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# Owner(s): ["module: cpp-extensions"]
import os
import unittest
import torch.testing._internal.common_utils as common
from torch.testing._internal.common_utils import IS_WINDOWS
from torch.testing._internal.common_cuda import TEST_CUDA
import torch
import torch.backends.cudnn
import torch.utils.cpp_extension
try:
import pytest
HAS_PYTEST = True
except ImportError as e:
HAS_PYTEST = False
# TODO: Rewrite these tests so that they can be collected via pytest without
# using run_test.py
try:
if HAS_PYTEST:
cpp_extension = pytest.importorskip("torch_test_cpp_extension.cpp")
ort_extension = pytest.importorskip("torch_test_cpp_extension.ort")
rng_extension = pytest.importorskip("torch_test_cpp_extension.rng")
else:
import torch_test_cpp_extension.cpp as cpp_extension
import torch_test_cpp_extension.ort as ort_extension
import torch_test_cpp_extension.rng as rng_extension
except ImportError as e:
raise RuntimeError(
"test_cpp_extensions_aot.py cannot be invoked directly. Run "
"`python run_test.py -i test_cpp_extensions_aot_ninja` instead."
) from e
class TestCppExtensionAOT(common.TestCase):
"""Tests ahead-of-time cpp extensions
NOTE: run_test.py's test_cpp_extensions_aot_ninja target
also runs this test case, but with ninja enabled. If you are debugging
a test failure here from the CI, check the logs for which target
(test_cpp_extensions_aot_no_ninja vs test_cpp_extensions_aot_ninja)
failed.
"""
def test_extension_function(self):
x = torch.randn(4, 4)
y = torch.randn(4, 4)
z = cpp_extension.sigmoid_add(x, y)
self.assertEqual(z, x.sigmoid() + y.sigmoid())
def test_extension_module(self):
mm = cpp_extension.MatrixMultiplier(4, 8)
weights = torch.rand(8, 4, dtype=torch.double)
expected = mm.get().mm(weights)
result = mm.forward(weights)
self.assertEqual(expected, result)
def test_backward(self):
mm = cpp_extension.MatrixMultiplier(4, 8)
weights = torch.rand(8, 4, dtype=torch.double, requires_grad=True)
result = mm.forward(weights)
result.sum().backward()
tensor = mm.get()
expected_weights_grad = tensor.t().mm(torch.ones([4, 4], dtype=torch.double))
self.assertEqual(weights.grad, expected_weights_grad)
expected_tensor_grad = torch.ones([4, 4], dtype=torch.double).mm(weights.t())
self.assertEqual(tensor.grad, expected_tensor_grad)
@unittest.skipIf(not TEST_CUDA, "CUDA not found")
def test_cuda_extension(self):
import torch_test_cpp_extension.cuda as cuda_extension
x = torch.zeros(100, device="cuda", dtype=torch.float32)
y = torch.zeros(100, device="cuda", dtype=torch.float32)
z = cuda_extension.sigmoid_add(x, y).cpu()
# 2 * sigmoid(0) = 2 * 0.5 = 1
self.assertEqual(z, torch.ones_like(z))
@common.skipIfRocm
@unittest.skipIf(common.IS_WINDOWS, "Windows not supported")
@unittest.skipIf(not TEST_CUDA, "CUDA not found")
def test_cublas_extension(self):
from torch_test_cpp_extension import cublas_extension
x = torch.zeros(100, device="cuda", dtype=torch.float32)
z = cublas_extension.noop_cublas_function(x)
self.assertEqual(z, x)
@common.skipIfRocm
@unittest.skipIf(common.IS_WINDOWS, "Windows not supported")
@unittest.skipIf(not TEST_CUDA, "CUDA not found")
def test_cusolver_extension(self):
from torch_test_cpp_extension import cusolver_extension
x = torch.zeros(100, device="cuda", dtype=torch.float32)
z = cusolver_extension.noop_cusolver_function(x)
self.assertEqual(z, x)
@unittest.skipIf(IS_WINDOWS, "Not available on Windows")
def test_no_python_abi_suffix_sets_the_correct_library_name(self):
# For this test, run_test.py will call `python setup.py install` in the
# cpp_extensions/no_python_abi_suffix_test folder, where the
# `BuildExtension` class has a `no_python_abi_suffix` option set to
# `True`. This *should* mean that on Python 3, the produced shared
# library does not have an ABI suffix like
# "cpython-37m-x86_64-linux-gnu" before the library suffix, e.g. "so".
root = os.path.join("cpp_extensions", "no_python_abi_suffix_test", "build")
matches = [f for _, _, fs in os.walk(root) for f in fs if f.endswith("so")]
self.assertEqual(len(matches), 1, msg=str(matches))
self.assertEqual(matches[0], "no_python_abi_suffix_test.so", msg=str(matches))
def test_optional(self):
has_value = cpp_extension.function_taking_optional(torch.ones(5))
self.assertTrue(has_value)
has_value = cpp_extension.function_taking_optional(None)
self.assertFalse(has_value)
@common.skipIfRocm
@unittest.skipIf(common.IS_WINDOWS, "Windows not supported")
@unittest.skipIf(not TEST_CUDA, "CUDA not found")
@unittest.skipIf(os.getenv('USE_NINJA', '0') == '0', "cuda extension with dlink requires ninja to build")
def test_cuda_dlink_libs(self):
from torch_test_cpp_extension import cuda_dlink
a = torch.randn(8, dtype=torch.float, device='cuda')
b = torch.randn(8, dtype=torch.float, device='cuda')
ref = a + b
test = cuda_dlink.add(a, b)
self.assertEqual(test, ref)
class TestORTTensor(common.TestCase):
def test_unregistered(self):
a = torch.arange(0, 10, device='cpu')
with self.assertRaisesRegex(RuntimeError, "Could not run"):
b = torch.arange(0, 10, device='ort')
def test_zeros(self):
a = torch.empty(5, 5, device='cpu')
self.assertEqual(a.device, torch.device('cpu'))
b = torch.empty(5, 5, device='ort')
self.assertEqual(b.device, torch.device('ort', 0))
self.assertEqual(ort_extension.get_test_int(), 0)
self.assertEqual(torch.get_default_dtype(), b.dtype)
c = torch.empty((5, 5), dtype=torch.int64, device='ort')
self.assertEqual(ort_extension.get_test_int(), 0)
self.assertEqual(torch.int64, c.dtype)
def test_add(self):
a = torch.empty(5, 5, device='ort', requires_grad=True)
self.assertEqual(ort_extension.get_test_int(), 0)
b = torch.empty(5, 5, device='ort')
self.assertEqual(ort_extension.get_test_int(), 0)
c = a + b
self.assertEqual(ort_extension.get_test_int(), 1)
def test_conv_backend_override(self):
# To simplify tests, we use 4d input here to avoid doing view4d( which
# needs more overrides) in _convolution.
input = torch.empty(2, 4, 10, 2, device='ort', requires_grad=True)
weight = torch.empty(6, 4, 2, 2, device='ort', requires_grad=True)
bias = torch.empty(6, device='ort')
# Make sure forward is overriden
out = torch.nn.functional.conv2d(input, weight, bias, 2, 0, 1, 1)
self.assertEqual(ort_extension.get_test_int(), 2)
self.assertEqual(out.shape[0], input.shape[0])
self.assertEqual(out.shape[1], weight.shape[0])
# Make sure backward is overriden
# Double backward is dispatched to _convolution_double_backward.
# It is not tested here as it involves more computation/overrides.
grad = torch.autograd.grad(out, input, out, create_graph=True)
self.assertEqual(ort_extension.get_test_int(), 3)
self.assertEqual(grad[0].shape, input.shape)
class TestRNGExtension(common.TestCase):
def setUp(self):
super(TestRNGExtension, self).setUp()
def test_rng(self):
fourty_two = torch.full((10,), 42, dtype=torch.int64)
t = torch.empty(10, dtype=torch.int64).random_()
self.assertNotEqual(t, fourty_two)
gen = torch.Generator(device='cpu')
t = torch.empty(10, dtype=torch.int64).random_(generator=gen)
self.assertNotEqual(t, fourty_two)
self.assertEqual(rng_extension.getInstanceCount(), 0)
gen = rng_extension.createTestCPUGenerator(42)
self.assertEqual(rng_extension.getInstanceCount(), 1)
copy = gen
self.assertEqual(rng_extension.getInstanceCount(), 1)
self.assertEqual(gen, copy)
copy2 = rng_extension.identity(copy)
self.assertEqual(rng_extension.getInstanceCount(), 1)
self.assertEqual(gen, copy2)
t = torch.empty(10, dtype=torch.int64).random_(generator=gen)
self.assertEqual(rng_extension.getInstanceCount(), 1)
self.assertEqual(t, fourty_two)
del gen
self.assertEqual(rng_extension.getInstanceCount(), 1)
del copy
self.assertEqual(rng_extension.getInstanceCount(), 1)
del copy2
self.assertEqual(rng_extension.getInstanceCount(), 0)
@unittest.skipIf(not TEST_CUDA, "CUDA not found")
class TestTorchLibrary(common.TestCase):
def test_torch_library(self):
import torch_test_cpp_extension.torch_library # noqa: F401
def f(a: bool, b: bool):
return torch.ops.torch_library.logical_and(a, b)
self.assertTrue(f(True, True))
self.assertFalse(f(True, False))
self.assertFalse(f(False, True))
self.assertFalse(f(False, False))
s = torch.jit.script(f)
self.assertTrue(s(True, True))
self.assertFalse(s(True, False))
self.assertFalse(s(False, True))
self.assertFalse(s(False, False))
self.assertIn('torch_library::logical_and', str(s.graph))
if __name__ == "__main__":
common.run_tests()
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