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zdevitozdevito
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JIT cleanup (#7631)
Cleans up dead code in the JIT: * Remove interpreter_autograd_function * Remove Handles * Remove HandleBuilder * Remove creates_handles, and tracing_autograd_python_function flags * Remove unused var_args * Fix submodules
1 parent e6f7e18 commit 286cd04

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+65
-555
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‎setup.py‎

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@@ -648,7 +648,6 @@ def run(self):
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"torch/csrc/jit/export.cpp",
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"torch/csrc/jit/import.cpp",
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"torch/csrc/jit/autodiff.cpp",
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"torch/csrc/jit/interpreter_autograd_function.cpp",
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"torch/csrc/jit/python_arg_flatten.cpp",
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"torch/csrc/jit/variable_flags.cpp",
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"torch/csrc/jit/passes/create_autodiff_subgraphs.cpp",

‎test/expect/TestJit.test_dropout.expect‎

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@@ -1,4 +1,4 @@
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graph(%0 : Double(2, 2)) {
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%1 : Double(2, 2), %2 : Handle = ^Dropout(0.6, True, False)(%0), scope: Dropout
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%1 : Double(2, 2) = ^Dropout(0.6, True, False)(%0), scope: Dropout
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return (%1);
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}

‎tools/cpp_build/libtorch/CMakeLists.txt‎

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@@ -206,7 +206,6 @@ set(TORCH_SRCS
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${TORCH_SRC_DIR}/csrc/jit/tracer_state.cpp
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${TORCH_SRC_DIR}/csrc/jit/autodiff.cpp
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${TORCH_SRC_DIR}/csrc/jit/type.cpp
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${TORCH_SRC_DIR}/csrc/jit/interpreter_autograd_function.cpp
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${TORCH_SRC_DIR}/csrc/jit/export.cpp
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${TORCH_SRC_DIR}/csrc/jit/import.cpp
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${TORCH_SRC_DIR}/csrc/onnx/onnx.cpp

‎torch/csrc/Module.cpp‎

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@@ -487,7 +487,6 @@ static PyObject* initModule() {
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ASSERT_TRUE(THPVariable_initModule(module));
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ASSERT_TRUE(THPFunction_initModule(module));
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ASSERT_TRUE(THPEngine_initModule(module));
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torch::autograd::initAutogradClosureBindings(module);
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torch::jit::initJITBindings(module);
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torch::onnx::initONNXBindings(module);
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torch::autograd::initNNFunctions(module);

‎torch/csrc/autograd/autograd.h‎

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@@ -6,8 +6,6 @@ void THPAutograd_initFunctions();
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namespace torch { namespace autograd {
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void initAutogradClosureBindings(PyObject* module);
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PyMethodDef* python_functions();
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}}

‎torch/csrc/autograd/function.cpp‎

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@@ -37,11 +37,7 @@ variable_list Function::traced_apply(variable_list inputs) {
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// Insert a CppOp in the trace.
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auto& graph = state->graph;
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std::vector<VariableFlags> var_flags;
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for(auto & input: inputs) {
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var_flags.push_back(VariableFlags::of(input));
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}
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auto* this_node = graph->createCppOp(get_shared_ptr(), std::move(var_flags));
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auto* this_node = graph->createCppOp(get_shared_ptr());
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#ifndef NO_PYTHON
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this_node->setSourceLocation(std::make_shared<StringSourceLocation>(
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jit::tracer::getPythonInterpreterStackTrace()

‎torch/csrc/autograd/functions/init.cpp‎

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@@ -3,7 +3,6 @@
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#include "basic_ops.h"
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#include "tensor.h"
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#include "special.h"
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#include "torch/csrc/jit/interpreter_autograd_function.h"
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#include "torch/csrc/autograd/functions/pybind.h"
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#include "torch/csrc/autograd/python_cpp_function.h"
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#include "torch/csrc/autograd/generated/python_functions.h"
@@ -99,9 +98,6 @@ void THPAutograd_initFunctions()
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static PyTypeObject EvalClass;
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addClass<Eval, NoCtor>(module, EvalClass, "Eval");
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static PyTypeObject InterpreterAutogradClass;
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addClass<torch::jit::InterpreterAutogradFunction, NoCtor>(module, InterpreterAutogradClass, "InterpreterAutogradFunction");
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static PyTypeObject CopyBackwardsClass;
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addClass<CopyBackwards, NoCtor>(module, CopyBackwardsClass, "CopyBackwards");
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@@ -118,18 +114,3 @@ void THPAutograd_initFunctions()
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throw python_error();
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}
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}
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namespace torch { namespace autograd {
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void initAutogradClosureBindings(PyObject* module) {
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auto m = py::handle(module).cast<py::module>();
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py::class_<jit::InterpreterFunctionFactory,std::shared_ptr<jit::InterpreterFunctionFactory>>(m, "InterpreterFunctionFactory")
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.def("__call__", &jit::InterpreterFunctionFactory::construct_function)
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;
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m.def("_jit_createInterpreterFactory", [](jit::tracer::TracingState* tracing_state) {
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return std::make_shared<jit::InterpreterFunctionFactory>(tracing_state);
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});
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}
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}}

‎torch/csrc/autograd/python_function.cpp‎

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@@ -602,19 +602,7 @@ static void _trace_post_record(
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auto state_lock = trace_info.state->lock();
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trace_info.n->i_(attr::inplace, is_inplace);
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// See definition in function.cpp.
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THPObjectPtr passes_py_bool {PyObject_GetAttrString(op_obj, "is_traceable")};
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if (!passes_py_bool) throw python_error();
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bool passes_state_transparently = passes_py_bool == Py_True;
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// NB: this path is executed only for forward of Python functions, so there's no need to check
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// tracing_state->in_eval_subgraph (it's always false, because they are never part of backward
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// subgraphs AND we don't even materialize the forward function).
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if (trace_info.state->creates_handles && !passes_state_transparently) {
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// TODO: sgross and ezyang don't know if this is right
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tracer::nontraceableBackwardSubgraph(input_vars, output_vars);
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Function::set_up_context_edge(trace_info.n, input_vars, output_vars);
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}
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}
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PyObject* process_outputs(PyObject *op_obj, THPFunction* grad_fn, const UnpackedInput& unpacked,

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