// 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. #include "paddle/fluid/inference/analysis/ir_pass_manager.h" #include #include #include #include #include #include #include #include "paddle/fluid/framework/ir/fuse_pass_base.h" #include "paddle/fluid/framework/ir/graph.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/inference/analysis/argument.h" #include "paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h" #include "paddle/fluid/string/pretty_log.h" namespace paddle { namespace inference { namespace analysis { using string::PrettyLogEndl; using string::PrettyLog; using string::Style; IRPassManager::IRPassManager(Argument *argument) { ARGUMENT_CHECK_FIELD(argument, main_program); graph_ = std::unique_ptr(new Graph(argument->main_program())); if (argument->Has("scope")) { graph_->Set(framework::ir::kParamScopeAttr, new framework::Scope *( const_cast(&argument->scope()))); } ARGUMENT_CHECK_FIELD(argument, ir_analysis_passes); CreatePasses(argument, argument->ir_analysis_passes()); } void IRPassManager::CreatePasses(Argument *argument, const std::vector &passes) { std::string pre_pass; int pass_num = 0; for (const std::string &pass_name : passes) { auto pass = framework::ir::PassRegistry::Instance().Get(pass_name); if (pass_name == "graph_viz_pass") { std::string dot_file_path = std::to_string(pass_num) + "_ir_" + (pre_pass.empty() ? "origin" : pre_pass) + ".dot"; pass->Set("graph_viz_path", new std::string(std::move(dot_file_path))); pass_num++; } else if (pass_name == "mkldnn_placement_pass") { pass->Set("mkldnn_enabled_op_types", new std::unordered_set( argument->mkldnn_enabled_op_types())); #ifdef PADDLE_WITH_MKLDNN } else if (pass_name == "cpu_quantize_placement_pass") { pass->Set("quantize_enabled_op_types", new std::unordered_set( argument->quantize_enabled_op_types())); pass->Set( "quantize_excluded_op_ids", new std::unordered_set(argument->quantize_excluded_op_ids())); } else if (pass_name == "cpu_quantize_pass") { pass->Set("quant_var_scales", new VarQuantScale(argument->quant_var_scales())); #endif } else if (pass_name == "tensorrt_subgraph_pass") { pass->Set("workspace_size", new int(argument->tensorrt_workspace_size())); pass->Set("max_batch_size", new int(argument->tensorrt_max_batch_size())); pass->Set("min_subgraph_size", new int(argument->tensorrt_min_subgraph_size())); pass->Set("program", new framework::ProgramDesc *(&argument->main_program())); bool enable_int8 = argument->tensorrt_precision_mode() == AnalysisConfig::Precision::kInt8; pass->Set("enable_int8", new bool(enable_int8)); bool use_static_engine = argument->tensorrt_use_static_engine(); bool model_from_memory = argument->model_from_memory(); bool int8_valid = !(model_from_memory && enable_int8); PADDLE_ENFORCE(int8_valid, "TRT INT8 Now don't support model load from memory."); if ((!model_from_memory && use_static_engine) || enable_int8) { std::string model_opt_cache_dir = argument->Has("model_dir") ? argument->model_dir() : GetDirRoot(argument->model_program_path()); pass->Set( "model_opt_cache_dir", new std::string(GetOrCreateModelOptCacheDir(model_opt_cache_dir))); } pass->Set("gpu_device_id", new int(argument->gpu_device_id())); pass->Set("use_static_engine", new bool(use_static_engine)); pass->Set("model_from_memory", new bool(argument->model_from_memory())); pass->Set("engine_opt_info", new std::map( argument->engine_opt_info())); } if (pass_name == "anakin_subgraph_pass") { pass->Set("program", new framework::ProgramDesc *(&argument->main_program())); pass->Set("use_gpu", new bool(argument->use_gpu())); pass->Set("gpu_device_id", new int(argument->gpu_device_id())); pass->Set("model_from_memory", new bool(argument->model_from_memory())); pass->Set("engine_opt_info", new std::map( argument->engine_opt_info())); pass->Set("predictor_id", new int(argument->predictor_id())); pass->Set("max_input_shape", new std::map>( argument->anakin_max_input_shape())); pass->Set("max_batch_size", new int(argument->anakin_max_batch_size())); } pre_pass = pass_name; passes_.emplace_back(std::move(pass)); } } std::unique_ptr IRPassManager::Apply(std::unique_ptr graph) { if (passes_.empty()) { return graph; } PADDLE_ENFORCE(graph.get()); // Apply all the passes for (const auto &pass : passes_) { if (pass->Type() != "graph_viz_pass") { PrettyLogEndl(Style::H2(), "--- Running IR pass [%s]", pass->Type()); } graph.reset(pass->Apply(graph.release())); } return graph; } framework::proto::ProgramDesc IRPassManager::AcquireProgram( std::unique_ptr *graph, ProgramDesc *program) const { auto pass = framework::ir::PassRegistry::Instance().Get("graph_to_program_pass"); // Direct using ProgramDesc desc(argument->main_program()) may cause // incomplete copies of information. ProgramDesc desc; desc.CopyFrom(*program->Proto()); pass->SetNotOwned("program", &desc); auto *the_graph = graph->release(); graph->reset(pass->Apply(the_graph)); return *desc.Proto(); } } // namespace analysis } // namespace inference } // namespace paddle