chore: import upstream snapshot with attribution

test/cpp/inference/api/analyzer_lac_tester.cc

@@ -0,0 +1,191 @@
+// 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 "test/cpp/inference/api/tester_helper.h"
+
+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() : data(), lod(), batched_lods(), batched_datas(), datasets() {}
+  explicit DataRecord(const std::string &path, int batch_size = 1)
+      : data(), lod(), batched_lods(), batched_datas(), datasets() {
+    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.empty()) {
+        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.dtype = PaddleDType::INT64;
+  TensorAssignData<int64_t>(&input_tensor, {one_batch.data}, one_batch.lod);
+  PADDLE_ENFORCE_EQ(
+      batch_size,
+      static_cast<int>(one_batch.lod.size() - 1),
+      ::common::errors::Fatal("The lod size of one batch is invalid."));
+  input_slots->assign({input_tensor});
+}
+
+void SetConfig(AnalysisConfig *cfg) {
+  cfg->SetModel(FLAGS_infer_model);
+  cfg->DisableGpu();
+  cfg->SwitchSpecifyInputNames();
+  cfg->SwitchIrOptim();
+}
+
+void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
+  DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
+  std::vector<PaddleTensor> input_slots;
+  int epoch = FLAGS_test_all_data ? data.batched_datas.size() : 1;
+  LOG(INFO) << "number of samples: " << epoch;
+  for (int bid = 0; bid < epoch; ++bid) {
+    GetOneBatch(&input_slots, &data, FLAGS_batch_size);
+    (*inputs).emplace_back(input_slots);
+  }
+}
+
+// Easy for profiling independently.
+TEST(Analyzer_LAC, profile) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  std::vector<std::vector<PaddleTensor>> outputs;
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                 input_slots_all,
+                 &outputs,
+                 FLAGS_num_threads);
+
+  if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
+    // the first inference result
+    const std::array<int64_t, 47> 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};
+    PADDLE_ENFORCE_GT(outputs.size(),
+                      0,
+                      ::common::errors::Fatal(
+                          "The size of output should be greater than 0."));
+    auto output = outputs.back();
+    PADDLE_ENFORCE_EQ(
+        output.size(),
+        1UL,
+        ::common::errors::Fatal("The size of output should be equal to 1."));
+    size_t size = GetSize(output[0]);
+    size_t batch1_size = sizeof(lac_ref_data) / sizeof(int64_t);
+    PADDLE_ENFORCE_GE(size,
+                      batch1_size,
+                      ::common::errors::Fatal("The size of batch is invalid."));
+    int64_t *pdata = static_cast<int64_t *>(output[0].data.data());
+    for (size_t i = 0; i < batch1_size; ++i) {
+      EXPECT_EQ(pdata[i], lac_ref_data[i]);
+    }
+  }
+}
+// Compare result of NativeConfig and AnalysisConfig
+TEST(Analyzer_LAC, compare) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareNativeAndAnalysis(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
+}
+
+// Compare Deterministic result
+TEST(Analyzer_LAC, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle