C++ MatrixPtr::addBias方法代码示例

void ExpandLayer::forward(PassType passType) {
  Layer::forward(passType);
  // Expand layer should have exactly 2 input, one for data, one for size
  CHECK_EQ(2U, inputLayers_.size());

  // using two input:
  // * first one for data;
  // * second one only for sequence info
  const Argument& shapeInput = getInput(1);
  const Argument& dataInput = getInput(0);
  size_t outputBatchSize = shapeInput.getBatchSize();
  auto startPositions = type_ ? shapeInput.subSequenceStartPositions
                              : shapeInput.sequenceStartPositions;
  size_t numSequences = startPositions->getSize() - 1;
  const int* starts = startPositions->getData(false);

  CHECK_EQ(starts[numSequences], shapeInput.getBatchSize());
  if (type_) {
    // when trans_type = seq, input[1] must hasSubseq
    CHECK_EQ(shapeInput.hasSubseq(), 1UL);
    CHECK_EQ(dataInput.getNumSequences(), shapeInput.getNumSequences());
  } else {
    CHECK_EQ(dataInput.getBatchSize(), shapeInput.getNumSequences());
  }

  // set output sequence info as shape sequence
  output_.sequenceStartPositions = shapeInput.sequenceStartPositions;
  if (shapeInput.hasSubseq()) {
    output_.subSequenceStartPositions = shapeInput.subSequenceStartPositions;
  }

  // reserve output: Expand output to batchsize of sequence data.
  reserveOutput(outputBatchSize, dataInput.value->getWidth());

  MatrixPtr inputValue = getInputValue(0);
  MatrixPtr outputValue = getOutputValue();

  ICpuGpuVector::resizeOrCreate(expandStartsPos_, outputBatchSize, false);
  int* expandStarts = expandStartsPos_->getMutableData(false);
  for (size_t sequenceId = 0; sequenceId < numSequences; ++sequenceId) {
    int sequenceLength = starts[sequenceId + 1] - starts[sequenceId];
    for (int j = 0; j < sequenceLength; j++) {
      expandStarts[starts[sequenceId] + j] = sequenceId;
    }
  }

  outputValue->copyByRowIndex(*inputValue,
                              *expandStartsPos_->getVector(useGpu_));

  if (biases_.get() != NULL) {
    outputValue->addBias(*(biases_->getW()), 1);
  }
}

void DeConv3DLayer::addBias() {
  MatrixPtr outMat = getOutputValue();
  MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
                                  1,
                                  biases_->getW()->getElementCnt(),
                                  false,
                                  useGpu_);
  if (this->sharedBiases_) {
    outMat->addSharedBias(*(bias), 1.0f);
  } else {
    outMat->addBias(*(bias), 1.0f);
  }
}

void AverageLayer::forward(PassType passType) {
  SequencePoolLayer::forward(passType);

  MatrixPtr inputValue = getInputValue(0);
  getOutputValue()->sequenceAvgForward(
      *inputValue, *startPositions_->getVector(useGpu_), mode_);

  /* add the bias-vector AFTER average operation */
  if (biases_.get() != NULL) {
    MatrixPtr outV = getOutputValue();
    outV->addBias(*(biases_->getW()), 1);
  }

  /* activation */ { forwardActivation(); }
}

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

  int batchSize = getInput(0).getBatchSize();
  int size = getSize();
  {
    REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
    resetOutput(batchSize, size);
  }

  MatrixPtr outV = getOutputValue();

  for (size_t i = 0; i != inputLayers_.size(); ++i) {
    if (projections_[i]) {
      projections_[i]->forward(&getInput(i), &output_, passType);
    }
  }

  std::vector<const Argument*> ins;
  for (auto& op : operators_) {
    ins.clear();
    for (auto& input_index : op->getConfig().input_indices()) {
      ins.push_back(&getInput(input_index));
    }
    op->forward(ins, &output_, passType);
  }

  /* add the bias-vector */
  if (biases_.get() != NULL) {
    REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
    outV->addBias(*(biases_->getW()), 1, sharedBias_);
  }

  /* activation */ {
    REGISTER_TIMER_INFO("FwAtvTimer", getName().c_str());
    forwardActivation();
  }
}

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

  size_t dim = getSize();

  const Argument& input1 = getInput(0);
  size_t numSequences1 = input1.getNumSequences();
  auto startPositions1 = input1.sequenceStartPositions->getVector(false);

  const Argument& input2 = getInput(1);
  size_t numSequences2 = input2.getNumSequences();
  auto startPositions2 = input2.sequenceStartPositions->getVector(false);

  CHECK_EQ(dim, input1.value->getWidth());
  CHECK_EQ(startPositions1->getData()[numSequences1], input1.getBatchSize());
  CHECK_EQ(numSequences1, startPositions1->getSize() - 1);

  CHECK_EQ(dim, input2.value->getWidth());
  CHECK_EQ(startPositions2->getData()[numSequences2], input2.getBatchSize());
  CHECK_EQ(numSequences2, startPositions2->getSize() - 1);

  CHECK_EQ(numSequences1, numSequences2);

  MatrixPtr inputValue1 = getInputValue(0);
  MatrixPtr inputValue2 = getInputValue(1);

  // reset output
  reserveOutput(inputValue1->getHeight() + inputValue2->getHeight(), dim);

  MatrixPtr outputValue = getOutputValue();

  const int* starts1 = startPositions1->getData();
  const int* starts2 = startPositions2->getData();

  {
    AsyncGpuBlock asyncGpuBlock;
    REGISTER_TIMER_INFO("SequenceConcatLayerForward", getName().c_str());

    size_t offset = 0;
    size_t leftNumIns = 0;
    size_t rightNumIns = 0;
    for (size_t seqId = 0; seqId < numSequences1; ++seqId) {
      leftNumIns = starts1[seqId + 1] - starts1[seqId];
      outputValue->subMatrix(offset, leftNumIns)
          ->assign(*(inputValue1->subMatrix(starts1[seqId], leftNumIns)));
      offset += leftNumIns;

      rightNumIns = starts2[seqId + 1] - starts2[seqId];
      outputValue->subMatrix(offset, rightNumIns)
          ->assign(*(inputValue2->subMatrix(starts2[seqId], rightNumIns)));
      offset += rightNumIns;
    }

    // modify the sequenceStartPositions
    ICpuGpuVector::resizeOrCreate(
        output_.sequenceStartPositions, numSequences1 + 1, false);

    int* tgtBuf = output_.sequenceStartPositions->getMutableData(false);

    for (size_t seqId = 0; seqId < numSequences1 + 1; ++seqId) {
      tgtBuf[seqId] = starts1[seqId] + starts2[seqId];
    }
  }

  if (biases_.get() != NULL) {
    MatrixPtr outV = getOutputValue();
    outV->addBias(*(biases_->getW()), 1);
  }

  /* activation */
  forwardActivation();
}