class TestCrashHandler(TestCase):
@unittest.skipIf(not has_breakpad(), "Crash handler lib was not linked in")
def test_python_exception_writing(self):
with tempfile.TemporaryDirectory() as temp_dir:
torch.utils._crash_handler.enable_minidumps(temp_dir)
torch.utils._crash_handler.enable_minidumps_on_exceptions()
files = os.listdir(temp_dir)
self.assertEqual(len(files), 0)
f = io.StringIO()
with contextlib.redirect_stderr(f):
try:
@torch.jit.script
def x(i: int):
return i + "2" # type: ignore[operator]
except RuntimeError as e:
pass
files = os.listdir(temp_dir)
self.assertEqual(len(files), 1)
self.assertTrue(files[0].endswith(".dmp"))
torch.utils._crash_handler.disable_minidumps()
class TestCppExtensionJIT(common.TestCase):
"""Tests just-in-time cpp extensions.
Don't confuse this with the PyTorch JIT (aka TorchScript).
"""
def setUp(self):
super().setUp()
# cpp extensions use relative paths. Those paths are relative to
# this file, so we'll change the working directory temporarily
self.old_working_dir = os.getcwd()
os.chdir(os.path.dirname(os.path.abspath(__file__)))
def tearDown(self):
super().tearDown()
# return the working directory (see setUp)
os.chdir(self.old_working_dir)
@classmethod
def setUpClass(cls):
remove_build_path()
@classmethod
def tearDownClass(cls):
remove_build_path()
def test_jit_compile_extension(self):
module = torch.utils.cpp_extension.load(
name="jit_extension",
sources=[
"cpp_extensions/jit_extension.cpp",
"cpp_extensions/jit_extension2.cpp",
],
extra_include_paths=["cpp_extensions"],
extra_cflags=["-g"],
verbose=True,
)
x = torch.randn(4, 4)
y = torch.randn(4, 4)
z = module.tanh_add(x, y)
self.assertEqual(z, x.tanh() + y.tanh())
# Checking we can call a method defined not in the main C++ file.
z = module.exp_add(x, y)
self.assertEqual(z, x.exp() + y.exp())
# Checking we can use this JIT-compiled class.
doubler = module.Doubler(2, 2)
self.assertIsNone(doubler.get().grad)
self.assertEqual(doubler.get().sum(), 4)
self.assertEqual(doubler.forward().sum(), 8)
@unittest.skipIf(not (TEST_CUDA or TEST_ROCM), "CUDA not found")
def test_jit_cuda_extension(self):
# NOTE: The name of the extension must equal the name of the module.
module = torch.utils.cpp_extension.load(
name="torch_test_cuda_extension",
sources=[
"cpp_extensions/cuda_extension.cpp",
"cpp_extensions/cuda_extension.cu",
],
extra_cuda_cflags=["-O2"],
verbose=True,
keep_intermediates=False,
)
x = torch.zeros(100, device="cuda", dtype=torch.float32)
y = torch.zeros(100, device="cuda", dtype=torch.float32)
z = module.sigmoid_add(x, y).cpu()
# 2 * sigmoid(0) = 2 * 0.5 = 1
self.assertEqual(z, torch.ones_like(z))
def _run_jit_cuda_archflags(self, flags, expected):
# Compile an extension with given `flags`
def _check_cuobjdump_output(expected_values, is_ptx=False):
elf_or_ptx = '--list-ptx' if is_ptx else '--list-elf'
lib_ext = '.pyd' if IS_WINDOWS else '.so'
# Note, .extension name may include _v1, _v2, so first find exact name
ext_filename = glob.glob(
os.path.join(temp_dir, 'cudaext_archflag*' + lib_ext))[0]
command = ['cuobjdump', elf_or_ptx, ext_filename]
p = subprocess.Popen(command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
output, err = p.communicate()
output = output.decode("ascii")
err = err.decode("ascii")
if not p.returncode == 0 or not err == '':
raise AssertionError("Flags: {}\nReturncode: {}\nStderr: {}\n"
"Output: {} ".format(
flags, p.returncode, err, output))
actual_arches = sorted(re.findall(r'sm_\d\d', output))
expected_arches = sorted(['sm_' + xx for xx in expected_values])
self.assertEqual(actual_arches,
expected_arches,
msg="Flags: {}, Actual: {}, Expected: {}\n"
"Stderr: {}\nOutput: {}".format(
flags, actual_arches, expected_arches, err,
output))
temp_dir = tempfile.mkdtemp()
old_envvar = os.environ.get('TORCH_CUDA_ARCH_LIST', None)
try:
os.environ['TORCH_CUDA_ARCH_LIST'] = flags
torch.utils.cpp_extension.load(
name="cudaext_archflags",
sources=[
"cpp_extensions/cuda_extension.cpp",
"cpp_extensions/cuda_extension.cu",
],
extra_cuda_cflags=["-O2"],
verbose=True,
build_directory=temp_dir,
)
# Expected output for --list-elf:
# ELF file 1: cudaext_archflags.1.sm_61.cubin
# ELF file 2: cudaext_archflags.2.sm_52.cubin
_check_cuobjdump_output(expected[0])
if expected[1] is not None:
# Expected output for --list-ptx:
# PTX file 1: cudaext_archflags.1.sm_61.ptx
_check_cuobjdump_output(expected[1], is_ptx=True)
finally:
if IS_WINDOWS:
print("Not wiping extensions build folder because Windows")
else:
shutil.rmtree(temp_dir)
if old_envvar is None:
os.environ.pop('TORCH_CUDA_ARCH_LIST')
else:
os.environ['TORCH_CUDA_ARCH_LIST'] = old_envvar
@unittest.skipIf(not TEST_CUDA, "CUDA not found")
@unittest.skipIf(TEST_ROCM, "disabled on rocm")
def test_jit_cuda_archflags(self):
# Test a number of combinations:
# - the default for the machine we're testing on
# - Separators, can be ';' (most common) or ' '
# - Architecture names
# - With/without '+PTX'
n = torch.cuda.device_count()
capabilities = {torch.cuda.get_device_capability(i) for i in range(n)}
# expected values is length-2 tuple: (list of ELF, list of PTX)
# note: there should not be more than one PTX value
archflags = {
'': ([
'{}{}'.format(capability[0], capability[1])
for capability in capabilities
], None),
"Maxwell+Tegra;6.1": (['53', '61'], None),
"Pascal 3.5": (['35', '60', '61'], None),
"Volta": (['70'], ['70']),
}
if int(torch.version.cuda.split('.')[0]) >= 10:
# CUDA 9 only supports compute capability <= 7.2
archflags["7.5+PTX"] = (['75'], ['75'])
archflags["5.0;6.0+PTX;7.0;7.5"] = (['50', '60', '70',
'75'], ['60'])
for flags, expected in archflags.items():
self._run_jit_cuda_archflags(flags, expected)
@unittest.skipIf(not TEST_CUDNN, "CuDNN not found")
def test_jit_cudnn_extension(self):
# implementation of CuDNN ReLU
if IS_WINDOWS:
extra_ldflags = ["cudnn.lib"]
else:
extra_ldflags = ["-lcudnn"]
module = torch.utils.cpp_extension.load(
name="torch_test_cudnn_extension",
sources=["cpp_extensions/cudnn_extension.cpp"],
extra_ldflags=extra_ldflags,
verbose=True,
with_cuda=True,
)
x = torch.randn(100, device="cuda", dtype=torch.float32)
y = torch.zeros(100, device="cuda", dtype=torch.float32)
module.cudnn_relu(x, y) # y=relu(x)
self.assertEqual(torch.nn.functional.relu(x), y)
with self.assertRaisesRegex(RuntimeError, "same size"):
y_incorrect = torch.zeros(20, device="cuda", dtype=torch.float32)
module.cudnn_relu(x, y_incorrect)
def test_inline_jit_compile_extension_with_functions_as_list(self):
cpp_source = """
torch::Tensor tanh_add(torch::Tensor x, torch::Tensor y) {
return x.tanh() + y.tanh();
}
"""
module = torch.utils.cpp_extension.load_inline(
name="inline_jit_extension_with_functions_list",
cpp_sources=cpp_source,
functions="tanh_add",
verbose=True,
)
self.assertEqual(module.tanh_add.__doc__.split("\n")[2], "tanh_add")
x = torch.randn(4, 4)
y = torch.randn(4, 4)
z = module.tanh_add(x, y)
self.assertEqual(z, x.tanh() + y.tanh())
def test_inline_jit_compile_extension_with_functions_as_dict(self):
cpp_source = """
torch::Tensor tanh_add(torch::Tensor x, torch::Tensor y) {
return x.tanh() + y.tanh();
}
"""
module = torch.utils.cpp_extension.load_inline(
name="inline_jit_extension_with_functions_dict",
cpp_sources=cpp_source,
functions={"tanh_add": "Tanh and then sum :D"},
verbose=True,
)
self.assertEqual(
module.tanh_add.__doc__.split("\n")[2], "Tanh and then sum :D")
def test_inline_jit_compile_extension_multiple_sources_and_no_functions(
self):
cpp_source1 = """
torch::Tensor sin_add(torch::Tensor x, torch::Tensor y) {
return x.sin() + y.sin();
}
"""
cpp_source2 = """
#include <torch/extension.h>
torch::Tensor sin_add(torch::Tensor x, torch::Tensor y);
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("sin_add", &sin_add, "sin(x) + sin(y)");
}
"""
module = torch.utils.cpp_extension.load_inline(
name="inline_jit_extension",
cpp_sources=[cpp_source1, cpp_source2],
verbose=True,
)
x = torch.randn(4, 4)
y = torch.randn(4, 4)
z = module.sin_add(x, y)
self.assertEqual(z, x.sin() + y.sin())
@unittest.skip("Temporarily disabled")
@unittest.skipIf(not (TEST_CUDA or TEST_ROCM), "CUDA not found")
def test_inline_jit_compile_extension_cuda(self):
cuda_source = """
__global__ void cos_add_kernel(
const float* __restrict__ x,
const float* __restrict__ y,
float* __restrict__ output,
const int size) {
const auto index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < size) {
output[index] = __cosf(x[index]) + __cosf(y[index]);
}
}
torch::Tensor cos_add(torch::Tensor x, torch::Tensor y) {
auto output = torch::zeros_like(x);
const int threads = 1024;
const int blocks = (output.numel() + threads - 1) / threads;
cos_add_kernel<<<blocks, threads>>>(x.data<float>(), y.data<float>(), output.data<float>(), output.numel());
return output;
}
"""
# Here, the C++ source need only declare the function signature.
cpp_source = "torch::Tensor cos_add(torch::Tensor x, torch::Tensor y);"
module = torch.utils.cpp_extension.load_inline(
name="inline_jit_extension_cuda",
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=["cos_add"],
verbose=True,
)
self.assertEqual(module.cos_add.__doc__.split("\n")[2], "cos_add")
x = torch.randn(4, 4, device="cuda", dtype=torch.float32)
y = torch.randn(4, 4, device="cuda", dtype=torch.float32)
z = module.cos_add(x, y)
self.assertEqual(z, x.cos() + y.cos())
@unittest.skip("Temporarily disabled")
@unittest.skipIf(not (TEST_CUDA or TEST_ROCM), "CUDA not found")
def test_inline_jit_compile_custom_op_cuda(self):
cuda_source = """
__global__ void cos_add_kernel(
const float* __restrict__ x,
const float* __restrict__ y,
float* __restrict__ output,
const int size) {
const auto index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < size) {
output[index] = __cosf(x[index]) + __cosf(y[index]);
}
}
torch::Tensor cos_add(torch::Tensor x, torch::Tensor y) {
auto output = torch::zeros_like(x);
const int threads = 1024;
const int blocks = (output.numel() + threads - 1) / threads;
cos_add_kernel<<<blocks, threads>>>(x.data_ptr<float>(), y.data_ptr<float>(), output.data_ptr<float>(), output.numel());
return output;
}
"""
# Here, the C++ source need only declare the function signature.
cpp_source = """
#include <torch/library.h>
torch::Tensor cos_add(torch::Tensor x, torch::Tensor y);
TORCH_LIBRARY(inline_jit_extension_custom_op_cuda, m) {
m.def("cos_add", cos_add);
}
"""
torch.utils.cpp_extension.load_inline(
name="inline_jit_extension_custom_op_cuda",
cpp_sources=cpp_source,
cuda_sources=cuda_source,
verbose=True,
is_python_module=False,
)
x = torch.randn(4, 4, device="cuda", dtype=torch.float32)
y = torch.randn(4, 4, device="cuda", dtype=torch.float32)
z = torch.ops.inline_jit_extension_custom_op_cuda.cos_add(x, y)
self.assertEqual(z, x.cos() + y.cos())
def test_inline_jit_compile_extension_throws_when_functions_is_bad(self):
with self.assertRaises(ValueError):
torch.utils.cpp_extension.load_inline(name="invalid_jit_extension",
cpp_sources="",
functions=5)
def test_lenient_flag_handling_in_jit_extensions(self):
cpp_source = """
torch::Tensor tanh_add(torch::Tensor x, torch::Tensor y) {
return x.tanh() + y.tanh();
}
"""
module = torch.utils.cpp_extension.load_inline(
name="lenient_flag_handling_extension",
cpp_sources=cpp_source,
functions="tanh_add",
extra_cflags=["-g\n\n", "-O0 -Wall"],
extra_include_paths=[" cpp_extensions\n"],
verbose=True,
)
x = torch.zeros(100, dtype=torch.float32)
y = torch.zeros(100, dtype=torch.float32)
z = module.tanh_add(x, y).cpu()
self.assertEqual(z, x.tanh() + y.tanh())
@unittest.skip("Temporarily disabled")
@unittest.skipIf(not (TEST_CUDA or TEST_ROCM), "CUDA not found")
def test_half_support(self):
"""
Checks for an issue with operator< ambiguity for half when certain
THC headers are included.
See https://github.com/pytorch/pytorch/pull/10301#issuecomment-416773333
for the corresponding issue.
"""
cuda_source = """
template<typename T, typename U>
__global__ void half_test_kernel(const T* input, U* output) {
if (input[0] < input[1] || input[0] >= input[1]) {
output[0] = 123;
}
}
torch::Tensor half_test(torch::Tensor input) {
auto output = torch::empty(1, input.options().dtype(torch::kFloat));
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "half_test", [&] {
half_test_kernel<scalar_t><<<1, 1>>>(
input.data<scalar_t>(),
output.data<float>());
});
return output;
}
"""
module = torch.utils.cpp_extension.load_inline(
name="half_test_extension",
cpp_sources="torch::Tensor half_test(torch::Tensor input);",
cuda_sources=cuda_source,
functions=["half_test"],
verbose=True,
)
x = torch.randn(3, device="cuda", dtype=torch.half)
result = module.half_test(x)
self.assertEqual(result[0], 123)
def test_reload_jit_extension(self):
def compile(code):
return torch.utils.cpp_extension.load_inline(
name="reloaded_jit_extension",
cpp_sources=code,
functions="f",
verbose=True,
)
module = compile("int f() { return 123; }")
self.assertEqual(module.f(), 123)
module = compile("int f() { return 456; }")
self.assertEqual(module.f(), 456)
module = compile("int f() { return 456; }")
self.assertEqual(module.f(), 456)
module = compile("int f() { return 789; }")
self.assertEqual(module.f(), 789)
def test_cpp_frontend_module_has_same_output_as_python(
self, dtype=torch.double):
extension = torch.utils.cpp_extension.load(
name="cpp_frontend_extension",
sources="cpp_extensions/cpp_frontend_extension.cpp",
verbose=True,
)
input = torch.randn(2, 5, dtype=dtype)
cpp_linear = extension.Net(5, 2)
cpp_linear.to(dtype)
python_linear = torch.nn.Linear(5, 2).to(dtype)
# First make sure they have the same parameters
cpp_parameters = dict(cpp_linear.named_parameters())
with torch.no_grad():
python_linear.weight.copy_(cpp_parameters["fc.weight"])
python_linear.bias.copy_(cpp_parameters["fc.bias"])
cpp_output = cpp_linear.forward(input)
python_output = python_linear(input)
self.assertEqual(cpp_output, python_output)
cpp_output.sum().backward()
python_output.sum().backward()
for p in cpp_linear.parameters():
self.assertFalse(p.grad is None)
self.assertEqual(cpp_parameters["fc.weight"].grad,
python_linear.weight.grad)
self.assertEqual(cpp_parameters["fc.bias"].grad,
python_linear.bias.grad)
def test_cpp_frontend_module_python_inter_op(self):
extension = torch.utils.cpp_extension.load(
name="cpp_frontend_extension",
sources="cpp_extensions/cpp_frontend_extension.cpp",
verbose=True,
)
# Create a torch.nn.Module which uses the C++ module as a submodule.
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.x = torch.nn.Parameter(torch.tensor(1.0))
self.net = extension.Net(3, 5)
def forward(self, input):
return self.net.forward(input) + self.x
net = extension.Net(5, 2)
net.double()
net.to(torch.get_default_dtype())
self.assertEqual(str(net), "Net")
# Further embed the torch.nn.Module into a Sequential, and also add the
# C++ module as an element of the Sequential.
sequential = torch.nn.Sequential(M(), torch.nn.Tanh(), net,
torch.nn.Sigmoid())
input = torch.randn(2, 3)
# Try calling the module!
output = sequential.forward(input)
# The call operator is bound to forward too.
self.assertEqual(output, sequential(input))
self.assertEqual(list(output.shape), [2, 2])
# Do changes on the module hierarchy.
old_dtype = torch.get_default_dtype()
sequential.to(torch.float64)
sequential.to(torch.float32)
sequential.to(old_dtype)
self.assertEqual(sequential[2].parameters()[0].dtype, old_dtype)
# Make sure we can access these methods recursively.
self.assertEqual(len(list(sequential.parameters())),
len(net.parameters()) * 2 + 1)
self.assertEqual(len(list(sequential.named_parameters())),
len(net.named_parameters()) * 2 + 1)
self.assertEqual(len(list(sequential.buffers())),
len(net.buffers()) * 2)
self.assertEqual(len(list(sequential.modules())), 8)
# Test clone()
net2 = net.clone()
self.assertEqual(len(net.parameters()), len(net2.parameters()))
self.assertEqual(len(net.buffers()), len(net2.buffers()))
self.assertEqual(len(net.modules()), len(net2.modules()))
# Try differentiating through the whole module.
for parameter in net.parameters():
self.assertIsNone(parameter.grad)
output.sum().backward()
for parameter in net.parameters():
self.assertFalse(parameter.grad is None)
self.assertGreater(parameter.grad.sum(), 0)
# Try calling zero_grad()
net.zero_grad()
for p in net.parameters():
self.assertEqual(p.grad, torch.zeros_like(p))
# Test train(), eval(), training (a property)
self.assertTrue(net.training)
net.eval()
self.assertFalse(net.training)
net.train()
self.assertTrue(net.training)
net.eval()
# Try calling the additional methods we registered.
biased_input = torch.randn(4, 5)
output_before = net.forward(biased_input)
bias = net.get_bias().clone()
self.assertEqual(list(bias.shape), [2])
net.set_bias(bias + 1)
self.assertEqual(net.get_bias(), bias + 1)
output_after = net.forward(biased_input)
self.assertNotEqual(output_before, output_after)
# Try accessing parameters
self.assertEqual(len(net.parameters()), 2)
np = net.named_parameters()
self.assertEqual(len(np), 2)
self.assertIn("fc.weight", np)
self.assertIn("fc.bias", np)
self.assertEqual(len(net.buffers()), 1)
nb = net.named_buffers()
self.assertEqual(len(nb), 1)
self.assertIn("buf", nb)
self.assertEqual(nb[0][1], torch.eye(5))
def test_cpp_frontend_module_has_up_to_date_attributes(self):
extension = torch.utils.cpp_extension.load(
name="cpp_frontend_extension",
sources="cpp_extensions/cpp_frontend_extension.cpp",
verbose=True,
)
net = extension.Net(5, 2)
self.assertEqual(len(net._parameters), 0)
net.add_new_parameter("foo", torch.eye(5))
self.assertEqual(len(net._parameters), 1)
self.assertEqual(len(net._buffers), 1)
net.add_new_buffer("bar", torch.eye(5))
self.assertEqual(len(net._buffers), 2)
self.assertEqual(len(net._modules), 1)
net.add_new_submodule("fc2")
self.assertEqual(len(net._modules), 2)
@unittest.skipIf(not (TEST_CUDA or TEST_ROCM), "CUDA not found")
def test_cpp_frontend_module_python_inter_op_with_cuda(self):
extension = torch.utils.cpp_extension.load(
name="cpp_frontend_extension",
sources="cpp_extensions/cpp_frontend_extension.cpp",
verbose=True,
)
net = extension.Net(5, 2)
for p in net.parameters():
self.assertTrue(p.device.type == "cpu")
cpu_parameters = [p.clone() for p in net.parameters()]
device = torch.device("cuda", 0)
net.to(device)
for i, p in enumerate(net.parameters()):
self.assertTrue(p.device.type == "cuda")
self.assertTrue(p.device.index == 0)
self.assertEqual(cpu_parameters[i], p)
net.cpu()
net.add_new_parameter("a", torch.eye(5))
net.add_new_parameter("b", torch.eye(5))
net.add_new_buffer("c", torch.eye(5))
net.add_new_buffer("d", torch.eye(5))
net.add_new_submodule("fc2")
net.add_new_submodule("fc3")
for p in net.parameters():
self.assertTrue(p.device.type == "cpu")
net.cuda()
for p in net.parameters():
self.assertTrue(p.device.type == "cuda")
def test_returns_shared_library_path_when_is_python_module_is_true(self):
source = """
#include <torch/script.h>
torch::Tensor func(torch::Tensor x) { return x; }
static torch::RegisterOperators r("test::func", &func);
"""
torch.utils.cpp_extension.load_inline(
name="is_python_module",
cpp_sources=source,
functions="func",
verbose=True,
is_python_module=False,
)
self.assertEqual(torch.ops.test.func(torch.eye(5)), torch.eye(5))
def test_set_default_type_also_changes_aten_default_type(self):
module = torch.utils.cpp_extension.load_inline(
name="test_set_default_type",
cpp_sources="torch::Tensor get() { return torch::empty({}); }",
functions="get",
verbose=True,
)
initial_default = torch.get_default_dtype()
try:
self.assertEqual(module.get().dtype, initial_default)
torch.set_default_dtype(torch.float64)
self.assertEqual(module.get().dtype, torch.float64)
torch.set_default_dtype(torch.float32)
self.assertEqual(module.get().dtype, torch.float32)
torch.set_default_dtype(torch.float16)
self.assertEqual(module.get().dtype, torch.float16)
finally:
torch.set_default_dtype(initial_default)
def test_compilation_error_formatting(self):
# Test that the missing-semicolon error message has linebreaks in it.
# This'll fail if the message has been munged into a single line.
# It's hard to write anything more specific as every compiler has it's own
# error formatting.
with self.assertRaises(RuntimeError) as e:
torch.utils.cpp_extension.load_inline(
name="test_compilation_error_formatting",
cpp_sources="int main() { return 0 }")
pattern = r'.*(\\n|\\r).*'
self.assertNotRegex(str(e), pattern)
def test_warning(self):
# Note: the module created from this source will include the py::key_error
# symbol. But because of visibility and the fact that it lives in a
# different compilation unit than pybind, this trips up ubsan even though
# it is fine. "ubsan.supp" thus needs to contain "vptr:warn_mod.so".
source = '''
// error_type:
// 0: no error
// 1: torch::TypeError
// 2: python_error()
// 3: py::error_already_set
at::Tensor foo(at::Tensor x, int error_type) {
std::ostringstream err_stream;
err_stream << "Error with " << x.type();
TORCH_WARN(err_stream.str());
if(error_type == 1) {
throw torch::TypeError(err_stream.str().c_str());
}
if(error_type == 2) {
PyObject* obj = PyTuple_New(-1);
TORCH_CHECK(!obj);
// Pretend it was caught in a different thread and restored here
auto e = python_error();
e.persist();
e.restore();
throw e;
}
if(error_type == 3) {
throw py::key_error(err_stream.str());
}
return x.cos();
}
'''
# Ensure double type for hard-coded c name below
t = torch.rand(2).double()
cpp_tensor_name = r"CPUDoubleType"
# Without error handling, the warnings cannot be catched
warn_mod = torch.utils.cpp_extension.load_inline(
name='warn_mod',
cpp_sources=[source],
functions=['foo'],
with_pytorch_error_handling=False)
with warnings.catch_warnings(record=True) as w:
warn_mod.foo(t, 0)
self.assertEqual(len(w), 0)
with self.assertRaisesRegex(TypeError, t.type()):
warn_mod.foo(t, 1)
self.assertEqual(len(w), 0)
with self.assertRaisesRegex(SystemError,
"bad argument to internal function"):
warn_mod.foo(t, 2)
self.assertEqual(len(w), 0)
with self.assertRaisesRegex(KeyError, cpp_tensor_name):
warn_mod.foo(t, 3)
self.assertEqual(len(w), 0)
warn_mod = torch.utils.cpp_extension.load_inline(
name='warn_mod',
cpp_sources=[source],
functions=['foo'],
with_pytorch_error_handling=True)
with warnings.catch_warnings(record=True) as w:
# Catched with no error should be detected
warn_mod.foo(t, 0)
self.assertEqual(len(w), 1)
# Catched with cpp error should also be detected
with self.assertRaisesRegex(TypeError, t.type()):
warn_mod.foo(t, 1)
self.assertEqual(len(w), 2)
# Catched with python error should also be detected
with self.assertRaisesRegex(SystemError,
"bad argument to internal function"):
warn_mod.foo(t, 2)
self.assertEqual(len(w), 3)
# Catched with pybind error should also be detected
# Note that there is no type name translation for pybind errors
with self.assertRaisesRegex(KeyError, cpp_tensor_name):
warn_mod.foo(t, 3)
self.assertEqual(len(w), 4)
# Make sure raising warnings are handled properly
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("error")
# No error, the warning should raise
with self.assertRaisesRegex(UserWarning, t.type()):
warn_mod.foo(t, 0)
self.assertEqual(len(w), 0)
# Another error happened, the warning is ignored
with self.assertRaisesRegex(TypeError, t.type()):
warn_mod.foo(t, 1)
self.assertEqual(len(w), 0)
def test_autograd_from_cpp(self):
source = '''
void run_back(at::Tensor x) {
x.backward({});
}
void run_back_no_gil(at::Tensor x) {
pybind11::gil_scoped_release no_gil;
x.backward({});
}
'''
class MyFn(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x.clone()
@staticmethod
def backward(ctx, gx):
return gx
test_backward_deadlock = torch.utils.cpp_extension.load_inline(
name='test_backward_deadlock',
cpp_sources=[source],
functions=['run_back', 'run_back_no_gil'],
)
# This used to deadlock
inp = torch.rand(20, requires_grad=True)
loss = MyFn.apply(inp).sum()
with self.assertRaisesRegex(
RuntimeError,
"The autograd engine was called while holding the GIL."):
test_backward_deadlock.run_back(loss)
inp = torch.rand(20, requires_grad=True)
loss = MyFn.apply(inp).sum()
test_backward_deadlock.run_back_no_gil(loss)
def test_custom_compound_op_autograd(self):
# Test that a custom compound op (i.e. a custom op that just calls other aten ops)
# correctly returns gradients of those other ops
source = """
#include <torch/library.h>
torch::Tensor my_add(torch::Tensor x, torch::Tensor y) {
return x + y;
}
TORCH_LIBRARY(my, m) {
m.def("add", &my_add);
}
"""
torch.utils.cpp_extension.load_inline(
name="is_python_module",
cpp_sources=source,
verbose=True,
is_python_module=False,
)
a = torch.randn(5, 5, requires_grad=True)
b = torch.randn(5, 5, requires_grad=True)
gradcheck(torch.ops.my.add, [a, b], eps=1e-2)
@staticmethod
def _crash_handler_test_process(stderr_file, destination):
# Code to enable dumps and trigger a segfault
if sys.platform == "win32":
destination = destination.replace("\\", "\\\\")
csrc = textwrap.dedent(f"""
#include <torch/torch.h>
#include <locale>
#include <iostream>
#include <codecvt>
#include <string>
int fail() {{
std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
std::string narrow("{destination}");
std::wstring wide = converter.from_bytes(narrow);
torch::crash_handler::enable_minidumps(wide.c_str());
volatile int* bad = nullptr;
return *bad;
}}
""")
else:
csrc = textwrap.dedent(f"""
#include <torch/torch.h>
int fail() {{
torch::crash_handler::enable_minidumps("{destination}");
volatile int* bad = nullptr;
return *bad;
}}
""")
# Some special stuff to overwrite stderr for a C++ extension
# Copied from: https://stackoverflow.com/questions/8804893/redirect-stdout-from-python-for-c-calls
sys.stdout.flush()
newstdout = os.dup(2)
devnull = os.open(stderr_file, os.O_WRONLY)
os.dup2(devnull, 2)
os.close(devnull)
sys.stdout = os.fdopen(newstdout, 'w')
module = torch.utils.cpp_extension.load_inline(
name="segfault",
cpp_sources=csrc,
functions=["fail"],
)
module.fail()
@unittest.skipIf(TEST_WITH_ASAN,
"ASAN disables the crash handler's signal handler")
@unittest.skipIf(not has_breakpad(), "Built without breakpad")
@unittest.skipIf(
os.environ.get("TEST_CONFIG") == "force_on_cpu",
"fails on force_on_cpu config, tracked w/ #65253")
def test_crash_handler(self):
with tempfile.TemporaryDirectory(
) as temp_dir, tempfile.NamedTemporaryFile(
delete=not sys.platform == "win32") as stderr:
# Use multiprocessing to spin up a separate process to make catching
# the segfault easier
p = Process(target=self._crash_handler_test_process,
args=(stderr.name, temp_dir))
p.start()
p.join()
with open(stderr.name) as f:
result = f.read().strip()
# Check that the signal handler was called
self.assertTrue(result.startswith(f"Wrote minidump to {temp_dir}"))
with open(result.replace("Wrote minidump to ", ""),
"rb") as dump_file:
dump_bytes = dump_file.read()
# Check that the file has the correct magic number
self.assertEqual(b"MDMP", dump_bytes[0:4])