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8d64a3848c
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34109 This change adds glue to GraphExecutor to give the RPC server access to the future-based Interpreter::runAsync() api. Previously, if a server encounted a TorchScript continuation-based block with fork/wait, it would simply block in the server thread until the handler completed, since it uses the synchronous Interpreter::run() api. With the ivalue::Future returned by the Interpreter, we can run the TorchScript code asynchronously from c++ simply by connecting its callback to the server callback. We add test cases to cover the new logic, both rpc_async and remote. ghstack-source-id: 101245438 Test Plan: buck test mode/dev-nosan caffe2/test/distributed/rpc/... Differential Revision: D20194321 fbshipit-source-id: 16785ec5d9ed0b16cb1ffab0a9771a77de30fcb0
76 lines
2.0 KiB
C++
76 lines
2.0 KiB
C++
#include <torch/csrc/jit/api/function_impl.h>
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#include <torch/csrc/jit/passes/inliner.h>
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#include <torch/csrc/jit/frontend/error_report.h>
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namespace torch {
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namespace jit {
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namespace {
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c10::FunctionSchema defaultSchemaFor(const Function& function) {
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std::vector<c10::Argument> args;
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std::vector<c10::Argument> returns;
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Graph& g = *function.graph();
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size_t num_inputs = function.num_inputs();
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for (size_t i = 0; i < num_inputs; ++i) {
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const Value* v = g.inputs().at(i);
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std::string name = v->hasDebugName() ? v->debugNameBase()
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: ("argument_" + c10::to_string(i));
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args.emplace_back(std::move(name), unshapedType(g.inputs()[i]->type()));
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}
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for (size_t i = 0; i < g.outputs().size(); ++i) {
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returns.emplace_back("", unshapedType(g.outputs()[i]->type()));
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}
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return {function.name(), "", std::move(args), std::move(returns)};
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}
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} // namespace
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void placeholderCreator(GraphFunction&) {
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throw RecursiveMethodCallError();
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}
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void GraphFunction::run(Stack& stack) {
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get_executor().run(stack);
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}
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void GraphFunction::run(Stack&& stack) {
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run(stack);
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}
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c10::intrusive_ptr<c10::ivalue::Future> GraphFunction::runAsync(Stack& stack) {
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return get_executor().runAsync(stack);
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}
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IValue GraphFunction::operator()(
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std::vector<IValue> stack,
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const Kwargs& kwargs) {
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getSchema().checkAndNormalizeInputs(stack, kwargs);
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run(stack);
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return stack.front();
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}
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void GraphFunction::ensure_defined() {
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if (function_creator_) {
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auto creator = function_creator_;
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function_creator_ = placeholderCreator;
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creator(*this);
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function_creator_ = nullptr;
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}
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check_single_output();
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}
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const c10::FunctionSchema& GraphFunction::getSchema() const {
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if (schema_ == nullptr) {
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schema_ = std::make_unique<c10::FunctionSchema>(defaultSchemaFor(*this));
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}
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return *schema_;
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}
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void preoptimizeGraph(std::shared_ptr<Graph>& graph) {
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// TODO: Invoke cleanup passes before and after inlining to reduce amount of
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// code we're copying.
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Inline(*graph);
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}
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} // namespace jit
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} // namespace torch
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