Merge pull request #3337 from tensor-tang/merge

Enable mkldnn_fc for general format

CMakeLists.txt

@@ -144,7 +144,7 @@ if(WITH_GPU)
 endif(WITH_GPU)
 
 if(WITH_MKLDNN)
-    list(APPEND EXTERNAL_LIBS ${MKLDNN_LIBRARY} ${MKLDNN_IOMP_LIB})
+    list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB} ${MKLDNN_IOMP_LIB})
 endif()
 
 if(USE_NNPACK)

paddle/gserver/CMakeLists.txt

@@ -23,6 +23,17 @@ endmacro()
 
 filter_test(GSERVER_HEADER)
 filter_test(GSERVER_SOURCES)
+
+if(NOT WITH_MKLDNN)
+    file(GLOB_RECURSE DNN_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.h")
+    file(GLOB_RECURSE DNN_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.cpp")
+    list(REMOVE_ITEM GSERVER_HEADER ${DNN_HEADER})
+    list(REMOVE_ITEM GSERVER_SOURCES ${DNN_SOURCES})
+    message(STATUS "Skip compiling with MKLDNNLayers and MKLDNNActivations")
+else()
+    message(STATUS "Compile with MKLDNNLayers and MKLDNNActivations")
+endif()
+
 if(NOT WITH_GPU)
     list(REMOVE_ITEM GSERVER_HEADER
         layers/CudnnConvBaseLayer.h

paddle/gserver/layers/MKLDNNBase.h

+/* Copyright (c) 2017 PaddlePaddle Authors. 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. */
+
+#pragma once
+
+#include "mkldnn.hpp"
+
+namespace paddle {
+
+typedef enum {
+  MKLDNN_BASE = 1,   // basical info of MKLDNN
+  MKLDNN_TESTS = 1,  // gtest info of MKLDNN
+  MKLDNN_SIZES = 2,  // size info of MKLDNN
+  MKLDNN_FMTS = 3,   // format info of MKLDNN
+  MKLDNN_ALL = 4,    // show all info of MKLDNN
+} MKLDNN_LOG_LEVEL;
+
+/**
+ * @brief MKLDNN CPU engine.
+ *
+ */
+class CPUEngine {
+public:
+  static CPUEngine& Instance() {
+    // Thread-safe in C++11.
+    static CPUEngine myInstance;
+    return myInstance;
+  }
+
+  // Disallow copy or move
+  CPUEngine(const CPUEngine&) = delete;             // Copy constructor
+  CPUEngine(CPUEngine&&) = delete;                  // Move constructor
+  CPUEngine& operator=(const CPUEngine&) = delete;  // Copy assignment
+  CPUEngine& operator=(CPUEngine&&) = delete;       // Move assignment
+
+  mkldnn::engine& getEngine() { return cpuEngine_; }
+
+protected:
+  CPUEngine() : cpuEngine_(mkldnn::engine::cpu, 0) {}
+  //    CPUEngine() : cpuEngine_(mkldnn::engine::cpu_lazy, 0) {}
+  ~CPUEngine() {}
+
+private:
+  mkldnn::engine cpuEngine_;
+};
+
+/**
+ * @brief MKLDNN Stream.
+ *
+ */
+class MKLDNNStream {
+public:
+  MKLDNNStream() : ready_(false) { resetState(); }
+
+  virtual ~MKLDNNStream() {}
+
+  /**
+   * @brief Submit stream
+   * @param prims The primitives vector
+   * @param block Waiting for the stream to complete
+   */
+  void submit(std::vector<mkldnn::primitive>& prims, bool block = true) {
+    resetState();
+    stream_->submit(prims).wait(block);
+    ready_ = false;
+  }
+
+  /**
+   * @brief Reset the mkldnn stream
+   */
+  void resetState() {
+    if (ready_) {
+      return;
+    }
+    // TODO(TJ): change me when mkldnn have method to reset this state
+    // stream_.reset(new mkldnn::stream(mkldnn::stream::kind::lazy));
+    stream_.reset(new mkldnn::stream(mkldnn::stream::kind::eager));
+    ready_ = true;
+  }
+
+private:
+  bool ready_;
+  std::shared_ptr<mkldnn::stream> stream_;
+};
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNFcLayer.cpp

+/* Copyright (c) 2017 PaddlePaddle Authors. 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 "MKLDNNFcLayer.h"
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
+
+using namespace mkldnn;  // NOLINT
+typedef memory::format format;
+typedef inner_product_forward fc_fwd;
+typedef inner_product_backward_weights fc_bwdWgt;
+typedef inner_product_backward_data fc_bwdData;
+
+namespace paddle {
+
+REGISTER_LAYER(mkldnn_fc, MKLDNNFcLayer);
+
+bool MKLDNNFcLayer::init(const LayerMap& layerMap,
+                         const ParameterMap& parameterMap) {
+  if (!MKLDNNLayer::init(layerMap, parameterMap)) {
+    return false;
+  }
+
+  CHECK_EQ(inputLayers_.size(), 1) << "Only support one input layer yet";
+  CHECK_EQ(inputLayers_.size(), parameters_.size());
+  CHECK(!parameters_[0]->isSparse()) << "Do not support sparse yet";
+
+  // output size, cat not be changed
+  oc_ = getSize();
+  oh_ = 1;
+  ow_ = 1;
+
+  // input size can not change in FC
+  iLayerSize_ = inputLayers_[0]->getSize();
+  CHECK_EQ(parameters_[0]->getSize(), iLayerSize_ * oc_);
+
+  // create weight
+  weight_ =
+      std::unique_ptr<Weight>(new Weight(oc_, iLayerSize_, parameters_[0], 0));
+
+  // create biases
+  if (biasParameter_.get() != NULL) {
+    biases_ = std::unique_ptr<Weight>(new Weight(1, oc_, biasParameter_));
+  }
+  return true;
+}
+
+void MKLDNNFcLayer::convertWeightsFromPaddle() {
+  if (FLAGS_use_mkldnn_wgt) {
+    return;
+  }
+
+  if (hasInitedWgt_) {
+    return;
+  }
+
+  // The weight_ is transposed from initial paddle weight
+  MatrixPtr paddleWgt = Matrix::create(
+      weight_->getW()->getData(), iLayerSize_, oc_, false, false);
+
+  // TODO(TJ): remove this print when do not need differ weights
+  std::ostringstream ostr;
+  paddleWgt->print(ostr);
+  VLOG(MKLDNN_ALL) << "Initial Weight from paddle: " << std::endl << ostr.str();
+
+  // The mkldnn weight is transposed from initial paddle matrix
+  MatrixPtr paddleWgtT;
+  paddleWgt->transpose(paddleWgtT, true);
+  weight_->getW()->copyFrom(*paddleWgtT);
+  hasInitedWgt_ = true;
+}
+
+void MKLDNNFcLayer::convertWeightsToPaddle() {
+  MatrixPtr dnnWgt = weight_->getW();
+  MatrixPtr paddleWgt;
+  dnnWgt->transpose(paddleWgt, true);
+
+  // copy paddle weight and override on weight_
+  MatrixPtr dnnWgtT = Matrix::create(
+      dnnWgt->getData(), dnnWgt->getWidth(), dnnWgt->getHeight(), false, false);
+  dnnWgtT->copyFrom(*paddleWgt);
+}
+
+void MKLDNNFcLayer::reshape() {
+  const Argument& input = getInput(0);
+  int batchSize = input.getBatchSize();
+  if (bs_ == batchSize) {
+    return;
+  }
+  bs_ = batchSize;
+  ih_ = input.getFrameHeight();
+  iw_ = input.getFrameWidth();
+  if (ih_ == 0) {
+    ih_ = 1;
+  }
+  if (iw_ == 0) {
+    iw_ = 1;
+  }
+  hasSpatial_ = true;
+  if (ih_ == 1 && iw_ == 1) {
+    hasSpatial_ = false;
+  }
+  CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
+  ic_ = iLayerSize_ / (ih_ * iw_);
+  CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
+  CHECK_EQ(size_t(oc_), getSize());
+  printSizeInfo();
+
+  // reset output
+  output_.setFrameHeight(oh_);
+  output_.setFrameWidth(ow_);
+  resetOutput(bs_, oc_);
+
+  // reset mkldnn forward
+  resetFwd();
+  needResetBwd_ = true;
+
+  convertWeightsFromPaddle();
+}
+
+void MKLDNNFcLayer::resetFwd() {
+  bool hasBias = biases_ && biases_->getW();
+  real* iData = getInputValue(0)->getData();
+  real* oData = getOutputValue()->getData();
+  real* wData = weight_->getW()->getData();
+  real* bData = hasBias ? biases_->getW()->getData() : NULL;
+
+  // TODO(TJ): below create should be covered in MkldnnMatrix
+  // create memory desc
+  memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw)
+                                 : createMD({bs_, ic_}, format::nc);
+  memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw)
+                                 : createMD({oc_, ic_}, format::oi);
+  memory::desc bMD = bData != NULL ? createMD({oc_}, format::x)
+                                   : createMD({}, format::format_undef);
+  memory::desc oMD = createMD({bs_, oc_}, format::nc);
+
+  // create memory primitive desc and memory self
+  inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData));
+  wgtVal_.reset(new memory(memory::primitive_desc(wMD, engine_), wData));
+  outVal_.reset(new memory(memory::primitive_desc(oMD, engine_), oData));
+
+  prop_kind pk = prop_kind::forward;
+  fc_fwd::desc fwdDesc = bData != NULL ? fc_fwd::desc(pk, iMD, wMD, bMD, oMD)
+                                       : fc_fwd::desc(pk, iMD, wMD, oMD);
+  fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
+
+  if (bData != NULL) {
+    biasVal_.reset(new memory(memory::primitive_desc(bMD, engine_), bData));
+    fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_));
+  } else {
+    fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_));
+  }
+  pipelineFwd_.clear();
+  pipelineFwd_.push_back(*fwd_);
+}
+
+void MKLDNNFcLayer::resetBwd() {
+  if (!needResetBwd_) {
+    return;
+  }
+  needResetBwd_ = false;
+
+  bool hasBias = biases_ && biases_->getWGrad();
+  real* iData = getInputValue(0)->getData();
+  real* iDiff = getInputGrad(0) != nullptr ? getInputGrad(0)->getData() : NULL;
+  real* oDiff = getOutputGrad()->getData();
+  real* wDiff = weight_->getWGrad()->getData();
+  real* bDiff = hasBias ? biases_->getWGrad()->getData() : NULL;
+
+  /// backward weight
+  // create memory desc for backward memory
+  memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw)
+                                 : createMD({bs_, ic_}, format::nc);
+  memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw)
+                                 : createMD({oc_, ic_}, format::oi);
+  memory::desc oMD = createMD({bs_, oc_}, format::nc);
+  memory::desc bMD = bDiff != NULL ? createMD({oc_}, format::x)
+                                   : createMD({}, format::format_undef);
+
+  if (inVal_) {
+    // update data
+    inVal_->set_data_handle(iData);
+  } else {
+    inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData));
+  }
+
+  // create memory primitive desc and memory self
+  wgtGrad_.reset(new memory(memory::primitive_desc(wMD, engine_), wDiff));
+  outGrad_.reset(new memory(memory::primitive_desc(oMD, engine_), oDiff));
+
+  fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, iMD, wMD, oMD);
+  fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
+  fc_bwdWgt::desc bwdWgtDesc = bDiff != NULL
+                                   ? fc_bwdWgt::desc(iMD, wMD, bMD, oMD)
+                                   : fc_bwdWgt::desc(iMD, wMD, oMD);
+  fc_bwdWgt::primitive_desc bwdWgtPD =
+      fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
+
+  if (bDiff != NULL) {
+    biasGrad_.reset(new memory(memory::primitive_desc(bMD, engine_), bDiff));
+    bwdWgt_.reset(
+        new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_));
+  } else {
+    bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_));
+  }
+  pipelineBwd_.clear();
+  pipelineBwd_.push_back(*bwdWgt_);
+
+  /// backward data
+  if (iDiff == NULL) {
+    return;
+  }
+  fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(iMD, wMD, oMD);
+  fc_bwdData::primitive_desc bwdDataPD =
+      fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
+  inGrad_.reset(new memory(memory::primitive_desc(iMD, engine_), iDiff));
+  CHECK(wgtVal_) << "Should have weight memory";
+  bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_));
+  pipelineBwd_.push_back(*bwdData_);
+}
+
+void MKLDNNFcLayer::forward(PassType passType) {
+  Layer::forward(passType);
+  reshape();
+
+  {
+    REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
+
+    // update input data
+    // since it might be changed if this is after data layer
+    real* iData = getInputValue(0)->getData();
+    inVal_->set_data_handle(iData);
+
+    // just submit forward pipeline
+    stream_->submit(pipelineFwd_);
+  }
+
+  /* activation */ {
+    REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
+    forwardActivation();
+  }
+}
+
+void MKLDNNFcLayer::backward(const UpdateCallback& callback) {
+  /* Do derivation */ {
+    REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
+    backwardActivation();
+  }
+
+  {
+    REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
+    resetBwd();
+
+    // update diff
+    real* oDiff = getOutputGrad()->getData();
+    outGrad_->set_data_handle(oDiff);
+
+    // just sumbmit backward pipeline
+    stream_->submit(pipelineBwd_);
+  }
+
+  {
+    REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
+    weight_->getParameterPtr()->incUpdate(callback);
+    if (biases_ && biases_->getWGrad()) {
+      biases_->getParameterPtr()->incUpdate(callback);
+    }
+  }
+}
+}  // namespace paddle

paddle/gserver/layers/MKLDNNFcLayer.h

+/* Copyright (c) 2017 PaddlePaddle Authors. 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. */
+
+#pragma once
+
+#include "MKLDNNLayer.h"
+#include "mkldnn.hpp"
+
+namespace paddle {
+
+/**
+ * @brief A subclass of MKLDNNLayer fc layer.
+ *
+ * The config file api is mkldnn_fc
+ */
+class MKLDNNFcLayer : public MKLDNNLayer {
+protected:
+  // input layer size, can not be change after init
+  size_t iLayerSize_;  // == ic * ih * iw
+
+  // if has already init the weight
+  bool hasInitedWgt_;
+
+  // if input layer has image size info (ih>1 && iw>1)
+  bool hasSpatial_;
+
+  // fc weight and bias
+  std::unique_ptr<Weight> weight_;
+  std::unique_ptr<Weight> biases_;
+
+public:
+  explicit MKLDNNFcLayer(const LayerConfig& config)
+      : MKLDNNLayer(config), hasInitedWgt_(false), hasSpatial_(true) {}
+
+  ~MKLDNNFcLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void convertWeightsFromPaddle() override;
+
+  void convertWeightsToPaddle() override;
+
+  void forward(PassType passType) override;
+
+  void backward(const UpdateCallback& callback) override;
+
+protected:
+  /**
+   * reshape the input image sizes
+   * and reset output buffer size
+   * and reset mkldnn forward
+   */
+  void reshape();
+
+  /**
+   * reset the forward primitve and memory
+   * only would be called when input size changes
+   */
+  void resetFwd();
+
+  /**
+   * reset the backward primitve and memory for mkldnn fc
+   * only would be called when needed
+   */
+  void resetBwd();
+};
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNLayer.h

+/* Copyright (c) 2017 PaddlePaddle Authors. 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. */
+
+#pragma once
+
+#include <vector>
+#include "Layer.h"
+#include "MKLDNNBase.h"
+#include "mkldnn.hpp"
+
+DECLARE_bool(use_mkldnn);
+DECLARE_bool(use_mkldnn_wgt);
+
+namespace paddle {
+
+class MKLDNNLayer;
+typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr;
+
+/**
+ * @brief Base class of MKLDNNlayer.
+ *
+ */
+class MKLDNNLayer : public Layer {
+protected:
+  // batch size
+  int bs_;
+  // input image channel, height and width
+  int ic_, ih_, iw_;
+  // output image channel, height and width
+  int oc_, oh_, ow_;
+
+  // backward also need reset after reset forward handle
+  bool needResetBwd_;
+
+  // mkldnn engine, stream and primivtives
+  mkldnn::engine engine_;
+  std::shared_ptr<MKLDNNStream> stream_;
+  std::shared_ptr<mkldnn::primitive> fwd_;
+  std::shared_ptr<mkldnn::primitive> bwdWgt_;
+  std::shared_ptr<mkldnn::primitive> bwdData_;
+  std::vector<mkldnn::primitive> pipelineFwd_;
+  std::vector<mkldnn::primitive> pipelineBwd_;
+
+  // TODO(TJ): change below memory as MKLDNNMatrixPtr type
+  std::shared_ptr<mkldnn::memory> inVal_;
+  std::shared_ptr<mkldnn::memory> inGrad_;
+  std::shared_ptr<mkldnn::memory> outVal_;
+  std::shared_ptr<mkldnn::memory> outGrad_;
+  std::shared_ptr<mkldnn::memory> wgtVal_;
+  std::shared_ptr<mkldnn::memory> wgtGrad_;
+  std::shared_ptr<mkldnn::memory> biasVal_;
+  std::shared_ptr<mkldnn::memory> biasGrad_;
+
+public:
+  explicit MKLDNNLayer(const LayerConfig& config)
+      : Layer(config),
+        bs_(0),
+        ic_(0),
+        ih_(0),
+        iw_(0),
+        oc_(0),
+        oh_(0),
+        ow_(0),
+        needResetBwd_(true),
+        engine_(mkldnn::engine::cpu, 0),
+        stream_(nullptr),
+        fwd_(nullptr),
+        bwdWgt_(nullptr),
+        bwdData_(nullptr) {}
+
+  ~MKLDNNLayer() {}
+
+  virtual bool init(const LayerMap& layerMap,
+                    const ParameterMap& parameterMap) {
+    if (!Layer::init(layerMap, parameterMap)) {
+      return false;
+    }
+
+    CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
+                            << "Please set WITH_MKLDNN=ON "
+                            << "and set use_mkldnn=True";
+    stream_.reset(new MKLDNNStream());
+    engine_ = CPUEngine::Instance().getEngine();
+
+    // TODO(TJ): deivecId
+    return true;
+  }
+
+  /**
+   * convert weight from paddle format to mkldnn format
+   * weight_ will be override
+   */
+  virtual void convertWeightsFromPaddle() {}
+
+  /**
+   * convert mkldnn weight to paddle format
+   * weight_ will be override
+   */
+  virtual void convertWeightsToPaddle() {}
+
+  /**
+   * print info about sizes
+   */
+  virtual void printSizeInfo() {
+    VLOG(MKLDNN_SIZES) << getName() << ": bs: " << bs_ << ", ic: " << ic_
+                       << ", ih: " << ih_ << ", iw: " << iw_ << ", oc: " << oc_
+                       << ", oh: " << oh_ << ", ow: " << ow_;
+  }
+
+  // TODO(TJ): move to MkldnnMatrix
+  // create memory desc
+  inline mkldnn::memory::desc createMD(
+      mkldnn::memory::dims dims,
+      mkldnn::memory::format fmt,
+      mkldnn::memory::data_type type = mkldnn::memory::data_type::f32) {
+    // TODO(TJ): isFmtSuppoted(fmt)
+    return mkldnn::memory::desc(dims, type, fmt);
+  }
+};
+
+}  // namespace paddle

paddle/gserver/tests/CMakeLists.txt

@@ -18,6 +18,15 @@ add_unittest_without_exec(test_LayerGrad
 add_test(NAME test_LayerGrad
     COMMAND test_LayerGrad)
 
+########## test_Mkldnn layers and activations ##########
+if(WITH_MKLDNN)
+    add_unittest_without_exec(test_MKLDNN
+        test_MKLDNN.cpp
+        MKLDNNTester.cpp
+        LayerGradUtil.cpp)
+    add_test(NAME test_MKLDNN COMMAND test_MKLDNN)
+endif()
+
 ################ test_CRFLayerGrad ####################
 add_unittest_without_exec(test_CRFLayerGrad
     test_CRFLayerGrad.cpp

paddle/gserver/tests/MKLDNNTester.cpp

+/* Copyright (c) 2017 PaddlePaddle Authors. 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 "MKLDNNTester.h"
+#include "paddle/gserver/layers/MKLDNNBase.h"
+#include "paddle/gserver/layers/MKLDNNLayer.h"
+
+namespace paddle {
+
+// init data layer and test layer of both dnn and reference
+void MKLDNNTester::reset(const TestConfig& dnn,
+                         const TestConfig& ref,
+                         size_t batchSize) {
+  const bool trans = false;
+  const bool useGpu = false;
+
+  // clear
+  configs_.clear();
+  layerNames_.clear();
+  dataLayers_.clear();
+  datas_.clear();
+  layerMaps_.clear();
+  parameters_.clear();
+  testLayers_.clear();
+
+  // resize
+  configs_.resize(NUM);
+  layerNames_.resize(NUM);
+  dataLayers_.resize(NUM);
+  datas_.resize(NUM);
+  layerMaps_.resize(NUM);
+  parameters_.resize(NUM);
+  testLayers_.resize(NUM);
+
+  // reset configs and layer names
+  configs_[DNN] = dnn;
+  configs_[REF] = ref;
+  layerNames_[DNN] = "mkldnn";     // the first is mkldnn layer
+  layerNames_[REF] = "reference";  // second is reference layer
+
+  // reset others
+  for (size_t i = 0; i < NUM; ++i) {
+    configs_[i].layerConfig.set_name(layerNames_[i]);
+    initDataLayer(configs_[i],
+                  &(dataLayers_[i]),
+                  &(datas_[i]),
+                  &(layerMaps_[i]),
+                  layerNames_[i],
+                  batchSize,
+                  trans,
+                  useGpu);
+    initTestLayer(
+        configs_[i], &(layerMaps_[i]), &(parameters_[i]), &(testLayers_[i]));
+  }
+  dnnLayer_ = testLayers_[DNN];
+  refLayer_ = testLayers_[REF];
+  EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size());
+  EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
+
+  setInputImgSize();
+}
+
+void MKLDNNTester::setInputImgSize() {
+  for (size_t n = 0; n < dataLayers_.size(); ++n) {
+    for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
+      // TODO(TJ): fix me when concat and elewise ready
+      dataLayers_[n][i]->getOutput().setFrameHeight(ih_);
+      dataLayers_[n][i]->getOutput().setFrameWidth(iw_);
+    }
+  }
+}
+
+// init randome parameters of ref, and copy to mkldnn
+void MKLDNNTester::randomWgtDatas() {
+  EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
+  for (size_t i = 0; i < parameters_[REF].size(); ++i) {
+    const VectorPtr& dnnValue = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
+    const VectorPtr& refValue = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
+    parameters_[REF][i]->randomize();
+    dnnValue->copyFrom(*refValue);
+
+    VLOG(lvl_) << "Random weight data " << parameters_[DNN][i]->getName();
+    printVector(dnnValue);
+  }
+}
+
+// random botdata of ref layer and copy same to mkldnn
+void MKLDNNTester::randomBotDatas() {
+  CHECK_EQ(dataLayers_.size(), NUM);
+  for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
+    dataLayers_[REF][i]->getOutputValue()->randomizeUniform();
+    dataLayers_[DNN][i]->getOutputValue()->copyFrom(
+        *(dataLayers_[REF][i]->getOutputValue()));
+    VLOG(lvl_) << "Input " << i << " data:";
+    printMatrix(dataLayers_[REF][i]->getOutputValue());
+  }
+}
+
+void MKLDNNTester::randomTopDiffs() {
+  refLayer_->getOutputGrad()->randomizeUniform();
+  dnnLayer_->getOutputGrad()->copyFrom(*(refLayer_->getOutputGrad()));
+  VLOG(lvl_) << "Random dom Backward Input, TopDiff: ";
+  printMatrix(refLayer_->getOutputGrad());
+}
+
+void MKLDNNTester::checkForward() {
+  printTopDatas();
+  double delta = compareMatrix(testLayers_[DNN]->getOutputValue(),
+                               testLayers_[REF]->getOutputValue());
+  VLOG(MKLDNN_ALL) << "Check Forward";
+  EXPECT_LE(fabs(delta), eps_);
+}
+
+void MKLDNNTester::checkBackwardData() {
+  // TODO(TJ): uncomment me when batch norm ready
+  // const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
+  for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
+    const MatrixPtr& dnnDiff = dataLayers_[DNN][i]->getOutputGrad();
+    const MatrixPtr& refDiff = dataLayers_[REF][i]->getOutputGrad();
+    VLOG(lvl_) << "Mkldnn Backward Output BotDiff " << i;
+    printMatrix(dnnDiff);
+    VLOG(lvl_) << "Reference Backward Output BotDiff " << i;
+    printMatrix(refDiff);
+
+    double delta = compareMatrix(dnnDiff, refDiff);
+    EXPECT_LE(fabs(delta), eps_);
+    // TODO(TJ): uncomment me when batch norm ready
+    // if (isBN) {
+    //  // the other two inputs in batch norm are for moving mean and var
+    //  break;
+    // }
+  }
+}
+
+void MKLDNNTester::checkBackwardWgts() {
+  CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
+  vector<VectorPtr> dnnWgts;  // used to temply save mkldnn weights
+  saveWgt(parameters_[DNN], dnnWgts);
+
+  const MKLDNNLayerPtr dnnlayer =
+      std::dynamic_pointer_cast<MKLDNNLayer>(dnnLayer_);
+  CHECK(dnnlayer);
+  dnnlayer->convertWeightsToPaddle();
+  for (size_t i = 0; i < parameters_[DNN].size(); ++i) {
+    const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
+    const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
+    VLOG(lvl_) << "Mkldnn Output weight " << parameters_[DNN][i]->getName();
+    printVector(dnn);
+    VLOG(lvl_) << "Reference Output weight " << parameters_[REF][i]->getName();
+    printVector(ref);
+
+    double delta = compareVector(dnn, ref);
+    EXPECT_LE(fabs(delta), eps_);
+  }
+
+  VLOG(MKLDNN_ALL) << "Restore dnn weights before comapre";
+  restoreWgt(dnnWgts, parameters_[DNN]);
+}
+
+void MKLDNNTester::saveWgt(const vector<ParameterPtr>& from,
+                           vector<VectorPtr>& to) {
+  const bool useGpu = false;
+  to.resize(from.size());
+  for (size_t i = 0; i < to.size(); ++i) {
+    const VectorPtr& wgt = from[i]->getBuf(PARAMETER_VALUE);
+    to[i] = Vector::create(wgt->getSize(), useGpu);
+    to[i]->copyFrom(*wgt);
+  }
+}
+
+void MKLDNNTester::restoreWgt(const vector<VectorPtr>& from,
+                              vector<ParameterPtr>& to) {
+  CHECK_EQ(from.size(), to.size());
+  for (size_t i = 0; i < from.size(); ++i) {
+    const VectorPtr& wgt = to[i]->getBuf(PARAMETER_VALUE);
+    wgt->copyFrom(*from[i]);
+  }
+}
+
+// clear parameters grad
+void MKLDNNTester::clearWgtDiffs() {
+  for (size_t n = 0; n < parameters_.size(); ++n) {
+    for (size_t i = 0; i < parameters_[n].size(); ++i) {
+      const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT);
+      if (grad) {
+        grad->zeroMem();
+      }
+    }
+  }
+}
+
+void MKLDNNTester::clearBotDiffs() {
+  // dnn and ref
+  for (size_t n = 0; n < dataLayers_.size(); ++n) {
+    // all inputs layers
+    for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
+      dataLayers_[n][i]->getOutputGrad()->zeroMem();
+    }
+  }
+}
+
+void MKLDNNTester::clearBotDiffs(int n) {
+  CHECK_LT(n, NUM);
+  // all inputs layers
+  for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
+    dataLayers_[n][i]->getOutputGrad()->zeroMem();
+  }
+}
+
+void MKLDNNTester::clearTopDatas() {
+  for (size_t i = 0; i < testLayers_.size(); ++i) {
+    testLayers_[i]->getOutputValue()->zeroMem();
+  }
+}
+
+void MKLDNNTester::printTopDatas() {
+  if (!log_) {
+    return;
+  }
+
+  for (int n = 0; n < NUM; ++n) {
+    VLOG(lvl_) << testLayers_[n]->getType() << " forward output TopData: ";
+    printMatrix(testLayers_[n]->getOutputValue());
+  }
+}
+
+void MKLDNNTester::printMatrix(const MatrixPtr& m) {
+  if (!log_) {
+    return;
+  }
+
+  std::ostringstream ostr;
+  m->print(ostr);
+  VLOG(lvl_) << std::endl << ostr.str();
+}
+
+void MKLDNNTester::printVector(const VectorPtr& v) {
+  if (!log_) {
+    return;
+  }
+
+  std::ostringstream ostr;
+  v->print(ostr, v->getSize());
+  VLOG(lvl_) << std::endl << ostr.str();
+}
+
+double MKLDNNTester::getDelta(const real* d1,
+                              const real* d2,
+                              size_t len,
+                              const float failRate,
+                              const float thres) {
+  double delta = 0, sum = 0;
+  int failCnt = 0;
+  const double eps = 1e-5;
+  double maxOut = 0;
+  for (size_t i = 0; i < len; ++i) {
+    double ref = fabs(d2[i]);
+    double diff = fabs(d1[i] - d2[i]);
+    delta += diff;
+    sum += ref;
+    if (ref > eps && fabs(d1[i]) > eps && diff / ref > thres) {
+      maxOut = std::max(maxOut, diff / ref);
+      failCnt++;
+    }
+  }
+  EXPECT_TRUE(std::isnormal(sum));
+  EXPECT_FALSE(std::isinf(sum));
+  EXPECT_FALSE(std::isnan(delta));
+  VLOG(MKLDNN_ALL) << "reference avg data: " << sum / len
+                   << ", delta: " << delta / sum << ", failCnt:" << failCnt;
+  return (failCnt / (float)len) > failRate ? maxOut : delta / sum;
+}
+
+double MKLDNNTester::compareMatrix(const MatrixPtr& m1, const MatrixPtr& m2) {
+  CHECK_EQ(m1->getElementCnt(), m2->getElementCnt());
+  return getDelta(m1->getData(), m2->getData(), m1->getElementCnt());
+}
+
+double MKLDNNTester::compareVector(const VectorPtr& v1, const VectorPtr& v2) {
+  CHECK_EQ(v1->getSize(), v2->getSize());
+  return getDelta(v1->getData(), v2->getData(), v1->getSize());
+}
+
+void MKLDNNTester::runOnce() {
+  // test forward
+  randomBotDatas();
+  dnnLayer_->forward(PASS_TRAIN);
+  refLayer_->forward(PASS_TRAIN);
+  checkForward();
+
+  // test backward
+  randomTopDiffs();
+  dnnLayer_->backward(nullptr);
+  refLayer_->backward(nullptr);
+  checkBackwardData();
+  checkBackwardWgts();
+
+  // clear buffers
+  // ref code will addto the diff, dnn code will writeto it
+  // and clearTopDatas() and clearWgtDiffs() should be coverd by test layers
+  clearBotDiffs(REF);
+}
+
+void MKLDNNTester::run(const TestConfig& dnn,
+                       const TestConfig& ref,
+                       size_t batchSize,
+                       size_t inputImgH,
+                       size_t inputImgW,
+                       size_t iter,
+                       float epsilon,
+                       bool log,
+                       int level) {
+  VLOG(MKLDNN_TESTS) << "Test MKLDNN functionality: " << dnn.layerConfig.type()
+                     << " vs " << ref.layerConfig.type();
+  ih_ = inputImgH;
+  iw_ = inputImgW;
+  iter_ = iter;
+  eps_ = epsilon;
+  log_ = log;
+  lvl_ = level;
+
+  // Firstly test FLAGS_use_mkldnn_wgt = false
+  FLAGS_use_mkldnn_wgt = false;
+  // reset and run once
+  reset(dnn, ref, batchSize);
+  randomWgtDatas();
+  clearWgtDiffs();
+  clearBotDiffs();
+  for (size_t i = 0; i < iter_; ++i) {
+    VLOG(MKLDNN_TESTS) << "Check Iteration " << i;
+    runOnce();
+  }
+
+  // Then test FLAGS_use_mkldnn_wgt = true
+  FLAGS_use_mkldnn_wgt = true;
+  // after run once the mkldnn weight has been stored in dnnlayer
+  // then save the weights and restart again
+  vector<VectorPtr> dnnWgts, refWgts;
+  CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
+  saveWgt(parameters_[DNN], dnnWgts);
+  saveWgt(parameters_[REF], refWgts);
+
+  // restart again with flag true
+  reset(dnn, ref, batchSize);
+
+  // restore wgt
+  restoreWgt(dnnWgts, parameters_[DNN]);
+  restoreWgt(refWgts, parameters_[REF]);
+  clearWgtDiffs();
+  clearBotDiffs();
+
+  for (size_t i = 0; i < iter_; ++i) {
+    VLOG(MKLDNN_TESTS) << "Check Iteration " << i;
+    runOnce();
+  }
+}
+
+}  //  namespace paddle

paddle/gserver/tests/MKLDNNTester.h

+/* Copyright (c) 2017 PaddlePaddle Authors. 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. */
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "LayerGradUtil.h"
+#include "paddle/gserver/layers/MKLDNNBase.h"
+
+namespace paddle {
+
+/**
+ * @brief test the functionality of Mkldnnlayers
+ * refer to paddle original function
+ */
+class MKLDNNTester {
+  enum {
+    DNN = 0,  // MKLDNN layer
+    REF = 1,  // Reference layer
+    NUM = 2,  // Number of total
+  };
+
+protected:
+  std::vector<TestConfig> configs_;
+  vector<string> layerNames_;
+  vector<vector<DataLayerPtr>> dataLayers_;
+  vector<vector<Argument>> datas_;
+  vector<LayerMap> layerMaps_;
+  vector<vector<ParameterPtr>> parameters_;
+  vector<LayerPtr> testLayers_;
+  LayerPtr dnnLayer_, refLayer_;
+
+  /// run some iterations, all the result should pass
+  size_t iter_;
+  /// whether to print out the details
+  bool log_;
+  /// vlog level to print the matrix details datas
+  int lvl_;
+  /// epsilon
+  float eps_;
+  /// input image size, default 1
+  size_t ih_, iw_;
+
+public:
+  explicit MKLDNNTester(size_t iter = 3, float epsilon = 1e-4) {
+    iter_ = iter;
+    eps_ = epsilon;
+    log_ = false;
+    lvl_ = MKLDNN_ALL;
+  }
+
+  ~MKLDNNTester() {}
+
+public:
+  void run(const TestConfig& dnn,
+           const TestConfig& ref,
+           size_t batchSize,
+           size_t inputImgH = 1,
+           size_t inputImgW = 1,
+           size_t iter = 3,
+           float epsilon = 1e-4,
+           bool log = false,
+           int level = MKLDNN_ALL);
+  void setLogLevel(int lvl) { lvl_ = lvl; }
+
+private:
+  void reset(const TestConfig& dnn, const TestConfig& ref, size_t batchSize);
+  void setInputImgSize();
+  void runOnce();
+
+  void randomWgtDatas();
+  void randomBotDatas();
+  void randomTopDiffs();
+
+  void checkForward();
+  void checkBackwardData();
+  void checkBackwardWgts();
+
+  void clearWgtDiffs();
+  void clearBotDiffs();
+  void clearBotDiffs(int n);  // clear specific layer
+  void clearTopDatas();
+
+  void printTopDatas();
+  void printMatrix(const MatrixPtr& m);
+  void printVector(const VectorPtr& v);
+
+  void saveWgt(const vector<ParameterPtr>& from, vector<VectorPtr>& to);
+  void restoreWgt(const vector<VectorPtr>& from, vector<ParameterPtr>& to);
+
+  double compareMatrix(const MatrixPtr& m1, const MatrixPtr& m2);
+  double compareVector(const VectorPtr& v1, const VectorPtr& v2);
+
+  /**
+   * Get delta percent
+   * if many(>failRate) wrong(abs(dnn-ref)/abs(ref)>thres) points return the
+   * max(diff/ref)
+   * else return sum(abs(a-b)) / sum(abs(b))
+   * The return value should smaller than eps when passing.
+   */
+  double getDelta(const real* d1,
+                  const real* d2,
+                  size_t len,
+                  const float failRate = 1e-3,
+                  const float thres = 0.1);
+};
+
+}  //  namespace paddle

paddle/gserver/tests/test_MKLDNN.cpp

+/* Copyright (c) 2017 PaddlePaddle Authors. 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 <gtest/gtest.h>
+#include <string>
+#include <vector>
+#include "MKLDNNTester.h"
+#include "ModelConfig.pb.h"
+
+using namespace paddle;  // NOLINT
+
+DECLARE_bool(thread_local_rand_use_global_seed);
+DECLARE_bool(use_gpu);
+DECLARE_bool(use_mkldnn);
+
+struct testFCDesc {
+  int bs;
+  int ic;
+  int oc;
+  int ih, iw;  // oh == ow == 1
+};
+
+void testFcLayer(const testFCDesc& pm) {
+  const std::string compareTypes[] = {"mkldnn_fc", "fc"};
+  TestConfig cfg;
+  cfg.layerConfig.set_type(compareTypes[0]);
+  cfg.layerConfig.set_size(pm.oc);
+  cfg.inputDefs.push_back(
+      {INPUT_DATA,
+       "layer_0",
+       /* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw),
+       /* size of weight= */ size_t(pm.oc * pm.ic * pm.ih * pm.iw)});
+  cfg.layerConfig.add_inputs();
+
+  MKLDNNTester tester;
+  for (auto biasSize : {pm.oc, 0}) {
+    cfg.biasSize = biasSize;
+    TestConfig ref = cfg;
+    ref.layerConfig.set_type(compareTypes[1]);
+    for (auto bs : {pm.bs, 1}) {
+      tester.run(cfg, ref, bs, pm.ih, pm.iw);
+    }
+  }
+}
+
+TEST(MKLDNNLayer, FcLayer) {
+  testFcLayer({/*bs*/ 2, /*ic*/ 2, /*oc*/ 3, /*ih*/ 1, /*iw*/ 1});
+  testFcLayer({/*bs*/ 3, /*ic*/ 7, /*oc*/ 19, /*ih*/ 1, /*iw*/ 1});
+  testFcLayer({/*bs*/ 8, /*ic*/ 16, /*oc*/ 32, /*ih*/ 13, /*iw*/ 13});
+  testFcLayer({/*bs*/ 4, /*ic*/ 12, /*oc*/ 18, /*ih*/ 13, /*iw*/ 11});
+  testFcLayer({/*bs*/ 2, /*ic*/ 64, /*oc*/ 32, /*ih*/ 16, /*iw*/ 16});
+  testFcLayer({/*bs*/ 15, /*ic*/ 3, /*oc*/ 6, /*ih*/ 16, /*iw*/ 16});
+}
+
+// TODO(TJ): add branch test
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  FLAGS_use_gpu = false;
+  FLAGS_use_mkldnn = true;
+  initMain(argc, argv);
+  FLAGS_thread_local_rand_use_global_seed = true;
+  srand(1);
+  return RUN_ALL_TESTS();
+}

paddle/trainer/TrainerConfigHelper.cpp

@@ -28,6 +28,8 @@ DECLARE_bool(with_cost);
 DECLARE_bool(with_gpu);
 DECLARE_bool(parallel_nn);
 DECLARE_string(config_args);
+DECLARE_bool(use_mkldnn);
+DECLARE_bool(use_mkldnn_wgt);
 
 const char *kConfigParserModuleName = "paddle.trainer.config_parser";
 const char *kConfigParserFuncName = "parse_config_and_serialize";
@@ -44,6 +46,8 @@ TrainerConfigHelper::TrainerConfigHelper(const std::string &configFilePath)
   configArgs << "trainer_id=" << FLAGS_trainer_id << ",local=" << FLAGS_local
              << ",with_cost=" << FLAGS_with_cost << ",use_gpu=" << FLAGS_use_gpu
              << ",parallel_nn=" << FLAGS_parallel_nn
+             << ",use_mkldnn=" << FLAGS_use_mkldnn
+             << ",use_mkldnn_wgt=" << FLAGS_use_mkldnn_wgt
              << ",cudnn_version=" << hl_get_cudnn_lib_version();
   if (!FLAGS_config_args.empty()) {
     configArgs << "," << FLAGS_config_args;

paddle/utils/Flags.cpp

@@ -20,6 +20,14 @@ DEFINE_bool(use_gpu, false, "Only support CPU training");
 DEFINE_bool(use_gpu, true, "Whether to use GPU for training");
 #endif
 
+#ifdef PADDLE_USE_MKLDNN
+// TODO(TJ): change to true when MKLDNN layers support multi-inputs
+DEFINE_bool(use_mkldnn, false, "Default still keep use CPU training");
+#else
+DEFINE_bool(use_mkldnn, false, "Only support CPU training");
+#endif
+
+DEFINE_bool(use_mkldnn_wgt, false, "Init weight from CPU weight");
 DEFINE_bool(parallel_nn,
             false,
             "Whether to use multi-threads to calculate one neural network."

paddle/utils/Flags.h

@@ -40,3 +40,5 @@ DECLARE_bool(show_layer_stat);
 DECLARE_string(predict_file);
 DECLARE_bool(prev_batch_state);
 DECLARE_string(init_model_path);
+DECLARE_bool(use_mkldnn);
+DECLARE_bool(use_mkldnn_wgt);

python/paddle/trainer/config_parser.py

@@ -1604,6 +1604,8 @@ class MultiClassCrossEntropySelfNormCostLayer(LayerBase):
 
 @config_layer('fc')
 class FCLayer(LayerBase):
+    layer_type = 'fc'
+
     def __init__(self,
                  name,
                  size,
@@ -1611,14 +1613,27 @@ class FCLayer(LayerBase):
                  bias=True,
                  error_clipping_threshold=None,
                  **xargs):
-        super(FCLayer, self).__init__(name, 'fc', size, inputs=inputs, **xargs)
+        use_mkldnn = bool(int(g_command_config_args.get("use_mkldnn", 0)))
+        use_mkldnn_wgt = bool(
+            int(g_command_config_args.get("use_mkldnn_wgt", 0)))
+        if use_mkldnn:
+            self.layer_type = 'mkldnn_fc'
+            config_assert(
+                len(inputs) == 1,
+                "MkldnnFCLayer support one and only one input!")
+        super(FCLayer, self).__init__(
+            name, self.layer_type, size, inputs=inputs, **xargs)
         for input_index in xrange(len(self.inputs)):
             input_layer = self.get_input_layer(input_index)
             psize = self.config.size * input_layer.size
             dims = [input_layer.size, self.config.size]
             format = self.inputs[input_index].format
             sparse = format == "csr" or format == "csc"
-
+            if use_mkldnn:
+                config_assert(not sparse,
+                              "MkldnnFCLayer do not support sparse format yet")
+                if use_mkldnn_wgt:
+                    dims = [self.config.size, input_layer.size]
             if sparse:
                 psize = self.inputs[input_index].nnz
             else:
@@ -1631,6 +1646,11 @@ class FCLayer(LayerBase):
             self.config.error_clipping_threshold = error_clipping_threshold
 
 
+@config_layer('mkldnn_fc')
+class MkldnnFcLayer(FCLayer):
+    layer_type = 'mkldnn_fc'
+
+
 @config_layer('selective_fc')
 class SelectiveFCLayer(LayerBase):
     def __init__(self,