add dam test

paddle/fluid/inference/tests/api/CMakeLists.txt

@@ -43,6 +43,11 @@ set(RNN2_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/rnn2")
 download_model_and_data(${RNN2_INSTALL_DIR} "rnn2_model.tar.gz" "rnn2_data.txt.tar.gz")
 inference_analysis_api_test(test_analyzer_rnn2 ${RNN2_INSTALL_DIR} analyzer_rnn2_tester.cc)
 
+# DAM
+set(DAM_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/dam")
+download_model_and_data(${DAM_INSTALL_DIR} "DAM_model.tar.gz" "DAM_data.txt.tar.gz")
+inference_analysis_api_test(test_analyzer_dam ${DAM_INSTALL_DIR} analyzer_dam_tester.cc)
+
 # chinese_ner
 set(CHINESE_NER_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/chinese_ner")
 download_model_and_data(${CHINESE_NER_INSTALL_DIR} "chinese_ner_model.tar.gz" "chinese_ner-data.txt.tar.gz")

paddle/fluid/inference/tests/api/analyzer_dam_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/tests/api/tester_helper.h"
+
+namespace paddle {
+namespace inference {
+using contrib::AnalysisConfig;
+#define MAX_TURN_NUM 9
+#define MAX_TURN_LEN 50
+static std::vector<float> result_data;
+
+struct DataRecord {
+  std::vector<std::vector<int64_t>>
+      turns[MAX_TURN_NUM];  // turns data : MAX_TURN_NUM
+  std::vector<std::vector<float>>
+      turns_mask[MAX_TURN_NUM];                // turns mask data : MAX_TURN_NUM
+  std::vector<std::vector<int64_t>> response;  // response data : 1
+  std::vector<std::vector<float>> response_mask;  // response mask data : 1
+  size_t batch_iter{0};
+  size_t batch_size{1};
+  size_t num_samples;  // total number of samples
+  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 <= response.size()) {
+      for (int i = 0; i < MAX_TURN_NUM; ++i) {
+        data.turns[i].assign(turns[i].begin() + batch_iter,
+                             turns[i].begin() + batch_end);
+      }
+      for (int i = 0; i < MAX_TURN_NUM; ++i) {
+        data.turns_mask[i].assign(turns_mask[i].begin() + batch_iter,
+                                  turns_mask[i].begin() + batch_end);
+      }
+      data.response.assign(response.begin() + batch_iter,
+                           response.begin() + batch_end);
+      data.response_mask.assign(response_mask.begin() + batch_iter,
+                                response_mask.begin() + batch_end);
+      CHECK(!data.response.empty());
+      CHECK(!data.response_mask.empty());
+      CHECK_EQ(data.response.size(), data.response_mask.size());
+    }
+    batch_iter += batch_size;
+    return data;
+  }
+  void Load(const std::string &path) {
+    std::ifstream file(path);
+    std::string line;
+    size_t num_lines = 0;
+    result_data.clear();
+    while (std::getline(file, line)) {
+      num_lines++;
+      std::vector<std::string> data;
+      split(line, ',', &data);
+      CHECK_EQ(data.size(), 2 * MAX_TURN_NUM + 3);
+      // load turn data
+      std::vector<int64_t> turns_tmp[MAX_TURN_NUM];
+      for (int i = 0; i < MAX_TURN_NUM; ++i) {
+        split_to_int64(data[i], ' ', &turns_tmp[i]);
+        turns[i].push_back(std::move(turns_tmp[i]));
+      }
+      // load turn_mask data
+      std::vector<float> turns_mask_tmp[MAX_TURN_NUM];
+      for (int i = 0; i < MAX_TURN_NUM; ++i) {
+        split_to_float(data[MAX_TURN_NUM + i], ' ', &turns_mask_tmp[i]);
+        turns_mask[i].push_back(std::move(turns_mask_tmp[i]));
+      }
+      // load response data
+      std::vector<int64_t> response_tmp;
+      split_to_int64(data[2 * MAX_TURN_NUM], ' ', &response_tmp);
+      response.push_back(std::move(response_tmp));
+      // load response_mask data
+      std::vector<float> response_mask_tmp;
+      split_to_float(data[2 * MAX_TURN_NUM + 1], ' ', &response_mask_tmp);
+      response_mask.push_back(std::move(response_mask_tmp));
+      // load result data
+      float result_tmp;
+      result_tmp = std::stof(data[2 * MAX_TURN_NUM + 2]);
+      result_data.push_back(result_tmp);
+    }
+    num_samples = num_lines;
+  }
+};
+
+void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
+                   int batch_size) {
+  PaddleTensor turns_tensor[MAX_TURN_NUM];
+  PaddleTensor turns_mask_tensor[MAX_TURN_NUM];
+  PaddleTensor response_tensor;
+  PaddleTensor response_mask_tensor;
+  std::string turn_pre = "turn_";
+  std::string turn_mask_pre = "turn_mask_";
+
+  auto one_batch = data->NextBatch();
+  int size = one_batch.response[0].size();
+  CHECK_EQ(size, MAX_TURN_LEN);
+
+  // turn tensor assignment
+  for (int i = 0; i < MAX_TURN_NUM; ++i) {
+    turns_tensor[i].name = turn_pre + std::to_string(i);
+    turns_tensor[i].shape.assign({batch_size, size, 1});
+    turns_tensor[i].dtype = PaddleDType::INT64;
+    TensorAssignData<int64_t>(&turns_tensor[i], one_batch.turns[i]);
+  }
+
+  // turn mask tensor assignment
+  for (int i = 0; i < MAX_TURN_NUM; ++i) {
+    turns_mask_tensor[i].name = turn_mask_pre + std::to_string(i);
+    turns_mask_tensor[i].shape.assign({batch_size, size, 1});
+    turns_mask_tensor[i].dtype = PaddleDType::FLOAT32;
+    TensorAssignData<float>(&turns_mask_tensor[i], one_batch.turns_mask[i]);
+  }
+
+  // response tensor assignment
+  response_tensor.name = "response";
+  response_tensor.shape.assign({batch_size, size, 1});
+  response_tensor.dtype = PaddleDType::INT64;
+  TensorAssignData<int64_t>(&response_tensor, one_batch.response);
+
+  // response mask tensor assignment
+  response_mask_tensor.name = "response_mask";
+  response_mask_tensor.shape.assign({batch_size, size, 1});
+  response_mask_tensor.dtype = PaddleDType::FLOAT32;
+  TensorAssignData<float>(&response_mask_tensor, one_batch.response_mask);
+
+  // Set inputs.
+  for (int i = 0; i < MAX_TURN_NUM; ++i) {
+    input_slots->push_back(std::move(turns_tensor[i]));
+  }
+  for (int i = 0; i < MAX_TURN_NUM; ++i) {
+    input_slots->push_back(std::move(turns_mask_tensor[i]));
+  }
+  input_slots->push_back(std::move(response_tensor));
+  input_slots->push_back(std::move(response_mask_tensor));
+}
+
+void SetConfig(contrib::AnalysisConfig *cfg) {
+  cfg->prog_file = FLAGS_infer_model + "/__model__";
+  cfg->param_file = FLAGS_infer_model + "/param";
+  cfg->use_gpu = false;
+  cfg->device = 0;
+  cfg->specify_input_name = true;
+  cfg->enable_ir_optim = true;
+}
+
+void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
+  DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
+  std::vector<PaddleTensor> input_slots;
+  int test_batch_num =
+      FLAGS_test_all_data ? data.num_samples / FLAGS_batch_size : 1;
+  LOG(INFO) << "The number of samples to be test: "
+            << test_batch_num * FLAGS_batch_size;
+  for (int bid = 0; bid < test_batch_num; ++bid) {
+    input_slots.clear();
+    PrepareInputs(&input_slots, &data, FLAGS_batch_size);
+    (*inputs).emplace_back(input_slots);
+  }
+}
+
+// Easy for profiling independently.
+TEST(Analyzer_dam, profile) {
+  contrib::AnalysisConfig cfg;
+  SetConfig(&cfg);
+  std::vector<PaddleTensor> outputs;
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
+
+  if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
+    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    size_t size = GetSize(outputs[0]);
+    PADDLE_ENFORCE_GT(size, 0);
+    float *result = static_cast<float *>(outputs[0].data.data());
+    for (size_t i = 0; i < size; i++) {
+      EXPECT_NEAR(result[i], result_data[i], 1e-3);
+    }
+  }
+}
+
+// Check the fuse status
+TEST(Analyzer_dam, fuse_statis) {
+  contrib::AnalysisConfig cfg;
+  // cfg.enable_ir_optim must be set true
+  SetConfig(&cfg);
+
+  int num_ops;
+  auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
+  auto fuse_statis = GetFuseStatis(
+      static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
+  ASSERT_TRUE(fuse_statis.count("fc_fuse"));
+  EXPECT_EQ(fuse_statis.at("fc_fuse"), 317);
+  EXPECT_EQ(num_ops, 2020);
+}
+
+// Compare result of NativeConfig and AnalysisConfig
+TEST(Analyzer_dam, compare) {
+  contrib::AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareNativeAndAnalysis(cfg, input_slots_all);
+}
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_ner_tester.cc

@@ -20,7 +20,6 @@ using contrib::AnalysisConfig;
 
 struct DataRecord {
   std::vector<std::vector<int64_t>> word_data_all, mention_data_all;
-  std::vector<std::vector<int64_t>> rnn_word_datas, rnn_mention_datas;
   std::vector<size_t> lod;  // two inputs have the same lod info.
   size_t batch_iter{0};
   size_t batch_size{1};
@@ -45,8 +44,6 @@ struct DataRecord {
       CHECK(!data.mention_data_all.empty());
       CHECK_EQ(data.word_data_all.size(), data.mention_data_all.size());
       for (size_t j = 0; j < data.word_data_all.size(); j++) {
-        data.rnn_word_datas.push_back(data.word_data_all[j]);
-        data.rnn_mention_datas.push_back(data.mention_data_all[j]);
         // calculate lod
         data.lod.push_back(data.lod.back() + data.word_data_all[j].size());
       }
@@ -87,8 +84,8 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
   lod_mention_tensor.shape.assign({size, 1});
   lod_mention_tensor.lod.assign({one_batch.lod});
   // assign data
-  TensorAssignData<int64_t>(&lod_word_tensor, one_batch.rnn_word_datas);
-  TensorAssignData<int64_t>(&lod_mention_tensor, one_batch.rnn_mention_datas);
+  TensorAssignData<int64_t>(&lod_word_tensor, one_batch.word_data_all);
+  TensorAssignData<int64_t>(&lod_mention_tensor, one_batch.mention_data_all);
   // Set inputs.
   input_slots->assign({lod_word_tensor, lod_mention_tensor});
   for (auto &tensor : *input_slots) {