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test_testing.py
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test_testing.py
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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()