src/model/optimizer/sgd.cc

/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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.
 */
#ifndef SRC_MODEL_OPTIMIZER_SGD_H_
#define SRC_MODEL_OPTIMIZER_SGD_H_
#include "singa/model/optimizer.h"
#include <functional>
namespace singa {

void SGD::Setup(const OptimizerConf& conf) {
  Optimizer::Setup(conf);
  if (conf.has_momentum()) {
    float m = conf.momentum();
    SetMomentumGenerator([m](int step) { return m; });
  }
}

// history = history * momentum + grad * lr
// value = value - history
void SGD::Apply(int epoch, float lr, const string& name, Tensor& grad,
                Tensor& value, int step) {
  if (grad.empty())
    return;
  ApplyRegularizerConstraint(epoch, name, value, grad, step);
  if (learning_rate_multplier_.find(name) != learning_rate_multplier_.end())
    lr *= learning_rate_multplier_.at(name);

  // LOG(INFO) << "param " << name  << " lr = " << lr << " grad = " << grad.L1() << " value = " << value.L1();
  if (momentum_generator_) {
    float mom = momentum_generator_(step);
    if (mom != 0) {
      if (history_gradient_.find(name) == history_gradient_.end()) {
        history_gradient_[name].ResetLike(value);
        history_gradient_[name].SetValue(0.0f);
      }
      Tensor& history = history_gradient_[name];
      history *= mom;
      Axpy(lr, grad, &history);
      value -= history;
      return;
    }
  }
  Axpy(-lr, grad, &value);
}
}  // namespace singa
#endif  // SRC_MODEL_OPTIMIZER_SGD_H_