parameter_optimizer_test.cc

//  Copyright (c) 2018 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.
/*
  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 "parameter_optimizer.h"
#include <cmath>
#include <map>
#include <vector>
#include "gtest/gtest.h"
#include "lr_policy.h"

paddle::optimizer::Tensor* FillTensor(size_t size) {
  paddle::optimizer::Tensor* param = new paddle::optimizer::Tensor(size);
  paddle::optimizer::Tensor& p = *param;
  for (size_t i = 0; i < p.size(); ++i) {
    p[i] = (float)rand() / (float)RAND_MAX;
  }
  return param;
}

paddle::optimizer::Tensor* FixedTensor(size_t size) {
  paddle::optimizer::Tensor* param = new paddle::optimizer::Tensor(size);
  paddle::optimizer::Tensor& p = *param;
  for (size_t i = 0; i < p.size(); ++i) {
    p[i] = i;
  }
  return param;
}

class OptimizerTest : public testing::Test {
public:
  virtual ~OptimizerTest() {}
  // init paddle::optimizer::Tensor shape
  const size_t kSize = 5;

  virtual void SetUp() {
    CreateSGD();
    CreateAdam();
  }
  virtual void TearDown() {}

  void CreateSGD() {
    paddle::optimizer::Tensor* parameter = FixedTensor(kSize);
    config_.set_optimizer(paddle::OptimizerConfig::SGD);
    config_.mutable_sgd()->set_momentum(0.0);
    config_.mutable_sgd()->set_decay(0.0);
    config_.mutable_sgd()->set_nesterov(false);
    config_.set_lr_policy(paddle::OptimizerConfig::Const);
    config_.mutable_const_lr()->set_learning_rate(0.1);
    std::string str = config_.SerializeAsString();
    paddle::optimizer::ParameterOptimizer* opt =
        paddle::optimizer::ParameterOptimizer::Create(str, parameter);
    opts_.push_back(opt);
  }

  void CreateAdam() {
    paddle::optimizer::Tensor* parameter = FixedTensor(kSize);
    config_.set_optimizer(paddle::OptimizerConfig::Adam);
    config_.mutable_adam()->set_beta_1(0.9);
    config_.mutable_adam()->set_beta_2(0.1);
    config_.mutable_adam()->set_epsilon(1e-3);
    config_.mutable_adam()->set_decay(0.0);
    config_.set_lr_policy(paddle::OptimizerConfig::Const);
    config_.mutable_const_lr()->set_learning_rate(0.1);
    std::string str = config_.SerializeAsString();
    paddle::optimizer::ParameterOptimizer* opt =
        paddle::optimizer::ParameterOptimizer::Create(str, parameter);
    opts_.push_back(opt);
  }

  void TestGetWeight() {
    paddle::optimizer::Tensor* p = FixedTensor(kSize);
    for (size_t i = 0; i < opts_.size(); ++i) {
      int s = 0;
      float* newp = (float*)opts_[i]->get_weight(&s);
      EXPECT_EQ(static_cast<size_t>(s), kSize);
      for (size_t j = 0; j < kSize; ++j) {
        EXPECT_EQ(newp[j], (*p)[j]);
      }
    }
  }

  void TestUpdate() {
    paddle::optimizer::Tensor* g = FixedTensor(kSize);
    for (size_t i = 0; i < opts_.size(); ++i) {
      opts_[i]->Update(g);
    }
  }

  void TestCheckPoint() {
    paddle::optimizer::Tensor* p = FixedTensor(kSize);
    for (size_t i = 0; i < opts_.size(); ++i) {
      auto state = opts_[i]->SerializeState();
      opts_[i]->DeserializeState(state);
      auto state1 = opts_[i]->SerializeState();
      opts_[i]->DeserializeState(state);
      EXPECT_EQ(state, state1);

      int s = 0;
      float* newp = (float*)opts_[i]->get_weight(&s);
      EXPECT_EQ(static_cast<size_t>(s), kSize);
      for (size_t j = 0; j < kSize; ++j) {
        EXPECT_EQ(newp[j], (*p)[j]);
      }
    }
  }

private:
  std::vector<paddle::optimizer::ParameterOptimizer*> opts_;
  paddle::OptimizerConfig config_;
};

TEST_F(OptimizerTest, TestGetWeight) { TestGetWeight(); }

TEST_F(OptimizerTest, TestUpdate) { TestUpdate(); }

TEST_F(OptimizerTest, TestCheckPoint) { TestCheckPoint(); }