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0ae5498079
This pass splits differentiable subgraphs into their own Node, similar to a fusion group. This initial implementation does not create optimal subgraphs, but it works well in the case where most things are differentiable, and has the building blocks (`mergeNodes`) to extend to the better implementation.
70 lines
2.3 KiB
C++
70 lines
2.3 KiB
C++
#include "torch/csrc/utils/pybind.h"
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#include "torch/csrc/jit/python_tracer.h"
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#include "torch/csrc/jit/python_ir.h"
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#include "torch/csrc/jit/python_arg_flatten.h"
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#include "torch/csrc/jit/export.h"
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#include "torch/csrc/jit/python_compiled_function.h"
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#include "torch/csrc/jit/passes/graph_fuser.h"
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#include "torch/csrc/jit/passes/onnx.h"
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#include "torch/csrc/jit/passes/dead_code_elimination.h"
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#include "torch/csrc/jit/passes/common_subexpression_elimination.h"
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#include "torch/csrc/jit/passes/peephole.h"
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#include "torch/csrc/jit/passes/canonicalize.h"
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#include "torch/csrc/jit/passes/onnx/peephole.h"
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namespace torch { namespace jit {
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namespace {
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bool loadPythonClasses() {
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// Leaving this code here, because it will likely be useful at some point
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//PyObject *jit_module = PyImport_ImportModule("torch.jit");
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//THPUtils_assert(jit_module, "class loader couldn't access "
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//"torch.jit module");
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//PyObject *jit_dict = PyModule_GetDict(jit_module);
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return true;
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}
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template<void (*F)(std::shared_ptr<Graph>& graph)>
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void graph_pass(const std::shared_ptr<tracer::TracingState>& state) {
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return F(state->graph);
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}
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} // anonymous namespace
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extern std::string runJITCPPTests();
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void initJITBindings(PyObject *module) {
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auto m = py::handle(module).cast<py::module>();
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py::class_<python::IODescriptor>(m, "IODescriptor");
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m.def("_jit_init", loadPythonClasses)
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.def("_jit_pass_onnx", ToONNX)
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.def("_jit_pass_onnx_peephole", graph_pass<PeepholeOptimizeONNX>)
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.def("_jit_pass_fuse", graph_pass<FuseGraph>)
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.def("_jit_pass_dce", graph_pass<EliminateDeadCode>)
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.def("_jit_pass_cse", graph_pass<EliminateCommonSubexpression>)
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.def("_jit_pass_peephole", graph_pass<PeepholeOptimize>)
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.def("_jit_pass_canonicalize", graph_pass<Canonicalize>)
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.def("_jit_pass_lint", graph_pass<LintGraph>)
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.def("_jit_run_cpp_tests", runJITCPPTests)
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.def("_jit_flatten", [](py::handle& obj) {
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auto res = python::flatten(obj);
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return std::make_pair(res.vars, res.desc);
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})
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.def("_jit_unflatten", [](autograd::variable_list vars, python::IODescriptor& desc) {
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return py::reinterpret_steal<py::object>(python::unflatten(vars, desc));
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});
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initPythonIRBindings(module);
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initPythonTracerBindings(module);
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python::initCompilerMixin(module);
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}
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}}
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