// Copyright (c) 2018 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 /*! \file */ /*! \namespace paddle */ namespace paddle { /** This is a pass builder based on string. It is part of inference API. */ class PaddlePassBuilder { public: explicit PaddlePassBuilder(const std::vector &passes) : passes_(passes) {} void SetPasses(std::initializer_list passes) { passes_ = passes; } /** Append a pass to the end of the passes. */ void AppendPass(const std::string &pass_type); /** Insert a pass to a specific position. * @param idx the position to insert. * @param pass_type the pass key. */ void InsertPass(size_t idx, const std::string &pass_type); /** Delete the `idx`-th pass. */ void DeletePass(size_t idx); /** Delete all the passes that has type `pass_type`. */ void DeletePass(const std::string &pass_type); void ClearPasses(); /** Append an analysis pass. */ void AppendAnalysisPass(const std::string &pass); /** Visualize the computation graph after each pass by generating a DOT * language file, one can draw them with the Graphviz toolkit. */ void TurnOnDebug(); /** Human-readible information. */ std::string DebugString(); const std::vector &AllPasses() const { return passes_; } std::vector AnalysisPasses() const { auto passes = analysis_passes_; // To make sure the ir_graph_to_program should be the last pass so any // modication of IR will persist to the program. passes.push_back("ir_graph_to_program_pass"); return passes; } protected: std::vector analysis_passes_{ {"ir_graph_build_pass", "ir_analysis_pass", "ir_params_sync_among_devices_pass", "adjust_cudnn_workspace_size_pass", "inference_op_replace_pass"}}; std::vector passes_; }; /**Pass strategy to help control the IR passes. */ class PassStrategy : public PaddlePassBuilder { public: explicit PassStrategy(const std::vector &passes) : PaddlePassBuilder(passes) {} /** The MKLDNN control exists in both CPU and GPU mode, because there can be * still some CPU kernels running in CPU mode. */ virtual void EnableMKLDNN() {} /** Enable NGRAPH optimization */ virtual void EnableNgraph() {} /** Enable MKLDNN quantize optimization */ virtual void EnableMkldnnQuantizer() {} bool use_gpu() const { return use_gpu_; } virtual ~PassStrategy() = default; protected: bool use_ngraph_{false}; bool use_gpu_{false}; bool use_mkldnn_{false}; }; /** The CPU passes controller, it is used in AnalysisPredictor with CPU mode. */ class CpuPassStrategy : public PassStrategy { public: CpuPassStrategy(); explicit CpuPassStrategy(const CpuPassStrategy &other) : PassStrategy(other.AllPasses()) { use_gpu_ = other.use_gpu_; use_ngraph_ = other.use_ngraph_; use_mkldnn_ = other.use_mkldnn_; use_mkldnn_quantizer_ = other.use_mkldnn_quantizer_; } virtual ~CpuPassStrategy() = default; void EnableNgraph() override; void EnableMKLDNN() override; void EnableMkldnnQuantizer() override; protected: bool use_ngraph_{false}; bool use_mkldnn_quantizer_{false}; }; /** The GPU passes strategy, it is used in AnalysisPredictor with GPU mode. */ class GpuPassStrategy : public PassStrategy { public: GpuPassStrategy(); explicit GpuPassStrategy(const GpuPassStrategy &other) : PassStrategy(other.AllPasses()) { use_gpu_ = true; } void EnableNgraph() override; void EnableMKLDNN() override; void EnableMkldnnQuantizer() override; virtual ~GpuPassStrategy() = default; }; extern const std::vector kTRTSubgraphPasses; extern const std::vector kAnakinSubgraphPasses; } // namespace paddle