From 703947928ff4d856d77d711ea7f238b919978d2f Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Wed, 25 Oct 2017 21:02:29 +0800
Subject: [PATCH] refine comment and code

---
 .../gserver/layers/MKLDNNBatchNormLayer.cpp   | 24 +++++++------------
 paddle/gserver/layers/MKLDNNBatchNormLayer.h  |  6 +++--
 2 files changed, 12 insertions(+), 18 deletions(-)

diff --git a/paddle/gserver/layers/MKLDNNBatchNormLayer.cpp b/paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
index 30b64ee941..f577616230 100644
--- a/paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
@@ -109,19 +109,10 @@ void MKLDNNBatchNormLayer::convertWeightsFromPaddle() {
 void MKLDNNBatchNormLayer::calMovingMeanAndVar() {
   // calculating and saving moving mean and variance
   CHECK_EQ(useGlobalStats_, false);
-  MatrixPtr movingMean = movingMean_->getW();
-  MatrixPtr movingVar = movingVar_->getW();
-  if (FLAGS_trainer_count > 1) {
-    auto mvMean = std::dynamic_pointer_cast<SharedCpuMatrix>(movingMean);
-    auto mvVar = std::dynamic_pointer_cast<SharedCpuMatrix>(movingVar);
-    CHECK(mvMean && mvVar);
-    mvMean->add(*mean_, movingAvgFraction_, 1.0 - movingAvgFraction_);
-    mvVar->add(*var_, movingAvgFraction_, 1.0 - movingAvgFraction_);
-  } else {
-    movingMean->add(*mean_, movingAvgFraction_, 1.0 - movingAvgFraction_);
-    // here var is v^2
-    movingVar->add(*var_, movingAvgFraction_, 1.0 - movingAvgFraction_);
-  }
+  movingMean_->getW()->add(
+      *mean_, movingAvgFraction_, 1.0 - movingAvgFraction_);
+  // here var is v^2
+  movingVar_->getW()->add(*var_, movingAvgFraction_, 1.0 - movingAvgFraction_);
 }
 
 void MKLDNNBatchNormLayer::reshape(
@@ -142,8 +133,9 @@ void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
                                     MKLDNNMatrixPtr& wgt,
                                     MKLDNNMatrixPtr& bias,
                                     MKLDNNMatrixPtr& out) {
-  // in training always calculate mean and var, so useGlobalStats must be false
-  // in test depends on useGlobalStats
+  // In training phase, it will always calculate mean and var,
+  // so useGlobalStats must be false.
+  // In scoring phase, it depends on useGlobalStats choice.
   if (passType_ != PASS_TEST && useGlobalStats_ == true) {
     LOG(WARNING) << "use_global_stats is invalid setting in training phase";
     useGlobalStats_ = false;
@@ -173,7 +165,7 @@ void MKLDNNBatchNormLayer::resetBwd(std::vector<primitive>& pipeline,
 void MKLDNNBatchNormLayer::forward(PassType passType) {
   MKLDNNLayer::forward(passType);
 
-  // calculating and saving moving mean and variance
+  // calculate and save moving mean and variance
   if (passType_ != PASS_TEST) {
     calMovingMeanAndVar();
   }
diff --git a/paddle/gserver/layers/MKLDNNBatchNormLayer.h b/paddle/gserver/layers/MKLDNNBatchNormLayer.h
index 19f32285fc..456c0424ec 100644
--- a/paddle/gserver/layers/MKLDNNBatchNormLayer.h
+++ b/paddle/gserver/layers/MKLDNNBatchNormLayer.h
@@ -56,8 +56,10 @@ protected:
   bool hasInitedWgt_;
 
   // local mean and variance
-  MKLDNNMatrixPtr mean_;  // output of mkldnn: m
-  MKLDNNMatrixPtr var_;   // output of mkldnn: v^2
+  // when useGlobalStats_ they are loaded from moving mean and variance
+  // when do not useGlobalStats_ they are calculated from this mini-batch
+  MKLDNNMatrixPtr mean_;
+  MKLDNNMatrixPtr var_;
 
 public:
   explicit MKLDNNBatchNormLayer(const LayerConfig& config)
-- 
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