frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-10-21 05:34:18 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
14744e1ab2f3b9226cc2f7d6a05311826e34354a
pytorch/torch/csrc/jit/python/pybind_utils.cpp
Scott Wolchok a8a187b2cf Overload _get_operation_for_overload_or_packet & friends to accept ArrayRef (#162219) 
Avoids requiring vector allocation to call this.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162219
Approved by: https://github.com/Skylion007
ghstack dependencies: #161591, #161595, #161633, #161634, #161692
2025-09-09 01:10:06 +00:00

955 lines
34 KiB
C++
Raw Blame History

#include <torch/csrc/jit/ir/graph_utils.h>
#include <torch/csrc/jit/python/module_python.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <torch/csrc/jit/python/python_dict.h>
#include <torch/csrc/jit/python/python_ivalue.h>
#include <torch/csrc/jit/python/python_list.h>
#include <torch/csrc/jit/python/utf8_decoding_ignore.h>
#include <ATen/ScalarOps.h>
#include <c10/core/QScheme.h>
#include <c10/util/irange.h>
#include <torch/csrc/utils/python_arg_parser.h>
#include <limits>
#include <optional>
#include <utility>
namespace torch::jit {
static thread_local bool allow_numbers_as_tensors = false;
ToIValueAllowNumbersAsTensors::ToIValueAllowNumbersAsTensors(bool enable)
: old_(allow_numbers_as_tensors) {
allow_numbers_as_tensors = enable;
}
ToIValueAllowNumbersAsTensors::~ToIValueAllowNumbersAsTensors() {
allow_numbers_as_tensors = old_;
}
// This is a hack to remove instances deleted in C++ from the PyBind cache
// C++->Python. We need this because otherwise we may get the old Python object
// if C++ creates a new object at the memory location of the deleted object.
void clear_registered_instances(void* ptr) {
#if IS_PYBIND_2_13_PLUS
py::detail::with_instance_map(
ptr, [&](py::detail::instance_map& registered_instances) {
auto range = registered_instances.equal_range(ptr);
for (auto it = range.first; it != range.second; ++it) {
auto vh = it->second->get_value_and_holder();
vh.set_instance_registered(false);
}
registered_instances.erase(ptr);
});
#else
auto& registered_instances =
pybind11::detail::get_internals().registered_instances;
auto range = registered_instances.equal_range(ptr);
for (auto it = range.first; it != range.second; ++it) {
auto vh = it->second->get_value_and_holder();
vh.set_instance_registered(false);
}
registered_instances.erase(ptr);
#endif
}
// WARNING: Precondition for this function is that, e.g., you have tested if a
// SymIntList is in fact only ints, and if so, you called this with T=int64_t.
// This precondition is NOT checked at runtime.
template <typename T>
static IValue listToIValue(py::handle obj) {
c10::List<T> rs;
for (auto it = obj.begin(); it != obj.end(); it++) {
auto elm = *it;
rs.push_back(py::cast<T>(elm));
}
// Promises that we have decayed the list appropriately
return c10::impl::toList<T>(rs);
}
IValue toIValue(py::handle obj, const TypePtr& type, std::optional<int32_t> N) {
switch (type->kind()) {
case TypeKind::TensorType: {
if (obj.ptr() == Py_None) {
// None gets converted to undefined Tensors
return autograd::Variable();
}
if (THPVariable_Check(obj.ptr())) {
auto var = py::cast<autograd::Variable>(obj);
guardAgainstNamedTensor<autograd::Variable>(var);
return var;
} else {
if (!allow_numbers_as_tensors) {
throw py::cast_error(
c10::str("Unable to cast ", py::str(obj), " to Tensor"));
}
bool save_symint = false;
at::Scalar scalar;
if (PyBool_Check(obj.ptr())) {
scalar = at::Scalar(THPUtils_unpackBool(obj.ptr()));
} else if (THPUtils_checkLong(obj.ptr())) {
scalar = THPUtils_unpackInteger<at::Scalar>(obj.ptr());
} else if (PyComplex_Check(obj.ptr())) {
scalar = at::Scalar(THPUtils_unpackComplexDouble(obj.ptr()));
} else if (THPUtils_checkDouble(obj.ptr())) {
scalar = at::Scalar(THPUtils_unpackDouble(obj.ptr()));
} else if (torch::is_symint(py::handle(obj))) {
save_symint = true;
scalar = at::Scalar(7777777);
} else if (torch::is_symfloat(py::handle(obj))) {
save_symint = true;
scalar = at::Scalar(std::numeric_limits<double>::quiet_NaN());
} else if (torch::is_symbool(py::handle(obj))) {
save_symint = true;
scalar = at::Scalar(true);
} else {
throw py::cast_error(
c10::str("Unable to cast ", py::str(obj), " to Tensor"));
}
at::Tensor tensor = at::scalar_to_tensor(scalar);
tensor.unsafeGetTensorImpl()->set_wrapped_number(true);
if (save_symint) {
auto py_tensor = py::cast(tensor);
if (PyObject_SetAttrString(
py_tensor.ptr(), "_wrapped_number", obj.ptr()) < 0) {
throw python_error();
}
}
return tensor;
}
}
case TypeKind::StorageType:
return py::cast<at::Storage>(obj);
case TypeKind::FloatType:
if (torch::is_symfloat(py::handle(obj))) {
return py::cast<c10::SymFloat>(obj).guard_float(__FILE__, __LINE__);
}
if (THPVariable_Check(obj.ptr())) {
auto var = py::cast<autograd::Variable>(obj);
// NB: We carefully test if the storage is meta, because that is
// always accurate even if you have a fake tensor (which is the
// primary case we are trying to detect here)
if (var.storage().device_type() == c10::kMeta) {
throw py::cast_error(
"cannot extract float from tensor with meta storage");
}
}
return py::cast<double>(obj);
case TypeKind::ComplexType: {
auto c_obj = py::cast<std::complex<double>>(obj.ptr());
return static_cast<c10::complex<double>>(c_obj);
}
case TypeKind::IntType:
// TODO: Properly fake this type
if (THPQScheme_Check(obj.ptr())) {
auto qscheme = reinterpret_cast<THPQScheme*>(obj.ptr());
return static_cast<uint8_t>(qscheme->qscheme);
}
// For backwards compatibility
if (THPDtype_Check(obj.ptr())) {
auto dtype = reinterpret_cast<THPDtype*>(obj.ptr());
return static_cast<int64_t>(dtype->scalar_type);
}
if (THPQScheme_Check(obj.ptr())) {
auto qscheme = reinterpret_cast<THPQScheme*>(obj.ptr());
return static_cast<uint8_t>(qscheme->qscheme);
}
if (THPLayout_Check(obj.ptr())) {
auto layout = reinterpret_cast<THPLayout*>(obj.ptr());
return static_cast<int8_t>(layout->layout);
}
if (THPMemoryFormat_Check(obj.ptr())) {
auto memory_format = reinterpret_cast<THPMemoryFormat*>(obj.ptr());
return static_cast<int8_t>(memory_format->memory_format);
}
if (torch::is_symint(py::handle(obj))) {
return py::cast<c10::SymInt>(obj).guard_int(__FILE__, __LINE__);
}
if (THPVariable_Check(obj.ptr())) {
auto var = py::cast<autograd::Variable>(obj);
if (var.storage().device_type() == c10::kMeta) {
throw py::cast_error(
"cannot extract int from tensor with meta storage");
}
}
return py::cast<int64_t>(obj);
case TypeKind::LayoutType: {
if (THPLayout_Check(obj.ptr())) {
auto layout = reinterpret_cast<THPLayout*>(obj.ptr());
return static_cast<int8_t>(layout->layout);
}
// For backwards compatibility
return py::cast<int64_t>(obj);
}
case TypeKind::ScalarTypeType: {
if (THPDtype_Check(obj.ptr())) {
auto dtype = reinterpret_cast<THPDtype*>(obj.ptr());
return static_cast<int64_t>(dtype->scalar_type);
}
// For backwards compatibility
return py::cast<int64_t>(obj);
}
case TypeKind::MemoryFormatType: {
if (THPMemoryFormat_Check(obj.ptr())) {
auto memory_format = reinterpret_cast<THPMemoryFormat*>(obj.ptr());
return static_cast<int8_t>(memory_format->memory_format);
}
// For backwards compatibility
return py::cast<int64_t>(obj);
}
case TypeKind::SymIntType:
if (torch::is_symint(obj.ptr())) {
return py::cast<c10::SymInt>(obj);
}
return py::cast<int64_t>(obj);
case TypeKind::SymFloatType:
if (torch::is_symfloat(obj.ptr())) {
return py::cast<c10::SymFloat>(obj);
}
return py::cast<double>(obj);
case TypeKind::SymBoolType:
if (torch::is_symbool(obj.ptr())) {
return py::cast<c10::SymBool>(obj);
}
return py::cast<bool>(obj);
case TypeKind::NoneType:
if (!obj.is_none()) {
throw py::cast_error(
c10::str("Cannot cast ", py::str(obj), " to None"));
}
return {};
case TypeKind::BoolType:
if (torch::is_symbool(obj.ptr())) {
return py::cast<c10::SymBool>(obj).guard_bool(__FILE__, __LINE__);
}
if (THPVariable_Check(obj.ptr())) {
auto var = py::cast<autograd::Variable>(obj);
if (var.storage().device_type() == c10::kMeta) {
throw py::cast_error(
"cannot extract bool from tensor with meta storage");
}
}
return py::cast<bool>(obj);
case TypeKind::TupleType: {
py::tuple tuple = py::cast<py::tuple>(obj);
size_t tuple_size = tuple.size();
auto tuple_type = type->cast<TupleType>();
const auto& elem_types = tuple_type->elements();
if (elem_types.size() != tuple_size) {
throw py::cast_error(c10::str(
"Object ",
py::str(obj),
" had a different number of elements than type ",
type->repr_str()));
}
std::vector<IValue> values;
values.reserve(tuple_size);
for (const auto i : c10::irange(tuple_size)) {
values.push_back(toIValue(tuple[i], elem_types[i]));
}
return tuple_type->name()
? c10::ivalue::Tuple::createNamed(std::move(values), tuple_type)
: c10::ivalue::Tuple::create(std::move(values));
}
case TypeKind::UnionType: {
auto actual_type = toTypeInferredIValue(obj);
auto actual_type_ptr = actual_type.type();
auto union_type = type->expect<UnionType>();
if (!actual_type_ptr->isSubtypeOf(union_type)) {
throw py::cast_error(c10::str(
"Expected a member of ",
union_type->annotation_str(),
" but instead found type ",
actual_type.type()->annotation_str()));
}
return actual_type;
}
case TypeKind::StringType:
return ConstantString::create(py::cast<std::string>(obj));
case TypeKind::DeviceObjType: {
if (THPDevice_Check(obj.ptr())) {
auto device = reinterpret_cast<THPDevice*>(obj.ptr());
return device->device;
}
return c10::Device(py::cast<std::string>(obj.ptr()));
}
case TypeKind::StreamObjType: {
auto thp_stream = reinterpret_cast<THPStream*>(obj.ptr());
auto stream = c10::Stream::unpack3(
thp_stream->stream_id,
static_cast<c10::DeviceIndex>(thp_stream->device_index),
static_cast<c10::DeviceType>(thp_stream->device_type));
return stream;
}
case TypeKind::ListType: {
// If the object is a ScriptList, retrieve the c10::List
// instance inside it.
if (py::isinstance<ScriptList>(obj)) {
return py::cast<ScriptList>(obj).list_;
}
// If not (i.e. it is a regular Python list), make a new
// c10::List.
const auto& elem_type = type->expectRef<ListType>().getElementType();
switch (elem_type->kind()) {
// allows single int/float to be broadcasted to a fixed size list
case TypeKind::IntType:
if (!N || !py::isinstance<py::int_>(obj)) {
return IValue(py::cast<std::vector<int64_t>>(obj));
} else {
int64_t value = py::cast<int64_t>(obj);
c10::List<int64_t> repeated;
repeated.reserve(*N);
for (int i = 0; i < *N; ++i) {
repeated.push_back(value);
}
return repeated;
}
case TypeKind::SymIntType: {
bool is_symbolic = false;
for (auto it = obj.begin(); it != obj.end(); it++) {
auto elm = *it;
if (torch::is_symint(elm) || THPVariable_Check(elm.ptr())) {
is_symbolic = true;
break;
}
}
if (is_symbolic) {
return listToIValue<c10::SymInt>(obj);
} else {
return listToIValue<int64_t>(obj);
}
}
case TypeKind::SymFloatType: {
bool is_symbolic = false;
for (auto it = obj.begin(); it != obj.end(); it++) {
auto elm = *it;
// TODO: what about SymInt conversion to SymFloat?
if (torch::is_symfloat(elm)) {
is_symbolic = true;
break;
}
}
if (is_symbolic) {
return listToIValue<c10::SymFloat>(obj);
} else {
return listToIValue<double>(obj);
}
}
case TypeKind::SymBoolType: {
bool is_symbolic = false;
for (auto it = obj.begin(); it != obj.end(); it++) {
auto elm = *it;
if (torch::is_symbool(elm)) {
is_symbolic = true;
break;
}
}
if (is_symbolic) {
return listToIValue<c10::SymBool>(obj);
} else {
return listToIValue<bool>(obj);
}
}
case TypeKind::FloatType:
if (!N || !py::isinstance<py::float_>(obj)) {
return IValue(py::cast<std::vector<double>>(obj));
} else {
double value = py::cast<double>(obj);
c10::List<double> repeated;
repeated.reserve(*N);
for (int i = 0; i < *N; ++i) {
repeated.push_back(value);
}
return repeated;
}
case TypeKind::BoolType:
return IValue(py::cast<std::vector<bool>>(obj));
case TypeKind::TensorType:
return IValue(py::cast<std::vector<at::Tensor>>(obj));
default:
return createGenericList(obj, elem_type);
}
}
case TypeKind::DictType: {
const auto& dict_type = type->expect<DictType>();
// If the object is a ScriptDict, retrieve the c10::Dict
// instance inside it.
try {
auto script_dict = py::cast<ScriptDict>(obj);
return script_dict.dict_;
} catch (py::cast_error& e) {
}
// If not (i.e. it is a regular Python dictionary), make a new
// c10::Dict.
return createGenericDict(
py::cast<py::dict>(obj),
dict_type->getKeyType(),
dict_type->getValueType());
}
case TypeKind::OptionalType: {
// check if it's a none obj since optional accepts NoneType
if (obj.is_none()) {
// check if it's a none obj since optional accepts NoneType
// return an IValue() to denote a NoneType
return {};
}
return toIValue(obj, type->expectRef<OptionalType>().getElementType(), N);
}
case TypeKind::ClassType: {
auto classType = type->expect<ClassType>();
auto object = py::cast<py::object>(obj);
if (auto mod = as_module(object)) {
// if obj is already a ScriptModule, just return its ivalue
return mod.value()._ivalue();
}
// Check if the obj is a ScriptObject.
if (auto script_obj = as_object(object)) {
return script_obj.value()._ivalue();
}
// otherwise is a normal class object, we create a fresh
// ivalue::Object to use from the py object.
// 1. create a bare ivalue
const size_t numAttrs = classType->numAttributes();
auto cu = classType->compilation_unit();
auto userObj = c10::ivalue::Object::create(
c10::StrongTypePtr(cu, classType), numAttrs);
// 2. copy all the contained types
for (const auto slot : c10::irange(numAttrs)) {
const auto& attrType = classType->getAttribute(slot);
const auto& attrName = classType->getAttributeName(slot);
if (!py::hasattr(obj, attrName.c_str())) {
throw py::cast_error(c10::str(
"Tried to cast object to type ",
type->repr_str(),
" but object",
" was missing attribute ",
attrName));
}
try {
const auto& contained = py::getattr(obj, attrName.c_str());
userObj->setSlot(slot, toIValue(contained, attrType));
} catch (std::exception& e) {
throw py::cast_error(c10::str(
"Could not cast attribute '",
attrName,
"' to type ",
attrType->repr_str(),
": ",
e.what()));
}
}
return userObj;
}
case TypeKind::InterfaceType: {
auto interfaceType = type->expect<InterfaceType>();
// When converting an pyobj to an interface, we check if rhs
// is module or normal torchscript class, get the type and ivalue
// from them correspondingly.
c10::ClassTypePtr classType = nullptr;
IValue res;
if (auto mod = as_module(py::cast<py::object>(obj))) {
classType = mod.value().type();
res = mod.value()._ivalue();
} else if (auto object = as_object(py::cast<py::object>(obj))) {
classType = object.value().type();
res = object.value()._ivalue();
} else {
// We inspect the value to found the compiled TorchScript class
// and then create a ivalue::Object from that class type.
py::str qualified_name =
py::module::import("torch._jit_internal")
.attr("_qualified_name")(py::type::handle_of(obj));
auto pyCu = get_python_cu();
classType = pyCu->get_class(c10::QualifiedName(qualified_name));
if (!classType) {
throw std::runtime_error(c10::str(
"Assigning the object ",
py::str(obj),
" to an interface fails because the value is not "
"a TorchScript compatible type, did you forget to",
"turn it into a user defined TorchScript class?"));
}
res = toIValue(obj, classType);
}
// check if the classType conform with the interface or not
std::stringstream why_not;
if (!classType->isSubtypeOfExt(*interfaceType, &why_not)) {
throw py::cast_error(c10::str(
"Object of type ",
classType->repr_str(),
" is not compatible with interface ",
interfaceType->repr_str(),
"\n",
why_not.str()));
}
return res;
}
case TypeKind::NumberType: {
if (THPDtype_Check(obj.ptr())) {
auto dtype = reinterpret_cast<THPDtype*>(obj.ptr());
return static_cast<int64_t>(dtype->scalar_type);
}
if (THPQScheme_Check(obj.ptr())) {
auto qscheme = reinterpret_cast<THPQScheme*>(obj.ptr());
return static_cast<uint8_t>(qscheme->qscheme);
}
if (THPLayout_Check(obj.ptr())) {
auto layout = reinterpret_cast<THPLayout*>(obj.ptr());
return static_cast<int8_t>(layout->layout);
}
if (py::isinstance<py::bool_>(obj)) {
return py::cast<bool>(obj);
} else if (py::isinstance<py::int_>(obj)) {
return THPUtils_unpackInteger<IValue>(obj.ptr());
} else if (py::isinstance<py::float_>(obj)) {
return py::cast<double>(obj);
} else if (PyComplex_CheckExact(obj.ptr())) {
auto c_obj = py::cast<std::complex<double>>(obj.ptr());
return static_cast<c10::complex<double>>(c_obj);
} else if (torch::is_symint(obj)) {
return py::cast<c10::SymInt>(obj);
} else if (torch::is_symfloat(obj)) {
return py::cast<c10::SymFloat>(obj);
} else if (torch::is_symbool(obj)) {
return py::cast<c10::SymBool>(obj);
} else {
throw py::cast_error(
c10::str("Cannot cast ", py::str(obj), " to ", type->repr_str()));
}
}
case TypeKind::RRefType: {
#ifdef USE_RPC
return obj.cast<torch::distributed::rpc::PyRRef>().toIValue();
#else
TORCH_CHECK(false, "RRef is only supported with the distributed package");
#endif
} break;
case TypeKind::PyObjectType: {
return c10::ivalue::ConcretePyObjectHolder::create(obj);
}
case TypeKind::CapsuleType: {
return IValue::make_capsule(py::cast<c10::Capsule>(obj).obj_ptr);
}
case TypeKind::FutureType: {
return obj.cast<std::shared_ptr<PythonFutureWrapper>>()->fut;
}
case TypeKind::AwaitType: {
return obj.cast<std::shared_ptr<PythonAwaitWrapper>>()->aw_;
}
case TypeKind::AnyType:
return toTypeInferredIValue(obj);
case TypeKind::QSchemeType: {
if (py::isinstance<py::int_>(obj)) {
return static_cast<at::QScheme>(py::cast<int64_t>(obj));
}
throw py::cast_error(
c10::str("Cannot cast ", py::str(obj), " to ", type->repr_str()));
}
case TypeKind::GeneratorType:
return py::cast<at::Generator>(obj);
case TypeKind::DynamicType:
case TypeKind::FunctionType:
case TypeKind::QuantizerType:
case TypeKind::VarType:
case TypeKind::AnyListType:
case TypeKind::AnyTupleType:
case TypeKind::AnyClassType:
case TypeKind::AnyEnumType:
break;
case TypeKind::EnumType:
EnumTypePtr enum_type = type->expect<EnumType>();
py::object py_obj = py::reinterpret_borrow<py::object>(obj);
std::string name = py::cast<std::string>(obj.attr("name"));
IValue value = toIValue(obj.attr("value"), enum_type->getValueType(), {});
auto enum_holder =
c10::make_intrusive<c10::ivalue::EnumHolder>(enum_type, name, value);
return IValue(enum_holder);
}
throw py::cast_error(c10::str(
"toIValue() cannot handle converting to type: ", type->repr_str()));
}
py::object toPyObject(IValue ivalue) {
if (ivalue.isNone()) {
return py::none();
} else if (ivalue.isTensor()) {
auto tensor = std::move(ivalue).toTensor();
if (tensor.unsafeGetTensorImpl()->is_wrapped_number()) {
TORCH_INTERNAL_ASSERT(tensor.device().is_cpu());
auto py_tensor = py::cast(tensor);
if (PyObject_HasAttrString(py_tensor.ptr(), "_wrapped_number")) {
return py_tensor.attr("_wrapped_number");
}
auto scalar_type = tensor.scalar_type();
switch (scalar_type) {
case at::ScalarType::Bool:
return py::cast(*tensor.const_data_ptr<bool>());
case at::ScalarType::Long:
return py::cast(*tensor.const_data_ptr<int64_t>());
case at::ScalarType::UInt64:
return py::cast(*tensor.const_data_ptr<uint64_t>());
case at::ScalarType::Double:
return py::cast(*tensor.const_data_ptr<double>());
case at::ScalarType::ComplexDouble:
// TODO: https://github.com/pytorch/pytorch/issues/77134
return py::cast(static_cast<std::complex<double>>(
*tensor.const_data_ptr<c10::complex<double>>()));
default:
TORCH_CHECK(
false,
"Missing cases in 'toPyObject' wrapped number handling! Can't convert ",
scalar_type,
" to a Python object");
}
} else {
guardAgainstNamedTensor<at::Tensor>(tensor);
return py::cast(std::move(tensor));
}
} else if (ivalue.isStorage()) {
return py::cast(std::move(ivalue).toStorage());
} else if (ivalue.isGenerator()) {
return py::cast(std::move(ivalue).toGenerator());
} else if (ivalue.isDouble()) {
return py::cast(std::move(ivalue).toDouble());
} else if (ivalue.isComplexDouble()) {
return py::cast(
static_cast<std::complex<double>>(std::move(ivalue).toComplexDouble()));
} else if (ivalue.isInt()) {
return py::cast(std::move(ivalue).toInt());
} else if (ivalue.isBool()) {
return py::cast(std::move(ivalue).toBool());
} else if (ivalue.isString()) {
if (getUTF8DecodingIgnore()) {
std::string s = std::move(ivalue).toStringRef();
PyObject* pyObj = PyUnicode_DecodeUTF8(s.data(), s.length(), "ignore");
return py::reinterpret_steal<py::object>(pyObj);
} else {
return py::cast(std::move(ivalue).toStringRef());
}
} else if (ivalue.isList()) {
auto list = std::move(ivalue).toList();
py::list t{list.size()};
for (const auto i : c10::irange(list.size())) {
t[i] = toPyObject(IValue{list.get(i)});
}
#if C10_RETURN_MOVE_IF_OLD_COMPILER
return std::move(t);
#else
return t;
#endif
} else if (ivalue.isTuple()) {
auto tuple = std::move(ivalue).toTuple();
const auto& elements = tuple->elements();
py::tuple t{elements.size()};
for (const auto i : c10::irange(elements.size())) {
t[i] = toPyObject(IValue{elements.at(i)});
}
// If we have a NamedTuple
if (tuple->type() && tuple->type()->schema() &&
!tuple->type()->schema()->name().empty()) {
auto unqualName = tuple->type()->name()->name();
std::vector<Argument> tuple_args = tuple->type()->schema()->arguments();
std::vector<pybind11::object> defaults;
auto it = std::find_if(
tuple_args.begin(), tuple_args.end(), [](const Argument& arg) {
return arg.default_value().has_value();
});
std::transform(
it,
tuple_args.end(),
std::back_inserter(defaults),
[](const Argument& arg) { return toPyObject(*arg.default_value()); });
std::vector<std::string> fieldNames =
fmap(tuple_args, [](const Argument& arg) { return arg.name(); });
return py::module::import("torch._jit_internal")
.attr("_create_named_tuple")(
t, unqualName, fieldNames, py::make_tuple(defaults));
} else {
#if C10_RETURN_MOVE_IF_OLD_COMPILER
return std::move(t);
#else
return t;
#endif
}
} else if (ivalue.isDevice()) {
return py::cast(std::move(ivalue).toDevice());
} else if (ivalue.isStream()) {
return py::cast(std::move(ivalue).toStream());
} else if (ivalue.isGenericDict()) {
auto dict = std::move(ivalue).toGenericDict();
py::dict py_dict;
for (auto& pair : dict) {
py_dict[toPyObject(IValue{pair.key()})] =
toPyObject(IValue{pair.value()});
}
#if C10_RETURN_MOVE_IF_OLD_COMPILER
return std::move(py_dict);
#else
return py_dict;
#endif
} else if (ivalue.isRRef()) {
#ifdef USE_RPC
auto RRefPtr =
c10::dynamic_intrusive_pointer_cast<torch::distributed::rpc::RRef>(
std::move(ivalue).toRRef());
return py::cast(torch::distributed::rpc::PyRRef(RRefPtr));
#else
TORCH_CHECK(false, "RRef is only supported with the distributed package");
#endif
} else if (ivalue.isObject()) {
const auto obj = std::move(ivalue).toObject();
if (obj->type()->is_module()) {
return py::cast(Module(obj));
}
auto pyCu = get_python_cu();
if (obj->name().find("__torch__.torch.classes") == 0) {
return py::cast(Object(obj));
}
const auto classType = pyCu->get_class(c10::QualifiedName(obj->name()));
AT_ASSERT(classType, c10::str(obj->name(), " is not found."));
auto pyClass = getScriptedClassOrError(obj->type());
auto pyObj = pyClass.attr("__new__")(pyClass);
const auto numAttrs = classType->numAttributes();
for (const auto slot : c10::irange(numAttrs)) {
const auto& attrName = classType->getAttributeName(slot);
IValue v = obj->getSlot(slot);
py::setattr(pyObj, attrName.c_str(), toPyObject(std::move(v)));
}
return pyObj;
} else if (ivalue.isPyObject()) {
// return borrowed reference to ensure it correctly incref the underlying
// PyObject
return py::reinterpret_borrow<py::object>(ivalue.toPyObject());
} else if (ivalue.isCapsule()) {
return py::cast(c10::Capsule(ivalue.toCapsule()));
} else if (ivalue.isFuture()) {
return py::cast(std::make_shared<PythonFutureWrapper>(ivalue.toFuture()));
} else if (ivalue.isAwait()) {
return py::cast(std::make_shared<PythonAwaitWrapper>(ivalue.toAwait()));
} else if (ivalue.isEnum()) {
auto enum_holder = ivalue.toEnumHolder();
auto py_class = getScriptedClassOrError(enum_holder->type());
return py_class.attr(enum_holder->name().c_str());
} else if (ivalue.isRRef()) {
#ifdef USE_RPC
return py::cast(torch::distributed::rpc::PyRRef(
c10::static_intrusive_pointer_cast<distributed::rpc::RRef>(
ivalue.toRRef())));
#else
TORCH_CHECK(false, "RRef is only supported with the distributed package");
#endif
} else if (ivalue.isSymInt()) {
return py::cast(std::move(ivalue).toSymInt());
} else if (ivalue.isSymFloat()) {
return py::cast(std::move(ivalue).toSymFloat());
} else if (ivalue.isSymBool()) {
return py::cast(std::move(ivalue).toSymBool());
} else if (ivalue.isUnsigned()) {
return py::cast(std::move(ivalue).toUInt());
} else {
TORCH_CHECK(
false,
"Missing cases in 'toPyObject'! Can't convert ",
ivalue.tagKind(),
" to a Python object");
}
}
std::pair<std::shared_ptr<Operator>, Stack> getOpWithStack(
const std::vector<std::shared_ptr<Operator>>& operations,
const py::args& args,
const py::kwargs& kwargs) {
return getOpWithStack(
c10::ArrayRef<std::shared_ptr<Operator>>(operations), args, kwargs);
}
std::pair<std::shared_ptr<Operator>, Stack> getOpWithStack(
c10::ArrayRef<std::shared_ptr<Operator>> operations,
const py::args& args,
const py::kwargs& kwargs) {
Stack stack;
if (operations.size() == 1) {
std::shared_ptr<Operator> op = operations[0];
// Create a stack full of the arguments and keyword arguments.
stack = createStackForSchema(op->schema(), args, kwargs, std::nullopt);
return std::make_pair(std::move(op), std::move(stack));
} else {
std::vector<schema_match_error> errors;
std::shared_ptr<Operator> found_op = nullptr;
for (const auto& op : operations) {
try {
stack = createStackForSchema(op->schema(), args, kwargs, std::nullopt);
found_op = op;
break;
} catch (schema_match_error& error) {
errors.push_back(std::move(error));
}
}
if (!found_op) {
std::stringstream ss;
ss << "Overloaded torch operator invoked from Python failed to match any schema:\n";
for (const auto& err : errors) {
ss << err.what() << "\n\n";
}
throw std::runtime_error(ss.str());
}
return std::make_pair(std::move(found_op), std::move(stack));
}
}
// This function is used to check if the schema is valid for the given args and
// kwargs. It checks script object by checking whether the FakeScriptObject is
// an instance of the corresponding fake class for the actual class used in
// schema.
bool checkSchemaAllowFakeScriptObject(
const FunctionSchema& schema,
const py::args& args,
const py::kwargs& kwargs) {
bool match = false;
try {
match = matchSchemaAllowFakeScriptObject(schema, args, kwargs);
} catch (schema_match_error& error) {
throw std::runtime_error(error.what());
}
return match;
}
py::object invokeOperatorFromPython(
const std::vector<std::shared_ptr<Operator>>& operations,
const py::args& args,
const py::kwargs& kwargs,
std::optional<c10::DispatchKey> dk) {
return invokeOperatorFromPython(
c10::ArrayRef<std::shared_ptr<Operator>>(operations), args, kwargs, dk);
}
py::object invokeOperatorFromPython(
c10::ArrayRef<std::shared_ptr<Operator>> operations,
const py::args& args,
const py::kwargs& kwargs,
std::optional<c10::DispatchKey> dk) {
auto [found_op, stack] = getOpWithStack(operations, args, kwargs);
{
pybind11::gil_scoped_release no_gil_guard;
if (dk) {
found_op->getOperationForDispatchKey (*dk)(stack);
} else {
found_op->getOperation()(stack);
}
}
return createPyObjectForStack(std::move(stack));
}
std::optional<py::object> _maybe_handle_torch_function(
const std::string& ns,
const std::string& method_name,
const std::string& overload_name,
bool is_overload,
const py::args& args,
const py::kwargs& kwargs) {
std::vector<PyObject*> overloaded_args;
const auto args_size = args.size();
size_t total_arg_num = args_size + kwargs.size();
for (const auto i : c10::irange(args_size)) {
is_tensor_and_append_overloaded(args[i].ptr(), &overloaded_args);
is_tensor_list_and_append_overloaded(
args[i].ptr(),
&overloaded_args,
static_cast<int>(total_arg_num),
false /* throw_error */);
}
// NB: for kwargs, we cannot guarantee the order of appending
// is the same as the argument order in operator's schema.
// This is suboptimal, but should be fine. Later when we have
// better schema matching and argument parsing, we could
// match the operator in `operations` first, then the order will
// be guaranteed.
for (auto item : kwargs) {
is_tensor_and_append_overloaded(item.second.ptr(), &overloaded_args);
is_tensor_list_and_append_overloaded(
item.second.ptr(),
&overloaded_args,
total_arg_num,
false /* throw_error */);
}
if (!overloaded_args.empty() || at::impl::torch_function_mode_enabled()) {
auto self_func = py::module::import("torch")
.attr("ops")
.attr(ns.c_str())
.attr(method_name.c_str());
if (is_overload) {
if (overload_name.empty()) {
self_func = self_func.attr("default");
} else {
self_func = self_func.attr(overload_name.c_str());
}
}
std::string module_name("torch.ops");
module_name.append(ns);
return {pybind11::reinterpret_steal<py::object>(
handle_torch_function_no_python_arg_parser(
overloaded_args,
args.ptr(),
kwargs.ptr(),
method_name.c_str(),
self_func.ptr(),
module_name.c_str()))};
}
return std::nullopt;
}
py::object _get_operation_for_overload_or_packet(
const std::vector<std::shared_ptr<Operator>>& operations,
Symbol symbol,
const py::args& args,
const py::kwargs& kwargs,
bool is_overload,
std::optional<c10::DispatchKey> dk) {
return _get_operation_for_overload_or_packet(
c10::ArrayRef(operations), symbol, args, kwargs, is_overload, dk);
}
py::object _get_operation_for_overload_or_packet(
c10::ArrayRef<std::shared_ptr<Operator>> operations,
Symbol symbol,
const py::args& args,
const py::kwargs& kwargs,
bool is_overload,
std::optional<c10::DispatchKey> dk) {
std::string ns = symbol.ns().toUnqualString();
std::string method_name = symbol.toUnqualString();
std::string overload_name = operations[0]->schema().overload_name();
auto res = _maybe_handle_torch_function(
ns, method_name, overload_name, is_overload, args, kwargs);
auto torch_function_called = res.has_value();
return torch_function_called
? *res
: invokeOperatorFromPython(operations, args, kwargs, dk);
}
} // namespace torch::jit
Reference in New Issue View Git Blame Copy Permalink