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a956c4ab1cb13079203a8f07eb26218724f54dc8
pytorch/torch/csrc/Module.cpp
Sherlock Huang a956c4ab1c Return NoOpDeviceGuardImpl in replace of CudaDeviceGuard when device is not available, or cpu-only build (#160532) 
Summary:

To support exporting a cuda model on a CPU-only machine under fake tensor mode.
User commonly need to move sample inputs to the cuda device with .to("cuda:0") or .to("cuda") call.
This diff supports this.
I expect the following pattern to work
```
with FakeTensorMode(allow_non_fake_inputs=True):
    cuda_module = module.to("cuda:0")
    cuda_sample_inputs = tuple([x.to("cuda:0") for x in sample_inputs])
    with torch.no_grad():
        ep = torch.export.export(cuda_module, cuda_sample_inputs)
```

Test Plan:
CI

Rollback Plan:

Differential Revision: D80181887

Pull Request resolved: https://github.com/pytorch/pytorch/pull/160532
Approved by: https://github.com/henryoier, https://github.com/ezyang
2025-09-13 01:50:51 +00:00

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#include <ATen/DeviceAccelerator.h>
#include <fmt/core.h>
#include <sys/types.h>
#include <torch/csrc/python_headers.h>
#include <csignal>
#include <optional>
#ifndef _MSC_VER
#include <sys/socket.h>
#endif
#include <ATen/ATen.h>
#include <ATen/BlasBackend.h>
#include <ATen/CachedTensorUtils.h>
#include <ATen/DLConvertor.h>
#include <ATen/ExpandUtils.h>
#include <ATen/LegacyVmapMode.h>
#include <ATen/LinalgBackend.h>
#include <ATen/Parallel.h>
#include <ATen/Utils.h>
#include <ATen/core/Vitals.h>
#include <ATen/dlpack.h>
#include <ATen/native/ConvUtils.h>
#include <ATen/native/ForeachUtils.h>
#include <ATen/native/Normalization.h>
#include <c10/core/Device.h>
#include <c10/core/DispatchKeySet.h>
#include <c10/core/impl/DeviceGuardImplInterface.h>
#include <c10/util/AbortHandler.h>
#include <c10/util/Backtrace.h>
#include <c10/util/Logging.h>
#include <c10/util/irange.h>
#include <c10/util/thread_name.h>
#include <libshm.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <torch/csrc/THConcat.h>
#include <torch/csrc/utils/pybind.h>
#include <cstdlib>
#include <iostream>
#include <unordered_map>
#include <ATen/ThreadLocalPythonObjects.h>
#include <torch/csrc/DataLoader.h>
#include <torch/csrc/Device.h>
#include <torch/csrc/DeviceAccelerator.h>
#include <torch/csrc/Dtype.h>
#include <torch/csrc/DynamicTypes.h>
#include <torch/csrc/Event.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/Generator.h>
#include <torch/csrc/Layout.h>
#include <torch/csrc/MemoryFormat.h>
#include <torch/csrc/QScheme.h>
#include <torch/csrc/Stream.h>
#include <torch/csrc/THP.h>
#include <torch/csrc/TypeInfo.h>
#include <torch/csrc/api/include/torch/python/init.h>
#include <torch/csrc/autograd/generated/python_return_types.h>
#include <torch/csrc/autograd/python_cpp_function.h>
#include <torch/csrc/autograd/python_enum_tag.h>
#include <torch/csrc/autograd/python_fft_functions.h>
#include <torch/csrc/autograd/python_function.h>
#include <torch/csrc/autograd/python_legacy_variable.h>
#include <torch/csrc/autograd/python_linalg_functions.h>
#include <torch/csrc/autograd/python_nested_functions.h>
#include <torch/csrc/autograd/python_nn_functions.h>
#include <torch/csrc/autograd/python_sparse_functions.h>
#include <torch/csrc/autograd/python_special_functions.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/cpu/Module.h>
#include <torch/csrc/dynamo/init.h>
#include <torch/csrc/export/pybind.h>
#include <torch/csrc/functorch/init.h>
#include <torch/csrc/fx/node.h>
#include <torch/csrc/inductor/aoti_package/pybind.h>
#include <torch/csrc/inductor/aoti_runner/pybind.h>
#include <torch/csrc/instruction_counter/Module.h>
#include <torch/csrc/jit/python/init.h>
#include <torch/csrc/jit/python/python_ir.h>
#include <torch/csrc/jit/python/python_tracer.h>
#include <torch/csrc/jit/serialization/pickler.h>
#include <torch/csrc/lazy/python/init.h>
#include <torch/csrc/monitor/python_init.h>
#include <torch/csrc/mps/Module.h>
#include <torch/csrc/mtia/Module.h>
#include <torch/csrc/multiprocessing/init.h>
#include <torch/csrc/onnx/init.h>
#include <torch/csrc/profiler/python/init.h>
#include <torch/csrc/tensor/python_tensor.h>
#include <torch/csrc/utils/disable_torch_function.h>
#include <torch/csrc/utils/init.h>
#include <torch/csrc/utils/pycfunction_helpers.h>
#include <torch/csrc/utils/python_arg_parser.h>
#include <torch/csrc/utils/python_compat.h>
#include <torch/csrc/utils/python_dispatch.h>
#include <torch/csrc/utils/python_strings.h>
#include <torch/csrc/utils/tensor_dtypes.h>
#include <torch/csrc/utils/tensor_layouts.h>
#include <torch/csrc/utils/tensor_memoryformats.h>
#include <torch/csrc/utils/tensor_new.h>
#include <torch/csrc/utils/tensor_numpy.h>
#include <torch/csrc/utils/tensor_qschemes.h>
#include <torch/csrc/utils/verbose.h>
#include <ATen/native/transformers/sdp_utils_cpp.h>
#include <torch/csrc/profiler/combined_traceback.h>
#include <torch/csrc/profiler/kineto_client_interface.h>
#include <sstream>
#ifdef USE_CUDA
#include <ATen/ROCmFABackend.h>
#include <ATen/cuda/CUDAConfig.h>
#include <ATen/native/transformers/cuda/sdp_utils.h>
#include <torch/csrc/inductor/static_cuda_launcher.h>
#ifdef __HIP_PLATFORM_AMD__
#include <ATen/native/cudnn/hip/BatchNorm.h>
#else
#include <ATen/native/cudnn/BatchNorm.h>
#endif
#endif
#include <torch/csrc/distributed/autograd/python_autograd.h>
#include <torch/csrc/distributed/c10d/c10d.h>
#include <torch/csrc/distributed/rpc/rpc.h>
#include <torch/csrc/distributed/rpc/testing/testing.h>
#if defined(USE_VALGRIND)
#include <callgrind.h>
#endif
#ifdef USE_ITT
#include <torch/csrc/itt.h>
#endif
#include <torch/nativert/python/Bindings.h>
namespace py = pybind11;
static PyObject* module;
static THPGenerator* THPDefaultCPUGenerator = nullptr;
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
static PyObject* THPModule_initNames(PyObject* self, PyObject* arg) {
HANDLE_TH_ERRORS
static std::vector<std::string> names;
THPObjectPtr types(PySequence_Fast(arg, "expected a sequence"));
if (!types)
return nullptr;
// NOLINTNEXTLINE(bugprone-branch-clone)
auto num_classes = PySequence_Fast_GET_SIZE(types.get());
names.reserve(names.size() + num_classes);
for (Py_ssize_t i = 0; i < num_classes; i++) {
PyObject* obj = PySequence_Fast_GET_ITEM(types.get(), i);
TORCH_CHECK(PyType_Check(obj), "expected a PyTypeObject");
PyTypeObject* type = (PyTypeObject*)obj;
THPObjectPtr module_name(PyObject_GetAttrString(obj, "__module__"));
if (!module_name)
return nullptr;
TORCH_CHECK(
THPUtils_checkString(module_name.get()),
"expected __module__ to be a string");
std::string name = THPUtils_unpackString(module_name.get());
names.emplace_back(name + "." + type->tp_name);
type->tp_name = names.back().c_str();
}
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
//
// Callback for python part. Used for additional initialization of python
// classes
static PyObject* THPModule_initExtension(
PyObject* _unused,
PyObject* shm_manager_path) {
HANDLE_TH_ERRORS
#if !defined(FBCODE_CAFFE2) && !defined(__aarch64__)
if (torch::get_cpp_stacktraces_enabled()) {
c10::SetStackTraceFetcher([]() -> std::string {
auto tb = torch::CapturedTraceback::gather(false, false, true);
if (torch::get_symbolize_mode() == torch::unwind::Mode::addr2line) {
LOG(WARNING)
<< "symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1..."
<< '\n';
}
auto s_tbs = torch::symbolize({tb.get()});
std::stringstream oss;
oss << "C++ CapturedTraceback:" << '\n';
const auto& s_tb = s_tbs.tracebacks.at(0);
for (auto idx : c10::irange(s_tb.size())) {
// Skip the first few frames:
// #1 torch::CapturedTraceback::gather(bool, bool, bool)
// #2 THPModule_initExtension
// #3 THPModule_initExtension(_object*, _object*)::{lambda()#1}
if (idx <= 3) {
continue;
}
auto frame_id = s_tb[idx];
const auto& frame = s_tbs.all_frames.at(frame_id);
oss << "#" << idx << " " << frame.funcname << " from " << frame.filename
<< ":" << frame.lineno << '\n';
}
return oss.str();
});
}
#endif
if (!THPUtils_checkString(shm_manager_path)) {
THPUtils_setError(
"initialization error - expected bytes/string object as shm_manager_path!");
return nullptr;
}
torch::utils::initializeLayouts();
torch::utils::initializeMemoryFormats();
torch::utils::initializeQSchemes();
torch::utils::initializeDtypes();
torch::tensors::initialize_python_bindings();
std::string path = THPUtils_unpackString(shm_manager_path);
libshm_init(path.c_str());
auto module = THPObjectPtr(PyImport_ImportModule("torch"));
if (!module)
throw python_error();
THPStorage_postInit(module);
THPAutograd_initFunctions();
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
// The idea behind these two functions is to make it easy to test if we are
// built with ASAN: they're designed not to crash if ASAN is not enabled, but
// to trigger ASAN if it is enabled. This lets us run a "canary" tests which
// checks if our build environment is misconfigured.
static PyObject* THPModule_crashIfCsrcASAN(PyObject* module, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkLong(arg),
"crash_if_csrc_asan expects an int, but got ",
THPUtils_typename(arg));
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays, modernize-avoid-c-arrays)
volatile char x[3];
x[THPUtils_unpackInt(arg)] = 0;
// NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage)
return THPUtils_packInt32(x[0]);
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_crashIfCsrcUBSAN(PyObject* module, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkLong(arg),
"crash_if_csrc_ubsan expects an int, but got ",
THPUtils_typename(arg));
int32_t x = THPUtils_unpackInt(arg);
double y = 1.0 / x;
return THPUtils_packInt32((int)y);
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_crashIfvptrUBSAN(PyObject* module, PyObject* noarg) {
// This code should work perfectly fine, as vtables are identical for Foo and
// Baz unless rtti and ubsan are enabled
struct Foo {
virtual int bar() = 0;
virtual ~Foo() = default;
};
struct Baz {
virtual int bar() {
return 17;
}
virtual ~Baz() = default;
};
Baz x{};
// Purposely cast through `void*` so there's no fixups applied.
// NOLINTNEXTLINE(bugprone-casting-through-void,-warnings-as-errors)
auto y = static_cast<Foo*>(static_cast<void*>(&x));
auto rc = y->bar();
return THPUtils_packInt32(rc);
}
static PyObject* THPModule_crashIfATenASAN(PyObject* module, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkLong(arg),
"crash_if_aten_asan expects an int, "
"but got ",
THPUtils_typename(arg));
return THPUtils_packInt32(at::_crash_if_asan(THPUtils_unpackInt(arg)));
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_abort(PyObject* module, PyObject* noargs) {
std::terminate();
Py_RETURN_NONE;
}
static PyObject* THPModule_crashIfDebugAssertsFail(
PyObject* module,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkLong(arg),
"crash_if_debug_asserts_fail expects an int, but got ",
THPUtils_typename(arg));
TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
THPUtils_unpackInt(arg) != 424242,
"Expect anything but 424242 as an input for debug builds");
return THPUtils_packInt32(0);
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getNumThreads(PyObject* module, PyObject* noargs) {
return THPUtils_packInt32(at::get_num_threads());
}
static PyObject* THPModule_setNumThreads(PyObject* module, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkLong(arg),
"set_num_threads expects an int, but got ",
THPUtils_typename(arg));
int nthreads = (int)THPUtils_unpackLong(arg);
TORCH_CHECK(nthreads > 0, "set_num_threads expects a positive integer");
at::set_num_threads(nthreads);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getNumInteropThreads(
PyObject* module,
PyObject* noargs) {
return THPUtils_packUInt64(at::get_num_interop_threads());
}
static PyObject* THPModule_setNumInteropThreads(
PyObject* module,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkLong(arg),
"set_num_interop_threads expects an int, "
"but got ",
THPUtils_typename(arg));
int nthreads = (int)THPUtils_unpackLong(arg);
TORCH_CHECK(
nthreads > 0, "set_num_interop_threads expects a positive integer");
at::set_num_interop_threads(nthreads);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setDefaultTensorType(
PyObject* _unused,
PyObject* type) {
HANDLE_TH_ERRORS
torch::tensors::py_set_default_tensor_type(type);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setDefaultDtype(PyObject* _unused, PyObject* dtype) {
HANDLE_TH_ERRORS
torch::tensors::py_set_default_dtype(dtype);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_swap_tensor_impl(PyObject* _unused, PyObject* args) {
HANDLE_TH_ERRORS
PyObject* a_ = nullptr;
PyObject* b_ = nullptr;
if (!PyArg_ParseTuple(args, "OO", &a_, &b_)) {
return nullptr;
}
// Ensure we have Tensors
TORCH_CHECK(THPVariable_Check(a_));
TORCH_CHECK(THPVariable_Check(b_));
THPVariable* a = reinterpret_cast<THPVariable*>(a_);
THPVariable* b = reinterpret_cast<THPVariable*>(b_);
// weak_use_count() adds 1 if use_count is non-zero
TORCH_CHECK(
a->cdata->weak_use_count() == 1,
"Expected no weakrefs to t1's Tensor object but got ",
a->cdata->weak_use_count() - 1);
TORCH_CHECK(
b->cdata->weak_use_count() == 1,
"Expected no weakrefs to t2's Tensor object but got ",
b->cdata->weak_use_count() - 1);
// Swap the Tensor Impl
c10::MaybeOwned<at::Tensor> tmp = a->cdata;
// The TensorImpls contain PyObjectSlots that have a reference to the PyObject
// associated with the TensorImpl. Swap this field as well.
std::optional<PyObject*> mb_obj_a =
a->cdata->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(
/*ignore_hermetic_tls=*/false);
std::optional<PyObject*> mb_obj_b =
b->cdata->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(
/*ignore_hermetic_tls=*/false);
TORCH_INTERNAL_ASSERT(
mb_obj_a.has_value() && mb_obj_b.has_value(),
"Both tensors should have PyObjects tagged by the current python interpreter");
TORCH_CHECK(mb_obj_a.value() == a_);
TORCH_CHECK(mb_obj_b.value() == b_);
a->cdata = b->cdata;
b->cdata = tmp;
a->cdata->unsafeGetTensorImpl()->pyobj_slot()->init_pyobj(a_);
b->cdata->unsafeGetTensorImpl()->pyobj_slot()->init_pyobj(b_);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_addDocStr(PyObject* _unused, PyObject* args) {
// adds a __doc__ string to a function, similar to numpy's arr_add_docstring
static std::vector<std::string> all_docs;
PyObject* obj = nullptr;
PyObject* doc_obj = nullptr;
if (!PyArg_ParseTuple(args, "OO", &obj, &doc_obj)) {
return nullptr;
}
const char* doc_str = "<invalid string>";
if (THPUtils_checkString(doc_obj)) {
all_docs.push_back(THPUtils_unpackString(doc_obj));
doc_str = all_docs.back().c_str();
}
if (Py_TYPE(obj) == &PyCFunction_Type) {
PyCFunctionObject* f = (PyCFunctionObject*)obj;
if (f->m_ml->ml_doc) {
return PyErr_Format(
PyExc_RuntimeError,
"function '%s' already has a docstring",
f->m_ml->ml_name);
}
f->m_ml->ml_doc = doc_str;
} else if (strcmp(Py_TYPE(obj)->tp_name, "method_descriptor") == 0) {
PyMethodDescrObject* m = (PyMethodDescrObject*)obj;
if (m->d_method->ml_doc) {
return PyErr_Format(
PyExc_RuntimeError,
"method '%s' already has a docstring",
m->d_method->ml_name);
}
m->d_method->ml_doc = doc_str;
} else if (strcmp(Py_TYPE(obj)->tp_name, "getset_descriptor") == 0) {
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-cstyle-cast)
PyGetSetDescrObject* m = (PyGetSetDescrObject*)obj;
if (m->d_getset->doc) {
return PyErr_Format(
PyExc_RuntimeError,
"attribute '%s' already has a docstring",
m->d_getset->name);
}
m->d_getset->doc = doc_str;
} else if (Py_TYPE(obj) == &PyType_Type) {
PyTypeObject* t = (PyTypeObject*)obj;
if (t->tp_doc) {
return PyErr_Format(
PyExc_RuntimeError, "Type '%s' already has a docstring", t->tp_name);
}
t->tp_doc = doc_str;
} else {
return PyErr_Format(
PyExc_TypeError,
"don't know how to add docstring to type '%s'",
Py_TYPE(obj)->tp_name);
}
Py_INCREF(obj);
return obj;
}
static PyObject* THPModule_inferSize(PyObject* _unused, PyObject* args) {
HANDLE_TH_ERRORS
Py_ssize_t num_args = args ? (Py_ssize_t)PyTuple_Size(args) : 0;
TORCH_CHECK(num_args == 2, "expected exactly 2 arguments");
PyObject* arg1 = PyTuple_GET_ITEM(args, 0);
TORCH_CHECK(THPSize_Check(arg1), "expected a torch.Size as argument 1");
PyObject* arg2 = PyTuple_GET_ITEM(args, 1);
TORCH_CHECK(THPSize_Check(arg2), "expected a torch.Size as argument 2");
auto size1 = THPUtils_unpackLongs(arg1);
auto size2 = THPUtils_unpackLongs(arg2);
auto sizes = at::infer_size(size1, size2);
return THPSize_NewFromSizes(static_cast<int64_t>(sizes.size()), sizes.data());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setBackcompatBroadcastWarn(
PyObject* module,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_backcompat_broadcast_warn expects a bool, "
"but got ",
THPUtils_typename(arg));
setBackCompatBroadcastWarn(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getBackcompatBroadcastWarn(
PyObject* module,
PyObject* noargs) {
if (getBackCompatBroadcastWarn())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setBackcompatKeepdimWarn(
PyObject* module,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_backcompat_keepdim_warn expects a bool, "
"but got ",
THPUtils_typename(arg));
setBackCompatKeepdimWarn(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getBackcompatKeepdimWarn(
PyObject* module,
PyObject* noargs) {
if (getBackCompatKeepdimWarn())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_hasDistributed(PyObject* _unused, PyObject* noargs) {
Py_RETURN_TRUE;
}
static PyObject* THPModule_showConfig(PyObject* module, PyObject* noargs) {
HANDLE_TH_ERRORS
return THPUtils_packString(at::show_config());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_cxxFlags(PyObject* module, PyObject* noargs) {
HANDLE_TH_ERRORS
return THPUtils_packString(at::get_cxx_flags());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_parallelInfo(PyObject* module, PyObject* noargs) {
HANDLE_TH_ERRORS
return THPUtils_packString(at::get_parallel_info());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getCpuCapability(
PyObject* module,
PyObject* noargs) {
HANDLE_TH_ERRORS
return THPUtils_packString(at::get_cpu_capability());
END_HANDLE_TH_ERRORS
}
namespace {
template <class T>
void DLPack_Capsule_Destructor(PyObject* data) {
if (C10_LIKELY(!PyCapsule_IsValid(data, at::DLPackTraits<T>::capsule))) {
// early out, see DLPack spec: if a consuming library sets the capsule
// name to something else, they own it and we don't need to do anything
return;
}
HANDLE_TH_ERRORS
// Causes overheads for validity checks again, but this case is rare
// since consuming libraries should rename the capsule according to spec.
// Note that this cannot set a python error (we checked validity above),
// so we don't need to handle python error state here.
T* tensor = (T*)PyCapsule_GetPointer(data, at::DLPackTraits<T>::capsule);
// the dlMTensor has not been consumed, call deleter ourselves.
// DLPack spec mentions that deleter may be NULL, but deleter from
// `at::toDLPack` is never NULL, so no need for an additional check here.
tensor->deleter(tensor);
END_HANDLE_TH_ERRORS_RET()
}
template <class T>
PyObject* THPModule_toDLPackImpl(
PyObject* self,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static torch::PythonArgParser parser(
{"_to_dlpack(Tensor data, *, IntArrayRef? dl_device=None, bool? copy=None)"});
torch::ParsedArgs<3> parsed_args{};
auto r = parser.parse(args, kwargs, parsed_args);
TORCH_INTERNAL_ASSERT(r.idx == 0);
auto data = r.tensor(0);
auto dl_device = r.intlist(1);
auto copy = r.toBoolOptional(2);
// Parse the int list into a tuple.
std::optional<DLDevice> optional_dl_device;
if (!dl_device.empty()) {
TORCH_CHECK(
dl_device.size() == 2,
"dl_device must be either None or a tuple of ints");
optional_dl_device = DLDevice{
static_cast<DLDeviceType>(dl_device[0]),
static_cast<int32_t>(dl_device[1])};
}
auto tensor = at::DLPackTraits<T>::toDLPack(
at::maybeCopyTensor(data, optional_dl_device, copy));
return PyCapsule_New(
tensor, at::DLPackTraits<T>::capsule, DLPack_Capsule_Destructor<T>);
END_HANDLE_TH_ERRORS
}
} // namespace
static PyObject* THPModule_toDLPack(
PyObject* self,
PyObject* args,
PyObject* kwargs) {
return THPModule_toDLPackImpl<DLManagedTensor>(self, args, kwargs);
}
static PyObject* THPModule_toDLPackVersioned(
PyObject* self,
PyObject* args,
PyObject* kwargs) {
return THPModule_toDLPackImpl<DLManagedTensorVersioned>(self, args, kwargs);
}
static PyObject* THPModule_fromDLPack(PyObject* _unused, PyObject* data) {
using namespace torch::autograd;
HANDLE_TH_ERRORS
auto tensor = torch::utils::tensor_fromDLPack(data);
return THPVariable_Wrap(tensor);
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_torchDeviceToDLDevice(
PyObject* _unused,
PyObject* data) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPDevice_Check(data),
"torchDeviceToDLDevice: expected torch.device argument.");
auto device = reinterpret_cast<THPDevice*>(data)->device;
auto dl_device = at::torchDeviceToDLDevice(device);
auto tuple = PyTuple_New(2);
if (!tuple) {
throw python_error();
}
PyTuple_SET_ITEM(tuple, 0, THPUtils_packInt64(dl_device.device_type));
PyTuple_SET_ITEM(tuple, 1, THPUtils_packInt64(dl_device.device_id));
return tuple;
END_HANDLE_TH_ERRORS
}
static PyObject* THModule_getCppBacktrace(PyObject* _unused, PyObject* args) {
HANDLE_TH_ERRORS
size_t frames_to_skip = 0;
size_t maximum_number_of_frames = 0;
if (!PyArg_ParseTuple(
args, "LL", &frames_to_skip, &maximum_number_of_frames)) {
return nullptr;
}
return THPUtils_packString(
c10::get_backtrace(frames_to_skip, maximum_number_of_frames, true));
END_HANDLE_TH_ERRORS
}
static PyObject* THModule_rename_privateuse1_backend(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkString(arg),
"_rename_privateuse1_backend expects a str, but got ",
THPUtils_typename(arg));
const std::string backend_name = THPUtils_unpackString(arg);
c10::register_privateuse1_backend(backend_name);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THModule_get_privateuse1_backend_name(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
return THPUtils_packString(c10::get_privateuse1_backend());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setAllowTF32CuDNN(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_allow_tf32_cublas expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setAllowTF32CuDNN(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_allowTF32CuDNN(PyObject* _unused, PyObject* noargs) {
HANDLE_TH_ERRORS
if (at::globalContext().allowTF32CuDNN())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setFloat32MatmulPrecision(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkString(arg),
"set_float32_matmul_precision expects a str, "
"but got ",
THPUtils_typename(arg));
std::string s = THPUtils_unpackString(arg);
at::globalContext().setFloat32MatmulPrecision(s);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_float32MatmulPrecision(
PyObject* _unused,
PyObject* noargs) {
HANDLE_TH_ERRORS
std::string s = "highest";
auto p = at::globalContext().float32MatmulPrecision();
if (p == at::Float32MatmulPrecision::HIGH) {
s = "high";
} else if (p == at::Float32MatmulPrecision::MEDIUM) {
s = "medium";
}
return THPUtils_packString(s);
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setSDPPriorityOrder(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
auto priority_order = THPUtils_unpackLongs(arg);
at::globalContext().setSDPPriorityOrder(priority_order);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_sDPPriorityOrder(
PyObject* _unused,
PyObject* noargs) {
auto ordervec = at::globalContext().sDPPriorityOrder();
auto order =
THPObjectPtr(PyList_New(static_cast<Py_ssize_t>(ordervec.size())));
for (const auto i : c10::irange(ordervec.size())) {
PyObject* i64 = THPUtils_packInt64(static_cast<int64_t>(ordervec[i]));
if (!i64)
return nullptr;
PyList_SET_ITEM(order.get(), i, i64);
}
return order.release();
}
static PyObject* THPModule_setSDPUseFlash(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_sdp_use_math expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setSDPUseFlash(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_userEnabledFlashSDP(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().userEnabledFlashSDP())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setSDPUseMemEfficient(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_sdp_use_math expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setSDPUseMemEfficient(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* userEnabledMemEfficientSDP(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().userEnabledMemEfficientSDP())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setSDPUseMath(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_sdp_use_math expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setSDPUseMath(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_userEnabledMathSDP(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().userEnabledMathSDP())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setAllowFP16BF16ReductionMathSDP(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_sdp_use_math expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setAllowFP16BF16ReductionMathSDP(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_allowFP16BF16ReductionMathSDP(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().allowFP16BF16ReductionMathSDP())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setSDPUseOverrideable(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_sdp_use_overrideable expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setSDPUseOverrideable(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_userEnabledOverrideableSDP(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().userEnabledOverrideableSDP())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setSDPUseCuDNN(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_sdp_use_cudnn expects a bool, "
"but got %s",
THPUtils_typename(arg));
at::globalContext().setSDPUseCuDNN(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_userEnabledCuDNNSDP(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().userEnabledCuDNNSDP())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setUserEnabledCuDNN(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_enabled_cudnn expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setUserEnabledCuDNN(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_userEnabledCuDNN(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().userEnabledCuDNN())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setUserEnabledMkldnn(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_enabled_mkldnn expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setUserEnabledMkldnn(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_userEnabledMkldnn(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().userEnabledMkldnn())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setDeterministicCuDNN(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_deterministic_cudnn expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setDeterministicCuDNN(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_deterministicCuDNN(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().deterministicCuDNN())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setDeterministicMkldnn(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_deterministic_mkldnn expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setDeterministicMkldnn(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_deterministicMkldnn(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().deterministicMkldnn())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setDeterministicAlgorithms(
PyObject* _unused,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
static torch::PythonArgParser parser(
{"_set_deterministic_algorithms(bool mode, *, bool warn_only=False)"});
torch::ParsedArgs<2> parsed_args{};
auto r = parser.parse(args, kwargs, parsed_args);
bool mode = r.toBool(0);
bool warn_only = r.toBool(1);
at::globalContext().setDeterministicAlgorithms(mode, warn_only);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setAllowTF32OneDNN(
PyObject* _unsued,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"_set_onednn_allow_tf32 expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setAllowTF32OneDNN(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_allowTF32OneDNN(
PyObject* _unused,
PyObject* noargs) {
#ifdef USE_XPU
if (at::globalContext().allowTF32OneDNN())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
#else
Py_RETURN_NONE;
#endif
}
static PyObject* THPModule_deterministicAlgorithms(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().deterministicAlgorithms()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
static PyObject* THPModule_deterministicAlgorithmsWarnOnly(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().deterministicAlgorithmsWarnOnly()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
static PyObject* THPModule_setDeterministicFillUninitializedMemory(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg), "expected a bool, but got ", THPUtils_typename(arg));
at::globalContext().setDeterministicFillUninitializedMemory(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_deterministicFillUninitializedMemory(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().deterministicFillUninitializedMemory())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setUserEnabledNNPACK(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_enabled_NNPACK expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setUserEnabledNNPACK(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_userEnabledNNPACK(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().userEnabledNNPACK())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setWarnAlways(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"setWarnOnlyOnce expects a bool, "
"but got ",
THPUtils_typename(arg));
c10::WarningUtils::set_warnAlways(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_warnAlways(PyObject* _unused, PyObject* noargs) {
if (c10::WarningUtils::get_warnAlways()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
// Used only for testing C++ to Python warning translations.
static PyObject* THPModule_warn(PyObject* _unused, PyObject* noargs) {
HANDLE_TH_ERRORS
TORCH_WARN("Test message for TORCH_WARN");
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
// Used only for testing C++ to Python warning translations.
static PyObject* THPModule_warnDeprecation(
PyObject* _unused,
PyObject* noargs) {
HANDLE_TH_ERRORS
TORCH_WARN_DEPRECATION("Test message for TORCH_WARN_DEPRECATION");
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setBenchmarkCuDNN(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_benchmark_cudnn expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setBenchmarkCuDNN(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_benchmarkCuDNN(PyObject* _unused, PyObject* noargs) {
if (at::globalContext().benchmarkCuDNN()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
static PyObject* THPModule_setImmediateMiopen(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_immediate_miopen expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setImmediateMiopen(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_immediateMiopen(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().immediateMiopen()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
static PyObject* THPModule_setAllowTF32CuBLAS(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_allow_tf32_cublas expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setAllowTF32CuBLAS(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_allowTF32CuBLAS(
PyObject* _unused,
PyObject* noargs) {
HANDLE_TH_ERRORS
if (at::globalContext().allowTF32CuBLAS()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setAllowFP16ReductionCuBLAS(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_allow_fp16_reduction_cublas expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setAllowFP16ReductionCuBLAS(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_allowFP16ReductionCuBLAS(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().allowFP16ReductionCuBLAS()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
static PyObject* THPModule_setAllowBF16ReductionCuBLAS(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_allow_bf16_reduction_cublas expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setAllowBF16ReductionCuBLAS(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_allowBF16ReductionCuBLAS(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().allowBF16ReductionCuBLAS()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
static PyObject* THPModule_setAllowFP16AccumulationCuBLAS(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_allow_fp16_accumulation_cublas expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setAllowFP16AccumulationCuBLAS(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_allowFP16AccumulationCuBLAS(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().allowFP16AccumulationCuBLAS()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
static PyObject* THPModule_setAllowFP16ReductionCPU(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_allow_fp16_reduction_cpu expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setAllowFP16ReductionCPU(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_allowFP16ReductionCPU(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().allowFP16ReductionCPU()) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
static PyObject* THPModule_setFlushDenormal(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"flush_denormal expects a bool, "
"but got ",
THPUtils_typename(arg));
if (!at::globalContext().setFlushDenormal(arg == Py_True)) {
Py_RETURN_FALSE;
};
Py_RETURN_TRUE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getDefaultDtype(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
auto scalar_type = torch::tensors::get_default_scalar_type();
return Py_NewRef(torch::getTHPDtype(scalar_type));
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getDefaultDevice(PyObject* _unused, PyObject* arg) {
HANDLE_TH_ERRORS
return THPUtils_packString(c10::DeviceTypeName(
dispatchKeyToDeviceType(torch::tensors::get_default_dispatch_key()),
/*lower_case=*/true));
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setQEngine(PyObject* /* unused */, PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
THPUtils_checkLong(arg),
"set_qengine expects an int, "
"but got ",
THPUtils_typename(arg));
auto qengine = THPUtils_unpackLong(arg);
// NOLINTNEXTLINE(clang-analyzer-optin.core.EnumCastOutOfRange)
at::globalContext().setQEngine(static_cast<at::QEngine>(qengine));
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_qEngine(PyObject* _unused, PyObject* noargs) {
return THPUtils_packInt64(
static_cast<int64_t>(at::globalContext().qEngine()));
}
static PyObject* THPModule_supportedQEngines(
PyObject* _unused,
PyObject* noargs) {
const auto& qengines = at::globalContext().supportedQEngines();
auto list =
THPObjectPtr(PyList_New(static_cast<Py_ssize_t>(qengines.size())));
if (!list)
return nullptr;
for (const auto i : c10::irange(qengines.size())) {
PyObject* i64 = THPUtils_packInt64(static_cast<int64_t>(qengines[i]));
if (!i64)
return nullptr;
PyList_SET_ITEM(list.get(), i, i64);
}
return list.release();
}
static PyObject* THPModule_isEnabledXNNPACK(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().isXNNPACKAvailable())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_setCheckSparseTensorInvariants(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"set_check_sparse_tensor_invariants expects a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setCheckSparseTensorInvariants(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_checkSparseTensorInvariants(
PyObject* _unused,
PyObject* noargs) {
if (at::globalContext().checkSparseTensorInvariants())
Py_RETURN_TRUE;
else
Py_RETURN_FALSE;
}
static PyObject* THPModule_willEngineExecuteNode(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
bool isTHPFunction = THPFunction_Check(arg);
bool isTHPCppFunction = torch::autograd::THPCppFunction_Check(arg);
TORCH_CHECK(
isTHPFunction || isTHPCppFunction,
"_will_engine_execute_node expects an grad_fn, "
"but got ",
THPUtils_typename(arg));
const auto exec_info = torch::autograd::get_current_graph_task_exec_info();
TORCH_CHECK(
exec_info,
"_get_should_execute_nodes should only be called during the backward pass");
torch::autograd::Node* node = nullptr;
std::shared_ptr<torch::autograd::Node> node_sp;
if (isTHPFunction) {
node_sp = ((THPFunction*)arg)->cdata.lock();
node = node_sp.get();
} else {
node = ((torch::autograd::THPCppFunction*)arg)->cdata.get();
}
const auto nodes_in_graph =
torch::autograd::get_current_graph_task_nodes_in_graph();
bool ret = nodes_in_graph->find(node) != nodes_in_graph->end();
if (ret && !exec_info->empty()) {
auto it = exec_info->find(node);
if (it == exec_info->end() || !it->second.should_execute()) {
ret = false;
} else {
TORCH_CHECK(
!(node->topological_nr() == 0 && it->second.captures_),
"A leaf node was passed to _will_engine_execute_node but we are "
"currently running autograd.grad(). This is currently not supported.");
}
}
if (ret) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getCurrentGraphTaskExecutionOrder(
PyObject* _unused,
PyObject* noargs) {
HANDLE_TH_ERRORS
std::vector<torch::autograd::Node*> nodes =
torch::autograd::get_current_graph_task_execution_order();
TORCH_CHECK(
!nodes.empty(),
"_current_graph_task_execution_order should only be called during the backward pass");
auto list = THPObjectPtr(PyList_New(static_cast<Py_ssize_t>(nodes.size())));
if (!list)
return nullptr;
for (const auto i : c10::irange(nodes.size())) {
// This node is guaranteed to be alive since the backward is still running
PyObject* pyobj_node =
torch::autograd::functionToPyObject(nodes[i]->getptr());
PyList_SET_ITEM(list.get(), i, pyobj_node);
}
return list.release();
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getCurrentGraphTaskId(
PyObject* _unused,
PyObject* noargs) {
HANDLE_TH_ERRORS
return THPUtils_packInt64(torch::autograd::get_current_graph_task_id());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_getCurrentNode(PyObject* _unused, PyObject* noargs) {
HANDLE_TH_ERRORS
return torch::autograd::functionToPyObject(
torch::autograd::get_current_node());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_isDefaultMobileCPUAllocatorSet(
PyObject* _unused,
PyObject* noargs) {
HANDLE_TH_ERRORS
return PyBool_FromLong(at::globalContext().isDefaultMobileCPUAllocatorSet());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_setDefaultMobileCPUAllocator(
PyObject* _unused,
PyObject* noargs) {
HANDLE_TH_ERRORS
at::globalContext().setDefaultMobileCPUAllocator();
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_unsetDefaultMobileCPUAllocator(
PyObject* _unused,
PyObject* noargs) {
HANDLE_TH_ERRORS
at::globalContext().unsetDefaultMobileCPUAllocator();
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_vmapmode_increment_nesting(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
return THPUtils_packInt64(at::impl::VmapMode::increment_nesting());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_vmapmode_decrement_nesting(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
return THPUtils_packInt64(at::impl::VmapMode::decrement_nesting());
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_set_display_vmap_fallback_warnings_mode(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
TORCH_CHECK(
PyBool_Check(arg),
"enabled must be a bool, "
"but got ",
THPUtils_typename(arg));
at::globalContext().setDisplayVmapFallbackWarnings(arg == Py_True);
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static PyObject* THPModule_are_vmap_fallback_warnings_enabled(
PyObject* _unused,
PyObject* arg) {
HANDLE_TH_ERRORS
if (at::globalContext().areVmapFallbackWarningsEnabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
END_HANDLE_TH_ERRORS
}
static PyObject* THCPModule_ensureCUDADeviceGuardSet(
PyObject* self,
PyObject* noargs) {
HANDLE_TH_ERRORS
c10::impl::ensureCUDADeviceGuardSet();
Py_RETURN_NONE;
END_HANDLE_TH_ERRORS
}
static std::initializer_list<PyMethodDef> TorchMethods = {
{"_initExtension", THPModule_initExtension, METH_O, nullptr},
{"_autograd_init", THPAutograd_initExtension, METH_NOARGS, nullptr},
{"_add_docstr", THPModule_addDocStr, METH_VARARGS, nullptr},
{"_swap_tensor_impl", THPModule_swap_tensor_impl, METH_VARARGS, nullptr},
{"_init_names", THPModule_initNames, METH_O, nullptr},
{"_has_distributed", THPModule_hasDistributed, METH_NOARGS, nullptr},
{"_set_default_tensor_type",
THPModule_setDefaultTensorType,
METH_O,
nullptr},
{"_set_default_dtype", THPModule_setDefaultDtype, METH_O, nullptr},
{"_infer_size", THPModule_inferSize, METH_VARARGS, nullptr},
{"_abort", THPModule_abort, METH_NOARGS, nullptr},
{"_crash_if_csrc_asan", THPModule_crashIfCsrcASAN, METH_O, nullptr},
{"_crash_if_csrc_ubsan", THPModule_crashIfCsrcUBSAN, METH_O, nullptr},
{"_crash_if_vptr_ubsan", THPModule_crashIfvptrUBSAN, METH_NOARGS, nullptr},
{"_crash_if_aten_asan", THPModule_crashIfATenASAN, METH_O, nullptr},
{"_crash_if_debug_asserts_fail",
THPModule_crashIfDebugAssertsFail,
METH_O,
nullptr},
{"_show_config", THPModule_showConfig, METH_NOARGS, nullptr},
{"_cxx_flags", THPModule_cxxFlags, METH_NOARGS, nullptr},
{"_parallel_info", THPModule_parallelInfo, METH_NOARGS, nullptr},
{"_get_cpu_capability", THPModule_getCpuCapability, METH_NOARGS, nullptr},
{"_set_backcompat_broadcast_warn",
THPModule_setBackcompatBroadcastWarn,
METH_O,
nullptr},
{"_get_backcompat_broadcast_warn",
THPModule_getBackcompatBroadcastWarn,
METH_NOARGS,
nullptr},
{"_set_backcompat_keepdim_warn",
THPModule_setBackcompatKeepdimWarn,
METH_O,
nullptr},
{"_get_backcompat_keepdim_warn",
THPModule_getBackcompatKeepdimWarn,
METH_NOARGS,
nullptr},
{"get_num_threads", THPModule_getNumThreads, METH_NOARGS, nullptr},
{"set_num_threads", THPModule_setNumThreads, METH_O, nullptr},
{"get_num_interop_threads",
THPModule_getNumInteropThreads,
METH_NOARGS,
nullptr},
{"set_num_interop_threads",
THPModule_setNumInteropThreads,
METH_O,
nullptr},
{"_get_flash_sdp_enabled",
THPModule_userEnabledFlashSDP,
METH_NOARGS,
nullptr},
{"_set_sdp_priority_order", THPModule_setSDPPriorityOrder, METH_O, nullptr},
{"_get_sdp_priority_order",
THPModule_sDPPriorityOrder,
METH_NOARGS,
nullptr},
{"_set_sdp_use_flash", THPModule_setSDPUseFlash, METH_O, nullptr},
{"_get_mem_efficient_sdp_enabled",
userEnabledMemEfficientSDP,
METH_NOARGS,
nullptr},
{"_set_sdp_use_mem_efficient",
THPModule_setSDPUseMemEfficient,
METH_O,
nullptr},
{"_get_math_sdp_enabled",
THPModule_userEnabledMathSDP,
METH_NOARGS,
nullptr},
{"_set_sdp_use_math", THPModule_setSDPUseMath, METH_O, nullptr},
{"_get_math_sdp_allow_fp16_bf16_reduction",
THPModule_allowFP16BF16ReductionMathSDP,
METH_NOARGS,
nullptr},
{"_set_math_sdp_allow_fp16_bf16_reduction",
THPModule_setAllowFP16BF16ReductionMathSDP,
METH_O,
nullptr},
{"_get_overrideable_sdp_enabled",
THPModule_userEnabledOverrideableSDP,
METH_NOARGS,
nullptr},
{"_set_sdp_use_overrideable",
THPModule_setSDPUseOverrideable,
METH_O,
nullptr},
{"_get_cudnn_sdp_enabled",
THPModule_userEnabledCuDNNSDP,
METH_NOARGS,
nullptr},
{"_set_sdp_use_cudnn", THPModule_setSDPUseCuDNN, METH_O, nullptr},
{"_get_cudnn_enabled", THPModule_userEnabledCuDNN, METH_NOARGS, nullptr},
{"_set_cudnn_enabled", THPModule_setUserEnabledCuDNN, METH_O, nullptr},
{"_get_mkldnn_enabled", THPModule_userEnabledMkldnn, METH_NOARGS, nullptr},
{"_set_mkldnn_enabled", THPModule_setUserEnabledMkldnn, METH_O, nullptr},
{"_get_cudnn_allow_tf32", THPModule_allowTF32CuDNN, METH_NOARGS, nullptr},
{"_set_cudnn_allow_tf32", THPModule_setAllowTF32CuDNN, METH_O, nullptr},
{"_get_onednn_allow_tf32", THPModule_allowTF32OneDNN, METH_NOARGS, nullptr},
{"_set_onednn_allow_tf32", THPModule_setAllowTF32OneDNN, METH_O, nullptr},
{"_get_cudnn_benchmark", THPModule_benchmarkCuDNN, METH_NOARGS, nullptr},
{"_set_cudnn_benchmark", THPModule_setBenchmarkCuDNN, METH_O, nullptr},
{"_get_miopen_immediate", THPModule_immediateMiopen, METH_NOARGS, nullptr},
{"_set_miopen_immediate", THPModule_setImmediateMiopen, METH_O, nullptr},
{"_get_cudnn_deterministic",
THPModule_deterministicCuDNN,
METH_NOARGS,
nullptr},
{"_set_cudnn_deterministic",
THPModule_setDeterministicCuDNN,
METH_O,
nullptr},
{"_get_mkldnn_deterministic",
THPModule_deterministicMkldnn,
METH_NOARGS,
nullptr},
{"_set_mkldnn_deterministic",
THPModule_setDeterministicMkldnn,
METH_O,
nullptr},
{"_get_deterministic_algorithms",
THPModule_deterministicAlgorithms,
METH_NOARGS,
nullptr},
{"_get_deterministic_algorithms_warn_only",
THPModule_deterministicAlgorithmsWarnOnly,
METH_NOARGS,
nullptr},
{"_set_deterministic_algorithms",
castPyCFunctionWithKeywords(THPModule_setDeterministicAlgorithms),
METH_VARARGS | METH_KEYWORDS,
nullptr},
{"_get_deterministic_fill_uninitialized_memory",
THPModule_deterministicFillUninitializedMemory,
METH_NOARGS,
nullptr},
{"_set_deterministic_fill_uninitialized_memory",
THPModule_setDeterministicFillUninitializedMemory,
METH_O,
nullptr},
{"_get_nnpack_enabled", THPModule_userEnabledNNPACK, METH_NOARGS, nullptr},
{"_set_nnpack_enabled", THPModule_setUserEnabledNNPACK, METH_O, nullptr},
{"_get_warnAlways", THPModule_warnAlways, METH_NOARGS, nullptr},
{"_set_warnAlways", THPModule_setWarnAlways, METH_O, nullptr},
{"_warn", THPModule_warn, METH_NOARGS, nullptr},
{"_warn_deprecation", THPModule_warnDeprecation, METH_NOARGS, nullptr},
{"_get_cublas_allow_tf32", THPModule_allowTF32CuBLAS, METH_NOARGS, nullptr},
{"_set_cublas_allow_tf32", THPModule_setAllowTF32CuBLAS, METH_O, nullptr},
{"_get_float32_matmul_precision",
THPModule_float32MatmulPrecision,
METH_NOARGS,
nullptr},
{"_set_float32_matmul_precision",
THPModule_setFloat32MatmulPrecision,
METH_O,
nullptr},
{"_get_cublas_allow_fp16_reduced_precision_reduction",
THPModule_allowFP16ReductionCuBLAS,
METH_NOARGS,
nullptr},
{"_set_cublas_allow_fp16_reduced_precision_reduction",
THPModule_setAllowFP16ReductionCuBLAS,
METH_O,
nullptr},
{"_get_cublas_allow_bf16_reduced_precision_reduction",
THPModule_allowBF16ReductionCuBLAS,
METH_NOARGS,
nullptr},
{"_set_cublas_allow_bf16_reduced_precision_reduction",
THPModule_setAllowBF16ReductionCuBLAS,
METH_O,
nullptr},
{"_get_cublas_allow_fp16_accumulation",
THPModule_allowFP16AccumulationCuBLAS,
METH_NOARGS,
nullptr},
{"_set_cublas_allow_fp16_accumulation",
THPModule_setAllowFP16AccumulationCuBLAS,
METH_O,
nullptr},
{"_get_cpu_allow_fp16_reduced_precision_reduction",
THPModule_allowFP16ReductionCPU,
METH_NOARGS,
nullptr},
{"_set_cpu_allow_fp16_reduced_precision_reduction",
THPModule_setAllowFP16ReductionCPU,
METH_O,
nullptr},
{"_vmapmode_increment_nesting",
THPModule_vmapmode_increment_nesting,
METH_NOARGS,
nullptr},
{"_vmapmode_decrement_nesting",
THPModule_vmapmode_decrement_nesting,
METH_NOARGS,
nullptr},
{"_debug_only_display_vmap_fallback_warnings",
THPModule_set_display_vmap_fallback_warnings_mode,
METH_O,
nullptr},
{"_debug_only_are_vmap_fallback_warnings_enabled",
THPModule_are_vmap_fallback_warnings_enabled,
METH_NOARGS,
nullptr},
{"_to_dlpack",
castPyCFunctionWithKeywords(THPModule_toDLPack),
METH_VARARGS | METH_KEYWORDS,
nullptr},
{"_to_dlpack_versioned",
castPyCFunctionWithKeywords(THPModule_toDLPackVersioned),
METH_VARARGS | METH_KEYWORDS,
nullptr},
{"_from_dlpack", THPModule_fromDLPack, METH_O, nullptr},
{"_torchDeviceToDLDevice",
THPModule_torchDeviceToDLDevice,
METH_O,
nullptr},
{"_get_cpp_backtrace", THModule_getCppBacktrace, METH_VARARGS, nullptr},
{"_rename_privateuse1_backend",
THModule_rename_privateuse1_backend,
METH_O,
nullptr},
{"_get_privateuse1_backend_name",
THModule_get_privateuse1_backend_name,
METH_NOARGS,
nullptr},
{"set_flush_denormal", THPModule_setFlushDenormal, METH_O, nullptr},
{"get_default_dtype", THPModule_getDefaultDtype, METH_NOARGS, nullptr},
{"_get_default_device", THPModule_getDefaultDevice, METH_NOARGS, nullptr},
{"_get_qengine", THPModule_qEngine, METH_NOARGS, nullptr},
{"_set_qengine", THPModule_setQEngine, METH_O, nullptr},
{"_supported_qengines", THPModule_supportedQEngines, METH_NOARGS, nullptr},
{"_is_xnnpack_enabled", THPModule_isEnabledXNNPACK, METH_NOARGS, nullptr},
{"_set_check_sparse_tensor_invariants",
THPModule_setCheckSparseTensorInvariants,
METH_O,
nullptr},
{"_check_sparse_tensor_invariants",
THPModule_checkSparseTensorInvariants,
METH_NOARGS,
nullptr},
{"_will_engine_execute_node",
THPModule_willEngineExecuteNode,
METH_O,
nullptr},
{"_current_graph_task_execution_order",
THPModule_getCurrentGraphTaskExecutionOrder,
METH_NOARGS,
nullptr},
{"_current_graph_task_id",
THPModule_getCurrentGraphTaskId,
METH_NOARGS,
nullptr},
{"_current_autograd_node", THPModule_getCurrentNode, METH_NOARGS, nullptr},
{"_is_default_mobile_cpu_allocator_set",
THPModule_isDefaultMobileCPUAllocatorSet,
METH_NOARGS,
nullptr},
{"_set_default_mobile_cpu_allocator",
THPModule_setDefaultMobileCPUAllocator,
METH_NOARGS,
nullptr},
{"_unset_default_mobile_cpu_allocator",
THPModule_unsetDefaultMobileCPUAllocator,
METH_NOARGS,
nullptr},
{"_is_torch_function_enabled",
THPModule_isEnabledTorchFunction,
METH_NOARGS,
nullptr},
{"_is_torch_function_all_disabled",
THPModule_isAllDisabledTorchFunction,
METH_NOARGS,
nullptr},
{"_disabled_torch_function_impl",
THPModule_disable_torch_function,
METH_VARARGS,
nullptr},
{"_disabled_torch_dispatch_impl",
THPModule_disable_torch_dispatch,
METH_VARARGS,
nullptr},
{"_has_torch_function", THPModule_has_torch_function, METH_O, nullptr},
{"_has_torch_function_unary",
THPModule_has_torch_function_unary,
METH_O,
nullptr},
{"_has_torch_function_variadic",
(PyCFunction)(void (*)())THPModule_has_torch_function_variadic,
METH_FASTCALL,
nullptr},
{"_ensureCUDADeviceGuardSet",
THCPModule_ensureCUDADeviceGuardSet,
METH_NOARGS,
nullptr},
{nullptr, nullptr, 0, nullptr}
};
#ifdef USE_CUDA
// NOLINTBEGIN(misc-use-internal-linkage)
void THCPStream_init(PyObject* module);
void THCPEvent_init(PyObject* module);
void THCPGraph_init(PyObject* module);
void THCPMemPool_init(PyObject* module);
PyMethodDef* THCPModule_methods();
namespace torch::cuda {
void initModule(PyObject* module);
} // namespace torch::cuda
// NOLINTEND(misc-use-internal-linkage)
#endif
#ifdef USE_XPU
PyMethodDef* THXPModule_methods();
void THXPStream_init(PyObject* module);
void THXPEvent_init(PyObject* module);
namespace torch::xpu {
void initModule(PyObject* module);
} // namespace torch::xpu
#endif
static std::vector<PyMethodDef> methods;
// In Python we can't use the trick of C10_LOG_API_USAGE_ONCE
// Guaranteed to be invoked from Python under GIL, no locking on map needed
static void LogAPIUsageOnceFromPython(const std::string& event) {
static std::unordered_set<std::string> seen;
if (!seen.count(event)) {
seen.insert(event);
c10::LogAPIUsage(event);
}
}
static void LogAPIUsageMetadataFromPython(
const std::string& event,
const std::map<std::string, std::string>& metadata_map) {
c10::LogAPIUsageMetadata(event, metadata_map);
}
// Weak reference to tensor, used to test a tensor isn't leaked
class WeakTensorRef {
c10::weak_intrusive_ptr<c10::TensorImpl> weakref_;
public:
WeakTensorRef(const at::Tensor& t) : weakref_(t.getIntrusivePtr()) {}
bool expired() {
return weakref_.expired();
}
};
namespace {
using SigHandler = void (*)(int);
SigHandler* _getOldHandler(int signum) {
#define SIG_CHECK(n) \
if (signum == (n)) { \
static SigHandler handler = nullptr; \
return &handler; \
}
SIG_CHECK(SIGSEGV);
SIG_CHECK(SIGILL);
throw std::runtime_error("unexpected signal number");
#undef SIG_CHECK
}
extern "C" void _signalHandler(int signum) {
// Note that technically there's not much you're allowed to do here - but
// we're probably dying anyway so give it a try...
auto oldAction = *_getOldHandler(signum);
*_getOldHandler(signum) = nullptr;
// If we hit another signal don't run this handler again.
std::signal(signum, oldAction);
#ifdef _WIN32
const char* signame = "<unknown>";
#else
const char* signame = strsignal(signum);
#endif
fprintf(
stderr,
"Process %d crashed with signal %s (%d):\n",
getpid(),
signame,
signum);
auto bt = c10::get_backtrace();
fwrite(bt.data(), 1, bt.size(), stderr);
// Try to run the previous signal handler
if (oldAction != SIG_IGN && oldAction != SIG_DFL) {
oldAction(signum);
}
if (oldAction != SIG_IGN) {
_exit(-1);
}
}
void _initCrashHandler() {
*_getOldHandler(SIGILL) = std::signal(SIGILL, _signalHandler);
*_getOldHandler(SIGSEGV) = std::signal(SIGSEGV, _signalHandler);
}
} // anonymous namespace
extern "C" TORCH_PYTHON_API PyObject* initModule();
// separate decl and defn for msvc error C2491
PyObject* initModule() {
HANDLE_TH_ERRORS
c10::initLogging();
c10::set_terminate_handler();
at::internal::lazy_init_num_threads();
C10_LOG_API_USAGE_ONCE("torch.python.import");
#define ASSERT_TRUE(cmd) \
if (!(cmd)) \
return nullptr
THPUtils_addPyMethodDefs(methods, std::data(TorchMethods));
THPUtils_addPyMethodDefs(methods, DataLoaderMethods);
THPUtils_addPyMethodDefs(methods, torch::autograd::python_functions());
THPUtils_addPyMethodDefs(methods, torch::multiprocessing::python_functions());
THPUtils_addPyMethodDefs(methods, torch::mps::python_functions());
#ifdef USE_CUDA
THPUtils_addPyMethodDefs(methods, THCPModule_methods());
#endif
#ifdef USE_XPU
THPUtils_addPyMethodDefs(methods, THXPModule_methods());
#endif
THPUtils_addPyMethodDefs(
methods, torch::distributed::c10d::python_functions());
#ifndef _WIN32
THPUtils_addPyMethodDefs(
methods, torch::distributed::rpc::python_functions());
THPUtils_addPyMethodDefs(
methods, torch::distributed::autograd::python_functions());
THPUtils_addPyMethodDefs(
methods, torch::distributed::rpc::testing::python_functions());
#endif
static struct PyModuleDef torchmodule = {
PyModuleDef_HEAD_INIT, "torch._C", nullptr, -1, methods.data()};
module = PyModule_Create(&torchmodule);
ASSERT_TRUE(module);
#ifdef Py_GIL_DISABLED
PyUnstable_Module_SetGIL(module, Py_MOD_GIL_NOT_USED);
#endif
ASSERT_TRUE(THPGenerator_init(module));
ASSERT_TRUE(THPException_init(module));
THPSize_init(module);
THPDtype_init(module);
THPDTypeInfo_init(module);
THPLayout_init(module);
THPMemoryFormat_init(module);
THPQScheme_init(module);
THPDevice_init(module);
THPStream_init(module);
THPEvent_init(module);
NodeBase_init(module);
NodeIter_init(module);
ASSERT_TRUE(THPVariable_initModule(module));
ASSERT_TRUE(THPFunction_initModule(module));
ASSERT_TRUE(THPEngine_initModule(module));
// NOTE: We need to be able to access OperatorExportTypes from ONNX for use in
// the export side of JIT, so this ONNX init needs to appear before the JIT
// init.
torch::onnx::initONNXBindings(module);
torch::autograd::initEnumTag(module);
torch::jit::initJITBindings(module);
torch::monitor::initMonitorBindings(module);
torch::impl::dispatch::initDispatchBindings(module);
torch::dynamo::initDynamoBindings(module);
torch::functorch::impl::initFuncTorchBindings(module);
torch::throughput_benchmark::initThroughputBenchmarkBindings(module);
torch::autograd::initReturnTypes(module);
torch::autograd::initNNFunctions(module);
torch::autograd::initFFTFunctions(module);
torch::autograd::initLinalgFunctions(module);
torch::autograd::initNestedFunctions(module);
torch::autograd::initSparseFunctions(module);
torch::autograd::initSpecialFunctions(module);
torch::autograd::init_legacy_variable(module);
torch::profiler::initPythonBindings(module);
torch::python::init_bindings(module);
torch::lazy::initLazyBindings(module);
torch::_export::initExportBindings(module);
torch::inductor::initAOTIRunnerBindings(module);
torch::inductor::initAOTIPackageBindings(module);
#ifdef USE_ITT
torch::profiler::initIttBindings(module);
#endif
#ifdef USE_CUDA
torch::cuda::initModule(module);
#endif
#if defined(USE_CUDA) && !defined(USE_ROCM)
ASSERT_TRUE(StaticCudaLauncher_init(module));
#endif
#ifdef USE_MPS
torch::mps::initModule(module);
#endif
#ifdef USE_XPU
torch::xpu::initModule(module);
#endif
torch::mtia::initModule(module);
torch::cpu::initModule(module);
torch::accelerator::initModule(module);
torch::instruction_counter::initModule(module);
torch::initVerboseBindings(module);
ASSERT_TRUE(THPStorage_init(module));
#ifdef USE_CUDA
// This will only initialise base classes and attach them to library namespace
// They won't be ready for real usage until importing cuda module, that will
// complete the process (but it defines Python classes before calling back
// into C, so these lines have to execute first)..
THCPStream_init(module);
THCPEvent_init(module);
THCPGraph_init(module);
THCPMemPool_init(module);
#endif
#ifdef USE_XPU
THXPStream_init(module);
THXPEvent_init(module);
#endif
auto set_module_attr =
[&](const char* name, PyObject* v, bool incref = true) {
// PyModule_AddObject steals reference
if (incref) {
Py_INCREF(v);
}
int ret = PyModule_AddObject(module, name, v);
if (ret != 0) {
Py_DECREF(v);
}
return ret == 0;
};
#if defined(USE_CUDNN) || defined(USE_ROCM)
PyObject* has_cudnn = Py_True;
#else
PyObject* has_cudnn = Py_False;
#endif
ASSERT_TRUE(set_module_attr("_has_cudnn", has_cudnn));
#if defined(USE_CUSPARSELT)
PyObject* has_cusparselt = Py_True;
#else
PyObject* has_cusparselt = Py_False;
#endif
ASSERT_TRUE(set_module_attr("_has_cusparselt", has_cusparselt));
#if AT_MKL_ENABLED() || AT_POCKETFFT_ENABLED()
PyObject* has_spectral = Py_True;
#else
PyObject* has_spectral = Py_False;
#endif
ASSERT_TRUE(set_module_attr("has_spectral", has_spectral));
// force ATen to initialize because it handles
// setting up TH Errors so that they throw C++ exceptions
at::init();
// Automatically translate errors thrown from pybind11 functions
// NOLINTNEXTLINE(performance-unnecessary-value-param)
py::register_exception_translator([](std::exception_ptr e) {
try {
if (e) {
std::rethrow_exception(e);
}
}
CATCH_TH_ERRORS()
});
auto py_module = py::reinterpret_borrow<py::module>(module);
py_module.def("_initCrashHandler", &_initCrashHandler);
py_module.def("_demangle", &c10::demangle);
py_module.def("_log_api_usage_once", &LogAPIUsageOnceFromPython);
py_module.def("_log_api_usage_metadata", &LogAPIUsageMetadataFromPython);
py_module.def("vitals_enabled", &at::vitals::torchVitalEnabled);
py_module.def(
"set_vital",
[](const std::string& vital,
const std::string& attr,
const std::string& value) {
return at::vitals::VitalsAPI.setVital(vital, attr, value);
});
py_module.def(
"read_vitals", []() { return at::vitals::VitalsAPI.readVitals(); });
py_module.def(
"init_num_threads",
torch::wrap_pybind_function(at::init_num_threads),
R"(
init_num_threads()
Initializes the number of parallel threads used on the current thread.
Call this whenever a new thread is created in order to propagate values from
:func:`torch.set_num_threads` onto the new thread.
)");
py_module.def("_set_cached_tensors_enabled", [](bool enabled) {
at::caching::set_cached_tensors_enabled(enabled);
});
py_module.def("_add_cached_tensor", [](const at::Tensor& t) {
at::caching::add_cached_tensor(t);
});
py_module.def("_remove_cached_tensor", [](const at::Tensor& t) {
at::caching::remove_cached_tensor(t);
});
py_module.def("_is_cached_tensor", [](const at::Tensor& t) {
return at::caching::is_cached_tensor(t);
});
py_module.def("_storage_Use_Count", [](size_t storage_impl_ptr) {
// NOLINTNEXTLINE(performance-no-int-to-ptr)
c10::StorageImpl* storage_impl = (c10::StorageImpl*)storage_impl_ptr;
return c10::raw::intrusive_ptr::use_count(storage_impl);
});
ASSERT_TRUE(
set_module_attr("has_openmp", at::hasOpenMP() ? Py_True : Py_False));
ASSERT_TRUE(set_module_attr("has_mkl", at::hasMKL() ? Py_True : Py_False));
ASSERT_TRUE(
set_module_attr("_has_kleidiai", at::hasKleidiAI() ? Py_True : Py_False));
ASSERT_TRUE(
set_module_attr("has_lapack", at::hasLAPACK() ? Py_True : Py_False));
ASSERT_TRUE(set_module_attr(
"_has_eigen_sparse", at::hasEigenSparse() ? Py_True : Py_False));
py_module.def("_valgrind_supported_platform", []() {
#if defined(USE_VALGRIND)
return true;
#else
return false;
#endif
});
py_module.def("_valgrind_toggle", []() {
#if defined(USE_VALGRIND)
CALLGRIND_TOGGLE_COLLECT;
#else
TORCH_CHECK(false, "Valgrind is not supported.");
#endif
});
py_module.def("_valgrind_toggle_and_dump_stats", []() {
#if defined(USE_VALGRIND)
// NB: If we don't toggle collect around dump stats, callgrind_annotate
// won't process the results correctly. Specifically,
// `callgrind_annotate --inclusive=no` will be almost completely empty.
CALLGRIND_TOGGLE_COLLECT;
CALLGRIND_DUMP_STATS;
#else
TORCH_CHECK(false, "Valgrind is not supported.");
#endif
});
py::class_<WeakTensorRef>(py_module, "_WeakTensorRef")
.def(py::init([](py::object tensor) {
return WeakTensorRef(THPVariable_Unpack(tensor.ptr()));
}))
.def("expired", &WeakTensorRef::expired);
py::enum_<at::native::ConvBackend>(py_module, "_ConvBackend")
.value("CudaDepthwise2d", at::native::ConvBackend::CudaDepthwise2d)
.value("CudaDepthwise3d", at::native::ConvBackend::CudaDepthwise3d)
.value("Cudnn", at::native::ConvBackend::Cudnn)
.value("CudnnTranspose", at::native::ConvBackend::CudnnTranspose)
.value("Empty", at::native::ConvBackend::Empty)
.value("Miopen", at::native::ConvBackend::Miopen)
.value("MiopenDepthwise", at::native::ConvBackend::MiopenDepthwise)
.value("MiopenTranspose", at::native::ConvBackend::MiopenTranspose)
.value("Mkldnn", at::native::ConvBackend::Mkldnn)
.value("MkldnnEmpty", at::native::ConvBackend::MkldnnEmpty)
.value("NnpackSpatial", at::native::ConvBackend::NnpackSpatial)
.value("Overrideable", at::native::ConvBackend::Overrideable)
.value("Slow2d", at::native::ConvBackend::Slow2d)
.value("Slow3d", at::native::ConvBackend::Slow3d)
.value("SlowDilated2d", at::native::ConvBackend::SlowDilated2d)
.value("SlowDilated3d", at::native::ConvBackend::SlowDilated3d)
.value("SlowTranspose2d", at::native::ConvBackend::SlowTranspose2d)
.value("SlowTranspose3d", at::native::ConvBackend::SlowTranspose3d)
.value(
"Winograd3x3Depthwise", at::native::ConvBackend::Winograd3x3Depthwise)
.value("Xnnpack2d", at::native::ConvBackend::Xnnpack2d)
.value("Mps", at::native::ConvBackend::Mps)
.value("MpsTranspose,", at::native::ConvBackend::MpsTranspose);
py_module.def(
"_select_conv_backend",
[](const at::Tensor& input,
const at::Tensor& weight,
const std::optional<at::Tensor>& bias_opt,
at::SymIntArrayRef stride_,
at::SymIntArrayRef padding_,
at::SymIntArrayRef dilation_,
bool transposed_,
at::SymIntArrayRef output_padding_,
c10::SymInt groups_) {
return at::native::select_conv_backend(
input,
weight,
bias_opt,
stride_,
padding_,
dilation_,
transposed_,
output_padding_,
std::move(groups_),
std::nullopt);
},
py::arg("input"),
py::arg("weight"),
py::arg("bias"),
py::arg("stride"),
py::arg("padding"),
py::arg("dilation"),
py::arg("transposed"),
py::arg("output_padding"),
py::arg("groups"));
// overload for bias_sizes_opt/backward TODO: figure out default value
py_module.def(
"_select_conv_backend",
[](const at::Tensor& input,
const at::Tensor& weight,
const std::optional<at::Tensor>& bias,
at::SymIntArrayRef stride_,
at::SymIntArrayRef padding_,
at::SymIntArrayRef dilation_,
bool transposed_,
at::SymIntArrayRef output_padding_,
c10::SymInt groups_,
std::optional<std::vector<c10::SymInt>> bias_sizes_opt) {
c10::OptionalArrayRef<c10::SymInt> ref = std::nullopt;
if (bias_sizes_opt) {
ref = (*bias_sizes_opt);
}
return at::native::select_conv_backend(
input,
weight,
bias,
stride_,
padding_,
dilation_,
transposed_,
output_padding_,
std::move(groups_),
ref);
},
py::arg("input"),
py::arg("weight"),
py::arg("bias"),
py::arg("stride"),
py::arg("padding"),
py::arg("dilation"),
py::arg("transposed"),
py::arg("output_padding"),
py::arg("groups"),
py::arg("bias_sizes"));
py_module.def(
"_conv_determine_backend_memory_format",
at::native::_determine_backend_memory_format);
////////////////////////////////////////////////////////////////////////////////
// Scaled Dot Product Attention utilities
////////////////////////////////////////////////////////////////////////////////
py::class_<sdp::sdp_params>(py_module, "_SDPAParams")
.def(py::init([](at::Tensor const& query,
at::Tensor const& key,
at::Tensor const& value,
std::optional<at::Tensor> attn_mask,
double dropout,
bool is_causal,
bool enable_gqa) {
return sdp::sdp_params{
query,
key,
value,
std::move(attn_mask),
dropout,
is_causal,
enable_gqa};
}))
.def_readonly("query", &sdp::sdp_params::query)
.def_readonly("key", &sdp::sdp_params::key)
.def_readonly("value", &sdp::sdp_params::value)
.def_readonly("attn_mask", &sdp::sdp_params::attn_mask)
.def_readonly("dropout", &sdp::sdp_params::dropout)
.def_readonly("is_causal", &sdp::sdp_params::is_causal)
.def_readonly("enable_gqa", &sdp::sdp_params::enable_gqa);
py::enum_<sdp::SDPBackend>(
py_module,
"_SDPBackend",
"An enum-like class that contains the different backends for scaled dot product attention.\n\n... warning:: This class is in beta and subject to change.\n\n"
"This backend class is designed to be used with the sdpa_kernel context manager."
"See :func: torch.nn.attention.sdpa_kernel for more details.")
.value("ERROR", sdp::SDPBackend::error)
.value("MATH", sdp::SDPBackend::math)
.value("FLASH_ATTENTION", sdp::SDPBackend::flash_attention)
.value("EFFICIENT_ATTENTION", sdp::SDPBackend::efficient_attention)
.value("CUDNN_ATTENTION", sdp::SDPBackend::cudnn_attention)
.value("OVERRIDEABLE", sdp::SDPBackend::overrideable);
py_module.def("_is_flash_attention_available", []() {
#ifdef USE_CUDA
return sdp::is_flash_attention_available();
#else
return false;
#endif
});
py_module.def(
"_can_use_flash_attention",
[](const sdp::sdp_params& params, bool debug) {
#ifdef USE_CUDA
return sdp::can_use_flash_attention(params, debug);
#else
return false;
#endif
});
py_module.def(
"_can_use_mem_efficient_attention",
[](const sdp::sdp_params& params, bool debug) {
#ifdef USE_CUDA
return sdp::can_use_mem_efficient_attention(params, debug);
#else
return false;
#endif
});
py_module.def(
"_can_use_cudnn_attention",
[](const sdp::sdp_params& params, bool debug) {
#ifdef USE_CUDA
return sdp::can_use_cudnn_attention(params, debug);
#else
return false;
#endif
});
py::enum_<at::LinalgBackend>(py_module, "_LinalgBackend")
.value("Default", at::LinalgBackend::Default)
.value("Cusolver", at::LinalgBackend::Cusolver)
.value("Magma", at::LinalgBackend::Magma);
py_module.def("_set_linalg_preferred_backend", [](at::LinalgBackend b) {
at::globalContext().setLinalgPreferredBackend(b);
});
py_module.def("_get_linalg_preferred_backend", []() {
return at::globalContext().linalgPreferredBackend();
});
py::enum_<at::BlasBackend>(py_module, "_BlasBackend")
.value("Default", at::BlasBackend::Default)
.value("Cublas", at::BlasBackend::Cublas)
.value("Cublaslt", at::BlasBackend::Cublaslt)
.value("Ck", at::BlasBackend::Ck);
py_module.def("_set_blas_preferred_backend", [](at::BlasBackend b) {
at::globalContext().setBlasPreferredBackend(b);
});
py_module.def("_get_blas_preferred_backend", []() {
return at::globalContext().blasPreferredBackend();
});
py::enum_<at::ROCmFABackend>(py_module, "_ROCmFABackend")
.value("Default", at::ROCmFABackend::Default)
.value("AOTriton", at::ROCmFABackend::AOTriton)
.value("Ck", at::ROCmFABackend::Ck);
py_module.def("_set_rocm_fa_preferred_backend", [](at::ROCmFABackend b) {
at::globalContext().setROCmFAPreferredBackend(b);
});
py_module.def("_get_rocm_fa_preferred_backend", []() {
return at::globalContext().getROCmFAPreferredBackend();
});
py_module.def(
"_set_sm_carveout_experimental", [](std::optional<int32_t> val) {
at::globalContext()._setSMCarveout_EXPERIMENTAL(val);
});
py_module.def("_get_sm_carveout_experimental", []() {
return at::globalContext()._SMCarveout_EXPERIMENTAL();
});
py_module.def(
"_construct_storage_from_data_pointer",
[](int64_t data_ptr, c10::Device device, size_t size_bytes) {
return c10::Storage(
c10::Storage::use_byte_size_t(),
size_bytes,
// NOLINTNEXTLINE(performance-no-int-to-ptr)
at::DataPtr(reinterpret_cast<void*>(data_ptr), device));
});
py_module.def(
"_get_fp32_precision_getter",
[](const std::string& backend, const std::string& op) {
return at::globalContext().float32Precision(backend, op);
});
py_module.def(
"_set_fp32_precision_setter",
[](const std::string& backend,
const std::string& op,
const std::string& precision) {
at::globalContext().setFloat32Precision(backend, op, precision);
return precision;
});
py_module.def(
"_stash_obj_in_tls", [](const std::string& key, py::handle arg) {
at::impl::ThreadLocalPythonObjects::get_state().set(
key,
std::make_shared<c10::SafePyObject>(arg.ptr(), getPyInterpreter()));
});
py_module.def("_get_obj_in_tls", [](const std::string& key) -> py::handle {
auto safe_pyobject =
at::impl::ThreadLocalPythonObjects::get_state().get(key);
auto obj = safe_pyobject->ptr(getPyInterpreter());
return py::handle(obj);
});
py_module.def("_is_key_in_tls", [](const std::string& key) -> bool {
return at::impl::ThreadLocalPythonObjects::get_state().contains(key);
});
py_module.def("_accelerator_hooks_device_count", []() {
auto device_type = at::getAccelerator();
if (device_type.has_value()) {
return at::globalContext()
.getAcceleratorHooksInterface(device_type)
.deviceCount();
}
return c10::DeviceIndex(-1);
});
py_module.def(
"_accelerator_hooks_set_current_device",
[](c10::DeviceIndex device_index) {
auto device_type = at::getAccelerator();
if (device_type.has_value()) {
at::globalContext()
.getAcceleratorHooksInterface(device_type)
.setCurrentDevice(device_index);
}
});
py_module.def("_accelerator_hooks_get_current_device", []() {
auto device_type = at::getAccelerator();
if (device_type.has_value()) {
return at::globalContext()
.getAcceleratorHooksInterface(device_type)
.getCurrentDevice();
}
return c10::DeviceIndex(-1);
});
py_module.def(
"_accelerator_hooks_exchange_device", [](c10::DeviceIndex device_index) {
auto device_type = at::getAccelerator();
if (device_type.has_value()) {
return at::globalContext()
.getAcceleratorHooksInterface(device_type)
.exchangeDevice(device_index);
}
return c10::DeviceIndex(-1);
});
py_module.def(
"_accelerator_hooks_maybe_exchange_device",
[](c10::DeviceIndex device_index) {
auto device_type = at::getAccelerator();
if (device_type.has_value()) {
return at::globalContext()
.getAcceleratorHooksInterface(device_type)
.maybeExchangeDevice(device_index);
}
return c10::DeviceIndex(-1);
});
py_module.def(
"_get_accelerator",
[](std::optional<bool> check = std::nullopt) {
auto acc = at::getAccelerator(check.value_or(false));
if (acc.has_value()) {
bool is_available = at::globalContext()
.getAcceleratorHooksInterface(acc)
.isAvailable();
if (!is_available) {
acc = std::nullopt;
}
}
return c10::Device(acc.value_or(c10::DeviceType::CPU), -1);
},
py::arg("check") = nullptr);
#ifdef USE_CUDA
PyObject* has_cuda = Py_True;
#else
PyObject* has_cuda = Py_False;
#endif
#ifdef USE_MPS
PyObject* has_mps = Py_True;
#else
PyObject* has_mps = Py_False;
#endif
#ifdef USE_XPU
PyObject* has_xpu = Py_True;
#else
PyObject* has_xpu = Py_False;
#endif
ASSERT_TRUE(set_module_attr("_has_cuda", has_cuda));
ASSERT_TRUE(
set_module_attr("_has_magma", at::hasMAGMA() ? Py_True : Py_False));
ASSERT_TRUE(set_module_attr("_has_mps", has_mps));
ASSERT_TRUE(set_module_attr("_has_xpu", has_xpu));
ASSERT_TRUE(
set_module_attr("_has_mkldnn", at::hasMKLDNN() ? Py_True : Py_False));
ASSERT_TRUE(set_module_attr("_GLIBCXX_USE_CXX11_ABI", Py_True));
py_module.def(
"_set_conj", [](const at::Tensor& x, bool conj) { x._set_conj(conj); });
py_module.def(
"_set_neg", [](const at::Tensor& x, bool neg) { x._set_neg(neg); });
py_module.def("_get_tensor_metadata", &torch::jit::getTensorMetadata);
py_module.def(
"_set_tensor_metadata",
static_cast<void (*)(
const at::Tensor&, std::unordered_map<std::string, bool>)>(
torch::jit::setTensorMetadata));
py_module.def("_dispatch_key_set", [](const at::Tensor& x) {
return toString(x.key_set());
});
py_module.def(
"_has_storage", [](const at::Tensor& x) { return x.has_storage(); });
py_module.def("_set_meta_in_tls_dispatch_include", [](bool meta_in_tls) {
auto local_keyset = c10::impl::tls_local_dispatch_key_set();
c10::DispatchKeySet key_set({at::DispatchKey::Meta});
if (meta_in_tls) {
local_keyset.included_ = local_keyset.included_ | key_set;
} else {
local_keyset.included_ =
local_keyset.included_.remove_backend(c10::BackendComponent::MetaBit);
}
c10::impl::_force_tls_local_dispatch_key_set(local_keyset);
});
py_module.def("_meta_in_tls_dispatch_include", []() {
auto local_keyset = c10::impl::tls_local_dispatch_key_set();
return local_keyset.included_.has_backend(c10::BackendComponent::MetaBit);
});
py_module.def("_dump_local_tls_set", []() {
auto local_keyset = c10::impl::tls_local_dispatch_key_set();
std::cout << "Included: " << toString(local_keyset.included_) << "\n";
std::cout << "Excluded: " << toString(local_keyset.excluded_) << "\n";
});
py_module.def(
"_should_allow_numbers_as_tensors", [](const std::string& name) {
return torch::should_allow_numbers_as_tensors(name);
});
py_module.def(
"_group_tensors_by_device_and_dtype",
[](const std::vector<std::vector<std::optional<at::Tensor>>>&
nested_tensorlist,
const bool with_indices) {
return at::native::_group_tensors_by_first_tensors_device_and_dtype(
nested_tensorlist, with_indices);
});
py_module.def(
"_storage_address",
[](const at::Tensor& tensor) {
return reinterpret_cast<std::intptr_t>(
tensor.storage().unsafeGetStorageImpl());
},
"Gets the memory address of the Tensor's StorageImpl.");
py_module.def(
"_data_address",
[](const at::Tensor& tensor) {
return reinterpret_cast<std::intptr_t>(tensor.storage().data());
},
"Gets the memory address of the Tensor's data pointer.");
py_module.def(
"_is_cow_tensor",
[](const at::Tensor& tensor) {
return c10::impl::cow::is_cow_data_ptr(tensor.storage().data_ptr());
},
"Checks if a tensor's data pointer is COW");
py_module.def(
"_get_cudnn_batch_norm_reserve_space_size",
[](const at::Tensor& input, bool training) {
#ifdef USE_CUDA
return at::native::_get_cudnn_batch_norm_reserve_space_size(
input, training);
#else
TORCH_CHECK(false, "PyTorch was not built with cuda");
#endif
},
py::arg("input"),
py::arg("training"));
py::enum_<at::native::BatchNormBackend>(py_module, "_BatchNormBackend")
.value("Native", at::native::BatchNormBackend::Native)
.value("Cudnn", at::native::BatchNormBackend::Cudnn)
.value("Miopen", at::native::BatchNormBackend::Miopen);
py_module.def(
"_select_batch_norm_backend",
[](const at::Tensor& input,
const at::Tensor& weight,
const at::Tensor& bias,
const at::Tensor& running_mean,
const at::Tensor& running_var,
bool training,
double eps) {
return at::native::_select_batch_norm_backend(
input, weight, bias, running_mean, running_var, training, eps);
},
py::arg("input"),
py::arg("weight"),
py::arg("bias"),
py::arg("running_mean"),
py::arg("running_var"),
py::arg("training"),
py::arg("eps"));
const auto& defaultGenerator = at::detail::getDefaultCPUGenerator();
THPDefaultCPUGenerator =
(THPGenerator*)THPGenerator_initDefaultGenerator(defaultGenerator);
// This reference is meant to be given away, so no need to incref here.
ASSERT_TRUE(set_module_attr(
"default_generator",
(PyObject*)THPDefaultCPUGenerator,
/* incref= */ false));
ASSERT_TRUE(set_module_attr(
"DisableTorchFunctionSubclass",
(PyObject*)THPModule_DisableTorchFunctionSubclassType(),
/* incref= */ false));
ASSERT_TRUE(set_module_attr(
"DisableTorchFunction",
(PyObject*)THPModule_DisableTorchFunctionType(),
/* incref= */ false));
py::enum_<at::impl::TorchFunctionDisabledState>(
py_module, "_TorchFunctionState")
.value("ENABLED", at::impl::TorchFunctionDisabledState::ENABLED)
.value(
"SUBCLASSES_DISABLED",
at::impl::TorchFunctionDisabledState::SUBCLASSES_DISABLED)
.value(
"ALL_DISABLED", at::impl::TorchFunctionDisabledState::ALL_DISABLED);
py_module.def(
"_set_torch_function_state",
[](at::impl::TorchFunctionDisabledState state) {
at::impl::PythonTorchFunctionTLS::set_disabled_state(state);
});
py_module.def("_get_torch_function_state", []() {
return at::impl::PythonTorchFunctionTLS::get_disabled_state();
});
torch::set_disabled_torch_function_impl(
PyObject_GetAttrString(module, "_disabled_torch_function_impl"));
ASSERT_TRUE(torch::disabled_torch_function_impl() != nullptr);
torch::set_disabled_torch_dispatch_impl(
PyObject_GetAttrString(module, "_disabled_torch_dispatch_impl"));
ASSERT_TRUE(torch::disabled_torch_dispatch_impl() != nullptr);
// init kineto here
#ifdef USE_KINETO
torch::global_kineto_init();
#endif
auto nativert_module = py_module.def_submodule("_nativert");
torch::nativert::initModelRunnerPybind(nativert_module);
return module;
END_HANDLE_TH_ERRORS
}
// Checks that the _C shared library isn't initialized multiple times. This
// can happen if the same csrc files are compiled into multiple shared
// libraries.
static void pytorch_duplicate_guard() {
static int initialized = 0;
if (initialized) {
fmt::print(stderr, "pytorch: _C shared library re-initialized\n");
abort();
}
initialized = 1;
;
}
struct call_duplicate_guard {
call_duplicate_guard() {
pytorch_duplicate_guard();
}
};
static call_duplicate_guard _call_duplicate_guard;
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