// 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 #include #include "lite/core/mir/generate_program_pass.h" #include "lite/core/mir/pass_manager.h" #include "lite/core/mir/pass_utils.h" #include "lite/core/mir/ssa_graph.h" #include "lite/core/mir/static_kernel_pick_pass.h" #include "lite/core/mir/type_target_cast_pass.h" #include "lite/core/program.h" #include "lite/core/types.h" #include "lite/model_parser/model_parser.h" #ifdef LITE_WITH_NPU #include "lite/core/mir/subgraph/generate_npu_program_pass.h" #endif #ifdef LITE_WITH_XPU #include "lite/core/mir/subgraph/generate_xpu_program_pass.h" #endif 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); graph_->SetValidPlaces(valid_places); SpecifyKernelPickTactic(kernel_pick_factor); InitTargetTypeTransformPass(); if (passes.empty()) { std::vector passes_local{ {"lite_quant_dequant_fuse_pass", // "lite_conv_elementwise_fuse_pass", // conv-elemwise-bn "lite_conv_bn_fuse_pass", // "lite_conv_elementwise_fuse_pass", // conv-bn-elemwise // TODO(Superjomn) Refine the fusion related design to select fusion // kernels for devices automatically. "lite_conv_activation_fuse_pass", // "lite_fc_fuse_pass", // "lite_shuffle_channel_fuse_pass", // "lite_transpose_softmax_transpose_fuse_pass", // "lite_interpolate_fuse_pass", // "identity_scale_eliminate_pass", // #ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK "lite_elementwise_add_activation_fuse_pass", // #endif "static_kernel_pick_pass", // pick original kernel from graph "variable_place_inference_pass", // inference arg/var's // info(target/precision/layout/device) // using kernel info "argument_type_display_pass", // debug pass: show arg-type-node's // info // (target/precision/layout/device) "type_target_cast_pass", // add io_copy/io_copy_once if meet // different targets when last and next // node "variable_place_inference_pass", // "argument_type_display_pass", // "io_copy_kernel_pick_pass", // "argument_type_display_pass", // "variable_place_inference_pass", // "argument_type_display_pass", // "type_precision_cast_pass", // "variable_place_inference_pass", // "argument_type_display_pass", // "type_layout_cast_pass", // add layout/layout_once op if meet // different layout when last and next node "argument_type_display_pass", // "variable_place_inference_pass", // "argument_type_display_pass", "runtime_context_assign_pass", "argument_type_display_pass", "memory_optimize_pass"}}; RunPasses(passes_local); } else { RunPasses(passes); } exec_scope_ = program.exec_scope(); } const lite::Scope* exec_scope() const { return exec_scope_; } // Generate a new program based on the mir graph. std::unique_ptr GenRuntimeProgram() { // Extra passes are applied for NPU and XPU, they depends on the shapes // of input tensors. so GenRuntimeProgram() must be called after the shapes // of input tensors are determined. std::vector subgraph_passes{"generate_npu_program_pass", "generate_xpu_program_pass"}; RunPasses(subgraph_passes); 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_cast_pass"); CHECK(pass); CHECK(!valid_places_.empty()); 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(); } lite::Scope* exec_scope() { return exec_scope_; } 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; mir::Pass* pass = mir::PassManager::Global().LookUp(x); if (!pass) { LOG(INFO) << " - Skip " << x << " because the pass isn't found."; continue; } std::set targets; for (const auto& place : valid_places_) { targets.insert(place.target); } bool matched = PassMatchesTarget(*pass, targets) && PassMatchesKernels(*pass); if (!matched) { LOG(INFO) << " - Skip " << x << " because the target or kernel does not match."; } else { pass->Apply(graph_); LOG(INFO) << "== Finished running: " << x; } } } private: std::unique_ptr graph_; std::vector valid_places_; lite::Scope* exec_scope_{}; Program* program_{}; }; } // namespace lite } // namespace paddle