chore: import upstream snapshot with attribution

test/cpp/inference/api/analyzer_seq_pool1_tester_helper.h

@@ -0,0 +1,178 @@
+/* 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. */
+#pragma once
+#include <algorithm>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "test/cpp/inference/api/tester_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+namespace seq_pool1_tester {
+
+// diff: similarity_norm.tmp_0, for speed: fc_4.tmp_1
+static const char out_var_name[] = "reduce_sum_0.tmp_0";
+
+// for diff: 154, for speed 111
+constexpr int num_slots = 154;
+
+struct OneSlotInBatch {
+  std::string name;
+  std::vector<std::vector<float>> data;
+  std::vector<int> shape;
+  std::vector<size_t> lod;
+};
+
+struct DataRecord {
+  std::vector<std::vector<OneSlotInBatch>> batched_data;
+  std::map<std::string, std::vector<std::vector<float>>> datasets;
+  size_t batch_iter{0}, num_samples;  // total number of samples
+
+  DataRecord() = default;
+  explicit DataRecord(const std::string &path, int batch_size = 1) {
+    Load(path);
+    Prepare(batch_size);
+  }
+
+  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, '\t', &data);
+      std::vector<float> slot_data;
+      split_to_float(data[1], ' ', &slot_data);
+      std::string name = data[0];
+      PADDLE_ENFORCE_EQ(
+          slot_data.size() % 11,
+          0UL,
+          ::common::errors::Fatal(
+              "line %d, %s should be divisible", num_lines, name));
+      datasets[name].emplace_back(std::move(slot_data));
+    }
+    num_samples = num_lines / num_slots;
+    PADDLE_ENFORCE_EQ(
+        num_samples * num_slots,
+        static_cast<size_t>(num_lines),
+        ::common::errors::Fatal("num samples should be divisible"));
+    PADDLE_ENFORCE_GT(num_samples,
+                      0UL,
+                      ::common::errors::Fatal(
+                          "The num of samples should be greater than 0."));
+  }
+
+  void Prepare(int bs) {
+    for (auto it = datasets.begin(); it != datasets.end(); ++it) {
+      PADDLE_ENFORCE_EQ(
+          it->second.size(),
+          num_samples,
+          ::common::errors::Fatal("size of each slot should be equal"));
+    }
+    size_t num_batches = num_samples / bs;
+    EXPECT_GT(num_batches, 0UL);
+    batched_data.resize(num_batches);
+    for (auto &one_batch : batched_data) {
+      one_batch.resize(datasets.size());
+      size_t i = 0;
+      for (auto it = datasets.begin(); it != datasets.end(); ++it) {
+        auto &slot = one_batch[i];
+        slot.name = it->first;
+        slot.data.resize(bs);
+        slot.lod.resize(bs + 1);
+        slot.lod[0] = 0;
+        auto &lod = slot.lod;
+        auto &datas = it->second;
+        for (int k = 0; k < bs; ++k) {
+          size_t id = k + batch_iter * bs;
+          std::copy(datas[id].begin(),
+                    datas[id].end(),
+                    std::back_inserter(slot.data[k]));
+          size_t len = datas[id].size() / 11;
+          PADDLE_ENFORCE_EQ(
+              len * 11,
+              datas[id].size(),
+              ::common::errors::Fatal(
+                  "%s %d size should be divisible", slot.name, id));
+          lod[k + 1] = lod[k] + len;
+        }
+        slot.shape.assign({static_cast<int>(lod[bs]), 11});
+        i++;
+      }
+    }
+  }
+
+  const std::vector<OneSlotInBatch> &NextBatch() {
+    if (batch_iter >= batched_data.size() - 1) {
+      batch_iter = -1;
+    }
+    return batched_data[++batch_iter];
+  }
+};
+
+static void TensorAssignSlot(PaddleTensor *tensor, const OneSlotInBatch &slot) {
+  tensor->name = slot.name + "_embed";
+  tensor->shape = slot.shape;
+  tensor->dtype = PaddleDType::FLOAT32;
+  tensor->lod.clear();
+  tensor->lod.emplace_back(slot.lod);
+  TensorAssignData(tensor, slot.data);
+}
+
+void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data) {
+  const auto &one_batch = data->NextBatch();
+  input_slots->resize(one_batch.size());
+  for (size_t i = 0; i < one_batch.size(); ++i) {
+    auto &slot = one_batch[i];
+    TensorAssignSlot(&((*input_slots)[i]), slot);
+  }
+}
+
+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_data.size() : 1;
+  LOG(INFO) << "number of samples: "
+            << data.batched_data.size() * FLAGS_batch_size;
+  for (int bid = 0; bid < epoch; ++bid) {
+    PrepareInputs(&input_slots, &data);
+    (*inputs).emplace_back(input_slots);
+  }
+}
+
+void SetConfig(AnalysisConfig *cfg, bool use_onednn = false) {
+  cfg->SetModel(FLAGS_infer_model + "/model", FLAGS_infer_model + "/params");
+  cfg->DisableGpu();
+  cfg->SwitchSpecifyInputNames();
+  cfg->SwitchIrDebug();
+  cfg->SetCpuMathLibraryNumThreads(FLAGS_cpu_num_threads);
+  if (use_onednn) {
+    cfg->EnableONEDNN();
+  }
+  // Enable seqpool_concat_fuse_pass, disabled by default since it takes much
+  // time
+  cfg->pass_builder()->InsertPass(2, "seqpool_concat_fuse_pass");
+}
+
+}  // namespace seq_pool1_tester
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle