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) instantiate_device_type_tests(TestTesting, globals()) if __name__ == '__main__': run_tests()