analysis/code-clean
Yancey1989
7 years ago
commit
6d01f10d56
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/contrib/inference/paddle_inference_api.h"
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#include "paddle/contrib/inference/paddle_inference_api_impl.h"
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#include "paddle/fluid/inference/analysis/analyzer.h"
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#include "paddle/fluid/inference/utils/singleton.h"
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namespace paddle {
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using inference::analysis::Argument;
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using inference::Singleton;
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using inference::analysis::Analyzer;
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using framework::proto::ProgramDesc;
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class TensorRTSubgraphPredictor : public NativePaddlePredictor {
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public:
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explicit TensorRTSubgraphPredictor(const TensorRTConfig& config)
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: NativePaddlePredictor(config), config_(config) {}
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bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
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VLOG(3) << "Predictor::init()";
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if (config_.use_gpu) {
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place_ = paddle::platform::CUDAPlace(config_.device);
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} else {
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place_ = paddle::platform::CPUPlace();
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}
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if (parent_scope) {
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scope_ = parent_scope;
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sub_scope_ = &(parent_scope->NewScope());
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} else {
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paddle::framework::InitDevices(false);
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scope_.reset(new paddle::framework::Scope());
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}
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executor_.reset(new paddle::framework::Executor(place_));
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// Initialize the inference program
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if (!config_.model_dir.empty()) {
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// Parameters are saved in separate files sited in
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// the specified `dirname`.
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inference_program_ = paddle::inference::Load(
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executor_.get(), scope_.get(), config_.model_dir);
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} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
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// All parameters are saved in a single file.
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// The file names should be consistent with that used
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// in Python API `fluid.io.save_inference_model`.
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inference_program_ = paddle::inference::Load(
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executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
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} else {
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LOG(ERROR) << "fail to load inference model.";
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return false;
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}
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// Analyze inference_program
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Argument argument;
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argument.origin_program_desc.reset(
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new ProgramDesc(*inference_program_->Proto()));
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Singleton<Analyzer>::Global().Run(&argument);
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CHECK(argument.transformed_program_desc);
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VLOG(5) << "transformed program:\n"
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<< argument.transformed_program_desc->SerializeAsString();
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VLOG(5) << "to prepare executor";
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*inference_program_->Proto() = *argument.transformed_program_desc;
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ctx_ = executor_->Prepare(*inference_program_, 0);
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VLOG(5) << "to create variables";
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executor_->CreateVariables(
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*inference_program_, sub_scope_ ? sub_scope_ : scope_.get(), 0);
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// Get the feed_target_names and fetch_target_names
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feed_target_names_ = inference_program_->GetFeedTargetNames();
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fetch_target_names_ = inference_program_->GetFetchTargetNames();
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return true;
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}
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private:
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TensorRTConfig config_;
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};
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template <>
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std::unique_ptr<PaddlePredictor>
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CreatePaddlePredictor<TensorRTConfig, PaddleEngineKind::kAutoMixedTensorRT>(
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const TensorRTConfig& config) {
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VLOG(3) << "create TensorRTSubgraphPredictor";
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if (config.use_gpu) {
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// 1. GPU memeroy
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PADDLE_ENFORCE_GT(
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config.fraction_of_gpu_memory,
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0.f,
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"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
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PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
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std::vector<std::string> flags;
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if (config.fraction_of_gpu_memory >= 0.0f ||
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config.fraction_of_gpu_memory <= 0.95f) {
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flags.push_back("dummpy");
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std::string flag = "--fraction_of_gpu_memory_to_use=" +
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std::to_string(config.fraction_of_gpu_memory);
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flags.push_back(flag);
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VLOG(3) << "set flag: " << flag;
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framework::InitGflags(flags);
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}
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}
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std::unique_ptr<PaddlePredictor> predictor(
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new TensorRTSubgraphPredictor(config));
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if (!dynamic_cast<TensorRTSubgraphPredictor*>(predictor.get())
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->Init(nullptr)) {
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return nullptr;
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}
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return std::move(predictor);
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}
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} // namespace paddle
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@ -0,0 +1,64 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <gflags/gflags.h>
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include "paddle/contrib/inference/paddle_inference_api.h"
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namespace paddle {
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DEFINE_string(dirname, "", "Directory of the inference model.");
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void Main(bool use_gpu) {
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//# 1. Create PaddlePredictor with a config.
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TensorRTConfig config;
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config.model_dir = FLAGS_dirname + "word2vec.inference.model";
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config.use_gpu = use_gpu;
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config.fraction_of_gpu_memory = 0.15;
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config.device = 0;
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auto predictor =
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CreatePaddlePredictor<TensorRTConfig,
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PaddleEngineKind::kAutoMixedTensorRT>(config);
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for (int batch_id = 0; batch_id < 3; batch_id++) {
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//# 2. Prepare input.
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int64_t data[4] = {1, 2, 3, 4};
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PaddleTensor tensor{.name = "",
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.shape = std::vector<int>({4, 1}),
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.data = PaddleBuf(data, sizeof(data)),
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.dtype = PaddleDType::INT64};
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// For simplicity, we set all the slots with the same data.
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std::vector<PaddleTensor> slots(4, tensor);
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//# 3. Run
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std::vector<PaddleTensor> outputs;
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CHECK(predictor->Run(slots, &outputs));
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//# 4. Get output.
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ASSERT_EQ(outputs.size(), 1UL);
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LOG(INFO) << "output buffer size: " << outputs.front().data.length();
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const size_t num_elements = outputs.front().data.length() / sizeof(float);
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// The outputs' buffers are in CPU memory.
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for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
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LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
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}
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}
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}
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TEST(paddle_inference_api_tensorrt_subgraph_engine, main) { Main(true); }
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} // namespace paddle
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@ -0,0 +1,82 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/inference/analysis/analyzer.h"
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#include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
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#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
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#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
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#include "paddle/fluid/inference/analysis/pass_manager.h"
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#include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h"
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#include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h"
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namespace paddle {
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namespace inference {
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namespace analysis {
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DEFINE_bool(inference_analysis_enable_tensorrt_subgraph_engine, false,
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"Enable subgraph to TensorRT engine for acceleration");
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DEFINE_string(inference_analysis_graphviz_log_root, "./",
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"Graphviz debuger for data flow graphs.");
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class DfgPassManagerImpl final : public DfgPassManager {
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public:
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DfgPassManagerImpl() {
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// TODO(Superjomn) set the key with pass reprs.
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AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
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if (FLAGS_inference_analysis_enable_tensorrt_subgraph_engine) {
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auto trt_teller = [](const Node* node) {
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if (!node->IsFunction()) return false;
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return static_cast<const Function*>(node)->func_type() == "mul";
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};
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AddPass("tensorrt-subgraph-marker",
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new TensorRTSubgraphNodeMarkPass(trt_teller));
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AddPass("tensorrt-subgraph", new TensorRTSubGraphPass(trt_teller));
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}
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AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
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}
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std::string repr() const override { return "dfg-pass-manager"; }
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std::string description() const override { return "DFG pass manager."; }
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private:
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void AddPass(const std::string& name, Pass* pass) {
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LOG(INFO) << "Adding pass " << name;
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Register(name, pass);
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AddGraphvizDebugerPass(pass);
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}
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// Add the graphviz debuger pass if the parent pass has one.
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void AddGraphvizDebugerPass(Pass* pass) {
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auto* debuger_pass = pass->CreateGraphvizDebugerPass();
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if (debuger_pass) {
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LOG(INFO) << " - register debug pass [" << debuger_pass->repr() << "]";
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Register(debuger_pass->repr(), debuger_pass);
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}
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}
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};
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Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
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void Analyzer::Run(Argument* argument) {
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for (auto& x : data_) {
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PADDLE_ENFORCE(x->Initialize(argument));
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x->RunAll();
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PADDLE_ENFORCE(x->Finalize());
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}
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}
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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@ -0,0 +1,66 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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/*
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* This file contains Analyzer, an class that exposed as a library that analyze
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* and optimize
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* Fluid ProgramDesc for inference. Similar to LLVM, it has multiple flags to
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* control whether
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* an process is applied on the program.
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*
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* The processes are called Passes in analysis, the Passes are placed in a
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* pipeline, the first
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* Pass is the FluidToDataFlowGraphPass which transforms a Fluid ProgramDesc to
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* a data flow
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* graph, the last Pass is DataFlowGraphToFluidPass which transforms a data flow
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* graph to a
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* Fluid ProgramDesc. The passes in the middle of the pipeline can be any Passes
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* which take a
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* node or data flow graph as input.
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*
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* The Analyzer can be used in two methods, the first is a executable file which
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* can be used to
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* pre-process the inference model and can be controlled by passing difference
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* command flags;
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* the other way is to compose inside the inference API as a runtime pre-process
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* phase in the
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* inference service.
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*/
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#include <gflags/gflags.h>
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#include "paddle/fluid/inference/analysis/pass.h"
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#include "paddle/fluid/inference/analysis/pass_manager.h"
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namespace paddle {
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namespace inference {
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namespace analysis {
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// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
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// flag if not available.
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DECLARE_bool(inference_analysis_enable_tensorrt_subgraph_engine);
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DECLARE_string(inference_analysis_graphviz_log_root);
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class Analyzer : public OrderedRegistry<PassManager> {
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public:
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// Register all the pass-managers.
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Analyzer();
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void Run(Argument* argument);
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DISABLE_COPY_AND_ASSIGN(Analyzer);
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};
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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@ -0,0 +1,29 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/inference/analysis/analyzer.h"
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#include "paddle/fluid/inference/analysis/ut_helper.h"
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namespace paddle {
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namespace inference {
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namespace analysis {
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TEST_F(DFG_Tester, main) {
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Analyzer analyser;
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analyser.Run(&argument);
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}
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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