Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into new_api

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

@@ -38,3 +38,11 @@ inference_test(recommender_system)
 #inference_test(rnn_encoder_decoder)
 #inference_test(understand_sentiment ARGS conv)
 inference_test(word2vec)
+
+# This is an unly work around to make this test run
+# TODO(TJ): clean me up
+cc_test(test_inference_nlp
+  SRCS test_inference_nlp.cc
+  DEPS paddle_fluid
+  ARGS
+  --model_path=${PADDLE_BINARY_DIR}/python/paddle/fluid/tests/book/recognize_digits_mlp.inference.model)

paddle/fluid/inference/tests/book/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 <fstream>
+#include <thread>  // NOLINT
+#include "gflags/gflags.h"
+#include "gtest/gtest.h"
+#include "paddle/fluid/inference/tests/test_helper.h"
+#ifdef PADDLE_WITH_MKLML
+#include <mkl_service.h>
+#include <omp.h>
+#endif
+
+DEFINE_string(model_path, "", "Directory of the inference model.");
+DEFINE_string(data_file, "", "File of input index data.");
+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_int32(num_threads, 1, "Number of threads should be used");
+
+inline double GetCurrentMs() {
+  struct timeval time;
+  gettimeofday(&time, NULL);
+  return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
+}
+
+// This function just give dummy data for recognize_digits model.
+size_t DummyData(std::vector<paddle::framework::LoDTensor>* out) {
+  paddle::framework::LoDTensor input;
+  SetupTensor<float>(&input, {1, 1, 28, 28}, -1.f, 1.f);
+  out->emplace_back(input);
+  return 1;
+}
+
+// Load the input word index data from file and save into LodTensor.
+// Return the size of words.
+size_t LoadData(std::vector<paddle::framework::LoDTensor>* out,
+                const std::string& filename) {
+  if (filename.empty()) {
+    return DummyData(out);
+  }
+
+  size_t sz = 0;
+  std::fstream fin(filename);
+  std::string line;
+  out->clear();
+  while (getline(fin, line)) {
+    std::istringstream iss(line);
+    std::vector<int64_t> ids;
+    std::string field;
+    while (getline(iss, field, ' ')) {
+      ids.push_back(stoi(field));
+    }
+    if (ids.size() >= 1024) {
+      // Synced with NLP guys, they will ignore input larger then 1024
+      continue;
+    }
+
+    paddle::framework::LoDTensor words;
+    paddle::framework::LoD lod{{0, ids.size()}};
+    words.set_lod(lod);
+    int64_t* pdata = words.mutable_data<int64_t>(
+        {static_cast<int64_t>(ids.size()), 1}, paddle::platform::CPUPlace());
+    memcpy(pdata, ids.data(), words.numel() * sizeof(int64_t));
+    out->emplace_back(words);
+    sz += ids.size();
+  }
+  return sz;
+}
+
+// Split input data samples into small pieces jobs as balanced as possible,
+// according to the number of threads.
+void SplitData(
+    const std::vector<paddle::framework::LoDTensor>& datasets,
+    std::vector<std::vector<const paddle::framework::LoDTensor*>>* jobs,
+    const int num_threads) {
+  size_t s = 0;
+  jobs->resize(num_threads);
+  while (s < datasets.size()) {
+    for (auto it = jobs->begin(); it != jobs->end(); it++) {
+      it->emplace_back(&datasets[s]);
+      s++;
+      if (s >= datasets.size()) {
+        break;
+      }
+    }
+  }
+}
+
+void ThreadRunInfer(
+    const int tid, paddle::framework::Executor* executor,
+    paddle::framework::Scope* scope,
+    const std::unique_ptr<paddle::framework::ProgramDesc>& inference_program,
+    const std::vector<std::vector<const paddle::framework::LoDTensor*>>& jobs) {
+  auto copy_program = std::unique_ptr<paddle::framework::ProgramDesc>(
+      new paddle::framework::ProgramDesc(*inference_program));
+  auto& sub_scope = scope->NewScope();
+
+  std::string feed_holder_name = "feed_" + paddle::string::to_string(tid);
+  std::string fetch_holder_name = "fetch_" + paddle::string::to_string(tid);
+  copy_program->SetFeedHolderName(feed_holder_name);
+  copy_program->SetFetchHolderName(fetch_holder_name);
+
+  const std::vector<std::string>& feed_target_names =
+      copy_program->GetFeedTargetNames();
+  const std::vector<std::string>& fetch_target_names =
+      copy_program->GetFetchTargetNames();
+
+  PADDLE_ENFORCE_EQ(fetch_target_names.size(), 1UL);
+  std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+  paddle::framework::LoDTensor outtensor;
+  fetch_targets[fetch_target_names[0]] = &outtensor;
+
+  std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
+  PADDLE_ENFORCE_EQ(feed_target_names.size(), 1UL);
+
+  auto& inputs = jobs[tid];
+  auto start_ms = GetCurrentMs();
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    feed_targets[feed_target_names[0]] = inputs[i];
+    executor->Run(*copy_program, &sub_scope, &feed_targets, &fetch_targets,
+                  true /*create_local_scope*/, true /*create_vars*/,
+                  feed_holder_name, fetch_holder_name);
+  }
+  auto stop_ms = GetCurrentMs();
+  scope->DeleteScope(&sub_scope);
+  LOG(INFO) << "Tid: " << tid << ", process " << inputs.size()
+            << " samples, avg time per sample: "
+            << (stop_ms - start_ms) / inputs.size() << " ms";
+}
+
+TEST(inference, nlp) {
+  if (FLAGS_model_path.empty()) {
+    LOG(FATAL) << "Usage: ./example --model_path=path/to/your/model";
+  }
+  if (FLAGS_data_file.empty()) {
+    LOG(WARNING) << "No data file provided, will use dummy data!"
+                 << "Note: if you use nlp model, please provide data file.";
+  }
+  LOG(INFO) << "Model Path: " << FLAGS_model_path;
+  LOG(INFO) << "Data File: " << FLAGS_data_file;
+
+  std::vector<paddle::framework::LoDTensor> datasets;
+  size_t num_total_words = LoadData(&datasets, FLAGS_data_file);
+  LOG(INFO) << "Number of samples (seq_len<1024): " << datasets.size();
+  LOG(INFO) << "Total number of words: " << num_total_words;
+
+  const bool model_combined = false;
+  // 0. Call `paddle::framework::InitDevices()` initialize all the devices
+  // 1. Define place, executor, scope
+  auto place = paddle::platform::CPUPlace();
+  auto executor = paddle::framework::Executor(place);
+  std::unique_ptr<paddle::framework::Scope> scope(
+      new paddle::framework::Scope());
+
+  // 2. Initialize the inference_program and load parameters
+  std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
+  inference_program =
+      InitProgram(&executor, scope.get(), FLAGS_model_path, model_combined);
+  if (FLAGS_use_mkldnn) {
+    EnableMKLDNN(inference_program);
+  }
+
+#ifdef PADDLE_WITH_MKLML
+  // only use 1 thread number per std::thread
+  omp_set_dynamic(0);
+  omp_set_num_threads(1);
+  mkl_set_num_threads(1);
+#endif
+
+  double start_ms = 0, stop_ms = 0;
+  if (FLAGS_num_threads > 1) {
+    std::vector<std::vector<const paddle::framework::LoDTensor*>> jobs;
+    SplitData(datasets, &jobs, FLAGS_num_threads);
+    std::vector<std::unique_ptr<std::thread>> threads;
+    start_ms = GetCurrentMs();
+    for (int i = 0; i < FLAGS_num_threads; ++i) {
+      threads.emplace_back(
+          new std::thread(ThreadRunInfer, i, &executor, scope.get(),
+                          std::ref(inference_program), std::ref(jobs)));
+    }
+    for (int i = 0; i < FLAGS_num_threads; ++i) {
+      threads[i]->join();
+    }
+    stop_ms = GetCurrentMs();
+  } else {
+    if (FLAGS_prepare_vars) {
+      executor.CreateVariables(*inference_program, scope.get(), 0);
+    }
+    // always prepare context
+    std::unique_ptr<paddle::framework::ExecutorPrepareContext> ctx;
+    ctx = executor.Prepare(*inference_program, 0);
+
+    // preapre fetch
+    const std::vector<std::string>& fetch_target_names =
+        inference_program->GetFetchTargetNames();
+    PADDLE_ENFORCE_EQ(fetch_target_names.size(), 1UL);
+    std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+    paddle::framework::LoDTensor outtensor;
+    fetch_targets[fetch_target_names[0]] = &outtensor;
+
+    // prepare feed
+    const std::vector<std::string>& feed_target_names =
+        inference_program->GetFeedTargetNames();
+    PADDLE_ENFORCE_EQ(feed_target_names.size(), 1UL);
+    std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
+
+    // feed data and run
+    start_ms = GetCurrentMs();
+    for (size_t i = 0; i < datasets.size(); ++i) {
+      feed_targets[feed_target_names[0]] = &(datasets[i]);
+      executor.RunPreparedContext(ctx.get(), scope.get(), &feed_targets,
+                                  &fetch_targets, !FLAGS_prepare_vars);
+    }
+    stop_ms = GetCurrentMs();
+    LOG(INFO) << "Tid: 0, process " << datasets.size()
+              << " samples, avg time per sample: "
+              << (stop_ms - start_ms) / datasets.size() << " ms";
+  }
+  LOG(INFO) << "Total inference time with " << FLAGS_num_threads
+            << " threads : " << (stop_ms - start_ms) / 1000.0
+            << " sec, QPS: " << datasets.size() / ((stop_ms - start_ms) / 1000);
+}