rename

paddle/gserver/layers/MKLDNNFcLayer.cpp

@@ -134,7 +134,7 @@ void MKLDNNFcLayer::resetFwd() {
   const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr;
   const MatrixPtr& out = output_.value;
 
-  if (prevIsOnlyMKLDNN()) {
+  if (inputIsOnlyMKLDNN()) {
     const MatrixPtr& in = getInputValue(0);
     inVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(in);
     CHECK(inVal_) << "Input should be MKLDNNMatrix";
@@ -154,7 +154,7 @@ void MKLDNNFcLayer::resetFwd() {
 
   // change original output value to mkldnn output value
   output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
-  if (!nextIsOnlyMKLDNN()) {
+  if (!outputIsOnlyMKLDNN()) {
     convertOutputToOtherDevice();
   }
 
@@ -194,19 +194,16 @@ void MKLDNNFcLayer::resetBwd() {
   const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr;
 
   // TODO(TJ): merge outgrad
-  if (nextIsOnlyMKLDNN()) {
-    // can not directly cast outputgrad to mkldnnmatrix,
-    // since each layer can not write the inputgrad to mkldnn inputgrad.
-    // So just create from matrix with outputvalue format.
-    const MatrixPtr& out = getOutput(MKLDNN_DEVICE).grad;
-    outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc());
-  } else {
-    const MatrixPtr& out = getOutput(CPU_DEVICE).grad;
-    // fc do not need to convert from cpu device since output always nc
-    // only need create from cpu device
-    outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc());
-  }
-
+  int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
+  // for MKLDNN device:
+  // can not directly cast outputgrad to mkldnnmatrix,
+  // since each layer can not write the inputgrad to mkldnn inputgrad.
+  // So just create from matrix with outputvalue format.
+  // for CPU device:
+  // fc do not need to convert from cpu device since output is always nc format
+  // only need create from cpu device
+  const MatrixPtr& out = getOutput(device).grad;
+  outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc());
   wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPrimitiveDesc());
   biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPrimitiveDesc())
                       : nullptr;
@@ -238,7 +235,7 @@ void MKLDNNFcLayer::resetBwd() {
   pipelineBwd_.push_back(*bwdWgt_);
 
   /// backward data
-  int device = prevIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
+  device = inputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
   const MatrixPtr& in = getInputGrad(0, device);
   if (in == nullptr) {
     return;

paddle/gserver/layers/MKLDNNLayer.h

@@ -151,6 +151,8 @@ class MKLDNNLayer : public Layer {
 protected:
   /**
    * copy image size and sequence info to other device
+   * @note: can not directly use Layer::copyOutputToOtherDevice since here only
+   *        copy base info and do not copy data value
    */
   void copyOutputInfoToOtherDevice() {
     for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
@@ -165,10 +167,10 @@ class MKLDNNLayer : public Layer {
   }
 
   /**
-   * Is previous layer only has MKLDNN type.
+   * If input only has MKLDNN device.
    * Otherwise, only support the previous layer using CPU device.
    */
-  bool prevIsOnlyMKLDNN(int index = 0) {
+  bool inputIsOnlyMKLDNN(int index = 0) {
     int prevDevice = getPrev(index)->getDeviceId();
     if (prevDevice == MKLDNN_DEVICE) {
       return true;
@@ -183,7 +185,7 @@ class MKLDNNLayer : public Layer {
    * If output only has MKLDNN device.
    * Otherwise, other devices should only using CPU device.
    */
-  bool nextIsOnlyMKLDNN() {
+  bool outputIsOnlyMKLDNN() {
     for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
       CHECK_EQ(outputOtherDevice_[i].deviceId, CPU_DEVICE)
           << "Only support other device is CPU yet";
@@ -195,7 +197,7 @@ class MKLDNNLayer : public Layer {
    * Sync input value data
    */
   void syncInputValue() {
-    if (prevIsOnlyMKLDNN()) {
+    if (inputIsOnlyMKLDNN()) {
       return;
     }
     real* iData = getInputValue(0, CPU_DEVICE)->getData();
@@ -208,7 +210,7 @@ class MKLDNNLayer : public Layer {
    * Sync output grad data
    */
   void syncOutputGrad() {
-    if (nextIsOnlyMKLDNN()) {
+    if (outputIsOnlyMKLDNN()) {
       return;
     }