import collections import functools import itertools import math import os import random import re import unittest from typing import Any, Callable, Iterator, List, Tuple import torch from torch.testing._internal.common_utils import \ (IS_SANDCASTLE, IS_WINDOWS, TestCase, make_tensor, run_tests, skipIfRocm, slowTest) from torch.testing._internal.framework_utils import calculate_shards from torch.testing._internal.common_device_type import \ (PYTORCH_TESTING_DEVICE_EXCEPT_FOR_KEY, PYTORCH_TESTING_DEVICE_ONLY_FOR_KEY, dtypes, get_device_type_test_bases, instantiate_device_type_tests, onlyCUDA, onlyOnCPUAndCUDA, deviceCountAtLeast) from torch.testing._asserts import UsageError # 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) @dtypes(torch.bool, torch.long, torch.float, torch.cfloat) def test_make_tensor(self, device, dtype): def check(size, low, high, requires_grad, noncontiguous): t = make_tensor(size, device, dtype, low=low, high=high, requires_grad=requires_grad, noncontiguous=noncontiguous) self.assertEqual(t.shape, size) self.assertEqual(t.device, torch.device(device)) self.assertEqual(t.dtype, dtype) low = -9 if low is None else low high = 9 if high is None else high if t.numel() > 0 and dtype in [torch.long, torch.float]: self.assertTrue(t.le(high).logical_and(t.ge(low)).all().item()) if dtype in [torch.float, torch.cfloat]: self.assertEqual(t.requires_grad, requires_grad) else: self.assertFalse(t.requires_grad) if t.numel() > 1: self.assertEqual(t.is_contiguous(), not noncontiguous) else: self.assertTrue(t.is_contiguous()) for size in (tuple(), (0,), (1,), (1, 1), (2,), (2, 3), (8, 16, 32)): check(size, None, None, False, False) check(size, 2, 4, True, True) 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")) # The following tests (test_cuda_assert_*) are added to ensure test suite terminates early # when CUDA assert was thrown. Because all subsequent test will fail if that happens. # These tests are slow because it spawn another process to run test suite. # See: https://github.com/pytorch/pytorch/issues/49019 @onlyCUDA @slowTest def test_cuda_assert_should_stop_common_utils_test_suite(self, device): # test to ensure common_utils.py override has early termination for CUDA. stderr = TestCase.runWithPytorchAPIUsageStderr("""\ #!/usr/bin/env python3 import torch from torch.testing._internal.common_utils import (TestCase, run_tests, slowTest) class TestThatContainsCUDAAssertFailure(TestCase): @slowTest def test_throw_unrecoverable_cuda_exception(self): x = torch.rand(10, device='cuda') # cause unrecoverable CUDA exception, recoverable on CPU y = x[torch.tensor([25])].cpu() @slowTest def test_trivial_passing_test_case_on_cpu_cuda(self): x1 = torch.tensor([0., 1.], device='cuda') x2 = torch.tensor([0., 1.], device='cpu') self.assertEqual(x1, x2) if __name__ == '__main__': run_tests() """) # should capture CUDA error self.assertIn('CUDA error: device-side assert triggered', stderr) # should run only 1 test because it throws unrecoverable error. self.assertIn('Ran 1 test', stderr) @onlyCUDA @slowTest def test_cuda_assert_should_stop_common_device_type_test_suite(self, device): # test to ensure common_device_type.py override has early termination for CUDA. stderr = TestCase.runWithPytorchAPIUsageStderr("""\ #!/usr/bin/env python3 import torch from torch.testing._internal.common_utils import (TestCase, run_tests, slowTest) from torch.testing._internal.common_device_type import instantiate_device_type_tests class TestThatContainsCUDAAssertFailure(TestCase): @slowTest def test_throw_unrecoverable_cuda_exception(self, device): x = torch.rand(10, device=device) # cause unrecoverable CUDA exception, recoverable on CPU y = x[torch.tensor([25])].cpu() @slowTest def test_trivial_passing_test_case_on_cpu_cuda(self, device): x1 = torch.tensor([0., 1.], device=device) x2 = torch.tensor([0., 1.], device='cpu') self.assertEqual(x1, x2) instantiate_device_type_tests( TestThatContainsCUDAAssertFailure, globals(), only_for='cuda' ) if __name__ == '__main__': run_tests() """) # should capture CUDA error self.assertIn('CUDA error: device-side assert triggered', stderr) # should run only 1 test because it throws unrecoverable error. self.assertIn('Ran 1 test', stderr) @onlyCUDA @slowTest def test_cuda_assert_should_not_stop_common_distributed_test_suite(self, device): # test to ensure common_distributed.py override should not early terminate CUDA. stderr = TestCase.runWithPytorchAPIUsageStderr("""\ #!/usr/bin/env python3 import torch from torch.testing._internal.common_utils import (run_tests, slowTest) from torch.testing._internal.common_device_type import instantiate_device_type_tests from torch.testing._internal.common_distributed import MultiProcessTestCase class TestThatContainsCUDAAssertFailure(MultiProcessTestCase): @slowTest def test_throw_unrecoverable_cuda_exception(self, device): x = torch.rand(10, device=device) # cause unrecoverable CUDA exception, recoverable on CPU y = x[torch.tensor([25])].cpu() @slowTest def test_trivial_passing_test_case_on_cpu_cuda(self, device): x1 = torch.tensor([0., 1.], device=device) x2 = torch.tensor([0., 1.], device='cpu') self.assertEqual(x1, x2) instantiate_device_type_tests( TestThatContainsCUDAAssertFailure, globals(), only_for='cuda' ) if __name__ == '__main__': run_tests() """) # we are currently disabling CUDA early termination for distributed tests. self.assertIn('Ran 2 test', stderr) instantiate_device_type_tests(TestTesting, globals()) class TestFrameworkUtils(TestCase): tests = [ 'super_long_test', 'long_test1', 'long_test2', 'normal_test1', 'normal_test2', 'normal_test3', 'short_test1', 'short_test2', 'short_test3', 'short_test4', 'short_test5', ] test_times = { 'super_long_test': 55, 'long_test1': 22, 'long_test2': 18, 'normal_test1': 9, 'normal_test2': 7, 'normal_test3': 5, 'short_test1': 1, 'short_test2': 0.6, 'short_test3': 0.4, 'short_test4': 0.3, 'short_test5': 0.01, } def test_calculate_2_shards_with_complete_test_times(self): expected_shards = [ (60, ['super_long_test', 'normal_test3']), (58.31, ['long_test1', 'long_test2', 'normal_test1', 'normal_test2', 'short_test1', 'short_test2', 'short_test3', 'short_test4', 'short_test5']) ] self.assertEqual(expected_shards, calculate_shards(2, self.tests, self.test_times)) def test_calculate_5_shards_with_complete_test_times(self): expected_shards = [ (55, ['super_long_test']), (22, ['long_test1', ]), (18, ['long_test2', ]), (11.31, ['normal_test1', 'short_test1', 'short_test2', 'short_test3', 'short_test4', 'short_test5']), (12, ['normal_test2', 'normal_test3']), ] self.assertEqual(expected_shards, calculate_shards(5, self.tests, self.test_times)) def test_calculate_2_shards_with_incomplete_test_times(self): incomplete_test_times = {k: v for k, v in self.test_times.items() if 'test1' in k} expected_shards = [ (22, ['long_test1', 'long_test2', 'normal_test3', 'short_test3', 'short_test5']), (10, ['normal_test1', 'short_test1', 'super_long_test', 'normal_test2', 'short_test2', 'short_test4']), ] self.assertEqual(expected_shards, calculate_shards(2, self.tests, incomplete_test_times)) def test_calculate_5_shards_with_incomplete_test_times(self): incomplete_test_times = {k: v for k, v in self.test_times.items() if 'test1' in k} expected_shards = [ (22, ['long_test1', 'normal_test2', 'short_test5']), (9, ['normal_test1', 'normal_test3']), (1, ['short_test1', 'short_test2']), (0, ['super_long_test', 'short_test3']), (0, ['long_test2', 'short_test4']), ] self.assertEqual(expected_shards, calculate_shards(5, self.tests, incomplete_test_times)) def test_calculate_2_shards_against_optimal_shards(self): for _ in range(100): random.seed(120) random_times = {k: random.random() * 10 for k in self.tests} # all test times except first two rest_of_tests = [i for k, i in random_times.items() if k != 'super_long_test' and k != 'long_test1'] sum_of_rest = sum(rest_of_tests) random_times['super_long_test'] = max(sum_of_rest / 2, max(rest_of_tests)) random_times['long_test1'] = sum_of_rest - random_times['super_long_test'] # An optimal sharding would look like the below, but we don't need to compute this for the test: # optimal_shards = [ # (sum_of_rest, ['super_long_test', 'long_test1']), # (sum_of_rest, [i for i in self.tests if i != 'super_long_test' and i != 'long_test1']), # ] calculated_shards = calculate_shards(2, self.tests, random_times) max_shard_time = max(calculated_shards[0][0], calculated_shards[1][0]) if sum_of_rest != 0: # The calculated shard should not have a ratio worse than 7/6 for num_shards = 2 self.assertGreaterEqual(7.0 / 6.0, max_shard_time / sum_of_rest) sorted_tests = sorted(self.tests) sorted_shard_tests = sorted(calculated_shards[0][1] + calculated_shards[1][1]) # All the tests should be represented by some shard self.assertEqual(sorted_tests, sorted_shard_tests) @skipIfRocm @unittest.skipIf(IS_WINDOWS, "Skipping because doesn't work for windows") @unittest.skipIf(IS_SANDCASTLE, "Skipping because doesn't work on sandcastle") def test_filtering_env_var(self): # Test environment variable selected device type test generator. test_filter_file_template = """\ #!/usr/bin/env python3 import torch from torch.testing._internal.common_utils import (TestCase, run_tests) from torch.testing._internal.common_device_type import instantiate_device_type_tests class TestEnvironmentVariable(TestCase): def test_trivial_passing_test(self, device): x1 = torch.tensor([0., 1.], device=device) x2 = torch.tensor([0., 1.], device='cpu') self.assertEqual(x1, x2) instantiate_device_type_tests( TestEnvironmentVariable, globals(), ) if __name__ == '__main__': run_tests() """ test_bases_count = len(get_device_type_test_bases()) # Test without setting env var should run everything. env = dict(os.environ) for k in ['IN_CI', PYTORCH_TESTING_DEVICE_ONLY_FOR_KEY, PYTORCH_TESTING_DEVICE_EXCEPT_FOR_KEY]: if k in env.keys(): del env[k] _, stderr = TestCase.run_process_no_exception(test_filter_file_template, env=env) self.assertIn(f'Ran {test_bases_count} test', stderr.decode('ascii')) # Test with setting only_for should only run 1 test. env[PYTORCH_TESTING_DEVICE_ONLY_FOR_KEY] = 'cpu' _, stderr = TestCase.run_process_no_exception(test_filter_file_template, env=env) self.assertIn('Ran 1 test', stderr.decode('ascii')) # Test with setting except_for should run 1 less device type from default. del env[PYTORCH_TESTING_DEVICE_ONLY_FOR_KEY] env[PYTORCH_TESTING_DEVICE_EXCEPT_FOR_KEY] = 'cpu' _, stderr = TestCase.run_process_no_exception(test_filter_file_template, env=env) self.assertIn(f'Ran {test_bases_count-1} test', stderr.decode('ascii')) # Test with setting both should throw exception env[PYTORCH_TESTING_DEVICE_ONLY_FOR_KEY] = 'cpu' _, stderr = TestCase.run_process_no_exception(test_filter_file_template, env=env) self.assertNotIn('OK', stderr.decode('ascii')) def assert_fns() -> List[Callable]: """Gets assert functions to be tested. Returns: List(Callable): Top-level assert functions from :mod:`torch.testing`. """ return [torch.testing.assert_equal, torch.testing.assert_close] def make_assert_inputs(actual: Any, expected: Any) -> List[Tuple[Any, Any]]: """Makes inputs for assert functions based on two examples. Args: actual (Any): Actual input. expected (Any): Expected input. Returns: List[Tuple[Any, Any]]: Pair of example inputs, as well as the example inputs wrapped in sequences (:class:`tuple`, :class:`list`), and mappings (:class:`dict`, :class:`~collections.OrderedDict`). """ return [ (actual, expected), # tuple vs. tuple ((actual,), (expected,)), # list vs. list ([actual], [expected]), # tuple vs. list ((actual,), [expected]), # dict vs. dict ({"t": actual}, {"t": expected}), # OrderedDict vs. OrderedDict (collections.OrderedDict([("t", actual)]), collections.OrderedDict([("t", expected)])), # dict vs. OrderedDict ({"t": actual}, collections.OrderedDict([("t", expected)])), # list of tuples vs. tuple of lists ([(actual,)], ([expected],)), # list of dicts vs. tuple of OrderedDicts ([{"t": actual}], (collections.OrderedDict([("t", expected)]),)), # dict of lists vs. OrderedDict of tuples ({"t": [actual]}, collections.OrderedDict([("t", (expected,))])), ] def assert_fns_with_inputs(actual: Any, expected: Any) -> Iterator[Callable]: """Yields assert functions with included positional inputs based on two examples. .. note:: This is a valid product of combinations from :meth:`assert_fns` and :meth:`make_inputs`. Every test that does not test for anything specific should iterate over this to maximize the coverage. Args: actual (Any): Actual input. expected (Any): Expected input. Yields: List[Callable]: Assert functions with predefined positional inputs. """ for assert_fn, inputs in itertools.product(assert_fns(), make_assert_inputs(actual, expected)): yield functools.partial(assert_fn, *inputs) class TestAsserts(TestCase): def test_sparse_support(self): actual = torch.empty(()) expected = torch.sparse_coo_tensor(size=()) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaises(UsageError): fn() def test_quantized_support(self): val = 1 actual = torch.tensor([val], dtype=torch.int32) expected = torch._empty_affine_quantized(actual.shape, scale=1, zero_point=0, dtype=torch.qint32) expected.fill_(val) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaises(UsageError): fn() def test_mismatching_shape(self): actual = torch.empty(()) expected = actual.clone().reshape((1,)) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, "shape"): fn() def test_mismatching_dtype(self): actual = torch.empty((), dtype=torch.float) expected = actual.clone().to(torch.int) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, "dtype"): fn() def test_mismatching_dtype_no_check(self): actual = torch.ones((), dtype=torch.float) expected = actual.clone().to(torch.int) for fn in assert_fns_with_inputs(actual, expected): fn(check_dtype=False) def test_mismatching_stride(self): actual = torch.empty((2, 2)) expected = torch.as_strided(actual.clone().t().contiguous(), actual.shape, actual.stride()[::-1]) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, "stride"): fn() def test_mismatching_stride_no_check(self): actual = torch.rand((2, 2)) expected = torch.as_strided(actual.clone().t().contiguous(), actual.shape, actual.stride()[::-1]) for fn in assert_fns_with_inputs(actual, expected): fn(check_stride=False) def test_mismatching_values(self): actual = torch.tensor(1) expected = torch.tensor(2) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaises(AssertionError): fn() def test_assert_equal(self): actual = torch.tensor(1) expected = actual.clone() torch.testing.assert_equal(actual, expected) def test_assert_close(self): actual = torch.tensor(1.0) expected = actual.clone() torch.testing.assert_close(actual, expected) def test_assert_close_only_rtol(self): actual = torch.empty(()) expected = actual.clone() with self.assertRaises(UsageError): torch.testing.assert_close(actual, expected, rtol=0.0) def test_assert_close_only_atol(self): actual = torch.empty(()) expected = actual.clone() with self.assertRaises(UsageError): torch.testing.assert_close(actual, expected, atol=0.0) def test_assert_close_mismatching_values_rtol(self): eps = 1e-3 actual = torch.tensor(1.0) expected = torch.tensor(1.0 + eps) with self.assertRaises(AssertionError): torch.testing.assert_close(actual, expected, rtol=eps / 2, atol=0.0) def test_assert_close_matching_values_rtol(self): eps = 1e-3 actual = torch.tensor(1.0) expected = torch.tensor(1.0 + eps) torch.testing.assert_close(actual, expected, rtol=eps * 2, atol=0.0) def test_assert_close_mismatching_values_atol(self): eps = 1e-3 actual = torch.tensor(0.0) expected = torch.tensor(eps) with self.assertRaises(AssertionError): torch.testing.assert_close(actual, expected, rtol=0.0, atol=eps / 2) def test_assert_close_matching_values_atol(self): eps = 1e-3 actual = torch.tensor(0.0) expected = torch.tensor(eps) torch.testing.assert_close(actual, expected, rtol=0.0, atol=eps * 2) def test_assert_close_nan(self): a = torch.tensor(float("NaN")) b = torch.tensor(float("NaN")) for inputs in make_assert_inputs(a, b): with self.assertRaises(AssertionError): torch.testing.assert_close(*inputs) def test_assert_close_equal_nan(self): a = torch.tensor(float("NaN")) b = torch.tensor(float("NaN")) for inputs in make_assert_inputs(a, b): torch.testing.assert_close(*inputs, equal_nan=True) def test_assert_close_equal_nan_complex(self): a = torch.tensor(complex(1, float("NaN"))) b = torch.tensor(complex(float("NaN"), 1)) for inputs in make_assert_inputs(a, b): with self.assertRaises(AssertionError): torch.testing.assert_close(*inputs, equal_nan=True) def test_assert_close_equal_nan_complex_relaxed(self): a = torch.tensor(complex(1, float("NaN"))) b = torch.tensor(complex(float("NaN"), 1)) for inputs in make_assert_inputs(a, b): torch.testing.assert_close(*inputs, equal_nan="relaxed") def test_mismatching_values_msg_mismatches(self): actual = torch.tensor([1, 2, 3, 4]) expected = torch.tensor([1, 2, 5, 6]) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, re.escape("Mismatched elements: 2 / 4 (50.0%)")): fn() def test_mismatching_values_msg_abs_diff(self): actual = torch.tensor([[1, 2], [3, 4]]) expected = torch.tensor([[1, 2], [5, 4]]) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, re.escape("Greatest absolute difference: 2 at (1, 0)")): fn() def test_mismatching_values_msg_rel_diff(self): actual = torch.tensor([[1, 2], [3, 4]]) expected = torch.tensor([[1, 4], [3, 4]]) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, re.escape("Greatest relative difference: 0.5 at (0, 1)")): fn() def test_mismatching_values_zero_div_zero(self): actual = torch.tensor([1.0, 0.0]) expected = torch.tensor([2.0, 0.0]) for fn in assert_fns_with_inputs(actual, expected): # Although it looks complicated, this regex just makes sure that the word 'nan' is not part of the error # message. That would happen if the 0 / 0 is used for the mismatch computation although it matches. with self.assertRaisesRegex(AssertionError, "((?!nan).)*"): fn() def test_mismatching_values_msg_complex_real(self): actual = torch.tensor(complex(0, 1)) expected = torch.tensor(complex(1, 1)) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, re.escape("The failure occurred for the real part")): fn() def test_mismatching_values_msg_complex_imag(self): actual = torch.tensor(complex(1, 0)) expected = torch.tensor(complex(1, 1)) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, re.escape("The failure occurred for the imaginary part")): fn() def test_assert_close_mismatching_values_msg_rtol(self): rtol = 1e-3 actual = torch.tensor(1) expected = torch.tensor(2) for inputs in make_assert_inputs(actual, expected): with self.assertRaisesRegex( AssertionError, re.escape(f"Greatest relative difference: 0.5 at 0 (up to {rtol} allowed)") ): torch.testing.assert_close(*inputs, rtol=rtol, atol=0.0) def test_assert_close_mismatching_values_msg_atol(self): atol = 1e-3 actual = torch.tensor(1) expected = torch.tensor(2) for inputs in make_assert_inputs(actual, expected): with self.assertRaisesRegex( AssertionError, re.escape(f"Greatest absolute difference: 1 at 0 (up to {atol} allowed)") ): torch.testing.assert_close(*inputs, rtol=0.0, atol=atol) def test_sequence_mismatching_len(self): actual = (torch.empty(()),) expected = () for fn in assert_fns(): with self.assertRaises(AssertionError): fn(actual, expected) def test_sequence_mismatching_values_msg(self): t1 = torch.tensor(1) t2 = torch.tensor(2) actual = (t1, t1) expected = (t1, t2) for fn in assert_fns(): with self.assertRaisesRegex(AssertionError, r"index\s+1"): fn(actual, expected) def test_mapping_mismatching_keys(self): actual = {"a": torch.empty(())} expected = {} for fn in assert_fns(): with self.assertRaises(AssertionError): fn(actual, expected) def test_mapping_mismatching_values_msg(self): t1 = torch.tensor(1) t2 = torch.tensor(2) actual = {"a": t1, "b": t1} expected = {"a": t1, "b": t2} for fn in assert_fns(): with self.assertRaisesRegex(AssertionError, r"key\s+'b'"): fn(actual, expected) def test_type_inequality(self): actual = torch.empty(2) expected = actual.tolist() for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, str(type(expected))): fn() def test_unknown_type(self): actual = "0" expected = "0" for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(UsageError, str(type(actual))): fn() def test_numpy(self): tensor = torch.rand(2, 2, dtype=torch.float32) actual = tensor.numpy() expected = actual.copy() for fn in assert_fns_with_inputs(actual, expected): fn() def test_scalar(self): number = torch.randint(10, size=()).item() for actual, expected in itertools.product((int(number), float(number), complex(number)), repeat=2): check_dtype = type(actual) is type(expected) for fn in assert_fns_with_inputs(actual, expected): fn(check_dtype=check_dtype) def test_msg_str(self): msg = "Custom error message!" actual = torch.tensor(1) expected = torch.tensor(2) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, msg): fn(msg=msg) def test_msg_callable(self): msg = "Custom error message!" def make_msg(actual, expected, trace): return msg actual = torch.tensor(1) expected = torch.tensor(2) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, msg): fn(msg=make_msg) class TestAssertsMultiDevice(TestCase): @deviceCountAtLeast(1) def test_mismatching_device(self, devices): for actual_device, expected_device in itertools.permutations(("cpu", *devices), 2): actual = torch.empty((), device=actual_device) expected = actual.clone().to(expected_device) for fn in assert_fns_with_inputs(actual, expected): with self.assertRaisesRegex(AssertionError, "device"): fn() @deviceCountAtLeast(1) def test_mismatching_device_no_check(self, devices): for actual_device, expected_device in itertools.permutations(("cpu", *devices), 2): actual = torch.rand((), device=actual_device) expected = actual.clone().to(expected_device) for fn in assert_fns_with_inputs(actual, expected): fn(check_device=False) instantiate_device_type_tests(TestAssertsMultiDevice, globals(), only_for="cuda") if __name__ == '__main__': run_tests()