CudnnBatchNormLayer.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 "paddle/utils/Stat.h"
#include "Layer.h"
#include "CudnnBatchNormLayer.h"

namespace paddle {

REGISTER_LAYER(cudnn_batch_norm, CudnnBatchNormLayer);

const double CudnnBatchNormLayer::EPS = 1E-5;

bool CudnnBatchNormLayer::init(const LayerMap& layerMap,
                               const ParameterMap& parameterMap) {
  /* Initialize the basic parent class */
  if (!BatchNormBaseLayer::init(layerMap, parameterMap)) return false;
  CHECK(useGpu_) << "CudnnBatchNorm only support GPU";

  hl_create_tensor_descriptor(&ioDesc_);
  hl_create_tensor_descriptor(&bnParamDesc_);
  hl_tensor_reshape(bnParamDesc_, 1, channels_, 1, 1);

  return true;
}

void CudnnBatchNormLayer::reshape(int batchSize) {
  hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_, imageW_);
}

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

  int batchSize = getInputValue(0)->getHeight();
  calFeatureMapSize();
  reshape(batchSize);
  resetOutput(batchSize, getInputValue(0)->getWidth());

  // for testing in training peroid.
  useGlobalStats_ = (passType == PASS_TEST);
  if (passType == PASS_TEST && config_.has_use_global_stats()) {
    useGlobalStats_ = config_.use_global_stats();
  }

  real* input = getInputValue(0)->getData();
  real* output = getOutputValue()->getData();
  real* gamma = weight_->getW()->getData();
  real* beta = biases_->getW()->getData();
  real* movingMean = movingMean_->getW()->getData();
  real* movingVar = movingVar_->getW()->getData();

  if (!useGlobalStats_) {
    REGISTER_TIMER_INFO("CudnnBatchFwTimer", getName().c_str());
    real* savedMean = savedMean_->getData();
    real* savedInvVar = savedInvVar_->getData();
    hl_batch_norm_forward_training(ioDesc_, input, ioDesc_, output,
                                   bnParamDesc_,
                                   gamma, beta, 1.0 - movingAvgFraction_,
                                   movingMean, movingVar,
                                   EPS, savedMean, savedInvVar);
  } else {
    // used movingMean and movingVar in testing
    hl_batch_norm_forward_inference(ioDesc_, input, ioDesc_, output,
                                    bnParamDesc_, gamma, beta,
                                    movingMean, movingVar, EPS);
  }

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

void CudnnBatchNormLayer::backward(const UpdateCallback& callback) {
  /* Do derivation */ {
    REGISTER_TIMER_INFO("BpAvtTimer", getName().c_str());
    backwardActivation();
  }

  real* input = getInputValue(0)->getData();
  real* outGrad = getOutputGrad()->getData();
  real* inGrad = getInputGrad(0)->getData();
  real* gamma = weight_->getW()->getData();
  real* savedMean = savedMean_->getData();
  real* savedInvVar = savedInvVar_->getData();

  auto create = [](MatrixPtr& m, size_t h, size_t w, real** p) {
    Matrix::resizeOrCreate(m, h, w, false, true);
    m->zeroMem();
    *p = m->getData();
  };

  real* gammaGrad = nullptr;
  real* betaGrad = nullptr;
  if (weight_->getWGrad()) {
    gammaGrad = weight_->getWGrad()->getData();
  } else {
    create(tmpWGrad_, 1, channels_, &gammaGrad);
  }
  if (biases_ && biases_->getWGrad()) {
    betaGrad = biases_->getWGrad()->getData();
  } else {
    create(tmpBiasGrad_, 1, channels_, &betaGrad);
  }

  // because of the different api of cudnn v4 and v5.
  if (hl_get_cudnn_lib_version() < 5000) {
    if (weight_->getWGrad()) {
      create(tmpWGrad_, 1, channels_, &gammaGrad);
    }
    if (biases_ && biases_->getWGrad()) {
      create(tmpBiasGrad_, 1, channels_, &betaGrad);
    }
  }

  hl_batch_norm_backward(ioDesc_, input, ioDesc_, outGrad,
                         ioDesc_, inGrad, bnParamDesc_,
                         gamma, gammaGrad, betaGrad,
                         EPS, savedMean, savedInvVar);

  // because of the different api of cudnn v4 and v5.
  if (hl_get_cudnn_lib_version() < 5000) {
    if (weight_->getWGrad() && biases_->getWGrad()) {
      weight_->getWGrad()->add(*tmpWGrad_);
      biases_->getWGrad()->add(*tmpBiasGrad_);
    }
  }

  {
    REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
    biases_->getParameterPtr()->incUpdate(callback);
    weight_->getParameterPtr()->incUpdate(callback);
  }
}

CudnnBatchNormLayer::~CudnnBatchNormLayer() {
  hl_destroy_tensor_descriptor(ioDesc_);
  hl_destroy_tensor_descriptor(bnParamDesc_);
}

}  // namespace paddle