test_inference_nlp.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 <sys/time.h>
#include <time.h>
#include <thread>  // NOLINT
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"

DEFINE_string(dirname, "", "Directory of the inference model.");
DEFINE_int32(repeat, 100, "Running the inference program repeat times");
DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run inference");
DEFINE_bool(prepare_vars, true, "Prepare variables before executor");
DEFINE_bool(prepare_context, true, "Prepare Context before executor");

inline double get_current_ms() {
  struct timeval time;
  gettimeofday(&time, NULL);
  return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
}

TEST(inference, understand_sentiment) {
  if (FLAGS_dirname.empty()) {
    LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
  }

  LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
  std::string dirname = FLAGS_dirname;
  const bool model_combined = false;
  int total_work = 100;
  int num_threads = 10;
  int work_per_thread = total_work / num_threads;
  std::vector<std::unique_ptr<std::thread>> infer_threads;
  for (int i = 0; i < num_threads; ++i) {
    infer_threads.emplace_back(new std::thread([&, i]() {
      for (int j = 0; j < work_per_thread; ++j) {
        // 0. Call `paddle::framework::InitDevices()` initialize all the devices
        // In unittests, this is done in paddle/testing/paddle_gtest_main.cc
        paddle::framework::LoDTensor words;
        /*
          paddle::framework::LoD lod{{0, 83}};
          int64_t word_dict_len = 198392;
          SetupLoDTensor(&words, lod, static_cast<int64_t>(0),
                         static_cast<int64_t>(word_dict_len - 1));
         */
        std::vector<int64_t> srcdata{
            784,   784,    1550,   6463,   56,     75693,  6189,  784,    784,
            1550,  198391, 6463,   42468,  4376,   10251,  10760, 6189,   297,
            396,   6463,   6463,   1550,   198391, 6463,   22564, 1612,   291,
            68,    164,    784,    784,    1550,   198391, 6463,  13659,  3362,
            42468, 6189,   2209,   198391, 6463,   2209,   2209,  198391, 6463,
            2209,  1062,   3029,   1831,   3029,   1065,   2281,  100,    11216,
            1110,  56,     10869,  9811,   100,    198391, 6463,  100,    9280,
            100,   288,    40031,  1680,   1335,   100,    1550,  9280,   7265,
            244,   1550,   198391, 6463,   1550,   198391, 6463,  42468,  4376,
            10251, 10760};
        paddle::framework::LoD lod{{0, srcdata.size()}};
        words.set_lod(lod);
        int64_t* pdata = words.mutable_data<int64_t>(
            {static_cast<int64_t>(srcdata.size()), 1},
            paddle::platform::CPUPlace());
        memcpy(pdata, srcdata.data(), words.numel() * sizeof(int64_t));

        LOG(INFO) << "number of input size:" << words.numel();
        std::vector<paddle::framework::LoDTensor*> cpu_feeds;
        cpu_feeds.push_back(&words);

        paddle::framework::LoDTensor output1;
        std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
        cpu_fetchs1.push_back(&output1);

        // Run inference on CPU
        if (FLAGS_prepare_vars) {
          if (FLAGS_prepare_context) {
            TestInference<paddle::platform::CPUPlace, false, true>(
                dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, model_combined,
                FLAGS_use_mkldnn);
          } else {
            TestInference<paddle::platform::CPUPlace, false, false>(
                dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, model_combined,
                FLAGS_use_mkldnn);
          }
        } else {
          if (FLAGS_prepare_context) {
            TestInference<paddle::platform::CPUPlace, true, true>(
                dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, model_combined,
                FLAGS_use_mkldnn);
          } else {
            TestInference<paddle::platform::CPUPlace, true, false>(
                dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, model_combined,
                FLAGS_use_mkldnn);
          }
        }
        LOG(INFO) << output1.lod();
        LOG(INFO) << output1.dims();
      }
    }));
  }
  auto start_ms = get_current_ms();
  for (int i = 0; i < num_threads; ++i) {
    infer_threads[i]->join();
  }
  auto stop_ms = get_current_ms();
  LOG(INFO) << "total: " << stop_ms - start_ms << " ms";
}