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99e13e68e9
Follows #131830 Pull Request resolved: https://github.com/pytorch/pytorch/pull/131903 Approved by: https://github.com/Skylion007
85 lines
2.6 KiB
C++
85 lines
2.6 KiB
C++
#include <torch/csrc/jit/runtime/interpreter.h>
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#include <torch/csrc/python_headers.h>
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#include <torch/csrc/autograd/edge.h>
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#include <torch/csrc/autograd/function.h>
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#include <torch/csrc/autograd/profiler.h>
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#include <torch/csrc/autograd/variable.h>
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#include <torch/csrc/jit/ir/ir.h>
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#include <torch/csrc/jit/python/pybind_utils.h>
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#include <torch/csrc/jit/python/python_ir.h>
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#include <torch/csrc/jit/runtime/custom_operator.h>
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#include <torch/csrc/jit/runtime/graph_executor.h>
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#include <torch/csrc/jit/runtime/operator.h>
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#include <pybind11/pybind11.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/autograd/python_engine.h>
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#include <torch/csrc/autograd/python_variable.h>
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#include <torch/csrc/jit/python/pybind.h>
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#include <torch/csrc/utils/pybind.h>
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namespace py = pybind11;
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namespace torch::jit {
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namespace {
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// Note: const_cast is used twice below to acquire a handle to a pyobject.
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Operation createPythonOperation(const Node* op_) {
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pybind11::gil_scoped_acquire gil;
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const ConcretePythonOp* op = static_cast<const ConcretePythonOp*>(op_);
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const py::function func = py::reinterpret_borrow<const py::function>(
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// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
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py::handle(const_cast<ConcretePythonOp*>(op)->pyobj.get()));
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size_t num_inputs = 0;
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for (auto arg_type : op->cconv) {
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if (arg_type == 'd')
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num_inputs++;
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}
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AT_ASSERT(op->outputs().size() == 1);
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return [=](Stack& stack) {
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pybind11::gil_scoped_acquire gil;
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py::tuple py_inputs(op->cconv.size());
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size_t i = 0;
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size_t next_scalar = 0;
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size_t next_tensor = 0;
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for (auto arg_type : op->cconv) {
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if (arg_type == 'c') {
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py_inputs[i] = py::reinterpret_borrow<const py::object>(
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// NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
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const_cast<ConcretePythonOp*>(op)
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->scalar_args[next_scalar++]
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.get());
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} else if (arg_type == 'd') {
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py_inputs[i] =
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toPyObject(std::move(peek(stack, next_tensor, num_inputs)));
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next_tensor++;
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}
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i++;
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}
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drop(stack, num_inputs);
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try {
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py::object py_output(func(*py_inputs));
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stack.push_back(returnToIValue(op->output()->type(), py_output));
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} catch (py::error_already_set& e) {
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throw std::runtime_error(e.what());
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}
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};
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}
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c10::AliasAnalysisKind aliasAnalysisIsSpecialCase() {
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return AliasAnalysisKind::INTERNAL_SPECIAL_CASE;
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}
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RegisterOperators reg({Operator(
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prim::PythonOp,
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createPythonOperation,
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aliasAnalysisIsSpecialCase())});
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} // namespace
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} // namespace torch::jit
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