luotao1
7 years ago
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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 <google/protobuf/text_format.h>
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#include <gtest/gtest.h>
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#include <thread> // NOLINT
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/pass.h"
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#include "paddle/fluid/inference/analysis/ut_helper.h"
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#include "paddle/fluid/inference/api/analysis_predictor.h"
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#include "paddle/fluid/inference/api/helper.h"
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#include "paddle/fluid/inference/api/paddle_inference_api.h"
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#include "paddle/fluid/inference/api/paddle_inference_pass.h"
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DEFINE_string(infer_model, "", "model path");
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DEFINE_string(infer_data, "", "data path");
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DEFINE_int32(batch_size, 1, "batch size.");
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DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
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DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
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namespace paddle {
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namespace inference {
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using namespace framework; // NOLINT
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struct DataRecord {
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std::vector<std::vector<std::vector<float>>> link_step_data_all;
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std::vector<size_t> lod;
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std::vector<std::vector<float>> rnn_link_data;
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std::vector<float> result_data;
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size_t batch_iter{0};
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size_t batch_size{1};
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DataRecord() = default;
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explicit DataRecord(const std::string &path, int batch_size = 1)
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: batch_size(batch_size) {
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Load(path);
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}
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DataRecord NextBatch() {
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DataRecord data;
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size_t batch_end = batch_iter + batch_size;
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// NOTE skip the final batch, if no enough data is provided.
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if (batch_end <= link_step_data_all.size()) {
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data.link_step_data_all.assign(link_step_data_all.begin() + batch_iter,
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link_step_data_all.begin() + batch_end);
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// Prepare LoDs
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data.lod.push_back(0);
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CHECK(!data.link_step_data_all.empty()) << "empty";
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for (size_t j = 0; j < data.link_step_data_all.size(); j++) {
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for (const auto &d : data.link_step_data_all[j]) {
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data.rnn_link_data.push_back(d);
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// calculate lod
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data.lod.push_back(data.lod.back() + 11);
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}
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}
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}
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batch_iter += batch_size;
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return data;
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}
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void Load(const std::string &path) {
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std::ifstream file(path);
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std::string line;
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int num_lines = 0;
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while (std::getline(file, line)) {
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num_lines++;
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std::vector<std::string> data;
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split(line, ':', &data);
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if (num_lines % 2) { // feature
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std::vector<std::string> feature_data;
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split(data[1], ' ', &feature_data);
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std::vector<std::vector<float>> link_step_data;
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int feature_count = 1;
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std::vector<float> feature;
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for (auto &step_data : feature_data) {
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std::vector<float> tmp;
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split_to_float(step_data, ',', &tmp);
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feature.insert(feature.end(), tmp.begin(), tmp.end());
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if (feature_count % 11 == 0) { // each sample has 11 features
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link_step_data.push_back(feature);
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feature.clear();
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}
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feature_count++;
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}
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link_step_data_all.push_back(std::move(link_step_data));
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} else { // result
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std::vector<float> tmp;
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split_to_float(data[1], ',', &tmp);
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result_data.insert(result_data.end(), tmp.begin(), tmp.end());
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}
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}
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}
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};
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void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
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int batch_size) {
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PaddleTensor feed_tensor;
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feed_tensor.name = "feed";
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auto one_batch = data->NextBatch();
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int token_size = one_batch.rnn_link_data.size();
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// each token has 11 features, each feature's dim is 54.
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std::vector<int> rnn_link_data_shape({token_size * 11, 54});
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feed_tensor.shape = rnn_link_data_shape;
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feed_tensor.lod.assign({one_batch.lod});
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feed_tensor.dtype = PaddleDType::FLOAT32;
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TensorAssignData<float>(&feed_tensor, one_batch.rnn_link_data);
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// Set inputs.
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input_slots->assign({feed_tensor});
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}
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void CompareResult(const std::vector<PaddleTensor> &outputs,
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const std::vector<float> &base_result) {
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PADDLE_ENFORCE_GT(outputs.size(), 0);
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for (size_t i = 0; i < outputs.size(); i++) {
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auto &out = outputs[i];
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size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
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[](int a, int b) { return a * b; });
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PADDLE_ENFORCE_GT(size, 0);
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float *data = static_cast<float *>(out.data.data());
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for (size_t i = 0; i < size; i++) {
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EXPECT_NEAR(data[i], base_result[i], 1e-3);
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}
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}
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}
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// Test with a really complicate model.
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void TestRNN2Prediction() {
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AnalysisConfig config;
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config.prog_file = FLAGS_infer_model + "/__model__";
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config.param_file = FLAGS_infer_model + "/param";
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config.use_gpu = false;
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config.device = 0;
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config.specify_input_name = true;
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config.enable_ir_optim = true;
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PADDLE_ENFORCE(config.ir_mode ==
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AnalysisConfig::IrPassMode::kExclude); // default
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int batch_size = FLAGS_batch_size;
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int num_times = FLAGS_repeat;
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auto base_predictor =
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CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
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auto predictor =
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CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
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config);
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std::vector<PaddleTensor> input_slots;
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DataRecord data(FLAGS_infer_data, batch_size);
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PrepareInputs(&input_slots, &data, batch_size);
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std::vector<PaddleTensor> outputs, base_outputs;
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Timer timer1;
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timer1.tic();
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for (int i = 0; i < num_times; i++) {
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base_predictor->Run(input_slots, &base_outputs);
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}
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PrintTime(batch_size, num_times, 1, 0, timer1.toc() / num_times);
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Timer timer2;
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timer2.tic();
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for (int i = 0; i < num_times; i++) {
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predictor->Run(input_slots, &outputs);
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}
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PrintTime(batch_size, num_times, 1, 0, timer2.toc() / num_times);
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CompareResult(base_outputs, data.result_data);
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CompareResult(outputs, data.result_data);
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}
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TEST(Analyzer, rnn2) { TestRNN2Prediction(); }
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} // namespace inference
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} // namespace paddle
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