// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include #include #include #include "paddle/fluid/lite/core/mir/generate_program_pass.h" #include "paddle/fluid/lite/core/mir/pass_manager.h" #include "paddle/fluid/lite/core/mir/ssa_graph.h" #include "paddle/fluid/lite/core/mir/static_kernel_pick_pass.h" #include "paddle/fluid/lite/core/mir/type_target_transform_pass.h" #include "paddle/fluid/lite/core/program.h" #include "paddle/fluid/lite/core/types.h" #include "paddle/fluid/lite/model_parser/model_parser.h" namespace paddle { namespace lite { /* * lite::Optimizer optimize a program. It utilize the mir passes to analysis the * program and export an optimized program. */ class Optimizer { public: void Run(Program&& program, const std::vector& valid_places, core::KernelPickFactor kernel_pick_factor, const std::vector& passes = {}) { program_ = &program; valid_places_ = valid_places; CHECK(!valid_places.empty()) << "At least one valid_place should be set"; CHECK(!graph_) << "duplicate optimize found"; graph_.reset(new mir::SSAGraph); graph_->Build(program, valid_places); SpecifyKernelPickTactic(kernel_pick_factor); InitTargetTypeTransformPass(); // #ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK if (passes.empty()) { RunPasses(std::vector{{ // "static_kernel_pick_pass", // // "variable_place_inference_pass", // // "argument_type_display_pass", // // "type_target_transform_pass", // // "argument_type_display_pass", // // "variable_place_inference_pass", // // "argument_type_display_pass", // // "io_copy_kernel_pick_pass", // // "variable_place_inference_pass", // "runtime_context_assign_pass", // }}); } else { RunPasses(passes); } // #endif exec_scope_ = program.exec_scope(); } void KernelPickPreferPlace(const Place& place) { auto* pass = mir::PassManager::Global().LookUp( "static_kernel_pick_pass"); CHECK(pass); pass->SetPreferPlace(place); } // Generate a new program based on the mir graph. std::unique_ptr GenRuntimeProgram() { LOG(INFO) << "generate program"; std::unique_ptr res; auto pass = mir::PassManager::Global().LookUp( "generate_program_pass"); pass->Apply(graph_); auto program = pass->GenProgram(); CHECK(exec_scope_); program->set_exec_scope(exec_scope_); return program; } void InitTargetTypeTransformPass() { auto* pass = mir::PassManager::Global().LookUp( "type_target_transform_pass"); CHECK(pass); CHECK(!valid_places_.empty()); LOG(INFO) << "valid_places.size " << valid_places_.size(); pass->SetValidPlaces(valid_places_); } // Generate C++ code which combines the inference program, model and weights. void GenCode(const std::string& code_dir); const mir::SSAGraph& ssa_graph() const { CHECK(graph_); return *graph_; } mir::SSAGraph* mutable_ssa_graph() { CHECK(graph_); return graph_.get(); } protected: void SpecifyKernelPickTactic(core::KernelPickFactor factor); // Specify the passes and run them. void RunPasses(const std::vector& passes) { for (auto& x : passes) { LOG(INFO) << "== Running pass " << x; auto* pass = mir::PassManager::Global().LookUp(x); CHECK(pass); pass->Apply(graph_); } } private: std::unique_ptr graph_; std::vector valid_places_; lite::Scope* exec_scope_{}; Program* program_{}; }; } // namespace lite } // namespace paddle