test_GradientMachine.cpp

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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 <gtest/gtest.h>
#include <paddle/gserver/gradientmachines/GradientMachine.h>
#include <paddle/trainer/TrainerConfigHelper.h>
#include <stdlib.h>
#include <string.h>
#include <type_traits>
#include "PaddleCAPI.h"
#include "paddle/utils/ThreadLocal.h"

static std::vector<pd_real> randomBuffer(size_t bufSize) {
  auto& eng = paddle::ThreadLocalRandomEngine::get();
  std::uniform_real_distribution<pd_real> dist(-1.0, 1.0);
  std::vector<pd_real> retv;
  retv.reserve(bufSize);
  for (size_t i = 0; i < bufSize; ++i) {
    retv.push_back(dist(eng));
  }
  return retv;
}

TEST(GradientMachine, testPredict) {
  paddle::TrainerConfigHelper config("./test_predict_network.py");
  std::string buffer;
  ASSERT_TRUE(config.getModelConfig().SerializeToString(&buffer));
  PD_GradientMachine machine;

  ASSERT_EQ(kPD_NO_ERROR,
            PDGradientMachineCreateForPredict(
                &machine, &buffer[0], (int)buffer.size()));
  std::unique_ptr<paddle::GradientMachine> gm(
      paddle::GradientMachine::create(config.getModelConfig()));
  ASSERT_NE(nullptr, gm);
  gm->randParameters();
  gm->saveParameters("./");

  ASSERT_EQ(kPD_NO_ERROR,
            PDGradientMachineLoadParameterFromDisk(machine, "./"));

  PD_GradientMachine machineSlave;
  ASSERT_EQ(kPD_NO_ERROR,
            PDGradientMachineCreateSharedParam(
                machine, &buffer[0], (int)buffer.size(), &machineSlave));
  std::swap(machineSlave, machine);
  PD_Arguments outArgs = PDArgsCreateNone();

  PD_Arguments inArgs = PDArgsCreateNone();
  ASSERT_EQ(kPD_NO_ERROR, PDArgsResize(inArgs, 1));
  PD_Matrix mat = PDMatCreate(1, 100, false);
  static_assert(std::is_same<pd_real, paddle::real>::value, "");

  auto data = randomBuffer(100);
  pd_real* rowPtr;
  ASSERT_EQ(kPD_NO_ERROR, PDMatGetRow(mat, 0, &rowPtr));
  memcpy(rowPtr, data.data(), data.size() * sizeof(pd_real));

  ASSERT_EQ(kPD_NO_ERROR, PDArgsSetValue(inArgs, 0, mat));
  ASSERT_EQ(kPD_NO_ERROR,
            PDGradientMachineForward(machine, inArgs, outArgs, false));

  uint64_t sz;
  ASSERT_EQ(kPD_NO_ERROR, PDArgsGetSize(outArgs, &sz));
  ASSERT_EQ(1UL, sz);

  ASSERT_EQ(kPD_NO_ERROR, PDArgsGetValue(outArgs, 0, mat));
  std::vector<paddle::Argument> paddleInArgs;
  std::vector<paddle::Argument> paddleOutArgs;
  paddleInArgs.resize(1);
  paddleInArgs[0].value =
      paddle::Matrix::create(data.data(), 1, 100, false, false);

  gm->forward(paddleInArgs, &paddleOutArgs, paddle::PASS_TEST);

  auto matPaddle = paddleOutArgs[0].value;

  uint64_t height, width;
  ASSERT_EQ(kPD_NO_ERROR, PDMatGetShape(mat, &height, &width));
  ASSERT_EQ(matPaddle->getHeight(), height);
  ASSERT_EQ(matPaddle->getWidth(), width);

  ASSERT_EQ(kPD_NO_ERROR, PDMatGetRow(mat, 0, &rowPtr));
  for (size_t i = 0; i < width; ++i) {
    ASSERT_NEAR(matPaddle->getData()[i], rowPtr[i], 1e-5);
  }

  ASSERT_EQ(kPD_NO_ERROR, PDMatDestroy(mat));
  ASSERT_EQ(kPD_NO_ERROR, PDArgsDestroy(inArgs));
  ASSERT_EQ(kPD_NO_ERROR, PDArgsDestroy(outArgs));
  std::swap(machineSlave, machine);
  ASSERT_EQ(kPD_NO_ERROR, PDGradientMachineDestroy(machineSlave));
  ASSERT_EQ(kPD_NO_ERROR, PDGradientMachineDestroy(machine));
}

int main(int argc, char** argv) {
  testing::InitGoogleTest(&argc, argv);
  std::vector<char*> argvs;
  argvs.push_back(strdup("--use_gpu=false"));
  PDInit((int)argvs.size(), argvs.data());
  for (auto each : argvs) {
    free(each);
  }
  return RUN_ALL_TESTS();
}