blob: b87345186cb381ecd595a84ed218d7cc46a9cc41 [file] [log] [blame]
import torch
import math
from torch.testing._internal.common_utils import \
(TestCase, run_tests, make_tensor)
from torch.testing._internal.common_device_type import \
(instantiate_device_type_tests, onlyOnCPUAndCUDA, dtypes)
# For testing TestCase methods and torch.testing functions
class TestTesting(TestCase):
# Ensure that assertEqual handles numpy arrays properly
@dtypes(*(torch.testing.get_all_dtypes(include_half=True, include_bfloat16=False,
include_bool=True, include_complex=True)))
def test_assertEqual_numpy(self, device, dtype):
S = 10
test_sizes = [
(),
(0,),
(S,),
(S, S),
(0, S),
(S, 0)]
for test_size in test_sizes:
a = make_tensor(test_size, device, dtype, low=-5, high=5)
a_n = a.cpu().numpy()
msg = f'size: {test_size}'
self.assertEqual(a_n, a, rtol=0, atol=0, msg=msg)
self.assertEqual(a, a_n, rtol=0, atol=0, msg=msg)
self.assertEqual(a_n, a_n, rtol=0, atol=0, msg=msg)
# Tests that when rtol or atol (including self.precision) is set, then
# the other is zeroed.
# TODO: this is legacy behavior and should be updated after test
# precisions are reviewed to be consistent with torch.isclose.
@onlyOnCPUAndCUDA
def test__comparetensors_legacy(self, device):
a = torch.tensor((10000000.,))
b = torch.tensor((10000002.,))
x = torch.tensor((1.,))
y = torch.tensor((1. + 1e-5,))
# Helper for reusing the tensor values as scalars
def _scalar_helper(a, b, rtol=None, atol=None):
return self._compareScalars(a.item(), b.item(), rtol=rtol, atol=atol)
for op in (self._compareTensors, _scalar_helper):
# Tests default
result, debug_msg = op(a, b)
self.assertTrue(result)
# Tests setting atol
result, debug_msg = op(a, b, atol=2, rtol=0)
self.assertTrue(result)
# Tests setting atol too small
result, debug_msg = op(a, b, atol=1, rtol=0)
self.assertFalse(result)
# Tests setting rtol too small
result, debug_msg = op(x, y, atol=0, rtol=1.05e-5)
self.assertTrue(result)
# Tests setting rtol too small
result, debug_msg = op(x, y, atol=0, rtol=1e-5)
self.assertFalse(result)
@onlyOnCPUAndCUDA
def test__comparescalars_debug_msg(self, device):
# float x float
result, debug_msg = self._compareScalars(4., 7.)
expected_msg = ("Comparing 4.0 and 7.0 gives a difference of 3.0, "
"but the allowed difference with rtol=1.3e-06 and "
"atol=1e-05 is only 1.9100000000000003e-05!")
self.assertEqual(debug_msg, expected_msg)
# complex x complex, real difference
result, debug_msg = self._compareScalars(complex(1, 3), complex(3, 1))
expected_msg = ("Comparing the real part 1.0 and 3.0 gives a difference "
"of 2.0, but the allowed difference with rtol=1.3e-06 "
"and atol=1e-05 is only 1.39e-05!")
self.assertEqual(debug_msg, expected_msg)
# complex x complex, imaginary difference
result, debug_msg = self._compareScalars(complex(1, 3), complex(1, 5.5))
expected_msg = ("Comparing the imaginary part 3.0 and 5.5 gives a "
"difference of 2.5, but the allowed difference with "
"rtol=1.3e-06 and atol=1e-05 is only 1.715e-05!")
self.assertEqual(debug_msg, expected_msg)
# complex x int
result, debug_msg = self._compareScalars(complex(1, -2), 1)
expected_msg = ("Comparing the imaginary part -2.0 and 0.0 gives a "
"difference of 2.0, but the allowed difference with "
"rtol=1.3e-06 and atol=1e-05 is only 1e-05!")
self.assertEqual(debug_msg, expected_msg)
# NaN x NaN, equal_nan=False
result, debug_msg = self._compareScalars(float('nan'), float('nan'), equal_nan=False)
expected_msg = ("Found nan and nan while comparing and either one is "
"nan and the other isn't, or both are nan and equal_nan "
"is False")
self.assertEqual(debug_msg, expected_msg)
# Checks that compareTensors provides the correct debug info
@onlyOnCPUAndCUDA
def test__comparetensors_debug_msg(self, device):
# Acquires atol that will be used
atol = max(1e-05, self.precision)
# Checks float tensor comparisons (2D tensor)
a = torch.tensor(((0, 6), (7, 9)), device=device, dtype=torch.float32)
b = torch.tensor(((0, 7), (7, 22)), device=device, dtype=torch.float32)
result, debug_msg = self._compareTensors(a, b)
expected_msg = ("With rtol=1.3e-06 and atol={0}, found 2 element(s) (out of 4) "
"whose difference(s) exceeded the margin of error (including 0 nan comparisons). "
"The greatest difference was 13.0 (9.0 vs. 22.0), "
"which occurred at index (1, 1).").format(atol)
self.assertEqual(debug_msg, expected_msg)
# Checks float tensor comparisons (with extremal values)
a = torch.tensor((float('inf'), 5, float('inf')), device=device, dtype=torch.float32)
b = torch.tensor((float('inf'), float('nan'), float('-inf')), device=device, dtype=torch.float32)
result, debug_msg = self._compareTensors(a, b)
expected_msg = ("With rtol=1.3e-06 and atol={0}, found 2 element(s) (out of 3) "
"whose difference(s) exceeded the margin of error (including 1 nan comparisons). "
"The greatest difference was nan (5.0 vs. nan), "
"which occurred at index 1.").format(atol)
self.assertEqual(debug_msg, expected_msg)
# Checks float tensor comparisons (with finite vs nan differences)
a = torch.tensor((20, -6), device=device, dtype=torch.float32)
b = torch.tensor((-1, float('nan')), device=device, dtype=torch.float32)
result, debug_msg = self._compareTensors(a, b)
expected_msg = ("With rtol=1.3e-06 and atol={0}, found 2 element(s) (out of 2) "
"whose difference(s) exceeded the margin of error (including 1 nan comparisons). "
"The greatest difference was nan (-6.0 vs. nan), "
"which occurred at index 1.").format(atol)
self.assertEqual(debug_msg, expected_msg)
# Checks int tensor comparisons (1D tensor)
a = torch.tensor((1, 2, 3, 4), device=device)
b = torch.tensor((2, 5, 3, 4), device=device)
result, debug_msg = self._compareTensors(a, b)
expected_msg = ("Found 2 different element(s) (out of 4), "
"with the greatest difference of 3 (2 vs. 5) "
"occuring at index 1.")
self.assertEqual(debug_msg, expected_msg)
# Checks bool tensor comparisons (0D tensor)
a = torch.tensor((True), device=device)
b = torch.tensor((False), device=device)
result, debug_msg = self._compareTensors(a, b)
expected_msg = ("Found 1 different element(s) (out of 1), "
"with the greatest difference of 1 (1 vs. 0) "
"occuring at index 0.")
self.assertEqual(debug_msg, expected_msg)
# Checks complex tensor comparisons (real part)
a = torch.tensor((1 - 1j, 4 + 3j), device=device)
b = torch.tensor((1 - 1j, 1 + 3j), device=device)
result, debug_msg = self._compareTensors(a, b)
expected_msg = ("Real parts failed to compare as equal! "
"With rtol=1.3e-06 and atol={0}, "
"found 1 element(s) (out of 2) whose difference(s) exceeded the "
"margin of error (including 0 nan comparisons). The greatest difference was "
"3.0 (4.0 vs. 1.0), which occurred at index 1.").format(atol)
self.assertEqual(debug_msg, expected_msg)
# Checks complex tensor comparisons (imaginary part)
a = torch.tensor((1 - 1j, 4 + 3j), device=device)
b = torch.tensor((1 - 1j, 4 - 21j), device=device)
result, debug_msg = self._compareTensors(a, b)
expected_msg = ("Imaginary parts failed to compare as equal! "
"With rtol=1.3e-06 and atol={0}, "
"found 1 element(s) (out of 2) whose difference(s) exceeded the "
"margin of error (including 0 nan comparisons). The greatest difference was "
"24.0 (3.0 vs. -21.0), which occurred at index 1.").format(atol)
self.assertEqual(debug_msg, expected_msg)
# Checks size mismatch
a = torch.tensor((1, 2), device=device)
b = torch.tensor((3), device=device)
result, debug_msg = self._compareTensors(a, b)
expected_msg = ("Attempted to compare equality of tensors "
"with different sizes. Got sizes torch.Size([2]) and torch.Size([]).")
self.assertEqual(debug_msg, expected_msg)
# Checks dtype mismatch
a = torch.tensor((1, 2), device=device, dtype=torch.long)
b = torch.tensor((1, 2), device=device, dtype=torch.float32)
result, debug_msg = self._compareTensors(a, b, exact_dtype=True)
expected_msg = ("Attempted to compare equality of tensors "
"with different dtypes. Got dtypes torch.int64 and torch.float32.")
self.assertEqual(debug_msg, expected_msg)
# Checks device mismatch
if self.device_type == 'cuda':
a = torch.tensor((5), device='cpu')
b = torch.tensor((5), device=device)
result, debug_msg = self._compareTensors(a, b, exact_device=True)
expected_msg = ("Attempted to compare equality of tensors "
"on different devices! Got devices cpu and cuda:0.")
self.assertEqual(debug_msg, expected_msg)
# Helper for testing _compareTensors and _compareScalars
# Works on single element tensors
def _comparetensors_helper(self, tests, device, dtype, equal_nan, exact_dtype=True, atol=1e-08, rtol=1e-05):
for test in tests:
a = torch.tensor((test[0],), device=device, dtype=dtype)
b = torch.tensor((test[1],), device=device, dtype=dtype)
# Tensor x Tensor comparison
compare_result, debug_msg = self._compareTensors(a, b, rtol=rtol, atol=atol,
equal_nan=equal_nan,
exact_dtype=exact_dtype)
self.assertEqual(compare_result, test[2])
# Scalar x Scalar comparison
compare_result, debug_msg = self._compareScalars(a.item(), b.item(),
rtol=rtol, atol=atol,
equal_nan=equal_nan)
self.assertEqual(compare_result, test[2])
def _isclose_helper(self, tests, device, dtype, equal_nan, atol=1e-08, rtol=1e-05):
for test in tests:
a = torch.tensor((test[0],), device=device, dtype=dtype)
b = torch.tensor((test[1],), device=device, dtype=dtype)
actual = torch.isclose(a, b, equal_nan=equal_nan, atol=atol, rtol=rtol)
expected = test[2]
self.assertEqual(actual.item(), expected)
# torch.close is not implemented for bool tensors
# see https://github.com/pytorch/pytorch/issues/33048
def test_isclose_comparetensors_bool(self, device):
tests = (
(True, True, True),
(False, False, True),
(True, False, False),
(False, True, False),
)
with self.assertRaises(RuntimeError):
self._isclose_helper(tests, device, torch.bool, False)
self._comparetensors_helper(tests, device, torch.bool, False)
@dtypes(torch.uint8,
torch.int8, torch.int16, torch.int32, torch.int64)
def test_isclose_comparetensors_integer(self, device, dtype):
tests = (
(0, 0, True),
(0, 1, False),
(1, 0, False),
)
self._isclose_helper(tests, device, dtype, False)
# atol and rtol tests
tests = [
(0, 1, True),
(1, 0, False),
(1, 3, True),
]
self._isclose_helper(tests, device, dtype, False, atol=.5, rtol=.5)
self._comparetensors_helper(tests, device, dtype, False, atol=.5, rtol=.5)
if dtype is torch.uint8:
tests = [
(-1, 1, False),
(1, -1, False)
]
else:
tests = [
(-1, 1, True),
(1, -1, True)
]
self._isclose_helper(tests, device, dtype, False, atol=1.5, rtol=.5)
self._comparetensors_helper(tests, device, dtype, False, atol=1.5, rtol=.5)
@onlyOnCPUAndCUDA
@dtypes(torch.float16, torch.float32, torch.float64)
def test_isclose_comparetensors_float(self, device, dtype):
tests = (
(0, 0, True),
(0, -1, False),
(float('inf'), float('inf'), True),
(-float('inf'), float('inf'), False),
(float('inf'), float('nan'), False),
(float('nan'), float('nan'), False),
(0, float('nan'), False),
(1, 1, True),
)
self._isclose_helper(tests, device, dtype, False)
self._comparetensors_helper(tests, device, dtype, False)
# atol and rtol tests
eps = 1e-2 if dtype is torch.half else 1e-6
tests = (
(0, 1, True),
(0, 1 + eps, False),
(1, 0, False),
(1, 3, True),
(1 - eps, 3, False),
(-.25, .5, True),
(-.25 - eps, .5, False),
(.25, -.5, True),
(.25 + eps, -.5, False),
)
self._isclose_helper(tests, device, dtype, False, atol=.5, rtol=.5)
self._comparetensors_helper(tests, device, dtype, False, atol=.5, rtol=.5)
# equal_nan = True tests
tests = (
(0, float('nan'), False),
(float('inf'), float('nan'), False),
(float('nan'), float('nan'), True),
)
self._isclose_helper(tests, device, dtype, True)
self._comparetensors_helper(tests, device, dtype, True)
# torch.close with equal_nan=True is not implemented for complex inputs
# see https://github.com/numpy/numpy/issues/15959
# Note: compareTensor will compare the real and imaginary parts of a
# complex tensors separately, unlike isclose.
@dtypes(torch.complex64, torch.complex128)
def test_isclose_comparetensors_complex(self, device, dtype):
tests = (
(complex(1, 1), complex(1, 1 + 1e-8), True),
(complex(0, 1), complex(1, 1), False),
(complex(1, 1), complex(1, 0), False),
(complex(1, 1), complex(1, float('nan')), False),
(complex(1, float('nan')), complex(1, float('nan')), False),
(complex(1, 1), complex(1, float('inf')), False),
(complex(float('inf'), 1), complex(1, float('inf')), False),
(complex(-float('inf'), 1), complex(1, float('inf')), False),
(complex(-float('inf'), 1), complex(float('inf'), 1), False),
(complex(float('inf'), 1), complex(float('inf'), 1), True),
(complex(float('inf'), 1), complex(float('inf'), 1 + 1e-4), False),
)
self._isclose_helper(tests, device, dtype, False)
self._comparetensors_helper(tests, device, dtype, False)
# atol and rtol tests
# atol and rtol tests
eps = 1e-6
tests = (
# Complex versions of float tests (real part)
(complex(0, 0), complex(1, 0), True),
(complex(0, 0), complex(1 + eps, 0), False),
(complex(1, 0), complex(0, 0), False),
(complex(1, 0), complex(3, 0), True),
(complex(1 - eps, 0), complex(3, 0), False),
(complex(-.25, 0), complex(.5, 0), True),
(complex(-.25 - eps, 0), complex(.5, 0), False),
(complex(.25, 0), complex(-.5, 0), True),
(complex(.25 + eps, 0), complex(-.5, 0), False),
# Complex versions of float tests (imaginary part)
(complex(0, 0), complex(0, 1), True),
(complex(0, 0), complex(0, 1 + eps), False),
(complex(0, 1), complex(0, 0), False),
(complex(0, 1), complex(0, 3), True),
(complex(0, 1 - eps), complex(0, 3), False),
(complex(0, -.25), complex(0, .5), True),
(complex(0, -.25 - eps), complex(0, .5), False),
(complex(0, .25), complex(0, -.5), True),
(complex(0, .25 + eps), complex(0, -.5), False),
)
self._isclose_helper(tests, device, dtype, False, atol=.5, rtol=.5)
self._comparetensors_helper(tests, device, dtype, False, atol=.5, rtol=.5)
# atol and rtol tests for isclose
tests = (
# Complex-specific tests
(complex(1, -1), complex(-1, 1), False),
(complex(1, -1), complex(2, -2), True),
(complex(-math.sqrt(2), math.sqrt(2)),
complex(-math.sqrt(.5), math.sqrt(.5)), True),
(complex(-math.sqrt(2), math.sqrt(2)),
complex(-math.sqrt(.501), math.sqrt(.499)), False),
(complex(2, 4), complex(1., 8.8523607), True),
(complex(2, 4), complex(1., 8.8523607 + eps), False),
(complex(1, 99), complex(4, 100), True),
)
self._isclose_helper(tests, device, dtype, False, atol=.5, rtol=.5)
# atol and rtol tests for compareTensors
tests = (
(complex(1, -1), complex(-1, 1), False),
(complex(1, -1), complex(2, -2), True),
(complex(1, 99), complex(4, 100), False),
)
self._comparetensors_helper(tests, device, dtype, False, atol=.5, rtol=.5)
# equal_nan = True tests
tests = (
(complex(1, 1), complex(1, float('nan')), False),
(complex(float('nan'), 1), complex(1, float('nan')), False),
(complex(float('nan'), 1), complex(float('nan'), 1), True),
)
with self.assertRaises(RuntimeError):
self._isclose_helper(tests, device, dtype, True)
self._comparetensors_helper(tests, device, dtype, True)
# Tests that isclose with rtol or atol values less than zero throws a
# RuntimeError
@dtypes(torch.bool, torch.uint8,
torch.int8, torch.int16, torch.int32, torch.int64,
torch.float16, torch.float32, torch.float64)
def test_isclose_atol_rtol_greater_than_zero(self, device, dtype):
t = torch.tensor((1,), device=device, dtype=dtype)
with self.assertRaises(RuntimeError):
torch.isclose(t, t, atol=-1, rtol=1)
with self.assertRaises(RuntimeError):
torch.isclose(t, t, atol=1, rtol=-1)
with self.assertRaises(RuntimeError):
torch.isclose(t, t, atol=-1, rtol=-1)
def test_assert_messages(self, device):
self.assertIsNone(self._get_assert_msg(msg=None))
self.assertEqual("\nno_debug_msg", self._get_assert_msg("no_debug_msg"))
self.assertEqual("no_user_msg", self._get_assert_msg(msg=None, debug_msg="no_user_msg"))
self.assertEqual("debug_msg\nuser_msg", self._get_assert_msg(msg="user_msg", debug_msg="debug_msg"))
instantiate_device_type_tests(TestTesting, globals())
if __name__ == '__main__':
run_tests()