/* 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 "NewRemoteParameterUpdater.h"
#include "Trainer.h"
#include "paddle/utils/Stat.h"

DECLARE_int32(trainer_id);
DECLARE_string(save_dir);

namespace paddle {
NewRemoteParameterUpdater::NewRemoteParameterUpdater(
    const OptimizationConfig &config, const std::string pserverSpec)
    : pserverSpec_(pserverSpec) {}

void NewRemoteParameterUpdater::init(
    const std::vector<ParameterPtr> &parameters) {
  ParameterUpdater::init(parameters);
  LOG(INFO) << "NewRemoteParameterUpdater init in";

  for (auto &para : parameters_) {
    para->getBuf(PARAMETER_VALUE)->zeroMem();
    para->getBuf(PARAMETER_GRADIENT)->zeroMem();
  }

  // create parameter server client.
  parameterClient_ =
      paddle_new_pserver_client((char *)pserverSpec_.c_str(), FLAGS_trainer_id);

  // init names_ for get parameter through paddle_cclient
  names_ = (char **)malloc(parameterSize() * sizeof(char *));
  for (int i = 0; i < parameterSize(); ++i) {
    names_[i] = (char *)parameters_[i]->getName().c_str();
  }

  // init new parameter and gradient.
  newParameters_ = initNewParameter(PARAMETER_VALUE);
  newGradients_ = initNewParameter(PARAMETER_GRADIENT);

  // init parameter, one trainer will get the opportunity to int parameter and
  // send them to parameter server. Others will get the initialized parameter
  // from parameter server
  if (paddle_begin_init_params(parameterClient_)) {
    LOG(INFO) << "paddle_begin_init_params start";
    for (int i = 0; i < parameterSize(); ++i) {
      paddle_init_param(parameterClient_, *newParameters_[i], NULL, 0);
    }
    paddle_finish_init_params(parameterClient_);
    LOG(INFO) << "paddle_begin_init_params done";
  } else {
    paddle_get_params(
        parameterClient_, names_, newParameters_, parameterSize());
  }

  LOG(INFO) << "NewRemoteParameterUpdater initialized";
}

void NewRemoteParameterUpdater::updateImpl(Parameter *para) {}

void NewRemoteParameterUpdater::finishBatch(real cost) {
  LOG(INFO) << "finishBatch in, cost: " << cost;

  // send gradient to parameter server.
  paddle_send_grads(parameterClient_, *newGradients_, parameterSize());
  // get the updated parameter from parameterClient.
  paddle_get_params(parameterClient_, names_, newParameters_, parameterSize());

  // clear gradient after update parameter.
  for (auto &para : parameters_) {
    para->getBuf(PARAMETER_GRADIENT)->zeroMem();
  }
}

void NewRemoteParameterUpdater::startPass() {}

bool NewRemoteParameterUpdater::finishPass() { return true; }
}