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a876432aea
Addresses: https://github.com/pytorch/pytorch/issues/83617 This PR a way to query the TLS graph task's exec_info which is a map mapping the Node to a bool indicating whether it will be executed in the current backward pass (as determined by the inputs= argument for .grad of .backward). - this works with both custom Function nodes and normal codegened nodes - to be able to verify whether the pyobject passed is an actual node, we now store pointers to PyTypeObjects into a set on registration. - error out when .backward without inputs= to avoid silently returning True Alternatives: - not sure if it is possible to bind to Python from a raw pointer to Node. At least we wouldn't be able to use existing logic, and the Python object should only hold a weak reference to the Node. - other solutions to the motivating issue seem to require more extensive modification to the engine See the issue linked for an example of usage Pull Request resolved: https://github.com/pytorch/pytorch/pull/84773 Approved by: https://github.com/albanD
103 lines
4.1 KiB
C++
103 lines
4.1 KiB
C++
#pragma once
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#include <torch/csrc/python_headers.h>
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#include <memory>
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#include <typeinfo>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/autograd/function.h>
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#include <torch/csrc/utils/object_ptr.h>
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namespace torch {
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namespace autograd {
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struct THPCppFunction {
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PyObject_HEAD std::shared_ptr<Node> cdata;
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};
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template <typename Ctor>
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PyObject* CppFunction_pynew(
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PyTypeObject* type,
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PyObject* args,
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PyObject* kwds) {
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THPObjectPtr obj(type->tp_alloc(type, 0));
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if (!obj)
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return nullptr;
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THPCppFunction* f = (THPCppFunction*)obj.get();
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HANDLE_TH_ERRORS
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new (&f->cdata) std::shared_ptr<Node>(Ctor()(args));
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END_HANDLE_TH_ERRORS
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if (!f->cdata) {
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return nullptr;
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}
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return obj.release();
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}
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#define THP_FUNCTION_DEFAULT_METHODS \
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{(char*)"_register_hook_dict", \
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THPCppFunction_register_hook_dict, \
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METH_O, \
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nullptr}, \
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{(char*)"register_hook", THPCppFunction_register_hook, METH_O, nullptr}, \
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{(char*)"register_prehook", \
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THPCppFunction_register_prehook, \
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METH_O, \
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nullptr}, \
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{ \
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(char*)"name", THPCppFunction_name, METH_NOARGS, nullptr \
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}
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#define THP_FUNCTION_DEFAULT_PROPERTIES \
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{(char*)"next_functions", \
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(getter)THPCppFunction_next_functions, \
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nullptr, \
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nullptr, \
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nullptr}, \
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{(char*)"requires_grad", \
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(getter)THPCppFunction_requires_grad, \
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nullptr, \
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nullptr, \
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nullptr}, \
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{ \
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(char*)"metadata", (getter)THPCppFunction_metadata, nullptr, nullptr, \
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nullptr \
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}
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PyObject* THPCppFunction_next_functions(THPCppFunction* self, PyObject* hook);
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PyObject* THPCppFunction_metadata(THPCppFunction* self, void* _unused);
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PyObject* THPCppFunction_requires_grad(THPCppFunction* self, void* _unused);
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PyObject* THPCppFunction_register_hook_dict(PyObject* self, PyObject* _var);
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PyObject* THPCppFunction_register_hook(PyObject* self, PyObject* hook);
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PyObject* THPCppFunction_register_prehook(PyObject* self, PyObject* hook);
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PyObject* THPCppFunction_name(PyObject* self, PyObject* noargs);
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PyTypeObject* _initFunctionPyTypeObject(
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PyTypeObject& type,
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const char* name,
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PyGetSetDef* function_properties,
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PyMethodDef* function_methods);
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PyObject* registerFunctionHook(Node& fn, PyObject* hook);
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PyObject* registerFunctionPreHook(Node& fn, PyObject* hook);
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template <typename Ctor>
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PyTypeObject* createForwardFunctionPyTypeObject(
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PyTypeObject& type,
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const char* name,
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PyGetSetDef* function_properties = nullptr,
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PyMethodDef* function_methods = nullptr) {
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type.tp_new = &CppFunction_pynew<Ctor>;
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return _initFunctionPyTypeObject(
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type, name, function_properties, function_methods);
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}
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void registerCppFunction(const std::type_info& type, PyTypeObject* pytype);
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PyObject* functionToPyObject(const std::shared_ptr<Node>& cdata);
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bool THPCppFunction_Check(PyObject* obj);
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} // namespace autograd
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} // namespace torch
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