/* 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 "RecurrentLayer.h"

DEFINE_bool(rnn_use_batch, false, "Using the batch method for calculation.");

namespace paddle {

REGISTER_LAYER(recurrent, RecurrentLayer);

bool RecurrentLayer::init(const LayerMap& layerMap,
                          const ParameterMap& parameterMap) {
  if (!Layer::init(layerMap, parameterMap)) return false;
  CHECK_EQ(1U, inputLayers_.size());
  CHECK_EQ(1U, parameters_.size());
  CHECK_EQ(getSize() * getSize(), parameters_[0]->getSize());
  weight_.reset(new Weight(getSize(), getSize(), parameters_[0]));
  if (biasParameter_.get() != NULL) {
    bias_.reset(new Weight(1, getSize(), biasParameter_));
  }
  reversed_ = config_.reversed();
  return true;
}

void RecurrentLayer::resetState() {
  CHECK(!reversed_) << "state is not allowed for reversed recurrent layer";
  Matrix::resizeOrCreate(
      prevOutput_, 1, getSize(), /* trans= */ false, useGpu_);
  prevOutput_->zeroMem();
}

void RecurrentLayer::setState(LayerStatePtr state) {
  CHECK(state->value.size() == 1) << "one matrix is expected for RNN state";
  prevOutput_->copyFrom(*(state->value[0]));
}

LayerStatePtr RecurrentLayer::getState() {
  LayerStatePtr res = std::make_shared<LayerState>();
  res->value.push_back(prevOutput_->clone(0, 0, useGpu_));
  res->value[0]->copyFrom(*prevOutput_);
  return res;
}

void RecurrentLayer::forward(PassType passType) {
  REGISTER_TIMER_INFO("RecurrentFwTimer", getName().c_str());
  Layer::forward(passType);
  const Argument& input = getInput(0);
  CHECK(input.sequenceStartPositions);
  int batchSize = input.getBatchSize();
  size_t numSequences = input.getNumSequences();
  resetOutput(batchSize, getSize());
  CHECK_EQ(getSize(), input.value->getWidth());
  const int* starts = input.sequenceStartPositions->getData(false);
  CHECK_EQ(starts[numSequences], batchSize);

  output_.value->assign(*input.value);
  if (bias_) {
    output_.value->addBias(*bias_->getW(), 1);
  }
  if (!FLAGS_rnn_use_batch) {
    forwardSequence(batchSize, numSequences, starts);
  } else {
    forwardBatch(batchSize, numSequences, starts);
  }
}

void RecurrentLayer::forwardSequence(int batchSize,
                                     size_t numSequences,
                                     const int* starts) {
  REGISTER_TIMER_INFO("RecurrentFwSequence", getName().c_str());
  frameOutput_.reserve(batchSize);
  for (int i = frameOutput_.size(); i < batchSize; ++i) {
    Argument arg;
    arg.value = Matrix::create(nullptr,
                               /* height= */ 1,
                               getSize(),
                               /* trans= */ false,
                               useGpu_);
    arg.grad = Matrix::create(nullptr,
                              /* height= */ 1,
                              getSize(),
                              /* trans= */ false,
                              useGpu_);
    frameOutput_.push_back(arg);
  }

  for (int i = 0; i < batchSize; ++i) {
    frameOutput_[i].value->setData(output_.value->getData() + i * getSize());
  }

  AsyncGpuBlock asyncGpuBlock;
  for (size_t i = 0; i < numSequences; ++i) {
    forwardOneSequence(starts[i], starts[i + 1] - starts[i]);
  }
}

void RecurrentLayer::forwardOneSequence(int start, int length) {
  if (!reversed_) {
    if (prevOutput_) {
      frameOutput_[start].value->mul(*prevOutput_, *weight_->getW(), 1, 1);
    }
    activation_->forward(frameOutput_[start]).check();

    for (int i = 1; i < length; ++i) {
      frameOutput_[start + i].value->mul(
          *frameOutput_[start + i - 1].value, *weight_->getW(), 1, 1);
      activation_->forward(frameOutput_[start + i]).check();
    }
    if (prevOutput_) {
      prevOutput_->assign(*frameOutput_[start + length - 1].value);
    }
  } else {
    activation_->forward(frameOutput_[start + length - 1]).check();
    for (int i = length - 2; i >= 0; --i) {
      frameOutput_[start + i].value->mul(
          *frameOutput_[start + i + 1].value, *weight_->getW(), 1, 1);
      activation_->forward(frameOutput_[start + i]).check();
    }
  }
}

void RecurrentLayer::backward(const UpdateCallback& callback) {
  REGISTER_TIMER_INFO("RecurrentBwTimer", getName().c_str());
  const Argument& input = getInput(0);
  CHECK(input.sequenceStartPositions);
  int batchSize = input.getBatchSize();
  const int* starts = input.sequenceStartPositions->getData(false);
  size_t numSequences = input.getNumSequences();

  if (!FLAGS_rnn_use_batch) {
    backwardSequence(batchSize, numSequences, starts);
  } else {
    backwardBatch(batchSize, numSequences, starts);
  }

  if (input.grad) {
    input.grad->add(*output_.grad);
  }

  if (bias_ && bias_->getWGrad()) {
    bias_->getWGrad()->collectBias(*output_.grad, 1);
    bias_->getParameterPtr()->incUpdate(callback);
  }
  weight_->getParameterPtr()->incUpdate(callback);
}

void RecurrentLayer::backwardSequence(int batchSize,
                                      size_t numSequences,
                                      const int* starts) {
  REGISTER_TIMER_INFO("RecurrentBwSequence", getName().c_str());
  for (int i = 0; i < batchSize; ++i) {
    frameOutput_[i].grad->setData(output_.grad->getData() + i * getSize());
  }

  AsyncGpuBlock asyncGpuBlock;
  for (size_t i = 0; i < numSequences; ++i) {
    backwardOneSequence(starts[i], starts[i + 1] - starts[i]);
  }
}

void RecurrentLayer::backwardOneSequence(int start, int length) {
  MatrixPtr weightT = weight_->getW()->getTranspose();
  if (!reversed_) {
    for (int i = length - 1; i > 0; --i) {
      activation_->backward(frameOutput_[start + i]).check();
      frameOutput_[start + i - 1].grad->mul(
          *frameOutput_[start + i].grad, *weightT, 1, 1);
    }
    activation_->backward(frameOutput_[start]).check();
    if (weight_->getWGrad()) {
      weight_->getWGrad()->mul(
          *output_.value->subMatrix(start, length - 1)->getTranspose(),
          *output_.grad->subMatrix(start + 1, length - 1),
          1,
          1);
    }
  } else {
    for (int i = 0; i < length - 1; ++i) {
      activation_->backward(frameOutput_[start + i]).check();
      frameOutput_[start + i + 1].grad->mul(
          *frameOutput_[start + i].grad, *weightT, 1, 1);
    }
    activation_->backward(frameOutput_[start + length - 1]).check();
    if (weight_->getWGrad()) {
      weight_->getWGrad()->mul(
          *output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
          *output_.grad->subMatrix(start, length - 1),
          1,
          1);
    }
  }
}

void RecurrentLayer::forwardBatch(int batchSize,
                                  size_t numSequences,
                                  const int* starts) {
  if (!batchValue_) {
    batchValue_.reset(new SequenceToBatch(useGpu_));
  }

  batchValue_->resizeOrCreateBatch(batchSize, numSequences, starts, reversed_);

  batchValue_->copyFromSeq(*output_.value);
  {
    REGISTER_TIMER_INFO("RecurrentFwBatch", getName().c_str());
    AsyncGpuBlock asyncGpuBlock;
    /* forward one batch */
    for (size_t n = 0; n < batchValue_->getNumBatch(); n++) {
      MatrixPtr batch2 = batchValue_->getBatchValue(n);

      if (n != 0) {
        MatrixPtr batch1 =
            batchValue_->getBatchValue(n - 1, batch2->getHeight());
        batch2->mul(*batch1, *weight_->getW(), 1, 1);
      }
      Argument arg;
      arg.value = batch2;
      activation_->forward(arg).check();
    }
  }
  batchValue_->copyBackSeq(*output_.value);
}

void RecurrentLayer::backwardBatch(int batchSize,
                                   size_t numSequences,
                                   const int* starts) {
  if (!batchGrad_) {
    batchGrad_.reset(new SequenceToBatch(useGpu_));
  }
  batchGrad_->shareIndexWith(*batchValue_);

  size_t numBatch = batchGrad_->getNumBatch();
  bool backwardByBatch = numBatch < numSequences;

  batchGrad_->copyFromSeq(*output_.grad);
  {
    REGISTER_TIMER_INFO("RecurrentBwData", getName().c_str());
    MatrixPtr weightT = weight_->getW()->getTranspose();
    AsyncGpuBlock asyncGpuBlock;
    /* backward one batch */
    for (int n = (int)numBatch - 1; n >= 0; n--) {
      MatrixPtr batch2 = batchGrad_->getBatchValue(n);
      MatrixPtr batch1 = batchValue_->getBatchValue(n, batch2->getHeight());

      Argument arg;
      arg.value = batch1;
      arg.grad = batch2;
      activation_->backward(arg).check();

      if (n != 0) {
        batch1 = batchGrad_->getBatchValue(n - 1, batch2->getHeight());
        batch1->mul(*batch2, *weightT, 1, 1);
      }

      if (backwardByBatch && weight_->getWGrad()) {
        if (n != 0) {
          /* backward weight */
          batch1 = batchValue_->getBatchValue(n - 1, batch2->getHeight());
          weight_->getWGrad()->mul(*batch1->getTranspose(), *batch2, 1, 1);
        }
      }
    }
  }

  batchGrad_->copyBackSeq(*output_.grad);

  if (!backwardByBatch && weight_->getWGrad()) {
    REGISTER_TIMER_INFO("RecurrentBwWeight", getName().c_str());
    AsyncGpuBlock asyncGpuBlock;
    for (size_t seq = 0; seq < numSequences; ++seq) {
      int len = starts[seq + 1] - starts[seq];
      if (!reversed_) {
        weight_->getWGrad()->mul(
            *output_.value->subMatrix(starts[seq], len - 1)->getTranspose(),
            *output_.grad->subMatrix(starts[seq] + 1, len - 1),
            1,
            1);
      } else {
        weight_->getWGrad()->mul(
            *output_.value->subMatrix(starts[seq] + 1, len - 1)->getTranspose(),
            *output_.grad->subMatrix(starts[seq], len - 1),
            1,
            1);
      }
    }
  }
}

}  // namespace paddle