text: /* 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 <map>
  #include <random>
  #include <string>
  #include <vector>
  
  #include "paddle/fluid/framework/lod_tensor.h"
  #include "paddle/fluid/inference/io.h"
  #include "paddle/fluid/platform/profiler.h"
  
  DECLARE_bool(use_mkldnn);
  
  template <typename T>
  void SetupTensor(paddle::framework::LoDTensor* input,
                   paddle::framework::DDim dims, T lower, T upper) {
    static unsigned int seed = 100;
    std::mt19937 rng(seed++);
    std::uniform_real_distribution<double> uniform_dist(0, 1);
  
    T* input_ptr = input->mutable_data<T>(dims, paddle::platform::CPUPlace());
    for (int i = 0; i < input->numel(); ++i) {
      input_ptr[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
    }
  }
  
  template <typename T>
  void SetupTensor(paddle::framework::LoDTensor* input,
                   paddle::framework::DDim dims, const std::vector<T>& data) {
    CHECK_EQ(paddle::framework::product(dims), static_cast<int64_t>(data.size()));
    T* input_ptr = input->mutable_data<T>(dims, paddle::platform::CPUPlace());
    memcpy(input_ptr, data.data(), input->numel() * sizeof(T));
  }
  
  template <typename T>
  void SetupLoDTensor(paddle::framework::LoDTensor* input,
                      const paddle::framework::LoD& lod, T lower, T upper) {
    input->set_lod(lod);
    int dim = lod[0][lod[0].size() - 1];
    SetupTensor<T>(input, {dim, 1}, lower, upper);
  }
  
  template <typename T>
  void SetupLoDTensor(paddle::framework::LoDTensor* input,
                      paddle::framework::DDim dims,
                      const paddle::framework::LoD lod,
                      const std::vector<T>& data) {
    const size_t level = lod.size() - 1;
    CHECK_EQ(dims[0], static_cast<int64_t>((lod[level]).back()));
    input->set_lod(lod);
    SetupTensor<T>(input, dims, data);
  }
  
  template <typename T>
  void CheckError(const paddle::framework::LoDTensor& output1,
                  const paddle::framework::LoDTensor& output2) {
    // Check lod information
    EXPECT_EQ(output1.lod(), output2.lod());
  
    EXPECT_EQ(output1.dims(), output2.dims());
    EXPECT_EQ(output1.numel(), output2.numel());
  
    T err = static_cast<T>(0);
    if (typeid(T) == typeid(float)) {
      err = 1E-3;
    } else if (typeid(T) == typeid(double)) {
      err = 1E-6;
    } else {
      err = 0;
    }
  
    size_t count = 0;
    for (int64_t i = 0; i < output1.numel(); ++i) {
      if (fabs(output1.data<T>()[i] - output2.data<T>()[i]) > err) {
        count++;
      }
    }
    EXPECT_EQ(count, 0U) << "There are " << count << " different elements.";
  }
  
  std::unique_ptr<paddle::framework::ProgramDesc> InitProgram(
      paddle::framework::Executor* executor, paddle::framework::Scope* scope,
      const std::string& dirname, const bool is_combined = false) {
    std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
    if (is_combined) {
      // All parameters are saved in a single file.
      // Hard-coding the file names of program and parameters in unittest.
      // The file names should be consistent with that used in Python API
      //  `fluid.io.save_inference_model`.
      std::string prog_filename = "__model_combined__";
      std::string param_filename = "__params_combined__";
      inference_program =
          paddle::inference::Load(executor, scope, dirname + "/" + prog_filename,
                                  dirname + "/" + param_filename);
    } else {
      // Parameters are saved in separate files sited in the specified
      // `dirname`.
      inference_program = paddle::inference::Load(executor, scope, dirname);
    }
    return inference_program;
  }
  
  std::vector<std::vector<int64_t>> GetFeedTargetShapes(
      const std::string& dirname, const bool is_combined = false) {
    auto place = paddle::platform::CPUPlace();
    auto executor = paddle::framework::Executor(place);
    auto* scope = new paddle::framework::Scope();
  
    auto inference_program = InitProgram(&executor, scope, dirname, is_combined);
    auto& global_block = inference_program->Block(0);
  
    const std::vector<std::string>& feed_target_names =
        inference_program->GetFeedTargetNames();
    std::vector<std::vector<int64_t>> feed_target_shapes;
    for (size_t i = 0; i < feed_target_names.size(); ++i) {
      auto* var = global_block.FindVar(feed_target_names[i]);
      std::vector<int64_t> var_shape = var->GetShape();
      feed_target_shapes.push_back(var_shape);
    }
  
    delete scope;
    return feed_target_shapes;
  }
  
  template <typename Place, bool CreateVars = true, bool PrepareContext = false>
  void TestInference(const std::string& dirname,
                     const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
                     const std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
                     const int repeat = 1, const bool is_combined = false) {
    // 1. Define place, executor, scope
    auto place = Place();
    auto executor = paddle::framework::Executor(place);
    auto* scope = new paddle::framework::Scope();
  
    // Profile the performance
    paddle::platform::ProfilerState state;
    if (paddle::platform::is_cpu_place(place)) {
      state = paddle::platform::ProfilerState::kCPU;
    } else {
  #ifdef PADDLE_WITH_CUDA
      state = paddle::platform::ProfilerState::kAll;
      // The default device_id of paddle::platform::CUDAPlace is 0.
      // Users can get the device_id using:
      //   int device_id = place.GetDeviceId();
      paddle::platform::SetDeviceId(0);
  #else
      PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
  #endif
    }
  
    // 2. Initialize the inference_program and load parameters
    std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
  
    // Enable the profiler
    paddle::platform::EnableProfiler(state);
    {
      paddle::platform::RecordEvent record_event(
          "init_program",
          paddle::platform::DeviceContextPool::Instance().Get(place));
      inference_program = InitProgram(&executor, scope, dirname, is_combined);
    }
    // Disable the profiler and print the timing information
    paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,
                                      "load_program_profiler");
    paddle::platform::ResetProfiler();
  
    // 3. Get the feed_target_names and fetch_target_names
    const std::vector<std::string>& feed_target_names =
        inference_program->GetFeedTargetNames();
    const std::vector<std::string>& fetch_target_names =
        inference_program->GetFetchTargetNames();
  
    // 4. Prepare inputs: set up maps for feed targets
    std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
    for (size_t i = 0; i < feed_target_names.size(); ++i) {
      // Please make sure that cpu_feeds[i] is right for feed_target_names[i]
      feed_targets[feed_target_names[i]] = cpu_feeds[i];
    }
  
    // 5. Define Tensor to get the outputs: set up maps for fetch targets
    std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
    for (size_t i = 0; i < fetch_target_names.size(); ++i) {
      fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
    }
  
    // 6. If export Flags_use_mkldnn=True, use mkldnn related ops.
    if (FLAGS_use_mkldnn) executor.EnableMKLDNN(*inference_program);
  
    // 7. Run the inference program
    {
      if (!CreateVars) {
        // If users don't want to create and destroy variables every time they
        // run, they need to set `create_vars` to false and manually call
        // `CreateVariables` before running.
        executor.CreateVariables(*inference_program, scope, 0);
      }
  
      // Ignore the profiling results of the first run
      std::unique_ptr<paddle::framework::ExecutorPrepareContext> ctx;
      if (PrepareContext) {
        ctx = executor.Prepare(*inference_program, 0);
        executor.RunPreparedContext(ctx.get(), scope, &feed_targets,
                                    &fetch_targets, true, CreateVars);
      } else {
        executor.Run(*inference_program, scope, &feed_targets, &fetch_targets,
                     true, CreateVars);
      }
  
      // Enable the profiler
      paddle::platform::EnableProfiler(state);
  
      // Run repeat times to profile the performance
      for (int i = 0; i < repeat; ++i) {
        paddle::platform::RecordEvent record_event(
            "run_inference",
            paddle::platform::DeviceContextPool::Instance().Get(place));
  
        if (PrepareContext) {
          // Note: if you change the inference_program, you need to call
          // executor.Prepare() again to get a new ExecutorPrepareContext.
          executor.RunPreparedContext(ctx.get(), scope, &feed_targets,
                                      &fetch_targets, CreateVars);
        } else {
          executor.Run(*inference_program, scope, &feed_targets, &fetch_targets,
                       CreateVars);
        }
      }
  
      // Disable the profiler and print the timing information
      paddle::platform::DisableProfiler(
          paddle::platform::EventSortingKey::kDefault, "run_inference_profiler");
      paddle::platform::ResetProfiler();
    }
  
    delete scope;
  }
  