add epsilon in bn

Author: peterzhang2029

paddle/gserver/layers/BatchNormBaseLayer.cpp

@@ -41,6 +41,7 @@ bool BatchNormBaseLayer::init(const LayerMap& layerMap,
     useGlobalStats_ = config_.use_global_stats();
   }
   movingAvgFraction_ = config_.moving_average_fraction();
+  EPS = config_.epsilon();
 
   weight_.reset(new Weight(1, channels_, parameters_[0]));
   movingMean_.reset(new Weight(1, channels_, parameters_[1]));

paddle/gserver/layers/BatchNormBaseLayer.h

@@ -94,6 +94,8 @@ class BatchNormBaseLayer : public Layer {
   bool useGlobalStats_;
   // use to compute moving mean and variance.
   real movingAvgFraction_;
+  // Epsilon value used in the batch normalization formula.
+  real EPS;
 };
 
 }  // namespace paddle

paddle/gserver/layers/BatchNormalizationLayer.cpp

@@ -22,8 +22,6 @@ namespace paddle {
 
 REGISTER_LAYER(batch_norm, BatchNormalizationLayer);
 
-const real BatchNormalizationLayer::EPS = 1E-5;
-
 bool BatchNormalizationLayer::init(const LayerMap& layerMap,
                                    const ParameterMap& parameterMap) {
   /* Initialize the basic parent class */

paddle/gserver/layers/BatchNormalizationLayer.h

@@ -39,9 +39,6 @@ class BatchNormalizationLayer : public BatchNormBaseLayer {
   void backward(const UpdateCallback& callback = nullptr) override;
 
 protected:
-  /// Epsilon value used in the batch normalization formula.
-  static const real EPS;
-
   /// Load pre-calculated mean and std.
   void setMeanAndStd();
 

paddle/gserver/layers/CudnnBatchNormLayer.cpp

@@ -21,7 +21,7 @@ namespace paddle {
 
 REGISTER_LAYER(cudnn_batch_norm, CudnnBatchNormLayer);
 
-const double CudnnBatchNormLayer::EPS = 1E-5;
+const double CudnnBatchNormLayer::MIN_EPS = 1E-5;
 
 bool CudnnBatchNormLayer::init(const LayerMap& layerMap,
                                const ParameterMap& parameterMap) {
@@ -60,6 +60,7 @@ void CudnnBatchNormLayer::forward(PassType passType) {
   real* beta = biases_->getW()->getData();
   real* movingMean = movingMean_->getW()->getData();
   real* movingVar = movingVar_->getW()->getData();
+  EPS_ = std::max(MIN_EPS, static_cast<double>(EPS));
 
   if (!useGlobalStats_) {
     REGISTER_TIMER_INFO("CudnnBatchFwTimer", getName().c_str());
@@ -75,7 +76,7 @@ void CudnnBatchNormLayer::forward(PassType passType) {
                                    1.0 - movingAvgFraction_,
                                    movingMean,
                                    movingVar,
-                                   EPS,
+                                   EPS_,
                                    savedMean,
                                    savedInvVar);
   } else {
@@ -90,7 +91,7 @@ void CudnnBatchNormLayer::forward(PassType passType) {
                                       beta,
                                       movingMean,
                                       movingVar,
-                                      EPS);
+                                      EPS_);
     } else {
       // There is a limitation in cudnn library.
       // When the batch size is larger than 1024 in cuDNN v5.1,
@@ -101,7 +102,7 @@ void CudnnBatchNormLayer::forward(PassType passType) {
                                    beta,
                                    movingMean,
                                    movingVar,
-                                   EPS,
+                                   EPS_,
                                    batchSize,
                                    channels_,
                                    imageH_ * imageD_,
@@ -127,6 +128,7 @@ void CudnnBatchNormLayer::backward(const UpdateCallback& callback) {
   real* gamma = weight_->getW()->getData();
   real* savedMean = savedMean_->getData();
   real* savedInvVar = savedInvVar_->getData();
+  EPS_ = std::max(MIN_EPS, static_cast<double>(EPS));
 
   auto create = [](MatrixPtr& m, size_t h, size_t w, real** p) {
     Matrix::resizeOrCreate(m, h, w, false, true);
@@ -157,7 +159,7 @@ void CudnnBatchNormLayer::backward(const UpdateCallback& callback) {
                          gamma,
                          gammaGrad,
                          betaGrad,
-                         EPS,
+                         EPS_,
                          savedMean,
                          savedInvVar);
 

paddle/gserver/layers/CudnnBatchNormLayer.h

@@ -47,11 +47,14 @@ class CudnnBatchNormLayer : public BatchNormBaseLayer {
 
 protected:
   /**
-   * Epsilon value used in the batch normalization formula.
    * Minimum allowed value is CUDNN_BN_MIN_EPSILON defined in cudnn.h.
    * Same epsilon value should be used in forward and backward functions.
    */
-  static const double EPS;
+  static const double MIN_EPS;
+
+  /// Epsilon value used in the batch normalization formula.
+  /// If EPS_ is smaller than MIN_EPS, MIN_EPS will be used.
+  double EPS_;
 
   /// Input/output tensor descriptor desc
   hl_tensor_descriptor ioDesc_;

paddle/gserver/layers/MKLDNNBatchNormLayer.cpp

@@ -21,8 +21,6 @@ namespace paddle {
 
 REGISTER_LAYER(mkldnn_batch_norm, MKLDNNBatchNormLayer);
 
-const real MKLDNNBatchNormLayer::EPS = 1E-5;
-
 bool MKLDNNBatchNormLayer::init(const LayerMap& layerMap,
                                 const ParameterMap& parameterMap) {
   if (!MKLDNNLayer::init(layerMap, parameterMap)) {
@@ -50,6 +48,8 @@ bool MKLDNNBatchNormLayer::init(const LayerMap& layerMap,
     useGlobalStats_ = config_.use_global_stats();
   }
   movingAvgFraction_ = config_.moving_average_fraction();
+  EPS = config_.epsilon();
+
   VLOG(MKLDNN_BASE) << "--- " << (useGlobalStats_ ? "use" : "do not use")
                     << " --- global stats";
   VLOG(MKLDNN_BASE) << "Moving average fraction: " << movingAvgFraction_;

paddle/gserver/layers/MKLDNNBatchNormLayer.h

@@ -32,7 +32,8 @@ class MKLDNNBatchNormLayer : public MKLDNNLayer {
   std::shared_ptr<bn_fwd::primitive_desc> fwdPD_;
 
   // Epsilon value used in the batch normalization formula.
-  static const real EPS;
+  real EPS;
+
   // weight and bias in paddle
   std::unique_ptr<Weight> weight_;
   std::unique_ptr<Weight> biases_;

proto/ModelConfig.proto

@@ -540,6 +540,10 @@ message LayerConfig {
 
   // for switch order layer
   optional ReshapeConfig reshape_conf = 59;
+
+  // for batch normalization layer
+  // small constant added to the variance to avoid numerical problems.
+  optional double epsilon = 60 [ default = 0.00001 ];
 }
 
 message EvaluatorConfig {

python/paddle/trainer/config_parser.py

@@ -2434,6 +2434,7 @@ def __init__(self,
                  bias=True,
                  img3D=False,
                  use_global_stats=True,
+                 epsilon=1e-5,
                  moving_average_fraction=0.9,
                  batch_norm_type=None,
                  mean_var_names=None,
@@ -2482,6 +2483,8 @@ def __init__(self,
             self.config.use_global_stats = use_global_stats
         if moving_average_fraction is not None:
             self.config.moving_average_fraction = moving_average_fraction
+        if epsilon is not None:
+            self.config.epsilon = epsilon
 
         input_layer = self.get_input_layer(0)
         image_conf = self.config.inputs[0].image_conf