Paddle/paddle/gserver/layers/MKLPackedRecurrentLayer.cpp

/* 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 "MKLPackedRecurrentLayer.h"

namespace paddle {

REGISTER_LAYER(mkl_packed_recurrent, MKLPackedRecurrentLayer);

bool MKLPackedRecurrentLayer::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();

  sgemm_packed_.reset(new MKLPackedGemm(weight_->getW()));

  return true;
}

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

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

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

void MKLPackedRecurrentLayer::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 MKLPackedRecurrentLayer::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());
  }

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

void MKLPackedRecurrentLayer::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 MKLPackedRecurrentLayer::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);
  sgemm_packed_.reset(new MKLPackedGemm(weight_->getW()));
}

void MKLPackedRecurrentLayer::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());
  }

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

void MKLPackedRecurrentLayer::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 MKLPackedRecurrentLayer::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());
    /* 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);
        sgemm_packed_->compute(batch2, batch1);
      }

#pragma omp parallel for collapse(2)
      for (size_t i = 0; i < batch2->getHeight(); i++) {
        for (size_t j = 0; j < batch2->getWidth(); j++) {
          *(batch2->getData() + i * batch2->getWidth() + j) =
              *(batch2->getData() + i * batch2->getWidth() + j) > 0
                  ? *(batch2->getData() + i * batch2->getWidth() + j)
                  : 0;
        }
      }
    }
  }

  batchValue_->copyBackSeq(*output_.value);
}

void MKLPackedRecurrentLayer::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());
    /* 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);
        sgemm_packed_->compute(batch1, batch2, true);
      }

      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());
    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