.2650),
-6.6545 - 3.7655j,
-2.7036 - 1.4470j,
0.3712 + 7.989j,
],
device=device,
dtype=dtype,
),
torch.tensor(
[
-6.1278 - 8.5019j,
0.5886 + 8.8816j,
complex(2.8871, nan),
6.3505 + 2.2683j,
0.3712 + 7.9659j,
0.3712 + 7.989j,
-6.1278 - 2.1172j,
5.1576 + 8.8816j,
complex(nan, -3.2650),
6.3505 + 2.2683j,
0.3712 + 7.9659j,
0.3712 + 7.989j,
],
device=device,
dtype=dtype,
),
),
):
actual = torch.eq(a, b)
expected = torch.tensor(
[
False,
False,
False,
False,
False,
True,
False,
False,
False,
False,
False,
True,
],
device=device,
dtype=torch.bool,
)
self.assertEqual(
actual, expected, msg=f"\neq\nactual {actual}\nexpected {expected}"
)
actual = torch.eq(a, a)
expected = torch.tensor(
[
True,
True,
False,
True,
True,
True,
True,
True,
False,
True,
True,
True,
],
device=device,
dtype=torch.bool,
)
self.assertEqual(
actual, expected, msg=f"\neq\nactual {actual}\nexpected {expected}"
)
actual = torch.full_like(b, complex(2, 2))
torch.eq(a, b, out=actual)
expected = torch.tensor(
[
complex(0),
complex(0),
complex(0),
complex(0),
complex(0),
complex(1),
complex(0),
complex(0),
complex(0),
complex(0),
complex(0),
complex(1),
],
device=device,
dtype=dtype,
)
self.assertEqual(
actual, expected, msg=f"\neq(out)\nactual {actual}\nexpected {expected}"
)
actual = torch.full_like(b, complex(2, 2))
torch.eq(a, a, out=actual)
expected = torch.tensor(
[
complex(1),
complex(1),
complex(0),
complex(1),
complex(1),
complex(1),
complex(1),
complex(1),
complex(0),
complex(1),
complex(1),
complex(1),
],
device=device,
dtype=dtype,
)
self.assertEqual(
actual, expected, msg=f"\neq(out)\nactual {actual}\nexpected {expected}"
)
@onlyCPU
@dtypes(*complex_types())
def test_ne(self, device, dtype):
"Test ne on complex types"
nan = float("nan")
# Non-vectorized operations
for a, b in (
(
torch.tensor([-0.0610 - 2.1172j], device=device, dtype=dtype),
torch.tensor([-6.1278 - 8.5019j], device=device, dtype=dtype),
),
(
torch.tensor([-0.0610 - 2.1172j], device=device, dtype=dtype),
torch.tensor([-6.1278 - 2.1172j], device=device, dtype=dtype),
),
(
torch.tensor([-0.0610 - 2.1172j], device=device, dtype=dtype),
torch.tensor([-0.0610 - 8.5019j], device=device, dtype=dtype),
),
):
actual = torch.ne(a, b)
expected = torch.tensor([True], device=device, dtype=torch.bool)
self.assertEqual(
actual, expected, msg=f"\nne\nactual {actual}\nexpected {expected}"
)
actual = torch.ne(a, a)
expected = torch.tensor([False], device=device, dtype=torch.bool)
self.assertEqual(
actual, expected, msg=f"\nne\nactual {actual}\nexpected {expected}"
)
actual = torch.full_like(b, complex(2, 2))
torch.ne(a, b, out=actual)
expected = torch.tensor([complex(1)], device=device, dtype=dtype)
self.assertEqual(
actual, expected, msg=f"\nne(out)\nactual {actual}\nexpected {expected}"
)
actual = torch.full_like(b, complex(2, 2))
torch.ne(a, a, out=actual)
expected = torch.tensor([complex(0)], device=device, dtype=dtype)
self.assertEqual(
actual, expected, msg=f"\nne(out)\nactual {actual}\nexpected {expected}"
)
# Vectorized operations
for a, b in (
(
torch.tensor(
[
-0.0610 - 2.1172j,
5.1576 + 5.4775j,
complex(2.8871, nan),
-6.6545 - 3.7655j,
-2.7036 - 1.4470j,
0.3712 + 7.989j,
-0.0610 - 2.1172j,
5.1576 + 5.4775j,
complex(nan, -3.2650),
-6.6545 - 3.7655j,
-2.7036 - 1.4470j,
0.3712 + 7.989j,
],
device=device,
dtype=dtype,
),
torch.tensor(
[
-6.1278 - 8.5019j,
0.5886 + 8.8816j,
complex(2.8871, nan),
6.3505 + 2.2683j,
0.3712 + 7.9659j,
0.3712 + 7.989j,
-6.1278 - 2.1172j,
5.1576 + 8.8816j,
complex(nan, -3.2650),
6.3505 + 2.2683j,
0.3712 + 7.9659j,
0.3712 + 7.989j,
],
device=device,
dtype=dtype,
),
),
):
actual = torch.ne(a, b)
expected = torch.tensor(
[
True,
True,
True,
True,
True,
False,
True,
True,
True,
True,
True,
False,
],
device=device,
dtype=torch.bool,
)
self.assertEqual(
actual, expected, msg=f"\nne\nactual {actual}\nexpected {expected}"
)
actual = torch.ne(a, a)
expected = torch.tensor(
[
False,
False,
True,
False,
False,
False,
False,
False,
True,
False,
False,
False,
],
device=device,
dtype=torch.bool,
)
self.assertEqual(
actual, expected, msg=f"\nne\nactual {actual}\nexpected {expected}"
)
actual = torch.full_like(b, complex(2, 2))
torch.ne(a, b, out=actual)
expected = torch.tensor(
[
complex(1),
complex(1),
complex(1),
complex(1),
complex(1),
complex(0),
complex(1),
complex(1),
complex(1),
complex(1),
complex(1),
complex(0),
],
device=device,
dtype=dtype,
)
self.assertEqual(
actual, expected, msg=f"\nne(out)\nactual {actual}\nexpected {expected}"
)
actual = torch.full_like(b, complex(2, 2))
torch.ne(a, a, out=actual)
expected = torch.tensor(
[
complex(0),
complex(0),
complex(1),
complex(0),
complex(0),
complex(0),
complex(0),
complex(0),
complex(1),
complex(0),
complex(0),
complex(0),
],
device=device,
dtype=dtype,
)
self.assertEqual(
actual, expected, msg=f"\nne(out)\nactual {actual}\nexpected {expected}"
)
instantiate_device_type_tests(TestComplexTensor, globals())
if __name__ == "__main__":
TestCase._default_dtype_check_enabled = True
run_tests()