analyzer bert tester

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

@@ -115,6 +115,11 @@ if (NOT EXISTS ${MOBILENET_INSTALL_DIR})
 endif()
 inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose ${MOBILENET_INSTALL_DIR} analyzer_vis_tester.cc SERIAL)
 
+# bert
+set(BERT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/bert")
+download_model_and_data(${BERT_INSTALL_DIR} "bert_model.tar.gz" "bert_data.txt.tar.gz")
+inference_analysis_api_test(test_analyzer_bert ${BERT_INSTALL_DIR} analyzer_bert_tester.cc)
+
 # resnet50
 inference_analysis_api_test_with_fake_data(test_analyzer_resnet50
   "${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz" SERIAL)

paddle/fluid/inference/tests/api/analyzer_bert_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 <gflags/gflags.h>
+#include <glog/logging.h>
+#include <chrono>
+#include <fstream>
+#include <numeric>
+#include <sstream>
+#include <string>
+#include <vector>
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+
+DEFINE_int32(repeat, 1, "repeat");
+
+namespace paddle {
+namespace inference {
+
+using paddle::PaddleTensor;
+using paddle::contrib::AnalysisConfig;
+
+template <typename T>
+void GetValueFromStream(std::stringstream *ss, T *t) {
+  (*ss) >> (*t);
+}
+
+template <>
+void GetValueFromStream<std::string>(std::stringstream *ss, std::string *t) {
+  *t = ss->str();
+}
+
+// Split string to vector
+template <typename T>
+void Split(const std::string &line, char sep, std::vector<T> *v) {
+  std::stringstream ss;
+  T t;
+  for (auto c : line) {
+    if (c != sep) {
+      ss << c;
+    } else {
+      GetValueFromStream<T>(&ss, &t);
+      v->push_back(std::move(t));
+      ss.str({});
+      ss.clear();
+    }
+  }
+
+  if (!ss.str().empty()) {
+    GetValueFromStream<T>(&ss, &t);
+    v->push_back(std::move(t));
+    ss.str({});
+    ss.clear();
+  }
+}
+
+template <typename T>
+constexpr paddle::PaddleDType GetPaddleDType();
+
+template <>
+constexpr paddle::PaddleDType GetPaddleDType<int64_t>() {
+  return paddle::PaddleDType::INT64;
+}
+
+template <>
+constexpr paddle::PaddleDType GetPaddleDType<float>() {
+  return paddle::PaddleDType::FLOAT32;
+}
+
+// Parse tensor from string
+template <typename T>
+bool ParseTensor(const std::string &field, paddle::PaddleTensor *tensor) {
+  std::vector<std::string> data;
+  Split(field, ':', &data);
+  if (data.size() < 2) return false;
+
+  std::string shape_str = data[0];
+
+  std::vector<int> shape;
+  Split(shape_str, ' ', &shape);
+
+  std::string mat_str = data[1];
+
+  std::vector<T> mat;
+  Split(mat_str, ' ', &mat);
+
+  tensor->shape = shape;
+  auto size =
+      std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>()) *
+      sizeof(T);
+  tensor->data.Resize(size);
+  std::copy(mat.begin(), mat.end(), static_cast<T *>(tensor->data.data()));
+  tensor->dtype = GetPaddleDType<T>();
+
+  return true;
+}
+
+// Parse input tensors from string
+bool ParseLine(const std::string &line,
+               std::vector<paddle::PaddleTensor> *tensors) {
+  std::vector<std::string> fields;
+  Split(line, ';', &fields);
+
+  if (fields.size() < 5) return false;
+
+  tensors->clear();
+  tensors->reserve(5);
+
+  int i = 0;
+  // src_id
+  paddle::PaddleTensor src_id;
+  ParseTensor<int64_t>(fields[i++], &src_id);
+  tensors->push_back(src_id);
+
+  // pos_id
+  paddle::PaddleTensor pos_id;
+  ParseTensor<int64_t>(fields[i++], &pos_id);
+  tensors->push_back(pos_id);
+
+  // segment_id
+  paddle::PaddleTensor segment_id;
+  ParseTensor<int64_t>(fields[i++], &segment_id);
+  tensors->push_back(segment_id);
+
+  // self_attention_bias
+  paddle::PaddleTensor self_attention_bias;
+  ParseTensor<float>(fields[i++], &self_attention_bias);
+  tensors->push_back(self_attention_bias);
+
+  // next_segment_index
+  paddle::PaddleTensor next_segment_index;
+  ParseTensor<int64_t>(fields[i++], &next_segment_index);
+  tensors->push_back(next_segment_index);
+
+  return true;
+}
+
+// Print outputs to log
+void PrintOutputs(const std::vector<paddle::PaddleTensor> &outputs) {
+  LOG(INFO) << "example_id\tcontradiction\tentailment\tneutral";
+
+  for (size_t i = 0; i < outputs.front().data.length(); i += 3) {
+    LOG(INFO) << (i / 3) << "\t"
+              << static_cast<float *>(outputs.front().data.data())[i] << "\t"
+              << static_cast<float *>(outputs.front().data.data())[i + 1]
+              << "\t"
+              << static_cast<float *>(outputs.front().data.data())[i + 2];
+  }
+}
+
+bool LoadInputData(std::vector<std::vector<paddle::PaddleTensor>> *inputs) {
+  if (FLAGS_infer_data.empty()) {
+    LOG(ERROR) << "please set input data path";
+    return false;
+  }
+
+  std::ifstream fin(FLAGS_infer_data);
+  std::string line;
+
+  int lineno = 0;
+  while (std::getline(fin, line)) {
+    std::vector<paddle::PaddleTensor> feed_data;
+    if (!ParseLine(line, &feed_data)) {
+      LOG(ERROR) << "Parse line[" << lineno << "] error!";
+    } else {
+      inputs->push_back(std::move(feed_data));
+    }
+  }
+
+  return true;
+}
+
+void SetConfig(contrib::AnalysisConfig *config) {
+  config->SetModel(FLAGS_infer_model);
+}
+
+void profile(bool use_mkldnn = false) {
+  contrib::AnalysisConfig config;
+  SetConfig(&config);
+
+  if (use_mkldnn) {
+    config.EnableMKLDNN();
+  }
+
+  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> inputs;
+  LoadInputData(&inputs);
+  TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&config),
+                 inputs, &outputs, FLAGS_num_threads);
+}
+
+void compare(bool use_mkldnn = false) {
+  AnalysisConfig config;
+  SetConfig(&config);
+
+  std::vector<std::vector<PaddleTensor>> inputs;
+  LoadInputData(&inputs);
+  CompareNativeAndAnalysis(
+      reinterpret_cast<const PaddlePredictor::Config *>(&config), inputs);
+}
+
+TEST(Analyzer_bert, profile) { profile(); }
+#ifdef PADDLE_WITH_MKLDNN
+TEST(Analyzer_bert, profile_mkldnn) { profile(true); }
+#endif
+}  // namespace inference
+}  // namespace paddle