/* 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 "Layer.h"

namespace paddle {

class KmaxSeqScoreLayer : public Layer {
private:
  MatrixPtr scores_;
  size_t beamSize_;
  void kmaxScorePerSeq(const real* score,
                       real* sortedRes,
                       const ICpuGpuVectorPtr seqStartPos);

public:
  explicit KmaxSeqScoreLayer(const LayerConfig& config) : Layer(config) {}

  bool init(const LayerMap& layerMap,
            const ParameterMap& parameterMap) override;

  void forward(PassType passType) override;
  void backward(const UpdateCallback& callback = nullptr) override;
};

REGISTER_LAYER(kmax_seq_score, KmaxSeqScoreLayer);

bool KmaxSeqScoreLayer::init(const LayerMap& layerMap,
                             const ParameterMap& parameterMap) {
  bool ret = Layer::init(layerMap, parameterMap);
  CHECK_EQ(1U, inputLayers_.size());

  beamSize_ = config_.beam_size();
  CHECK_GE(beamSize_, 1U);

  setNeedSequenceInfo(false);
  setNeedGradient(false);
  return ret;
}

void KmaxSeqScoreLayer::kmaxScorePerSeq(const real* scores,
                                        real* sortedIds,
                                        const ICpuGpuVectorPtr seqStartPos) {
  int* starts = seqStartPos->getMutableData(false);
  std::vector<real> indices;
  for (size_t i = 0; i < seqStartPos->getSize() - 1; ++i) {
    int seqLen = starts[i + 1] - starts[i];
    int k = std::min(static_cast<int>(beamSize_), seqLen);

    indices.resize(seqLen, 0);
    std::iota(begin(indices), end(indices), 0.);
    std::vector<real> tmpScore(scores + starts[i], scores + starts[i + 1]);
    std::partial_sort(
        begin(indices),
        begin(indices) + k,
        end(indices),
        [&](size_t a, size_t b) { return tmpScore[a] > tmpScore[b]; });
    memcpy(sortedIds + (i * beamSize_), indices.data(), k * sizeof(real));
  }
}

void KmaxSeqScoreLayer::forward(PassType passType) {
  Layer::forward(passType);

  const Argument& input = getInput(0);
  const MatrixPtr inputScore = getInputValue(0);

  CHECK(input.hasSeq() || input.hasSubseq())
      << "input of " << getName()
      << " must be a sequence or a nested sequence.";
  CHECK_EQ(input.value->getWidth(), 1UL)
      << "input of " << getName()
      << " is score over a sequence or a nested sequence, so its width "
      << " must be 1.";

  if (useGpu_) {
    // this Layer runs only in CPU, if the model is runing on GPU,
    // then copy the input to this layer from GPU to CPU.
    Matrix::resizeOrCreate(scores_,
                           inputScore->getHeight(),
                           1,
                           false /* trans */,
                           false /* useGpu */);
    scores_->copyFrom(*inputScore);
  } else {
    scores_ = inputScore;
  }

  int seqNum =
      input.hasSubseq() ? input.getNumSubSequences() : input.getNumSequences();
  Matrix::resizeOrCreate(output_.value, seqNum, beamSize_, false, false);
  output_.value->one();
  output_.value->mulScalar(-1.);

  kmaxScorePerSeq(scores_->getData(), output_.value->getData(), seqNum);
}

void KmaxSeqScoreLayer::backward(const UpdateCallback& callback) {}

}  // namespace paddle