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script_init.cpp
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#include <pybind11/detail/common.h>
#include <pybind11/pytypes.h>
#include <torch/csrc/jit/api/object.h>
#include <torch/csrc/jit/python/script_init.h>
#include <torch/csrc/utils/pybind.h>
#include <caffe2/serialize/versions.h>
#include <torch/csrc/Device.h>
#include <torch/csrc/DynamicTypes.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/frontend/sugared_value.h>
#include <torch/csrc/jit/mobile/code.h>
#include <torch/csrc/jit/mobile/compatibility/backport.h>
#include <torch/csrc/jit/mobile/compatibility/model_compatibility.h>
#include <torch/csrc/jit/mobile/file_format.h>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/module.h>
#include <torch/csrc/jit/mobile/quantization.h>
#include <torch/csrc/jit/operator_upgraders/upgraders.h>
#include <torch/csrc/jit/operator_upgraders/upgraders_entry.h>
#include <torch/csrc/jit/operator_upgraders/utils.h>
#include <torch/csrc/jit/operator_upgraders/version_map.h>
#include <torch/csrc/jit/python/module_python.h>
#include <torch/csrc/jit/python/python_ivalue.h>
#include <torch/csrc/jit/python/python_sugared_value.h>
#include <torch/csrc/jit/serialization/export_bytecode.h>
#include <torch/csrc/jit/serialization/import.h>
#include <torch/csrc/jit/testing/file_check.h>
#include <c10/util/intrusive_ptr.h>
#include <c10/util/irange.h>
#include <torch/csrc/jit/frontend/parser.h>
#include <torch/csrc/jit/frontend/tracer.h>
#include <torch/csrc/jit/ir/constants.h>
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/passes/inliner.h>
#include <torch/csrc/jit/passes/shape_analysis.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <torch/csrc/jit/python/python_dict.h>
#include <torch/csrc/jit/python/python_list.h>
#include <torch/csrc/jit/python/python_tracer.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <torch/csrc/jit/runtime/instruction.h>
#include <torch/csrc/jit/runtime/interpreter.h>
#include <torch/csrc/jit/runtime/logging.h>
#include <torch/csrc/jit/serialization/export_bytecode.h>
#include <torch/csrc/jit/serialization/import_source.h>
#include <torch/csrc/jit/serialization/python_print.h>
#include <torch/csrc/jit/testing/hooks_for_testing.h>
#include <torch/csrc/api/include/torch/ordered_dict.h>
#include <ATen/ATen.h>
#include <ATen/core/function_schema.h>
#include <ATen/core/ivalue.h>
#include <ATen/core/qualified_name.h>
#include <pybind11/functional.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <pybind11/stl_bind.h>
#include <torch/csrc/jit/mobile/train/export_data.h>
#include <chrono>
#include <cstddef>
#include <memory>
#include <sstream>
#include <string>
#include <tuple>
#include <utility>
#include <vector>
namespace torch {
namespace jit {
using ::c10::Argument;
using ::c10::FunctionSchema;
using ResolutionCallback = std::function<py::object(std::string)>;
using FunctionDefaults = std::unordered_map<std::string, py::object>;
using ClassMethodDefaults = std::unordered_map<std::string, FunctionDefaults>;
namespace {
// A resolver that will inspect the outer Python scope to find `name`.
struct PythonResolver : public Resolver {
explicit PythonResolver(ResolutionCallback rcb) : rcb_(std::move(rcb)) {}
/**
* While compiling classes, the class type we're compiling will not be
* available in Python, since we haven't fowner_ defining the class yet. So
* in order to make the class type available to its own methods, we need to
* explicitly resolve it.
*
* @param rcb Python function to resolve a name to its Python object in the
* enclosing scope
* @param classname The unqualified classname of the class currently being
* compiled.
* @param classType The class's type.
*/
explicit PythonResolver(
ResolutionCallback rcb,
std::string classname,
ClassTypePtr classType)
: rcb_(std::move(rcb)),
classname_(std::move(classname)),
classType_(std::move(classType)) {}
std::shared_ptr<SugaredValue> resolveValue(
const std::string& name,
GraphFunction& m,
const SourceRange& loc) override {
pybind11::gil_scoped_acquire ag;
py::object obj = rcb_(name);
if (obj.is_none()) {
return nullptr;
}
return toSugaredValue(obj, m, loc);
}
static bool isNamedTupleClass(py::object obj) {
auto tuple_type = reinterpret_cast<PyObject*>(&PyTuple_Type);
return PyObject_IsSubclass(obj.ptr(), tuple_type) &&
py::hasattr(obj, "_fields");
}
TypePtr resolveTypeFromObject(const py::object& obj, const SourceRange& loc) {
if (py::isinstance<ScriptClass>(obj)) {
auto script_class = py::cast<ScriptClass>(obj);
return script_class.class_type_.type_;
}
py::bool_ isClass = py::module::import("inspect").attr("isclass")(obj);
if (!py::cast<bool>(isClass)) {
return nullptr;
}
if (isNamedTupleClass(obj)) {
return registerNamedTuple(obj, loc);
}
auto qualifiedName = c10::QualifiedName(
py::cast<std::string>(py::module::import("torch._jit_internal")
.attr("_qualified_name")(obj)));
return get_python_cu()->get_type(qualifiedName);
}
TypePtr resolveType(const std::string& name, const SourceRange& loc)
override {
if (classType_ && name == classname_) {
return classType_;
}
pybind11::gil_scoped_acquire ag;
py::object obj = rcb_(name);
if (obj.is_none()) {
return nullptr;
}
auto annotation_type = py::module::import("torch.jit.annotations")
.attr("try_ann_to_type")(obj, loc);
if (!annotation_type.is_none()) {
return py::cast<TypePtr>(annotation_type);
}
return resolveTypeFromObject(obj, loc);
}
private:
ResolutionCallback rcb_;
std::string classname_;
ClassTypePtr classType_;
};
std::shared_ptr<PythonResolver> pythonResolver(const ResolutionCallback& rcb) {
return std::make_shared<PythonResolver>(rcb);
}
std::shared_ptr<PythonResolver> pythonResolver(
const ResolutionCallback& rcb,
std::string classname,
ClassTypePtr classType) {
return std::make_shared<PythonResolver>(
rcb, std::move(classname), std::move(classType));
}
void checkOverloadDecl(const Decl& new_decl, const Decl& old_decl) {
const auto& new_params = new_decl.params();
const auto& old_params = old_decl.params();
// TODO. same number of parameters not strictly necessary.
TORCH_INTERNAL_ASSERT(
new_params.size() == old_params.size(),
"Overload must have same number of parameters\n",
new_decl.range(),
old_decl.range());
for (const auto i : c10::irange(new_decl.params().size())) {
TORCH_INTERNAL_ASSERT(
new_params[i].ident().name() == old_params[i].ident().name(),
"Overload parameters must have the same names\n",
new_params[i].ident(),
old_params[i].ident());
}
}
c10::optional<IValue> tryCalculateDefaultParam(
const Argument& arg,
const py::object& def_value) {
auto n = arg.N();
auto list_type = arg.type()->cast<ListType>();
try {
if (n && *n > 0 && list_type) {
// BroadcastingList, allow default values T for arg types List[T]
return toIValue(def_value, list_type->getElementType());
} else {
return toIValue(def_value, arg.type());
}
} catch (...) {
return c10::nullopt;
}
}
// An overloaded function may have a default that does not subtype all overloads
// @overload
// def foo(x: str)
// def foo(x=1)
FunctionDefaults calcOverloadedFunctionDefaults(
const FunctionSchema& schema,
const FunctionDefaults& defaults) {
FunctionDefaults updated_defaults;
for (const auto& arg : schema.arguments()) {
const std::string& arg_name = arg.name();
auto value = defaults.find(arg_name);
if (value == defaults.end()) {
continue;
}
auto maybe_ivalue = tryCalculateDefaultParam(arg, value->second);
if (maybe_ivalue) {
updated_defaults[arg_name] = value->second;
}
}
return updated_defaults;
}
} // namespace
bool checkMutableFunctionDefault(const py::object& def_arg) {
if (py::isinstance<py::list>(def_arg) || py::isinstance<py::dict>(def_arg)) {
return true;
}
if (py::isinstance<py::tuple>(def_arg)) {
auto pytuple = def_arg.cast<py::tuple>();
for (py::handle t : pytuple) {
py::object obj = py::reinterpret_borrow<py::object>(t);
if (checkMutableFunctionDefault(obj)) {
return true;
}
}
}
return false;
}
void checkMutableFunctionDefault(
const SourceRange& range,
const Argument& arg,
const py::object& def_arg) {
if (checkMutableFunctionDefault(def_arg) || arg.type()->cast<ClassType>()) {
throw ErrorReport(range)
<< "Mutable default parameters are not supported because Python binds them to the function"
<< " and they persist across function calls.\n As a workaround, make the default None and instantiate"
<< " the default parameter within the body of the function. Found "
<< def_arg.get_type() << " on parameter " << arg.name();
}
}
FunctionSchema getSchemaWithNameAndDefaults(
const SourceRange& range,
const FunctionSchema& schema,
const at::optional<std::string>& new_name,
const FunctionDefaults& default_args) {
std::vector<Argument> new_args;
for (auto& arg : schema.arguments()) {
auto it = default_args.find(arg.name());
if (it != default_args.end()) {
checkMutableFunctionDefault(range, arg, it->second);
c10::optional<IValue> value = tryCalculateDefaultParam(arg, it->second);
if (!value) {
ErrorReport error(range);
error << "Expected a default value of type " << arg.type()->repr_str()
<< " on parameter \"" << arg.name() << "\".";
if (arg.is_inferred_type()) {
error << "Because \"" << arg.name()
<< "\" was not annotated with an explicit type "
<< "it is assumed to be type 'Tensor'.";
}
throw error;
}
new_args.emplace_back(
arg.name(), arg.type(), arg.N(), *value, arg.kwarg_only());
} else {
new_args.push_back(arg);
}
}
return FunctionSchema(
new_name.value_or(schema.name()),
schema.overload_name(),
new_args,
schema.returns(),
schema.is_vararg(),
schema.is_varret());
}
static Decl mergeDefaultsAndExtraParametersToOverloadDecl(
const Decl& overload_decl,
const Decl& impl_decl,
const FunctionDefaults& defaults) {
std::vector<Param> adjusted_params;
const auto& overload_params = overload_decl.params();
const auto& impl_params = impl_decl.params();
// following PEP specification that the following should work:
// @overload
// def mouse_event(x1: int, y1: int) -> ClickEvent: ...
// ...
// def mouse_event(x1: int, y1: int, x2: Optional[int] = None, y2:
// Optional[int] = None)
TORCH_CHECK(
overload_params.size() <= impl_params.size(),
"Overload should not have more parameters than implementation function",
overload_decl.range(),
impl_decl.range());
for (const auto i : c10::irange(overload_params.size())) {
auto overload_name = overload_params[i].ident().name();
auto impl_name = impl_params[i].ident().name();
if (overload_name != impl_name) {
throw ErrorReport(overload_decl.range())
<< "Overload parameters must have the same names. "
<< "Found " << overload_name << " and " << impl_name
<< " on argument " << i;
}
adjusted_params.push_back(overload_params[i]);
}
for (size_t i = overload_params.size(); i < impl_params.size(); ++i) {
if (!defaults.count(impl_params[i].ident().name())) {
throw ErrorReport(impl_decl.range())
<< "Expected to find default parameter on argument"
<< impl_params[i].ident().name()
<< " because it is not defined on the overloaded declaration";
}
if (!impl_params[i].type().present()) {
throw ErrorReport(impl_decl.range())
<< "Parameters not specified on the overloaded declaration must have a type annotation in the implementation function."
<< " Did not find type for param " << impl_params[i].ident().name();
}
adjusted_params.push_back(impl_params[i]);
}
return Decl::create(
overload_decl.range(),
List<Param>::create(overload_decl.range(), adjusted_params),
overload_decl.return_type());
}
static StrongFunctionPtr script_compile_overloaded_function(
const c10::QualifiedName& name,
const Decl& overload_decl,
const Def& implementation_def,
const ResolutionCallback& rcb,
const FunctionDefaults& implementation_defaults,
const py::object& signature) {
if (signature.is_none()) {
throw ErrorReport(overload_decl.range())
<< "Must explicitly add type annotations to overloaded functions";
}
auto adjusted_decl = mergeDefaultsAndExtraParametersToOverloadDecl(
overload_decl, implementation_def.decl(), implementation_defaults);
auto new_def = implementation_def.withDecl(adjusted_decl);
auto cu = get_python_cu();
auto defined_functions = cu->define(
QualifiedName(name.prefix()),
/*properties=*/{},
/*propResolvers=*/{},
{new_def},
{pythonResolver(rcb)},
nullptr,
true);
TORCH_INTERNAL_ASSERT(defined_functions.size() == 1);
auto& defined = defined_functions[0];
FunctionDefaults updated_defaults = calcOverloadedFunctionDefaults(
defined->getSchema(), implementation_defaults);
defined->setSchema(getSchemaWithNameAndDefaults(
new_def.range(),
defined->getSchema(),
new_def.name().name(),
updated_defaults));
StrongFunctionPtr ret(std::move(cu), defined);
didFinishEmitFunction(ret);
return ret;
}
static StrongFunctionPtr script_compile_function(
const c10::QualifiedName& name,
const Def& def,
const FunctionDefaults& defaults,
const ResolutionCallback& rcb) {
auto cu = get_python_cu();
auto defined_functions = cu->define(
QualifiedName(name.prefix()),
/*properties=*/{},
/*propResolvers=*/{},
{def},
{pythonResolver(rcb)},
nullptr,
true);
TORCH_INTERNAL_ASSERT(defined_functions.size() == 1);
auto& defined = defined_functions[0];
defined->setSchema(getSchemaWithNameAndDefaults(
def.range(), defined->getSchema(), def.name().name(), defaults));
StrongFunctionPtr ret(std::move(cu), defined);
didFinishEmitFunction(ret);
return ret;
}
struct VISIBILITY_HIDDEN ModuleSelf : public Self {
ModuleSelf(std::shared_ptr<ConcreteModuleType> concreteType)
: Self(), concreteType_(std::move(concreteType)) {}
std::shared_ptr<SugaredValue> makeSugared(Value* v) const override {
v->setType(getClassType());
return std::make_shared<ModuleValue>(v, concreteType_);
}
ClassTypePtr getClassType() const override {
return concreteType_->getJitType()->expect<ClassType>();
}
private:
std::shared_ptr<ConcreteModuleType> concreteType_;
};
static TypePtr getTensorType(const at::Tensor& t, bool complete) {
auto r = TensorType::create(t);
if (!complete) {
r = r->dimensionedOnly();
}
return r;
}
static TypePtr inferShapeAndTypeForInput(
TypePtr input_type,
Stack::const_iterator& s_iter,
const Stack::const_iterator& s_iter_end,
bool complete) {
if (auto tuple_type = input_type->cast<TupleType>()) {
std::vector<TypePtr> types;
for (const auto& sub_type : tuple_type->containedTypes()) {
TORCH_INTERNAL_ASSERT(s_iter != s_iter_end);
types.emplace_back(
inferShapeAndTypeForInput(sub_type, s_iter, s_iter_end, complete));
}
return TupleType::create(types);
} else if (auto list_type = input_type->cast<ListType>()) {
const TypePtr& sub_type = list_type->getElementType();
auto elem_type =
inferShapeAndTypeForInput(sub_type, s_iter, s_iter_end, complete);
return ListType::create(elem_type);
} else if (auto tensor_type = input_type->cast<TensorType>()) {
auto type = getTensorType(s_iter->toTensor(), complete);
s_iter++;
return type;
} else if (auto optional_type = input_type->cast<OptionalType>()) {
const TypePtr& sub_type = optional_type->getElementType();
auto elem_type =
inferShapeAndTypeForInput(sub_type, s_iter, s_iter_end, complete);
return OptionalType::create(elem_type);
} else {
// Primitive type, keep as is.
s_iter++;
return input_type;
}
}
static void setInputTensorTypes(
Graph& g,
const Stack& stack,
bool complete,
const std::vector<int>& param_count_list = {}) {
at::ArrayRef<Value*> input_values = g.inputs();
auto s_iter = stack.begin();
size_t list_idx = 0;
if (!param_count_list.empty()) {
TORCH_INTERNAL_ASSERT(
input_values.size() == param_count_list.size(),
" input_values:",
input_values.size(),
" vs param_count_list:",
param_count_list.size());
}
for (auto v : input_values) {
// Leave packed param types alone. This is needed for downstream passes
// (like alias analysis) to work properly. This will be unpacked later
// in unpackQuantizedWeights.
if (auto named_type = v->type()->cast<c10::NamedType>()) {
if (auto qualname = named_type->name()) {
if (getCustomClass(qualname->qualifiedName())) {
if (param_count_list.empty()) {
AT_ASSERT(s_iter != stack.end());
s_iter++;
} else {
if (param_count_list[list_idx] > 0) {
AT_ASSERT(s_iter != stack.end());
}
s_iter += param_count_list[list_idx];
}
list_idx++;
continue;
}
}
}
v->setType(
inferShapeAndTypeForInput(v->type(), s_iter, stack.end(), complete));
list_idx++;
}
}
static std::shared_ptr<Graph> _propagate_shapes(
Graph& graph,
std::vector<at::Tensor> inputs,
bool with_grad = false) {
Stack stack(inputs.begin(), inputs.end());
auto retval = graph.copy();
setInputTensorTypes(*retval, stack, /*complete=*/false);
PropagateInputShapes(retval);
return retval;
}
static std::shared_ptr<Graph> _propagate_and_assign_input_shapes(
Graph& graph,
const std::vector<at::Tensor>& inputs,
const std::vector<int>& param_count_list,
bool with_grad = false,
bool propagate = true) {
auto retval = graph.copy();
setInputTensorTypes(
*retval, fmap<IValue>(inputs), /*complete=*/true, param_count_list);
if (propagate) {
PropagateInputShapes(retval);
}
return retval;
}
void addFunctionToModule(Module& module, const StrongFunctionPtr& func) {
// Make a graph with a fake self argument
auto graph = toGraphFunction(*func.function_).graph()->copy();
auto v = graph->insertInput(0, "self");
v->setType(module._ivalue()->type());
const auto name = QualifiedName(*module.type()->name(), "forward");
auto method =
module._ivalue()->compilation_unit()->create_function(name, graph);
module.type()->addMethod(method);
}
// this is used in our test suite to check that we correctly preserved type tags
bool ivalue_tags_match(const Module& lhs, const Module& rhs) {
struct Work {
IValue a;
IValue b;
};
std::unordered_set<const void*> visited;
std::vector<Work> work = {{lhs._ivalue(), rhs._ivalue()}};
while (!work.empty()) {
Work item = work.back();
work.pop_back();
if (item.a.isPtrType()) {
// uncomment to debug type matching errors
// std::cout << "MATCHING " << /*item.a <<*/ "(" << *item.a.type() << ") "
// << item.a.internalToPointer() << " " << /*item.b <<*/ " ("
// << *item.b.type() << ") " << item.b.internalToPointer() <<
// "\n";
if (visited.count(item.a.internalToPointer())) {
continue;
}
visited.emplace(item.a.internalToPointer());
}
if (!unshapedType(item.b.type())
->isSubtypeOf(unshapedType(item.b.type()))) {
// Since named types are saved and loaded in the test suite, we cannot
// expect them to be equal. We should still check their slots however.
if (!item.a.type()->cast<c10::NamedType>()) {
return false;
}
}
// check tags for objects that contain subobjects
if (item.a.isObject()) {
auto ao = item.a.toObject();
auto bo = item.b.toObject();
for (size_t i = 0; i < ao->slots().size(); ++i) {
work.emplace_back(Work{ao->slots().at(i), bo->slots().at(i)});
}
} else if (item.a.isTuple()) {
auto at = item.a.toTuple();
auto bt = item.b.toTuple();
for (size_t i = 0; i < at->elements().size(); ++i) {
work.emplace_back(Work{at->elements().at(i), bt->elements().at(i)});
}
} else if (item.a.isList()) {
auto al = item.a.toList();
auto bl = item.b.toList();
for (const auto i : c10::irange(al.size())) {
work.emplace_back(Work{al.get(i), bl.get(i)});
}
} else if (item.a.isGenericDict()) {
auto ad = item.a.toGenericDict();
auto bd = item.b.toGenericDict();
for (auto& item : ad) {
// Dictionaory keys cannot contain List/Dicts that require tags
// so we do not have to check them.
// Furthermore without ordered dicts it is expensive to find the
// equivalent key
work.emplace_back(Work{item.value(), bd.at(item.key())});
}
} else if (item.a.isFuture()) {
auto af = item.a.toFuture();
auto bf = item.b.toFuture();
af->wait();
bf->wait();
work.emplace_back(Work{af->value(), bf->value()});
}
}
return true;
}
// helper used to implement ._parameters, ._buffers, ._modules dicts
// inside of script nn.Module
template <typename Policy>
struct slot_dict_impl {
slot_dict_impl(ModulePtr module) : module_(std::move(module)) {}
bool contains(const std::string& name) const {
if (auto slot = module_->type()->findAttributeSlot(name)) {
if (Policy::valid(module_->type(), *slot, module_->getSlot(*slot))) {
return true;
}
}
return false;
}
std::vector<std::pair<std::string, py::object>> items() const {
std::vector<std::pair<std::string, py::object>> result;
for (size_t i = 0, N = module_->type()->numAttributes(); i < N; ++i) {
if (Policy::valid(module_->type(), i, module_->getSlot(i))) {
result.emplace_back(
module_->type()->getAttributeName(i),
toPyObject(module_->getSlot(i)));
}
}
return result;
}
void setattr(const std::string& name, py::object value) {
const TypePtr& type = module_->type()->getAttribute(name);
Module(module_).setattr(name, toIValue(std::move(value), type));
}
py::object getattr(const std::string& name) {
return toPyObject(Module(module_).attr(name));
}
static void bind(const py::module& m, const char* name) {
py::class_<slot_dict_impl<Policy>>(m, name)
.def(py::init(
[](Module& m) { return slot_dict_impl<Policy>(m._ivalue()); }))
.def("contains", &slot_dict_impl<Policy>::contains)
.def("items", &slot_dict_impl<Policy>::items)
.def("setattr", &slot_dict_impl<Policy>::setattr)
.def("getattr", &slot_dict_impl<Policy>::getattr);
}
private:
ModulePtr module_;
};
template <typename T>
py::list debugMakeList(const T& list) {
py::list result;
for (const auto& elem : list) {
result.append(py::cast(elem));
}
return result;
}
template <typename T>
py::list debugMakeNamedList(const T& list) {
py::list result;
for (auto elem : list) {
result.append(py::cast(std::make_pair(elem.name, elem.value)));
}
return result;
}
template <typename T>
py::set debugMakeSet(const T& list) {
py::set result;
for (const auto& elem : list) {
result.add(py::cast(elem));
}
return result;
}
static py::dict _jit_debug_module_iterators(Module& module) {
py::dict result;
result["children"] = debugMakeList(module.children());
result["named_children"] = debugMakeNamedList(module.named_children());
result["modules"] = debugMakeList(module.modules());
result["named_modules"] = debugMakeNamedList(module.named_modules());
result["parameters"] = debugMakeList(module.parameters(false));
result["named_parameters"] =
debugMakeNamedList(module.named_parameters(false));
result["parameters_r"] = debugMakeList(module.parameters(true));
result["named_parameters_r"] =
debugMakeNamedList(module.named_parameters(true));
result["buffers"] = debugMakeList(module.buffers(false));
result["named_buffers"] = debugMakeNamedList(module.named_buffers(false));
result["buffers_r"] = debugMakeList(module.buffers(true));
result["named_buffers_r"] = debugMakeNamedList(module.named_buffers(true));
result["named_attributes"] =
debugMakeNamedList(module.named_attributes(false));
result["named_attributes_r"] =
debugMakeNamedList(module.named_attributes(true));
return result;
}
static constexpr std::array<const char*, 47> magic_method_names = {
"__lt__", "__le__", "__eq__", "__ne__",
"__ge__", "__gt__", "__not__", "__abs__",
"__add__", "__and__", "__floordiv__", "__index__",
"__inv__", "__invert__", "__lshift__", "__mod__",
"__mul__", "__matmul__", "__neg__", "__or__",
"__pos__", "__pow__", "__rshift__", "__sub__",
"__truediv__", "__xor__", "__concat__", "__contains__",
"__delitem__", "__getitem__", "__setitem__", "__iadd__",
"__iand__", "__iconcat__", "__ifloordiv__", "__ilshift__",
"__imod__", "__imul__", "__imatmul__", "__ior__",
"__ipow__", "__irshift__", "__isub__", "__itruediv__",
"__ixor__", "__str__", "__len__",
};
struct DeepCopyMemoTable {
std::shared_ptr<IValue::HashAliasedIValueMap> map;
};
IValue pyIValueDeepcopy(const IValue& ivalue, const py::dict& memo) {
if (!memo.contains(py::str("__torch_script_memo_table"))) {
memo["__torch_script_memo_table"] =
DeepCopyMemoTable{std::make_shared<IValue::HashAliasedIValueMap>()};
}
auto& ivalue_memo =
*py::cast<DeepCopyMemoTable>(memo["__torch_script_memo_table"]).map;
return ivalue.deepcopy(ivalue_memo);
}
ExtraFilesMap extra_files_from_python(const py::dict& pydict) {
ExtraFilesMap r;
for (const auto& it : pydict) {
r[py::cast<std::string>(it.first)] = "";
}
return r;
}
void extra_files_to_python(const ExtraFilesMap& m, const py::dict& pydict) {
// py::dict is pointer-like type so it gets modified despite const&
for (const auto& it : m) {
pydict[py::str(it.first)] = py::bytes(it.second);
}
}
void pyCompilationUnitDefine(
CompilationUnit& cu,
const std::string& src,
const ResolutionCallback* rcb,
const uint32_t _frames_up) {
if (rcb && *rcb) {
cu.define(c10::nullopt, src, pythonResolver(*rcb), nullptr);
} else {
py::object py_default_rcb =
py::module::import("torch._jit_internal")
.attr("createResolutionCallbackFromFrame")(_frames_up);
auto default_rcb = py_default_rcb.cast<ResolutionCallback>();
cu.define(c10::nullopt, src, pythonResolver(default_rcb), nullptr);
}
}
void initJitScriptBindings(PyObject* module) {
auto m = py::handle(module).cast<py::module>();
// NOLINTNEXTLINE(bugprone-unused-raii)
py::class_<c10::Capsule>(m, "Capsule");
auto object_class =
py::class_<Object>(m, "ScriptObject")
.def("_type", [](Module& m) { return m.type(); })
.def(
"_get_method",
[](Object& self, const std::string& name) -> Method {
return self.get_method(name);
},
py::keep_alive<0, 1>())
.def(
"setattr",
[](Object& self, const std::string& name, py::object value) {
if (self.type()->hasConstant(name)) {
TORCH_CHECK(
false,
"Can't set constant '",
name,
"' which has value:",
self.type()->getConstant(name));
}
TypePtr type = self.type()->getAttribute(name);
try {
auto ivalue = toIValue(std::move(value), type);
self.setattr(name, ivalue);
} catch (std::exception& e) {
throw py::cast_error(c10::str(
"Could not cast attribute '",
name,
"' to type ",
type->repr_str(),
": ",
e.what()));
}
})
.def(
"getattr",
[](Object& self, const std::string& name) {
try {
return toPyObject(self.attr(name));
} catch (const ObjectAttributeError& err) {
throw AttributeError("%s", err.what());
}
})
.def(
"__getattr__",
[](Object& self, const std::string& name) -> py::object {
try {
if (name == "__qualname__") {
return py::cast(self.type()->name()->name());
}
if (auto method = self.find_method(name)) {
return py::cast(*method);
}
if (self.has_property(name)) {
auto prop = self.get_property(name);
// wrap the Method into callable PyObject
auto getter_func = py::cast(prop.getter_func);
return getter_func();
}
return toPyObject(self.attr(name));
} catch (const ObjectAttributeError& err) {
throw AttributeError("%s", err.what());
}
})
.def(
"__setattr__",
[](Object& self, const std::string& name, py::object value) {
try {
if (self.has_property(name)) {
auto prop = self.get_property(name);
if (!prop.setter_func.has_value()) {
TORCH_CHECK(false, "can't set attribute");
}
// wrap the Method into callable PyObject
auto setter_func = py::cast(prop.setter_func);
setter_func(value);
return;
}
if (self.type()->hasConstant(name)) {
TORCH_CHECK(
false,
"Can't set constant '",
name,
"' which has value:",
self.type()->getConstant(name));
}
TypePtr type = self.type()->getAttribute(name);
auto ivalue = toIValue(std::move(value), type);
self.setattr(name, ivalue);
} catch (const ObjectAttributeError& err) {
throw AttributeError("%s", err.what());
}
})
.def(
"hasattr",
[](Object& self, const std::string& name) {
return self.hasattr(name);
})
.def(
"_has_method",
[](Object& self, const std::string& name) {
return bool(self.find_method(name));
})
.def(
"_method_names",
[](Object& self) {
return fmap(self.get_methods(), [](const Method& method) {
return method.name();
});
})
.def(
"_properties", [](Object& self) { return self.get_properties(); })
.def("__copy__", &Object::copy)
.def(
"__hash__",
[](const Object& self) {
// Similar to Tensor's `__hash__`, which is `id()`.
return std::hash<c10::ivalue::Object*>{}(self._ivalue().get());
})
.def(py::pickle(
[](const Object& self)
-> std::tuple<py::object, std::string> { // __getstate__
if (auto getstate_method = self.find_method("__getstate__")) {
auto object_state = toPyObject((*getstate_method)(Stack{}));
TORCH_INTERNAL_ASSERT(self.type()->name());
return std::make_tuple(
object_state, self.type()->name()->qualifiedName());
}
std::stringstream err;
err << "Tried to serialize object ";
if (auto qualname = self.type()->name()) {
err << qualname->qualifiedName() << " ";
}
err << "which does not have a __getstate__ method defined!";
throw std::runtime_error(err.str());
},
[](const std::tuple<py::object, std::string>& state_tup)
-> Object {
py::object state;
std::string qualname;
std::tie(state, qualname) = state_tup;
auto class_type = getCustomClass(qualname);
TORCH_CHECK(
class_type,
"Tried to deserialize class ",
qualname,
" which is not known to the runtime. "
"If this is a custom C++ class, make "
"sure the appropriate code is linked.");
auto self = Object(c10::ivalue::Object::create(
c10::StrongTypePtr(
std::shared_ptr<torch::jit::CompilationUnit>(),
class_type),
1));
if (auto setstate_method = self.find_method("__setstate__")) {
auto setstate_schema =
setstate_method->function().getSchema();
TORCH_INTERNAL_ASSERT(
setstate_schema.arguments().size() == 2,
"__setstate__ method for class ",
class_type->repr_str(),
" must have exactly 2 arguments!");
auto state_type = setstate_schema.arguments().at(1).type();
(*setstate_method)(Stack{toIValue(state, state_type)});
return self;
}
std::stringstream err;
err << "Tried to deserialize object ";
if (auto qualname = class_type->name()) {
err << qualname->qualifiedName() << " ";
}
err << "which does not have a __setstate__ method defined!";
throw std::runtime_error(err.str());
}));
py::class_<Object::Property>(m, "ScriptObjectProperty")
.def_property_readonly(
"name", [](const Object::Property& self) { return self.name; })
.def_property_readonly(
"getter",
[](const Object::Property& self) { return self.getter_func; })
.def_property_readonly("setter", [](const Object::Property& self) {
return self.setter_func;
});
// Special case __str__ to make sure we can print Objects/Modules
// regardless of if the user defined a __str__
using MagicMethodImplType = std::function<py::object(
const Object& self, py::args args, py::kwargs kwargs)>;
std::unordered_map<std::string, MagicMethodImplType> special_magic_methods{
{"__str__",
[](const Object& self, py::args args, py::kwargs kwargs) -> py::object {
auto method = self.find_method("__str__");
if (!method) {
return py::str("ScriptObject");
}
return invokeScriptMethodFromPython(
*method,
// NOLINTNEXTLINE(performance-move-const-arg)