add lac test

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -50,7 +50,7 @@ endfunction(inference_download_and_uncompress)
 set(DITU_RNN_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fmodel.tar.gz")
 set(DITU_RNN_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fdata.txt.tar.gz")
 set(DITU_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/ditu_rnn" CACHE PATH "Ditu RNN model and data root." FORCE)
-if (NOT EXISTS ${DITU_INSTALL_DIR})
+if (NOT EXISTS ${DITU_INSTALL_DIR} AND WITH_TESTING)
   inference_download_and_uncompress(${DITU_INSTALL_DIR} ${DITU_RNN_MODEL_URL} "ditu_rnn_fluid%2Fmodel.tar.gz")
   inference_download_and_uncompress(${DITU_INSTALL_DIR} ${DITU_RNN_DATA_URL} "ditu_rnn_fluid%2Fdata.txt.tar.gz")
 endif()
@@ -86,7 +86,7 @@ inference_analysis_test(test_model_store_pass SRCS model_store_pass_tester.cc)
 set(CHINESE_NER_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner_model.tar.gz")
 set(CHINESE_NER_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner-data.txt.tar.gz")
 set(CHINESE_NER_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/chinese_ner" CACHE PATH "Chinese ner model and data root." FORCE)
-if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR})
+if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR} AND WITH_TESTING)
   inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_MODEL_URL} "chinese_ner_model.tar.gz")
   inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_DATA_URL} "chinese_ner-data.txt.tar.gz")
 endif()
@@ -95,3 +95,16 @@ inference_analysis_test(test_analyzer_ner SRCS analyzer_ner_tester.cc
     EXTRA_DEPS paddle_inference_api paddle_fluid_api
     ARGS --infer_model=${CHINESE_NER_INSTALL_DIR}/model
         --infer_data=${CHINESE_NER_INSTALL_DIR}/data.txt)
+
+set(LAC_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/lac_model.tar.gz")
+set(LAC_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/lac_data.txt.tar.gz")
+set(LAC_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/lac" CACHE PATH "LAC model and data root." FORCE)
+if (NOT EXISTS ${LAC_INSTALL_DIR} AND WITH_TESTING)
+    inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_MODEL_URL} "lac_model.tar.gz")
+    inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_DATA_URL} "lac_data.txt.tar.gz")
+endif()
+
+inference_analysis_test(test_analyzer_lac SRCS analyzer_lac_tester.cc
+    EXTRA_DEPS paddle_inference_api paddle_fluid_api
+    ARGS --infer_model=${LAC_INSTALL_DIR}/model
+        --infer_data=${LAC_INSTALL_DIR}/data.txt)

paddle/fluid/inference/analysis/analyzer_lac_tester.cc

+// 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/analyzer.h"
+#include <google/protobuf/text_format.h>
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/platform/profiler.h"
+DEFINE_string(infer_model, "", "model path for LAC");
+DEFINE_string(infer_data, "", "data file for LAC");
+DEFINE_int32(batch_size, 1, "batch size.");
+DEFINE_int32(burning, 0, "Burning before repeat.");
+DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
+DEFINE_bool(test_all_data, false, "Test the all dataset in data file.");
+namespace paddle {
+namespace inference {
+namespace analysis {
+struct DataRecord {
+  std::vector<int64_t> data;
+  std::vector<size_t> lod;
+  // for dataset and nextbatch
+  size_t batch_iter{0};
+  std::vector<std::vector<size_t>> batched_lods;
+  std::vector<std::vector<int64_t>> batched_datas;
+  std::vector<std::vector<int64_t>> datasets;
+  DataRecord() = default;
+  explicit DataRecord(const std::string &path, int batch_size = 1) {
+    Load(path);
+    Prepare(batch_size);
+    batch_iter = 0;
+  }
+  void Load(const std::string &path) {
+    std::ifstream file(path);
+    std::string line;
+    int num_lines = 0;
+    datasets.resize(0);
+    while (std::getline(file, line)) {
+      num_lines++;
+      std::vector<std::string> data;
+      split(line, ';', &data);
+      std::vector<int64_t> words_ids;
+      split_to_int64(data[1], ' ', &words_ids);
+      datasets.emplace_back(words_ids);
+    }
+  }
+  void Prepare(int bs) {
+    if (bs == 1) {
+      batched_datas = datasets;
+      for (auto one_sentence : datasets) {
+        batched_lods.push_back({0, one_sentence.size()});
+      }
+    } else {
+      std::vector<int64_t> one_batch;
+      std::vector<size_t> lod{0};
+      int bs_id = 0;
+      for (auto one_sentence : datasets) {
+        bs_id++;
+        one_batch.insert(one_batch.end(), one_sentence.begin(),
+                         one_sentence.end());
+        lod.push_back(lod.back() + one_sentence.size());
+        if (bs_id == bs) {
+          bs_id = 0;
+          batched_datas.push_back(one_batch);
+          batched_lods.push_back(lod);
+          one_batch.clear();
+          one_batch.resize(0);
+          lod.clear();
+          lod.resize(0);
+          lod.push_back(0);
+        }
+      }
+      if (one_batch.size() != 0) {
+        batched_datas.push_back(one_batch);
+        batched_lods.push_back(lod);
+      }
+    }
+  }
+  DataRecord NextBatch() {
+    DataRecord data;
+    data.data = batched_datas[batch_iter];
+    data.lod = batched_lods[batch_iter];
+    batch_iter++;
+    if (batch_iter >= batched_datas.size()) {
+      batch_iter = 0;
+    }
+    return data;
+  }
+};
+void GetOneBatch(std::vector<PaddleTensor> *input_slots, DataRecord *data,
+                 int batch_size) {
+  auto one_batch = data->NextBatch();
+  PaddleTensor input_tensor;
+  input_tensor.name = "word";
+  input_tensor.shape.assign({static_cast<int>(one_batch.data.size()), 1});
+  input_tensor.lod.assign({one_batch.lod});
+  input_tensor.dtype = PaddleDType::INT64;
+  TensorAssignData<int64_t>(&input_tensor, {one_batch.data});
+  PADDLE_ENFORCE_EQ(batch_size, static_cast<int>(one_batch.lod.size() - 1));
+  input_slots->assign({input_tensor});
+}
+static void PrintTime(const double latency, const int bs, const int repeat) {
+  LOG(INFO) << "===========profile result===========";
+  LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
+            << ", avg latency: " << latency / repeat << "ms";
+  LOG(INFO) << "=====================================";
+}
+void BenchAllData(const std::string &model_path, const std::string &data_file,
+                  const int batch_size, const int repeat) {
+  NativeConfig config;
+  config.model_dir = model_path;
+  config.use_gpu = false;
+  config.device = 0;
+  config.specify_input_name = true;
+  std::vector<PaddleTensor> input_slots, outputs_slots;
+  DataRecord data(data_file, batch_size);
+  auto predictor =
+      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
+  GetOneBatch(&input_slots, &data, batch_size);
+  for (int i = 0; i < FLAGS_burning; i++) {
+    predictor->Run(input_slots, &outputs_slots);
+  }
+  Timer timer;
+  double sum = 0;
+  for (int i = 0; i < repeat; i++) {
+    for (size_t bid = 0; bid < data.batched_datas.size(); ++bid) {
+      GetOneBatch(&input_slots, &data, batch_size);
+      timer.tic();
+      predictor->Run(input_slots, &outputs_slots);
+      sum += timer.toc();
+    }
+  }
+  PrintTime(sum, batch_size, repeat);
+}
+const int64_t lac_ref_data[] = {24, 25, 25, 25, 38, 30, 31, 14, 15, 44, 24, 25,
+                                25, 25, 25, 25, 44, 24, 25, 25, 25, 36, 42, 43,
+                                44, 14, 15, 44, 14, 15, 44, 14, 15, 44, 38, 39,
+                                14, 15, 44, 22, 23, 23, 23, 23, 23, 23, 23};
+void TestLACPrediction(const std::string &model_path,
+                       const std::string &data_file, const int batch_size,
+                       const int repeat, bool test_all_data) {
+  if (test_all_data) {
+    BenchAllData(model_path, data_file, batch_size, repeat);
+    return;
+  }
+  NativeConfig config;
+  config.model_dir = model_path;
+  config.use_gpu = false;
+  config.device = 0;
+  config.specify_input_name = true;
+  std::vector<PaddleTensor> input_slots, outputs_slots;
+  DataRecord data(data_file, batch_size);
+  GetOneBatch(&input_slots, &data, batch_size);
+  auto predictor =
+      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
+  for (int i = 0; i < FLAGS_burning; i++) {
+    predictor->Run(input_slots, &outputs_slots);
+  }
+  Timer timer;
+  timer.tic();
+  for (int i = 0; i < repeat; i++) {
+    predictor->Run(input_slots, &outputs_slots);
+  }
+  PrintTime(timer.toc(), batch_size, repeat);
+  EXPECT_EQ(outputs_slots.size(), 1UL);
+  auto &out = outputs_slots[0];
+  size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
+                                [](int a, int b) { return a * b; });
+  size_t batch1_size = sizeof(lac_ref_data) / sizeof(int64_t);
+  PADDLE_ENFORCE_GT(size, 0);
+  EXPECT_GE(size, batch1_size);
+  int64_t *pdata = static_cast<int64_t *>(out.data.data());
+  for (size_t i = 0; i < batch1_size; ++i) {
+    EXPECT_EQ(pdata[i], lac_ref_data[i]);
+  }
+}
+TEST(Analyzer_LAC, native) {
+  LOG(INFO) << "LAC with native";
+  TestLACPrediction(FLAGS_infer_model, FLAGS_infer_data, FLAGS_batch_size,
+                    FLAGS_repeat, FLAGS_test_all_data);
+}
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle