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pytorch/torch/csrc/utils/disable_torch_function.cpp

386 lines
12 KiB
C++

#include <torch/csrc/Exceptions.h>
#include <torch/csrc/autograd/python_variable.h>
#include <torch/csrc/utils/disable_torch_function.h>
#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/python_strings.h>
#include <ATen/PythonTorchFunctionTLS.h>
namespace torch {
PyObject* disabled_torch_function = nullptr;
PyObject* disabled_torch_dispatch = nullptr;
bool torch_function_enabled() {
return at::impl::PythonTorchFunctionTLS::get_disabled_state() ==
at::impl::TorchFunctionDisabledState::ENABLED;
}
PyObject* disabled_torch_function_impl() {
return disabled_torch_function;
}
void set_disabled_torch_function_impl(PyObject* value) {
disabled_torch_function = value;
}
PyObject* disabled_torch_dispatch_impl() {
return disabled_torch_dispatch;
}
void set_disabled_torch_dispatch_impl(PyObject* value) {
disabled_torch_dispatch = value;
}
} // namespace torch
typedef struct {
PyObject_HEAD
/* Type-specific fields go here. */
at::impl::TorchFunctionDisabledState old_state;
} DisableTorchFunctionSubclass;
PyObject* DisableTorchFunctionSubclass__enter(
PyObject* self,
PyObject* unused) {
const auto old_state = at::impl::PythonTorchFunctionTLS::get_disabled_state();
((DisableTorchFunctionSubclass*)self)->old_state = old_state;
if (old_state == at::impl::TorchFunctionDisabledState::ENABLED) {
at::impl::PythonTorchFunctionTLS::set_disabled_state(
at::impl::TorchFunctionDisabledState::SUBCLASSES_DISABLED);
}
Py_RETURN_NONE;
}
PyObject* DisableTorchFunctionSubclass__exit(PyObject* self, PyObject* unused) {
at::impl::PythonTorchFunctionTLS::set_disabled_state(
((DisableTorchFunctionSubclass*)self)->old_state);
Py_RETURN_NONE;
}
PyObject* THPModule_isEnabledTorchFunction(PyObject* self, PyObject* unused) {
if (torch::torch_function_enabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
}
PyObject* THPModule_isAllDisabledTorchFunction(
PyObject* self,
PyObject* unused) {
if (at::impl::torch_function_all_disabled()) {
Py_RETURN_TRUE;
} else {
Py_RETURN_FALSE;
}
}
static PyMethodDef DisableTorchFunctionSubclass_methods[] = { // NOLINT
{"__enter__", DisableTorchFunctionSubclass__enter, METH_NOARGS, nullptr},
{"__exit__", DisableTorchFunctionSubclass__exit, METH_VARARGS, nullptr},
{nullptr, nullptr, 0, nullptr}};
PyTypeObject DisableTorchFunctionSubclassType = {
PyVarObject_HEAD_INIT(nullptr, 0)
"torch._C.DisableTorchFunctionSubclass", /* tp_name */
sizeof(DisableTorchFunctionSubclass), /* tp_basicsize */
0, /* tp_itemsize */
nullptr, /* tp_dealloc */
0, /* tp_vectorcall_offset */
nullptr, /* tp_getattr */
nullptr, /* tp_setattr */
nullptr, /* tp_reserved */
nullptr, /* tp_repr */
nullptr, /* tp_as_number */
nullptr, /* tp_as_sequence */
nullptr, /* tp_as_mapping */
nullptr, /* tp_hash */
nullptr, /* tp_call */
nullptr, /* tp_str */
nullptr, /* tp_getattro */
nullptr, /* tp_setattro */
nullptr, /* tp_as_buffer */
Py_TPFLAGS_DEFAULT, /* tp_flags */
nullptr, /* tp_doc */
nullptr, /* tp_traverse */
nullptr, /* tp_clear */
nullptr, /* tp_richcompare */
0, /* tp_weaklistoffset */
nullptr, /* tp_iter */
nullptr, /* tp_iternext */
DisableTorchFunctionSubclass_methods, /* tp_methods */
nullptr, /* tp_members */
nullptr, /* tp_getset */
nullptr, /* tp_base */
nullptr, /* tp_dict */
nullptr, /* tp_descr_get */
nullptr, /* tp_descr_set */
0, /* tp_dictoffset */
nullptr, /* tp_init */
PyType_GenericAlloc, /* tp_alloc */
PyType_GenericNew, /* tp_new */
};
PyObject* THPModule_DisableTorchFunctionSubclassType() {
if (PyType_Ready(&DisableTorchFunctionSubclassType) < 0) {
return nullptr;
}
return (PyObject*)(&DisableTorchFunctionSubclassType);
}
typedef struct {
PyObject_HEAD
/* Type-specific fields go here. */
at::impl::TorchFunctionDisabledState old_state;
} DisableTorchFunction;
PyObject* DisableTorchFunction__enter(PyObject* self, PyObject* unused) {
((DisableTorchFunctionSubclass*)self)->old_state =
at::impl::PythonTorchFunctionTLS::get_disabled_state();
at::impl::PythonTorchFunctionTLS::set_disabled_state(
at::impl::TorchFunctionDisabledState::ALL_DISABLED);
Py_RETURN_NONE;
}
PyObject* DisableTorchFunction__exit(PyObject* self, PyObject* unused) {
at::impl::PythonTorchFunctionTLS::set_disabled_state(
((DisableTorchFunctionSubclass*)self)->old_state);
Py_RETURN_NONE;
}
static PyMethodDef DisableTorchFunction_methods[] = { // NOLINT
{"__enter__", DisableTorchFunction__enter, METH_NOARGS, nullptr},
{"__exit__", DisableTorchFunction__exit, METH_VARARGS, nullptr},
{nullptr, nullptr, 0, nullptr}};
PyTypeObject DisableTorchFunctionType = {
PyVarObject_HEAD_INIT(nullptr, 0)
"torch._C.DisableTorchFunction", /* tp_name */
sizeof(DisableTorchFunction), /* tp_basicsize */
0, /* tp_itemsize */
nullptr, /* tp_dealloc */
0, /* tp_vectorcall_offset */
nullptr, /* tp_getattr */
nullptr, /* tp_setattr */
nullptr, /* tp_reserved */
nullptr, /* tp_repr */
nullptr, /* tp_as_number */
nullptr, /* tp_as_sequence */
nullptr, /* tp_as_mapping */
nullptr, /* tp_hash */
nullptr, /* tp_call */
nullptr, /* tp_str */
nullptr, /* tp_getattro */
nullptr, /* tp_setattro */
nullptr, /* tp_as_buffer */
Py_TPFLAGS_DEFAULT, /* tp_flags */
nullptr, /* tp_doc */
nullptr, /* tp_traverse */
nullptr, /* tp_clear */
nullptr, /* tp_richcompare */
0, /* tp_weaklistoffset */
nullptr, /* tp_iter */
nullptr, /* tp_iternext */
DisableTorchFunction_methods, /* tp_methods */
nullptr, /* tp_members */
nullptr, /* tp_getset */
nullptr, /* tp_base */
nullptr, /* tp_dict */
nullptr, /* tp_descr_get */
nullptr, /* tp_descr_set */
0, /* tp_dictoffset */
nullptr, /* tp_init */
PyType_GenericAlloc, /* tp_alloc */
PyType_GenericNew, /* tp_new */
};
PyObject* THPModule_DisableTorchFunctionType() {
if (PyType_Ready(&DisableTorchFunctionType) < 0) {
return nullptr;
}
return (PyObject*)(&DisableTorchFunctionType);
}
PyObject* THPModule_disable_torch_function(PyObject* self, PyObject* a) {
HANDLE_TH_ERRORS
PyObject *func = nullptr, *types = nullptr, *args = nullptr,
*kwargs = nullptr;
if (!PyArg_ParseTuple(a, "OO|OO", &func, &types, &args, &kwargs)) {
return nullptr;
}
py::tuple py_args;
if (args == nullptr) {
py_args = py::make_tuple();
} else if (PyList_Check(args)) {
py_args = py::reinterpret_steal<py::tuple>(PyList_AsTuple(args));
} else if (PyTuple_Check(args)) {
py_args = py::reinterpret_borrow<py::tuple>(args);
} else {
throw torch::TypeError(
"expected List or Tuple (got %s)", Py_TYPE(args)->tp_name);
}
// These are all C-API calls so no exceptions will be raised
// and therefore no need for RAII approach to storing
// the old value.
auto old_value = at::impl::PythonTorchFunctionTLS::get_disabled_state();
if (old_value == at::impl::TorchFunctionDisabledState::ENABLED) {
at::impl::PythonTorchFunctionTLS::set_disabled_state(
at::impl::TorchFunctionDisabledState::SUBCLASSES_DISABLED);
}
// kwargs can safely be nullptr here.
PyObject* result = PyObject_Call(func, py_args.ptr(), kwargs);
at::impl::PythonTorchFunctionTLS::set_disabled_state(old_value);
return result;
END_HANDLE_TH_ERRORS
}
PyObject* THPModule_disable_torch_dispatch(PyObject* self, PyObject* a) {
HANDLE_TH_ERRORS
PyObject *func = nullptr, *types = nullptr, *args = nullptr,
*kwargs = nullptr;
if (!PyArg_ParseTuple(a, "OO|OO", &func, &types, &args, &kwargs)) {
return nullptr;
}
py::tuple py_args;
if (args == nullptr) {
py_args = py::make_tuple();
} else if (PyList_Check(args)) {
py_args = py::reinterpret_steal<py::tuple>(PyList_AsTuple(args));
} else if (PyTuple_Check(args)) {
py_args = py::reinterpret_borrow<py::tuple>(args);
} else {
throw torch::TypeError(
"expected List or Tuple (got %s)", Py_TYPE(args)->tp_name);
}
// This implementation is not completely correct. The moral
// meaning of this function is that we should do a redispatch
// "after" PythonKey, aka a redispatch() call. But we don't have a
// dispatcher call here; we have an opaque Python object.
//
// What we have here is a close approximation: instead of redispatch(), we
// just exclude Python and all the keys before it, so that we will go
// to the next key after Python. The difference, however, is we are
// now PERMANENTLY after Python. We don't think there are any legitimate
// cases where we want to go for another round on the entire dispatcher key
// set, but if there are, then we will have to do something else here.
c10::impl::ExcludeDispatchKeyGuard guard_(
// TODO: add constructor for this specifically
c10::DispatchKeySet(c10::DispatchKeySet::FULL) -
c10::DispatchKeySet(
c10::DispatchKeySet::FULL_AFTER, c10::DispatchKey::Python)
// NB: off by one hazard here, but it works out: python key is not
// included in AFTER, so it is included in the negation (and that's
// correct: we want to exclude Python key and everything BEFORE it.)
);
auto r = PyObject_Call(func, py_args.ptr(), kwargs);
if (r == nullptr)
throw python_error();
return r;
END_HANDLE_TH_ERRORS
}
// Makes sure that we don't check for __torch_function__ on basic Python types
static bool is_basic_python_type(PyTypeObject* tp) {
return (
/* Basic number types */
tp == &PyBool_Type ||
tp == &PyLong_Type || tp == &PyFloat_Type || tp == &PyComplex_Type ||
/* Basic sequence types */
tp == &PyList_Type || tp == &PyTuple_Type || tp == &PyDict_Type ||
tp == &PySet_Type || tp == &PyFrozenSet_Type || tp == &PyUnicode_Type ||
tp == &PyBytes_Type ||
/* other builtins */
tp == &PySlice_Type || tp == Py_TYPE(Py_None) ||
tp == Py_TYPE(Py_Ellipsis) || tp == Py_TYPE(Py_NotImplemented) ||
PyModule_Check(tp) ||
/* sentinel to swallow trailing || */
false);
}
inline bool has_torch_function_attr(PyObject* obj) {
auto attr = PyObject_FastGetAttrString(obj, "__torch_function__");
return (
attr.ptr() != nullptr && attr.ptr() != torch::disabled_torch_function);
}
namespace torch {
auto check_has_torch_function(PyObject* obj, bool ignore_mode) -> bool {
if (!ignore_mode && at::impl::torch_function_mode_enabled())
return true;
PyTypeObject* tp = Py_TYPE(obj);
return (
!THPVariable_CheckTypeExact(tp) && !is_basic_python_type(tp) &&
torch::torch_function_enabled() && has_torch_function_attr(obj));
}
} // namespace torch
inline bool sequence_has_torch_function(PyObject* args) {
// NOLINTNEXTLINE(bugprone-branch-clone)
Py_ssize_t nargs = PySequence_Fast_GET_SIZE(args);
for (Py_ssize_t i = 0; i < nargs; i++) {
PyObject* obj = PySequence_Fast_GET_ITEM(args, i);
if (torch::check_has_torch_function(obj)) {
return true;
}
}
return false;
}
inline bool array_has_torch_function(PyObject* const* args, Py_ssize_t nargs) {
for (Py_ssize_t i = 0; i < nargs; i++) {
if (torch::check_has_torch_function(args[i])) {
return true;
}
}
return false;
}
PyObject* THPModule_has_torch_function(PyObject*, PyObject* arg) {
bool result; // NOLINT(cppcoreguidelines-init-variables)
if (PyTuple_CheckExact(arg) || PyList_CheckExact(arg)) {
// Fast path:
// If we know that we have a tuple or list, we can skip an INCREF and
// DECREF from PySequence_Fast. Core functions will always follow this
// convention (almost always tuples), and it shaves ~3.5% off the cost of
// the check.
result = sequence_has_torch_function(arg);
} else {
auto args = py::reinterpret_steal<py::object>(
PySequence_Fast(arg, "expected a sequence"));
if (!args) {
return nullptr;
}
result = sequence_has_torch_function(args.ptr());
}
if (result) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
PyObject* THPModule_has_torch_function_unary(PyObject*, PyObject* obj) {
// Special case `THPModule_has_torch_function` for the single arg case.
if (torch::check_has_torch_function(obj)) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}
PyObject* THPModule_has_torch_function_variadic(
PyObject*,
PyObject* const* args,
Py_ssize_t nargs) {
if (array_has_torch_function(args, nargs)) {
Py_RETURN_TRUE;
}
Py_RETURN_FALSE;
}