move analyzer_rnn1_test out of analyzer_test

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -35,11 +35,15 @@ function (inference_analysis_test TARGET)
         cc_test(${TARGET}
                 SRCS "${analysis_test_SRCS}"
                 DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}
-                ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt} ${analysis_test_ARGS})
+                ARGS ${mem_opt} ${analysis_test_ARGS})
         set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
     endif(WITH_TESTING)
 endfunction(inference_analysis_test)
 
+inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
+    EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
+    ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
+
 function (inference_download_and_uncompress install_dir url gz_filename)
     message(STATUS "Download inference test stuff ${gz_filename} from ${url}")
     execute_process(COMMAND bash -c "mkdir -p ${install_dir}")
@@ -56,7 +60,7 @@ if (NOT EXISTS ${RNN1_INSTALL_DIR} AND WITH_TESTING)
   inference_download_and_uncompress(${RNN1_INSTALL_DIR} ${RNN1_DATA_URL} "rnn1%2Fdata.txt.tar.gz")
 endif()
 
-inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
+inference_analysis_test(test_analyzer_rnn1 SRCS analyzer_rnn1_tester.cc
     EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
     ARGS --infer_model=${RNN1_INSTALL_DIR}/model
          --infer_data=${RNN1_INSTALL_DIR}/data.txt)

paddle/fluid/inference/analysis/analyzer_rnn1_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 <thread>  // NOLINT
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/analysis_predictor.h"
+#include "paddle/fluid/inference/api/helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
+
+DEFINE_string(infer_model, "", "model path");
+DEFINE_string(infer_data, "", "data path");
+DEFINE_int32(batch_size, 10, "batch size.");
+DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
+DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
+
+namespace paddle {
+namespace inference {
+
+using namespace framework;  // NOLINT
+
+struct DataRecord {
+  std::vector<std::vector<std::vector<float>>> link_step_data_all;
+  std::vector<std::vector<float>> week_data_all, minute_data_all;
+  std::vector<size_t> lod1, lod2, lod3;
+  std::vector<std::vector<float>> rnn_link_data, rnn_week_datas,
+      rnn_minute_datas;
+  size_t batch_iter{0};
+  size_t batch_size{1};
+  DataRecord() = default;
+  explicit DataRecord(const std::string &path, int batch_size = 1)
+      : batch_size(batch_size) {
+    Load(path);
+  }
+  DataRecord NextBatch() {
+    DataRecord data;
+    size_t batch_end = batch_iter + batch_size;
+    // NOTE skip the final batch, if no enough data is provided.
+    if (batch_end <= link_step_data_all.size()) {
+      data.link_step_data_all.assign(link_step_data_all.begin() + batch_iter,
+                                     link_step_data_all.begin() + batch_end);
+      data.week_data_all.assign(week_data_all.begin() + batch_iter,
+                                week_data_all.begin() + batch_end);
+      data.minute_data_all.assign(minute_data_all.begin() + batch_iter,
+                                  minute_data_all.begin() + batch_end);
+      // Prepare LoDs
+      data.lod1.push_back(0);
+      data.lod2.push_back(0);
+      data.lod3.push_back(0);
+      CHECK(!data.link_step_data_all.empty()) << "empty";
+      CHECK(!data.week_data_all.empty());
+      CHECK(!data.minute_data_all.empty());
+      CHECK_EQ(data.link_step_data_all.size(), data.week_data_all.size());
+      CHECK_EQ(data.minute_data_all.size(), data.link_step_data_all.size());
+      for (size_t j = 0; j < data.link_step_data_all.size(); j++) {
+        for (const auto &d : data.link_step_data_all[j]) {
+          data.rnn_link_data.push_back(d);
+        }
+        data.rnn_week_datas.push_back(data.week_data_all[j]);
+        data.rnn_minute_datas.push_back(data.minute_data_all[j]);
+        // calculate lod
+        data.lod1.push_back(data.lod1.back() +
+                            data.link_step_data_all[j].size());
+        data.lod3.push_back(data.lod3.back() + 1);
+        for (size_t i = 1; i < data.link_step_data_all[j].size() + 1; i++) {
+          data.lod2.push_back(data.lod2.back() +
+                              data.link_step_data_all[j].size());
+        }
+      }
+    }
+    batch_iter += batch_size;
+    return data;
+  }
+  void Load(const std::string &path) {
+    std::ifstream file(path);
+    std::string line;
+    int num_lines = 0;
+    while (std::getline(file, line)) {
+      num_lines++;
+      std::vector<std::string> data;
+      split(line, ':', &data);
+      std::vector<std::vector<float>> link_step_data;
+      std::vector<std::string> link_datas;
+      split(data[0], '|', &link_datas);
+      for (auto &step_data : link_datas) {
+        std::vector<float> tmp;
+        split_to_float(step_data, ',', &tmp);
+        link_step_data.push_back(tmp);
+      }
+      // load week data
+      std::vector<float> week_data;
+      split_to_float(data[2], ',', &week_data);
+      // load minute data
+      std::vector<float> minute_data;
+      split_to_float(data[1], ',', &minute_data);
+      link_step_data_all.push_back(std::move(link_step_data));
+      week_data_all.push_back(std::move(week_data));
+      minute_data_all.push_back(std::move(minute_data));
+    }
+  }
+};
+void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
+                   int batch_size) {
+  PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor,
+      week_tensor, minute_tensor;
+  lod_attention_tensor.name = "data_lod_attention";
+  init_zero_tensor.name = "cell_init";
+  lod_tensor_tensor.name = "data";
+  week_tensor.name = "week";
+  minute_tensor.name = "minute";
+  auto one_batch = data->NextBatch();
+  std::vector<int> rnn_link_data_shape(
+      {static_cast<int>(one_batch.rnn_link_data.size()),
+       static_cast<int>(one_batch.rnn_link_data.front().size())});
+  lod_attention_tensor.shape.assign({1, 2});
+  lod_attention_tensor.lod.assign({one_batch.lod1, one_batch.lod2});
+  init_zero_tensor.shape.assign({batch_size, 15});
+  init_zero_tensor.lod.assign({one_batch.lod3});
+  lod_tensor_tensor.shape = rnn_link_data_shape;
+  lod_tensor_tensor.lod.assign({one_batch.lod1});
+  // clang-format off
+  week_tensor.shape.assign(
+      {static_cast<int>(one_batch.rnn_week_datas.size()),
+       static_cast<int>(one_batch.rnn_week_datas.front().size())});
+  week_tensor.lod.assign({one_batch.lod3});
+  minute_tensor.shape.assign(
+      {static_cast<int>(one_batch.rnn_minute_datas.size()),
+       static_cast<int>(one_batch.rnn_minute_datas.front().size())});
+  minute_tensor.lod.assign({one_batch.lod3});
+  // clang-format on
+  // assign data
+  TensorAssignData<float>(&lod_attention_tensor,
+                          std::vector<std::vector<float>>({{0, 0}}));
+  std::vector<float> tmp_zeros(batch_size * 15, 0.);
+  TensorAssignData<float>(&init_zero_tensor, {tmp_zeros});
+  TensorAssignData<float>(&lod_tensor_tensor, one_batch.rnn_link_data);
+  TensorAssignData<float>(&week_tensor, one_batch.rnn_week_datas);
+  TensorAssignData<float>(&minute_tensor, one_batch.rnn_minute_datas);
+  // Set inputs.
+  auto init_zero_tensor1 = init_zero_tensor;
+  init_zero_tensor1.name = "hidden_init";
+  input_slots->assign({week_tensor, init_zero_tensor, minute_tensor,
+                       init_zero_tensor1, lod_attention_tensor,
+                       lod_tensor_tensor});
+  for (auto &tensor : *input_slots) {
+    tensor.dtype = PaddleDType::FLOAT32;
+  }
+}
+
+void CompareResult(const std::vector<PaddleTensor> &outputs,
+                   const std::vector<PaddleTensor> &base_outputs) {
+  PADDLE_ENFORCE_GT(outputs.size(), 0);
+  PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
+  for (size_t i = 0; i < outputs.size(); i++) {
+    auto &out = outputs[i];
+    auto &base_out = base_outputs[i];
+    size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
+                                  [](int a, int b) { return a * b; });
+    size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
+                                   1, [](int a, int b) { return a * b; });
+    PADDLE_ENFORCE_EQ(size, size1);
+    PADDLE_ENFORCE_GT(size, 0);
+    float *data = static_cast<float *>(out.data.data());
+    float *base_data = static_cast<float *>(base_out.data.data());
+    for (size_t i = 0; i < size; i++) {
+      EXPECT_NEAR(data[i], base_data[i], 1e-3);
+    }
+  }
+}
+// Test with a really complicate model.
+void TestRNN1Prediction(bool use_analysis, bool activate_ir, int num_threads) {
+  AnalysisConfig config;
+  config.prog_file = FLAGS_infer_model + "/__model__";
+  config.param_file = FLAGS_infer_model + "/param";
+  config.use_gpu = false;
+  config.device = 0;
+  config.specify_input_name = true;
+  config.enable_ir_optim = activate_ir;
+  PADDLE_ENFORCE(config.ir_mode ==
+                 AnalysisConfig::IrPassMode::kExclude);  // default
+  config.ir_passes.clear();  // Do not exclude any pass.
+
+  int batch_size = FLAGS_batch_size;
+  int num_times = FLAGS_repeat;
+
+  auto base_predictor =
+      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
+  auto predictor =
+      CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
+          config);
+  std::vector<PaddleTensor> input_slots;
+  DataRecord data(FLAGS_infer_data, batch_size);
+  // Prepare inputs.
+  PrepareInputs(&input_slots, &data, batch_size);
+  std::vector<PaddleTensor> outputs, base_outputs;
+
+  base_predictor->Run(input_slots, &base_outputs);
+
+  if (num_threads == 1) {
+    // Prepare inputs.
+    Timer timer;
+    timer.tic();
+    for (int i = 0; i < num_times; i++) {
+      predictor->Run(input_slots, &outputs);
+    }
+    PrintTime(batch_size, num_times, 1, 0, timer.toc() / num_times);
+    CompareResult(outputs, base_outputs);
+  } else {
+    std::vector<std::thread> threads;
+    std::vector<std::unique_ptr<PaddlePredictor>> predictors;
+    // TODO(yanchunwei): Bug here, the analyzer phase can't be parallelled
+    // because AttentionLSTM's hard code nodeid will be damanged.
+    for (int tid = 0; tid < num_threads; ++tid) {
+      predictors.emplace_back(
+          CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
+              config));
+    }
+    for (int tid = 0; tid < num_threads; ++tid) {
+      threads.emplace_back([&, tid]() {
+        // Each thread should have local input_slots and outputs.
+        std::vector<PaddleTensor> input_slots;
+        DataRecord data(FLAGS_infer_data, batch_size);
+        PrepareInputs(&input_slots, &data, batch_size);
+        std::vector<PaddleTensor> outputs;
+        Timer timer;
+        timer.tic();
+        for (int i = 0; i < num_times; i++) {
+          predictors[tid]->Run(input_slots, &outputs);
+        }
+        PrintTime(batch_size, num_times, num_threads, tid,
+                  timer.toc() / num_times);
+        CompareResult(outputs, base_outputs);
+      });
+    }
+    for (int i = 0; i < num_threads; ++i) {
+      threads[i].join();
+    }
+  }
+
+  if (use_analysis && activate_ir) {
+    AnalysisPredictor *analysis_predictor =
+        dynamic_cast<AnalysisPredictor *>(predictor.get());
+    auto &fuse_statis = analysis_predictor->analysis_argument()
+                            .Get<std::unordered_map<std::string, int>>(
+                                framework::ir::kFuseStatisAttr);
+    for (auto &item : fuse_statis) {
+      LOG(INFO) << "fused " << item.first << " " << item.second;
+    }
+
+    int num_ops = 0;
+    for (auto &node :
+         analysis_predictor->analysis_argument().main_dfg->nodes.nodes()) {
+      if (node->IsFunction()) {
+        ++num_ops;
+      }
+    }
+    LOG(INFO) << "has num ops: " << num_ops;
+
+    ASSERT_TRUE(fuse_statis.count("fc_fuse"));
+    EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
+    EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2);  // bi-directional LSTM
+    EXPECT_EQ(fuse_statis.at("seq_concat_fc_fuse"), 1);
+    EXPECT_EQ(num_ops,
+              13);  // After graph optimization, only 13 operators exists.
+  }
+}
+
+// Inference with analysis and IR, easy for profiling independently.
+TEST(Analyzer, rnn1) { TestRNN1Prediction(true, true, FLAGS_num_threads); }
+
+// Other unit-tests of RNN1, test different options of use_analysis,
+// activate_ir and multi-threads.
+TEST(Analyzer, RNN_tests) {
+  int num_threads[2] = {1, 4};
+  for (auto i : num_threads) {
+    // Directly infer with the original model.
+    TestRNN1Prediction(false, false, i);
+    // Inference with the original model with the analysis turned on, the
+    // analysis
+    // module will transform the program to a data flow graph.
+    TestRNN1Prediction(true, false, i);
+    // Inference with analysis and IR. The IR module will fuse some large
+    // kernels.
+    TestRNN1Prediction(true, true, i);
+  }
+}
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -16,21 +16,9 @@
 
 #include <google/protobuf/text_format.h>
 #include <gtest/gtest.h>
-#include <thread>  // NOLINT
-#include "paddle/fluid/framework/ir/fuse_pass_base.h"
-#include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
-#include "paddle/fluid/inference/api/analysis_predictor.h"
-#include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/inference/api/paddle_inference_pass.h"
-#include "paddle/fluid/inference/utils/singleton.h"
-
-DEFINE_string(infer_model, "", "model path");
-DEFINE_string(infer_data, "", "data path");
-DEFINE_int32(batch_size, 10, "batch size.");
-DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
-DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
 
 namespace paddle {
 namespace inference {
@@ -91,274 +79,8 @@ void TestWord2vecPrediction(const std::string &model_path) {
   }
 }
 
-namespace {
-
-struct DataRecord {
-  std::vector<std::vector<std::vector<float>>> link_step_data_all;
-  std::vector<std::vector<float>> week_data_all, minute_data_all;
-  std::vector<size_t> lod1, lod2, lod3;
-  std::vector<std::vector<float>> rnn_link_data, rnn_week_datas,
-      rnn_minute_datas;
-  size_t batch_iter{0};
-  size_t batch_size{1};
-  DataRecord() = default;
-  explicit DataRecord(const std::string &path, int batch_size = 1)
-      : batch_size(batch_size) {
-    Load(path);
-  }
-  DataRecord NextBatch() {
-    DataRecord data;
-    size_t batch_end = batch_iter + batch_size;
-    // NOTE skip the final batch, if no enough data is provided.
-    if (batch_end <= link_step_data_all.size()) {
-      data.link_step_data_all.assign(link_step_data_all.begin() + batch_iter,
-                                     link_step_data_all.begin() + batch_end);
-      data.week_data_all.assign(week_data_all.begin() + batch_iter,
-                                week_data_all.begin() + batch_end);
-      data.minute_data_all.assign(minute_data_all.begin() + batch_iter,
-                                  minute_data_all.begin() + batch_end);
-      // Prepare LoDs
-      data.lod1.push_back(0);
-      data.lod2.push_back(0);
-      data.lod3.push_back(0);
-      CHECK(!data.link_step_data_all.empty()) << "empty";
-      CHECK(!data.week_data_all.empty());
-      CHECK(!data.minute_data_all.empty());
-      CHECK_EQ(data.link_step_data_all.size(), data.week_data_all.size());
-      CHECK_EQ(data.minute_data_all.size(), data.link_step_data_all.size());
-      for (size_t j = 0; j < data.link_step_data_all.size(); j++) {
-        for (const auto &d : data.link_step_data_all[j]) {
-          data.rnn_link_data.push_back(d);
-        }
-        data.rnn_week_datas.push_back(data.week_data_all[j]);
-        data.rnn_minute_datas.push_back(data.minute_data_all[j]);
-        // calculate lod
-        data.lod1.push_back(data.lod1.back() +
-                            data.link_step_data_all[j].size());
-        data.lod3.push_back(data.lod3.back() + 1);
-        for (size_t i = 1; i < data.link_step_data_all[j].size() + 1; i++) {
-          data.lod2.push_back(data.lod2.back() +
-                              data.link_step_data_all[j].size());
-        }
-      }
-    }
-    batch_iter += batch_size;
-    return data;
-  }
-  void Load(const std::string &path) {
-    std::ifstream file(path);
-    std::string line;
-    int num_lines = 0;
-    while (std::getline(file, line)) {
-      num_lines++;
-      std::vector<std::string> data;
-      split(line, ':', &data);
-      std::vector<std::vector<float>> link_step_data;
-      std::vector<std::string> link_datas;
-      split(data[0], '|', &link_datas);
-      for (auto &step_data : link_datas) {
-        std::vector<float> tmp;
-        split_to_float(step_data, ',', &tmp);
-        link_step_data.push_back(tmp);
-      }
-      // load week data
-      std::vector<float> week_data;
-      split_to_float(data[2], ',', &week_data);
-      // load minute data
-      std::vector<float> minute_data;
-      split_to_float(data[1], ',', &minute_data);
-      link_step_data_all.push_back(std::move(link_step_data));
-      week_data_all.push_back(std::move(week_data));
-      minute_data_all.push_back(std::move(minute_data));
-    }
-  }
-};
-void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
-                   int batch_size) {
-  PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor,
-      week_tensor, minute_tensor;
-  lod_attention_tensor.name = "data_lod_attention";
-  init_zero_tensor.name = "cell_init";
-  lod_tensor_tensor.name = "data";
-  week_tensor.name = "week";
-  minute_tensor.name = "minute";
-  auto one_batch = data->NextBatch();
-  std::vector<int> rnn_link_data_shape(
-      {static_cast<int>(one_batch.rnn_link_data.size()),
-       static_cast<int>(one_batch.rnn_link_data.front().size())});
-  lod_attention_tensor.shape.assign({1, 2});
-  lod_attention_tensor.lod.assign({one_batch.lod1, one_batch.lod2});
-  init_zero_tensor.shape.assign({batch_size, 15});
-  init_zero_tensor.lod.assign({one_batch.lod3});
-  lod_tensor_tensor.shape = rnn_link_data_shape;
-  lod_tensor_tensor.lod.assign({one_batch.lod1});
-  // clang-format off
-  week_tensor.shape.assign(
-      {static_cast<int>(one_batch.rnn_week_datas.size()),
-       static_cast<int>(one_batch.rnn_week_datas.front().size())});
-  week_tensor.lod.assign({one_batch.lod3});
-  minute_tensor.shape.assign(
-      {static_cast<int>(one_batch.rnn_minute_datas.size()),
-       static_cast<int>(one_batch.rnn_minute_datas.front().size())});
-  minute_tensor.lod.assign({one_batch.lod3});
-  // clang-format on
-  // assign data
-  TensorAssignData<float>(&lod_attention_tensor,
-                          std::vector<std::vector<float>>({{0, 0}}));
-  std::vector<float> tmp_zeros(batch_size * 15, 0.);
-  TensorAssignData<float>(&init_zero_tensor, {tmp_zeros});
-  TensorAssignData<float>(&lod_tensor_tensor, one_batch.rnn_link_data);
-  TensorAssignData<float>(&week_tensor, one_batch.rnn_week_datas);
-  TensorAssignData<float>(&minute_tensor, one_batch.rnn_minute_datas);
-  // Set inputs.
-  auto init_zero_tensor1 = init_zero_tensor;
-  init_zero_tensor1.name = "hidden_init";
-  input_slots->assign({week_tensor, init_zero_tensor, minute_tensor,
-                       init_zero_tensor1, lod_attention_tensor,
-                       lod_tensor_tensor});
-  for (auto &tensor : *input_slots) {
-    tensor.dtype = PaddleDType::FLOAT32;
-  }
-}
-
-}  // namespace
-
-void CompareResult(const std::vector<PaddleTensor> &outputs,
-                   const std::vector<PaddleTensor> &base_outputs) {
-  PADDLE_ENFORCE_GT(outputs.size(), 0);
-  PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
-  for (size_t i = 0; i < outputs.size(); i++) {
-    auto &out = outputs[i];
-    auto &base_out = base_outputs[i];
-    size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
-                                  [](int a, int b) { return a * b; });
-    size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
-                                   1, [](int a, int b) { return a * b; });
-    PADDLE_ENFORCE_EQ(size, size1);
-    PADDLE_ENFORCE_GT(size, 0);
-    float *data = static_cast<float *>(out.data.data());
-    float *base_data = static_cast<float *>(base_out.data.data());
-    for (size_t i = 0; i < size; i++) {
-      EXPECT_NEAR(data[i], base_data[i], 1e-3);
-    }
-  }
-}
-// Test with a really complicate model.
-void TestRNN1Prediction(bool use_analysis, bool activate_ir, int num_threads) {
-  AnalysisConfig config;
-  config.prog_file = FLAGS_infer_model + "/__model__";
-  config.param_file = FLAGS_infer_model + "/param";
-  config.use_gpu = false;
-  config.device = 0;
-  config.specify_input_name = true;
-  config.enable_ir_optim = activate_ir;
-  PADDLE_ENFORCE(config.ir_mode ==
-                 AnalysisConfig::IrPassMode::kExclude);  // default
-  config.ir_passes.clear();  // Do not exclude any pass.
-
-  int batch_size = FLAGS_batch_size;
-  int num_times = FLAGS_repeat;
-
-  auto base_predictor =
-      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
-  auto predictor =
-      CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
-          config);
-  std::vector<PaddleTensor> input_slots;
-  DataRecord data(FLAGS_infer_data, batch_size);
-  // Prepare inputs.
-  PrepareInputs(&input_slots, &data, batch_size);
-  std::vector<PaddleTensor> outputs, base_outputs;
-
-  base_predictor->Run(input_slots, &base_outputs);
-
-  if (num_threads == 1) {
-    // Prepare inputs.
-    Timer timer;
-    timer.tic();
-    for (int i = 0; i < num_times; i++) {
-      predictor->Run(input_slots, &outputs);
-    }
-    PrintTime(batch_size, num_times, 1, 0, timer.toc() / num_times);
-    CompareResult(outputs, base_outputs);
-  } else {
-    std::vector<std::thread> threads;
-    std::vector<std::unique_ptr<PaddlePredictor>> predictors;
-    // TODO(yanchunwei): Bug here, the analyzer phase can't be parallelled
-    // because AttentionLSTM's hard code nodeid will be damanged.
-    for (int tid = 0; tid < num_threads; ++tid) {
-      predictors.emplace_back(
-          CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
-              config));
-    }
-    for (int tid = 0; tid < num_threads; ++tid) {
-      threads.emplace_back([&, tid]() {
-        // Each thread should have local input_slots and outputs.
-        std::vector<PaddleTensor> input_slots;
-        DataRecord data(FLAGS_infer_data, batch_size);
-        PrepareInputs(&input_slots, &data, batch_size);
-        std::vector<PaddleTensor> outputs;
-        Timer timer;
-        timer.tic();
-        for (int i = 0; i < num_times; i++) {
-          predictors[tid]->Run(input_slots, &outputs);
-        }
-        PrintTime(batch_size, num_times, num_threads, tid,
-                  timer.toc() / num_times);
-        CompareResult(outputs, base_outputs);
-      });
-    }
-    for (int i = 0; i < num_threads; ++i) {
-      threads[i].join();
-    }
-  }
-
-  if (use_analysis && activate_ir) {
-    AnalysisPredictor *analysis_predictor =
-        dynamic_cast<AnalysisPredictor *>(predictor.get());
-    auto &fuse_statis = analysis_predictor->analysis_argument()
-                            .Get<std::unordered_map<std::string, int>>(
-                                framework::ir::kFuseStatisAttr);
-    for (auto &item : fuse_statis) {
-      LOG(INFO) << "fused " << item.first << " " << item.second;
-    }
-
-    int num_ops = 0;
-    for (auto &node :
-         analysis_predictor->analysis_argument().main_dfg->nodes.nodes()) {
-      if (node->IsFunction()) {
-        ++num_ops;
-      }
-    }
-    LOG(INFO) << "has num ops: " << num_ops;
-
-    ASSERT_TRUE(fuse_statis.count("fc_fuse"));
-    EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
-    EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2);  // bi-directional LSTM
-    EXPECT_EQ(fuse_statis.at("seq_concat_fc_fuse"), 1);
-    EXPECT_EQ(num_ops,
-              13);  // After graph optimization, only 13 operators exists.
-  }
-}
-
-// Inference with analysis and IR, easy for profiling independently.
-TEST(Analyzer, rnn1) { TestRNN1Prediction(true, true, FLAGS_num_threads); }
-
-// Other unit-tests of RNN1, test different options of use_analysis,
-// activate_ir and multi-threads.
-TEST(Analyzer, RNN_tests) {
-  int num_threads[2] = {1, 4};
-  for (auto i : num_threads) {
-    // Directly infer with the original model.
-    TestRNN1Prediction(false, false, i);
-    // Inference with the original model with the analysis turned on, the
-    // analysis
-    // module will transform the program to a data flow graph.
-    TestRNN1Prediction(true, false, i);
-    // Inference with analysis and IR. The IR module will fuse some large
-    // kernels.
-    TestRNN1Prediction(true, true, i);
-  }
+TEST(Analyzer, word2vec_without_analysis) {
+  TestWord2vecPrediction(FLAGS_inference_model_dir);
 }
 
 }  // namespace analysis