/* 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 "PaddleAPI.h"

#include "PaddleAPIPrivate.h"
#include "paddle/trainer/NewRemoteParameterUpdater.h"
#include "paddle/trainer/RemoteParameterUpdater.h"
#include "paddle/trainer/ThreadParameterUpdater.h"

ParameterUpdater::ParameterUpdater() : m(new ParameterUpdaterPrivate()) {}

ParameterUpdater *ParameterUpdater::createLocalUpdater(
    OptimizationConfig *config) {
  auto updater = new ParameterUpdater();
  updater->m->updater.reset(
      new paddle::SgdThreadUpdater(config->m->getConfig()));
  return updater;
}

ParameterUpdater *ParameterUpdater::createNewRemoteUpdater(
    OptimizationConfig *config, const std::string pserverSpec) {
  auto updater = new ParameterUpdater();
  updater->m->updater.reset(new paddle::NewRemoteParameterUpdater(
      config->m->getConfig(), pserverSpec));
  return updater;
}

ParameterUpdater *ParameterUpdater::createRemoteUpdater(
    OptimizationConfig *config, int passCount, bool useSparseUpdater) {
  auto updater = new ParameterUpdater();
  auto remoteUpdater = new paddle::RemoteParameterUpdater(
      config->m->getConfig(), passCount, nullptr);
  if (useSparseUpdater) {
    std::unique_ptr<paddle::ParameterUpdater> remoteUpdaterPtr(remoteUpdater);
    auto sparseRemoteUpdater =
        new paddle::SparseRemoteParameterUpdaterComposite(
            config->m->getConfig(),
            passCount,
            false,
            std::move(remoteUpdaterPtr));
    updater->m->updater.reset(sparseRemoteUpdater);
  } else {
    updater->m->updater.reset(remoteUpdater);
  }
  return updater;
}

ParameterUpdater::~ParameterUpdater() { delete m; }

void ParameterUpdater::init(const GradientMachine &gm) {
  m->updater->init(gm.m->machine->getNonStaticParameters());
}

void ParameterUpdater::startPass() { m->updater->startPass(); }

void ParameterUpdater::finishPass() { m->updater->finishPass(); }

PassType ParameterUpdater::startBatch(size_t batchSize) {
  return m->updater->startBatch((int64_t)batchSize);
}

void ParameterUpdater::finishBatch(float cost) {
  m->updater->finishBatch(cost);
}

void ParameterUpdater::update(Parameter *param) {
  auto paddleParam = param->m->getPtr();
  m->updater->update(paddleParam);
}

void ParameterUpdater::getParametersRemote(bool fullSize, bool apply) {
  m->updater->getParametersRemote(fullSize, apply);
}

void ParameterUpdater::restore() { m->updater->restore(); }

void ParameterUpdater::apply() { m->updater->apply(); }

void ParameterUpdater::catchUpWith() { m->updater->catchUpWith(); }