remove unused code

refine comments and bias
fix typo and todo

Author: tensor-tang

paddle/gserver/layers/MKLDNNConvLayer.cpp

@@ -210,11 +210,11 @@ void MKLDNNConvLayer::resetFwdBuffers(
 
   resetWithMatrix(wgt, weight_->getW(), pd->weights_primitive_desc());
 
-  bias = nullptr;
-  if (biases_ == nullptr || biases_->getW() == nullptr) {
-    return;
+  if (biases_ && biases_->getW()) {
+    resetWithMatrix(bias, biases_->getW(), pd->bias_primitive_desc());
+  } else {
+    bias = nullptr;
   }
-  resetWithMatrix(bias, biases_->getW(), pd->bias_primitive_desc());
 }
 
 void MKLDNNConvLayer::resetFwdPipeline(

paddle/gserver/layers/MKLDNNFcLayer.cpp

@@ -134,10 +134,6 @@ void MKLDNNFcLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
   CHECK(in);
   in->downSpatial();
 
-  //  if (extInVal_) {
-  //    extInVal_->downSpatial();
-  //  }
-
   auto outPD =
       MKLDNNMatrix::createPrimitiveDesc({bs_, oc_}, format::nc, engine_);
   resetOutValue(out, outPD);
@@ -153,11 +149,12 @@ void MKLDNNFcLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
   resetWithMatrix(wgt, weight_->getW(), wgtPD);
   wgt->downSpatial();
 
-  if (biases_ == nullptr || biases_->getW() == nullptr) {
-    return;
+  if (biases_ && biases_->getW()) {
+    auto biasPD = MKLDNNMatrix::createPrimitiveDesc({oc_}, format::x, engine_);
+    resetWithMatrix(bias, biases_->getW(), biasPD);
+  } else {
+    bias = nullptr;
   }
-  auto biasPD = MKLDNNMatrix::createPrimitiveDesc({oc_}, format::x, engine_);
-  resetWithMatrix(bias, biases_->getW(), biasPD);
 }
 
 void MKLDNNFcLayer::resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
@@ -207,11 +204,11 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
   CHECK(wgtVal_);
   resetWithMatrix(wgt, weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
 
-  bias = nullptr;
-  if (biasVal_ == nullptr) {
-    return;
+  if (biasVal_) {
+    resetWithMatrix(bias, biases_->getWGrad(), biasVal_->getPrimitiveDesc());
+  } else {
+    bias = nullptr;
   }
-  resetWithMatrix(bias, biases_->getWGrad(), biasVal_->getPrimitiveDesc());
 }
 
 void MKLDNNFcLayer::resetBwdWgtPD(

paddle/gserver/layers/MKLDNNLayer.cpp

@@ -60,7 +60,7 @@ void MKLDNNLayer::forward(PassType passType) {
       resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
       // MKLDNNLayer output value should be MKLDNNMatrix
       // so external output value is necessary.
-      // then external input value is not necessary,
+      // Then external input value is not necessary,
       // since input may be mkldnn internal buffer.
       CHECK(extOutVal_) << "external output value is necessary";
       output_.value = std::dynamic_pointer_cast<Matrix>(extOutVal_);
@@ -235,8 +235,8 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
   in = MKLDNNMatrix::create(intPD, inMat);
   Argument& arg = input->getOutput(this->getName());
   arg.grad = std::dynamic_pointer_cast<Matrix>(in);
-  CHECK(inVal_ != nullptr && inVal_->getPrimitiveDesc() == intPD)
-      << "should have internal input value and primitive desc must equal";
+  CHECK(inVal_);
+  CHECK(inVal_->getPrimitiveDesc() == intPD) << "the primitive desc must equal";
   if (inputIsOnlyMKLDNN()) {
     return;
   }
@@ -246,6 +246,7 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
     return;
   }
   // need create reorder
+  // TODO(TJ): add macro definition to simplify it
   CHECK(extInVal_ != nullptr && isPaddleFormat(extInVal_->getFormat()))
       << "should have external input value and the format must be nchw(nc)";
   extInGrad_ = MKLDNNMatrix::create(extInVal_->getPrimitiveDesc(), inMat);

paddle/gserver/layers/MKLDNNLayer.h

@@ -58,14 +58,15 @@ class MKLDNNLayer : public Layer {
   std::vector<mkldnn::primitive> pipelineFwd_;
   std::vector<mkldnn::primitive> pipelineBwd_;
 
-  /// value and grad are seperated as internal and external buffers.
-  /// each MKLDNNLayer must init or reset internal buffer at least,
-  /// and the external buffer format is always nchw of nc(when h==w==1),
-  /// which is the same format as paddle.
-  /// The output_.value and output_.grad always save the external data,
-  /// when mixed with cpu device.
-  /// When all layers are mkldnn layers, they could save internal data.
-  /// below MKLDNNMatrix buffers are all internal buffers
+  /* Value and grad are seperated as internal and external buffers.
+   * Each MKLDNNLayer must init or reset internal buffer at least,
+   * and the external buffer format is always nchw of nc(when h==w==1),
+   * which is the same format as paddle.
+   * The output_.value and output_.grad always save the external data,
+   * when mixed with cpu device.
+   * When all layers are mkldnn layers, they could save internal data.
+   */
+  // below MKLDNNMatrix buffers are all internal buffers
   MKLDNNMatrixPtr inVal_;
   MKLDNNMatrixPtr inGrad_;
   MKLDNNMatrixPtr outVal_;
@@ -120,8 +121,8 @@ class MKLDNNLayer : public Layer {
   ~MKLDNNLayer() {}
 
   virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
-  void forward(PassType passType) override;
-  void backward(const UpdateCallback& callback) override;
+  virtual void forward(PassType passType);
+  virtual void backward(const UpdateCallback& callback);
 
   /**
    * reshape the input image sizes
@@ -217,15 +218,15 @@ class MKLDNNLayer : public Layer {
    * reset output grad from internal primitive desc.
    * merge grad if necessary.
    * reset both internal and external buffer and create reorder if necessary.
-   * note: about merge grad, when this layer has serval outputs,
+   * note: about merge grad, when this layer has several outputs,
    *       it could not be mixed with cpu device,
    *       since it can not get memory desc from cpu device.
    */
   void resetOutGrad(MKLDNNMatrixPtr& out, mkldnn::memory::primitive_desc intPD);
 
   /**
    * reset the merge grad primitive if necessary.
-   * note: do not support the grads are mixed with cpu device,
+   * note: do not support the grads mixed with cpu device,
    *       since it can not get memory desc from cpu device.
    */
   void resetMergeGrad(MKLDNNMatrixPtr& out);
@@ -313,17 +314,17 @@ class MKLDNNLayer : public Layer {
    * print the mkldnn memory format of grad
    */
   virtual void printGradFormat() {
-    if (extInGrad_) {
-      VLOG(MKLDNN_FMTS) << extInGrad_->getFormat() << " <<< ";
-    }
-    if (inGrad_) {
-      VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<<";
+    if (extOutGrad_) {
+      VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
     }
     if (outGrad_) {
       VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
     }
-    if (extOutGrad_) {
-      VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
+    if (inGrad_) {
+      VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<<";
+    }
+    if (extInGrad_) {
+      VLOG(MKLDNN_FMTS) << extInGrad_->getFormat() << " <<< ";
     }
     if (wgtGrad_) {
       VLOG(MKLDNN_FMTS) << "Weight grad format: " << wgtGrad_->getFormat();