test_helper.h

/* 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"

template <typename T>
void SetupTensor(paddle::framework::LoDTensor* input,
                 paddle::framework::DDim dims, T lower, T upper) {
  std::mt19937 rng(100);  // An arbitrarily chosen but fixed 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.";
}

template <typename Place, 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::kCUDA;
    // 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));

    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);
    }
  }
  // Disable the profiler and print the timing information
  paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,
                                    "load_program_profiler.txt");
  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. Run the inference program
  {
    // 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);
    } else {
      executor.Run(*inference_program, scope, feed_targets, fetch_targets);
    }

    // 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 changed the inference_program, you need to call
        // executor.Prepare() again to get a new ExecutorPrepareContext.
        executor.RunPreparedContext(ctx.get(), scope, feed_targets,
                                    fetch_targets);
      } else {
        executor.Run(*inference_program, scope, feed_targets, fetch_targets);
      }
    }

    // Disable the profiler and print the timing information
    paddle::platform::DisableProfiler(
        paddle::platform::EventSortingKey::kDefault,
        "run_inference_profiler.txt");
    paddle::platform::ResetProfiler();
  }

  delete scope;
}