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https://github.com/zebrajr/pytorch.git
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ba0fc71a3a
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68693 Generation of python bindings for native functions is split over 8 different files. One for each namespace, with the torch namespace split into 3 shards, and methods in their own file as well. This change ensures that editing any single (non-method) operator only causes one of these files to be rebuilt. Test Plan: Imported from OSS Reviewed By: jbschlosser Differential Revision: D32596270 Pulled By: albanD fbshipit-source-id: 0570ec69e7476b8f1bc21138ba18fe8f95ebbe3f
113 lines
3.4 KiB
C++
113 lines
3.4 KiB
C++
#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
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// ${generated_comment}
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#include "torch/csrc/Device.h"
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#include "torch/csrc/DynamicTypes.h"
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#include "torch/csrc/Exceptions.h"
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#include "torch/csrc/autograd/python_nn_functions.h"
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#include "torch/csrc/autograd/python_return_types.h"
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#include "torch/csrc/autograd/python_variable.h"
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#include "torch/csrc/autograd/utils/wrap_outputs.h"
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#include "torch/csrc/autograd/utils/python_arg_parsing.h"
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#include "torch/csrc/utils/pycfunction_helpers.h"
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#include "torch/csrc/utils/python_arg_parser.h"
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#include "torch/csrc/utils/structseq.h"
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#ifndef AT_PER_OPERATOR_HEADERS
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#include <ATen/Functions.h>
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#else
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$ops_headers
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#endif
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using at::Tensor;
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using at::Scalar;
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using at::MemoryFormat;
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using at::Generator;
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using at::IntArrayRef;
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using at::ArrayRef;
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using namespace torch::autograd::utils;
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namespace torch { namespace autograd {
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static PyObject* THPNNVariableFunctionsModule = NULL;
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static PyObject * THPVariable__parse_to(PyObject* module, PyObject* args, PyObject* kwargs)
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{
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HANDLE_TH_ERRORS
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static PythonArgParser parser({
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"to(Device device=None, ScalarType dtype=None, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
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"to(ScalarType dtype, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
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"to(Tensor tensor, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
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});
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ParsedArgs<5> parsed_args;
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auto r = parser.parse(args, kwargs, parsed_args);
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if (r.has_torch_function()) {
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return handle_torch_function(r, args, kwargs, THPNNVariableFunctionsModule, "torch.nn");
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}
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auto parsed = parse_to_conversion(r, /*allow_copy*/ false); // we don't want copy for nn.Module.to
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auto& device = std::get<0>(parsed);
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auto& scalarType = std::get<1>(parsed);
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auto non_blocking = std::get<2>(parsed);
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auto opt_memory_format = std::get<4>(parsed);
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auto tuple = THPObjectPtr{PyTuple_New(4)};
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if (!tuple) throw python_error();
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if (device) {
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PyTuple_SET_ITEM(tuple.get(), 0, THPDevice_New(*device));
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} else {
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Py_INCREF(Py_None);
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PyTuple_SET_ITEM(tuple.get(), 0, Py_None);
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}
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if (scalarType) {
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PyTuple_SET_ITEM(tuple.get(), 1, torch::autograd::utils::wrap(torch::getTHPDtype(*scalarType)));
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} else {
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Py_INCREF(Py_None);
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PyTuple_SET_ITEM(tuple.get(), 1, Py_None);
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}
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PyTuple_SET_ITEM(tuple.get(), 2, torch::autograd::utils::wrap(non_blocking));
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if (opt_memory_format.has_value()) {
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PyTuple_SET_ITEM(tuple.get(), 3, THPMemoryFormat_New(opt_memory_format.value(), "unused_name"));
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} else {
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Py_INCREF(Py_None);
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PyTuple_SET_ITEM(tuple.get(), 3, Py_None);
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}
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return tuple.release();
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END_HANDLE_TH_ERRORS
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}
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// generated forward declarations start here
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${py_forwards}
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static PyMethodDef nn_functions[] = {
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{"_parse_to", castPyCFunctionWithKeywords(THPVariable__parse_to),
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METH_VARARGS | METH_KEYWORDS, nullptr},
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${py_method_defs}
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{NULL}
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};
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void initNNFunctions(PyObject* module) {
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static struct PyModuleDef def = {
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PyModuleDef_HEAD_INIT,
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"torch._C._nn",
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NULL,
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-1,
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nn_functions
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};
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PyObject* nn = PyModule_Create(&def);
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THPNNVariableFunctionsModule = nn;
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if (!nn) {
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throw python_error();
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}
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// steals a reference to nn
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if (PyModule_AddObject(module, "_nn", nn) != 0) {
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throw python_error();
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}
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}
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// generated methods start here
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${py_methods}
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}} // namespace torch::autograd
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