// 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 #include #include #include #include #include #include #include #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/inference/anakin/convert/op_converter.h" #include "paddle/fluid/inference/anakin/op_teller.h" #include "paddle/fluid/inference/analysis/helper.h" #include "paddle/fluid/inference/analysis/ir_passes/anakin_subgraph_pass.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 framework::ir::Node; std::unique_ptr analysis::AnakinSubgraphPass::ApplyImpl( std::unique_ptr graph) const { framework::ir::FusePassBase::Init("anakin_subgraph_pass", graph.get()); auto teller = [](const framework::ir::Node *node) { if (!node->IsOp() || !node->Op()) return false; return anakin::OpTeller::Global().Tell(node->Op()->Type(), *node->Op()); }; SubGraphFuser fuser(graph.get(), teller, 6 /* min_subgraph_size */); fuser(); std::vector graph_param_names = ExtractParameters(graph->Nodes()); // those parameter already exist in anakin, and should not have another copy // in fluid. std::vector repetitive_params; for (auto *node : graph->Nodes()) { if (node->IsOp() && !Agent(node).subgraph()->empty()) { CreateAnakinOp(node, graph.get(), graph_param_names, &repetitive_params); std::unordered_set nodes2remove( Agent(node).subgraph()->begin(), Agent(node).subgraph()->end()); framework::ir::GraphSafeRemoveNodes(graph.get(), nodes2remove); } } std::unordered_set nodes2remove; for (auto *node : graph->Nodes()) { if (node->IsOp() && Agent(node).deleted()) { nodes2remove.insert(node); } } framework::ir::GraphSafeRemoveNodes(graph.get(), nodes2remove); graph->Set(framework::ir::kRepetitiveParamAttr, new std::vector(repetitive_params)); return graph; } std::string GenerateAnakinEngineKey(const std::set &engine_inputs, const std::set &engine_outputs, std::string id) { std::string engine_hash_key = ""; for (auto name : engine_inputs) { engine_hash_key += name; } for (auto name : engine_outputs) { engine_hash_key += name; } engine_hash_key += id; auto engine_key = std::to_string(std::hash()(engine_hash_key)); return engine_key; } void AnakinSubgraphPass::CreateAnakinOp( framework::ir::Node *node, Graph *graph, const std::vector &graph_params, std::vector *repetitive_params) const { auto *op_desc = node->Op(); auto &subgraph = *Agent(node).subgraph(); PADDLE_ENFORCE(!subgraph.empty()); framework::ProgramDesc *program_desc = Get("program"); // Add new block for TensorRTEngineOP const framework::BlockDesc &main_block = program_desc->Block(framework::kRootBlockIndex); // const framework::BlockDesc& main_block = program_desc->Block(0); framework::BlockDesc *new_block = program_desc->AppendBlock(main_block); // An fake block desc. framework::proto::BlockDesc block_proto; framework::BlockDesc block_desc(nullptr, &block_proto); block_desc.Proto()->set_parent_idx(-1); block_desc.Proto()->set_idx(0); string::PrettyLogDetail("--- detect a sub-graph with %d nodes", subgraph.size()); for (auto *node : subgraph) { auto *new_block_op = new_block->AppendOp(); auto *op = block_desc.AppendOp(); *new_block_op->Proto() = *node->Op()->Proto(); *op->Proto() = *node->Op()->Proto(); } // Then, we will use the input_names_with_id and output_names_with_id to // generate the eigine key. // So, We use set instead of unordered_set here to ensure that the engine key // is unique. std::set input_names; std::set input_names_with_id; std::vector params; for (auto *x : node->inputs) { input_names.insert(x->Name()); input_names_with_id.insert(x->Name() + std::to_string(x->id())); if (std::count(graph_params.begin(), graph_params.end(), x->Name()) > 0) { params.push_back(x->Name()); } } std::copy(params.begin(), params.end(), std::back_inserter(*repetitive_params)); op_desc->SetInput( "Xs", std::vector(input_names.begin(), input_names.end())); std::set output_names; std::set output_names_with_id; for (auto *x : node->outputs) { output_names.insert(x->Name()); output_names_with_id.insert(x->Name() + std::to_string(x->id())); } op_desc->SetOutput( "Ys", std::vector(output_names.begin(), output_names.end())); op_desc->SetType("anakin_engine"); std::unordered_map output_name_map; auto &subgraph_nodes = *Agent(node).subgraph(); // The following procedure is used to rename all the intermediate // variables and the output variables of the subgraph. RenameAndGetOutputs(subgraph_nodes, &block_desc, input_names_with_id, &output_names_with_id, &output_names, &output_name_map, false); // When anakin engine runs at the end of the operation, // output_mapping help us copy the data from the renamed ITensor // to Tensor. std::vector output_mapping; for (auto name : output_names) { PADDLE_ENFORCE(output_name_map.count(name) != 0); output_mapping.push_back(output_name_map[name]); } auto *vars = block_desc.Proto()->mutable_vars(); for (framework::ir::Node *node : graph->Nodes()) { if (node->IsVar() && node->Var()) { *vars->Add() = *node->Var()->Proto(); } } PADDLE_ENFORCE(!block_desc.Proto()->vars().empty(), "the block has no var-desc"); PADDLE_ENFORCE(!output_mapping.empty()); op_desc->SetBlockAttr("sub_block", new_block); SetAttr(op_desc->Proto(), "subgraph", block_desc.Proto()->SerializeAsString()); // Set attrs SetAttr(op_desc->Proto(), "parameters", params); SetAttr(op_desc->Proto(), "output_name_mapping", output_mapping); int predictor_id = Get("predictor_id"); auto engine_key = GenerateAnakinEngineKey( input_names_with_id, output_names_with_id, std::to_string(predictor_id)); SetAttr(op_desc->Proto(), "engine_key", engine_key); auto max_input_shape = Get>>("max_input_shape"); auto max_batch_size = Get("max_batch_size"); auto *anakin_engine = inference::Singleton::Global().Create( true, Get("gpu_device_id"), max_batch_size, max_input_shape, engine_key); auto *scope = param_scope(); std::unordered_set param_set(params.begin(), params.end()); framework::BlockDesc block_desc_temp(nullptr, block_desc.Proto()); inference::Singleton::Global() .ConvertBlockToAnakinEngine( &block_desc_temp, scope, std::vector(input_names.begin(), input_names.end()), param_set, output_mapping, anakin_engine); } } // namespace analysis } // namespace inference } // namespace paddle REGISTER_PASS(anakin_subgraph_pass, paddle::inference::analysis::AnakinSubgraphPass);