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#include <Python.h>
#include <c10/util/irange.h>
#include <torch/csrc/autograd/functions/accumulate_grad.h>
#include <torch/csrc/autograd/functions/basic_ops.h>
#include <torch/csrc/autograd/functions/pybind.h>
#include <torch/csrc/autograd/functions/tensor.h>
#include <torch/csrc/autograd/generated/python_functions.h>
#include <torch/csrc/autograd/python_cpp_function.h>
#include <torch/csrc/autograd/python_variable.h>
#ifdef USE_DISTRIBUTED
#include <torch/csrc/distributed/autograd/functions/sendrpc_backward.h>
#endif
#include <torch/csrc/jit/python/python_tracer.h>
#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/python_numbers.h>
#include <torch/csrc/utils/python_strings.h>
using namespace torch::autograd;
struct DelayedErrorCtor {
DelayedError* operator()(PyObject* args) {
TORCH_CHECK(
PyTuple_GET_SIZE(args) == 2,
"Requires two arguments, got ",
PyTuple_GET_SIZE(args));
auto arg1 = PyTuple_GET_ITEM(args, 0);
TORCH_CHECK(THPUtils_checkString(arg1), "argument 'msg' must be a string");
std::string msg = THPUtils_unpackString(arg1);
auto arg2 = PyTuple_GET_ITEM(args, 1);
TORCH_CHECK(
THPUtils_checkLong(arg2), "argument 'num_inputs' must be an int");
int num_inputs = THPUtils_unpackLong(arg2);
return new DelayedError(msg, num_inputs);
}
};
struct UndefinedGradCtor {
UndefinedGrad* operator()(PyObject* args) {
TORCH_CHECK(
PyTuple_GET_SIZE(args) == 0,
"Requires zero arguments, got ",
PyTuple_GET_SIZE(args));
return new UndefinedGrad();
}
};
struct NoCtor {
Node* operator()(PyObject* args) {
throw std::runtime_error("Cannot construct");
}
};
template <typename C, typename T>
static void addClass(
PyObject* module,
PyTypeObject& type,
const char* name,
PyGetSetDef* function_properties = nullptr,
PyMethodDef* function_methods = nullptr) {
createForwardFunctionPyTypeObject<T>(
type, name, function_properties, function_methods);
Py_INCREF(&type);
PyModule_AddObject(module, name, (PyObject*)&type);
registerCppFunction(typeid(C), &type);
}
template <
typename T,
typename ValueT,
typename ParamsT,
ValueT ParamsT::*ptr,
typename ConvertArgT,
PyObject* (*Convert)(ConvertArgT)>
PyObject* getTupleAttr(PyObject* obj, void* _unused) {
HANDLE_TH_ERRORS
THPCppFunction* self = (THPCppFunction*)obj;
auto& arr = ((T*)(self->cdata.get()))->*ptr;
auto num_elems = arr.size();
THPObjectPtr py_tuple(PyTuple_New(num_elems));
if (!py_tuple)
return nullptr;
for (const auto i : c10::irange(num_elems)) {
PyTuple_SET_ITEM(py_tuple.get(), i, Convert(arr[i]));
}
return py_tuple.release();
END_HANDLE_TH_ERRORS
}
template <
typename T,
typename ValueT,
typename ParamsT,
ValueT ParamsT::*ptr,
typename ConvertArgT,
PyObject* (*Convert)(ConvertArgT)>
PyObject* getValueAttr(PyObject* obj, void* _unused) {
HANDLE_TH_ERRORS
THPCppFunction* self = (THPCppFunction*)obj;
auto& val = ((T*)(self->cdata.get()))->*ptr;
return Convert(val);
END_HANDLE_TH_ERRORS
}
static PyObject* accumulateGradVar(PyObject* _self, void* _unused) {
THPCppFunction* self = (THPCppFunction*)_self;
auto grad_acc = (AccumulateGrad*)self->cdata.get();
return THPVariable_Wrap(grad_acc->variable);
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables,cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
static struct PyGetSetDef accumulate_grad_properties[] = {
THP_FUNCTION_DEFAULT_PROPERTIES,
{(char*)"variable", accumulateGradVar, nullptr, nullptr, nullptr},
{nullptr}};
void THPAutograd_initFunctions() {
THPObjectPtr module(PyModule_New("torch._C._functions"));
if (!module)
throw python_error();
static PyTypeObject AccumulateGradClass;
addClass<AccumulateGrad, NoCtor>(
module,
AccumulateGradClass,
"AccumulateGrad",
accumulate_grad_properties);
static PyTypeObject ErrorClass;
addClass<Error, NoCtor>(module, ErrorClass, "Error");
static PyTypeObject NotImplementedClass;
addClass<NotImplemented, NoCtor>(
module, NotImplementedClass, "NotImplemented");
static PyTypeObject DelayedErrorClass;
addClass<DelayedError, DelayedErrorCtor>(
module, DelayedErrorClass, "DelayedError");
static PyTypeObject UndefinedGradBackwardClass;
addClass<UndefinedGradBackward, NoCtor>(
module, UndefinedGradBackwardClass, "UndefinedGradBackward");
static PyTypeObject UndefinedGradClass;
addClass<UndefinedGrad, UndefinedGradCtor>(
module, UndefinedGradClass, "UndefinedGrad");
static PyTypeObject CopyBackwardsClass;
addClass<CopyBackwards, NoCtor>(module, CopyBackwardsClass, "CopyBackwards");
#ifdef USE_DISTRIBUTED
static PyTypeObject SendRpcBackwardClass;
addClass<torch::distributed::autograd::SendRpcBackward, NoCtor>(
module, SendRpcBackwardClass, "SendRpcBackward");
#endif
static PyTypeObject CopySlicesClass;
addClass<CopySlices, NoCtor>(module, CopySlicesClass, "CopySlices");
generated::initialize_autogenerated_functions();
auto c_module = THPObjectPtr(PyImport_ImportModule("torch._C"));
if (!c_module)
throw python_error();
Py_INCREF(module.get());
if (PyModule_AddObject(c_module, "_functions", module) < 0) {
Py_DECREF(module.get());
throw python_error();
}
}
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