RecurrentLayer.cpp

/* Copyright (c) 2016 Baidu, Inc. 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"
#include "paddle/utils/Stat.h"
#include "SequenceToBatch.h"
#include "paddle/utils/CommandLineParser.h"

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

namespace paddle {

/*
RecurrentLayer takes 1 input layer with the same size.
For each sequence [start, end] it performs the following computation:
out_i = act(in_i)                 for i = start
out_i = act(in_i + out_{i-1} * W) for start < i <= end

If reversed is true, the order is reversed:
out_i = act(in_i)                 for i = end
out_i = act(in_i + out_{i+1} * W) for start <= i < end
*/
class RecurrentLayer : public Layer {
public:
  explicit RecurrentLayer(const LayerConfig& config) : Layer(config) {}

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

  void forward(PassType passType);

  void backward(const UpdateCallback& callback);

  void resetState();

  void setState(LayerStatePtr state);

  LayerStatePtr getState();

protected:
  void forwardSequence(int batchSize, size_t numSequences, const int* starts);
  void forwardOneSequence(int start, int length);
  void backwardSequence(int batchSize, size_t numSequences, const int* starts);
  void backwardOneSequence(int start, int length);

  void forwardBatch(int batchSize, size_t numSequences, const int* starts);
  void backwardBatch(int batchSize, size_t numSequences, const int* starts);

protected:
  std::unique_ptr<Weight> weight_;
  std::unique_ptr<Weight> bias_;

  // frameOutput_[i] is used to hold the i-th sample of output_
  std::vector<Argument> frameOutput_;
  MatrixPtr prevOutput_;
  bool reversed_;
  std::unique_ptr<SequenceToBatch> batchValue_;
  std::unique_ptr<SequenceToBatch> batchGrad_;
};

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]);
    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]);
    }
    if (prevOutput_) {
      prevOutput_->assign(*frameOutput_[start + length - 1].value);
    }
  } else {
    activation_->forward(frameOutput_[start + length - 1]);
    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]);
    }
  }
}

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]);
      frameOutput_[start + i - 1].grad->mul(frameOutput_[start + i].grad,
                                            weightT, 1, 1);
    }
    activation_->backward(frameOutput_[start]);
    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]);
      frameOutput_[start + i + 1].grad->mul(frameOutput_[start + i].grad,
                                            weightT, 1, 1);
    }
    activation_->backward(frameOutput_[start + length - 1]);
    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);
    }
  }
  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);

      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