remove conflict

CMakeLists.txt

@@ -127,6 +127,7 @@ include(external/warpctc)   # download, build, install warpctc
 include(external/any)       # download libn::any
 include(external/eigen)     # download eigen3
 include(external/pybind11)    # download pybind11
+include(external/nccl)
 
 include(cudnn)              # set cudnn libraries, must before configure
 include(configure)          # add paddle env configuration
@@ -159,7 +160,7 @@ set(EXTERNAL_LIBS
 if(WITH_GPU)
     list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
     if(NOT WITH_DSO)
-        list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY})
+        list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
     endif(NOT WITH_DSO)
 endif(WITH_GPU)
 

cmake/configure.cmake

@@ -62,11 +62,11 @@ else()
     FIND_PACKAGE(CUDA REQUIRED)
 
     if(${CUDA_VERSION_MAJOR} VERSION_LESS 7)
-        message(FATAL_ERROR "Paddle need CUDA >= 7.0 to compile")
+        message(FATAL_ERROR "Paddle needs CUDA >= 7.0 to compile")
     endif()
 
     if(NOT CUDNN_FOUND)
-        message(FATAL_ERROR "Paddle need cudnn to compile")
+        message(FATAL_ERROR "Paddle needs cudnn to compile")
     endif()
 
     set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler ${SIMD_FLAG}")

cmake/external/nccl.cmake

@@ -0,0 +1,50 @@
+INCLUDE(ExternalProject)
+
+SET(NCCL_SOURCE_DIR ${THIRD_PARTY_PATH}/nccl)
+
+INCLUDE_DIRECTORIES(${NCCL_SOURCE_DIR}/src/extern_nccl/src)
+
+
+if(WITH_DSO)
+  # If we use DSO, we do not build nccl, just download the dependencies
+  set(NCCL_BUILD_COMMAND "")
+  set(NCCL_INSTALL_COMMAND "")
+  set(NCCL_INSTALL_DIR "")
+else()
+  # otherwise, we build nccl and link it.
+  set(NCCL_BUILD_COMMAND "make -j 8")
+  set(NCCL_INSTALL_COMMAND  "make install")
+  SET(NCCL_INSTALL_DIR ${THIRD_PARTY_PATH}/install/nccl)
+endif()
+
+ExternalProject_Add(
+        extern_nccl
+        ${EXTERNAL_PROJECT_LOG_ARGS}
+        GIT_REPOSITORY  "https://github.com/NVIDIA/nccl.git"
+        GIT_TAG         "v1.3.4-1"
+        PREFIX          "${NCCL_SOURCE_DIR}"
+        UPDATE_COMMAND  ""
+        CONFIGURE_COMMAND ""
+        BUILD_COMMAND     "${NCCL_BUILD_COMMAND}"
+        INSTALL_COMMAND   "${NCCL_INSTALL_COMMAND}"
+        INSTALL_DIR       "${NCCL_INSTALL_DIR}"
+        TEST_COMMAND      ""
+)
+
+if (WITH_DSO)
+  if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
+    set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/lib_any_dummy.c)
+    file(WRITE ${dummyfile} "const char * dummy_any = \"${dummyfile}\";")
+    add_library(nccl STATIC ${dummyfile})
+  else()
+    add_library(nccl INTERFACE)
+  endif()
+else()
+  ADD_LIBRARY(nccl STATIC IMPORTED GLOBAL)
+  SET_PROPERTY(TARGET nccl PROPERTY IMPORTED_LOCATION
+          ${NCCL_INSTALL_DIR}/lib/libnccl.a)
+endif()
+
+add_dependencies(nccl extern_nccl)
+
+LIST(APPEND external_project_dependencies nccl)

doc/faq/local/index_cn.rst

@@ -174,7 +174,7 @@ decoder_inputs = paddle.layer.fc(
 1. 两者都是对梯度的截断，但截断时机不同，前者在 :code:`optimzier` 更新网络参数时应用；后者在激活函数反向计算时被调用；
 2. 截断对象不同：前者截断可学习参数的梯度，后者截断回传给前层的梯度;
 
-除此之外，还可以通过减小学习律或者对数据进行归一化处理来解决这类问题。
+除此之外，还可以通过减小学习率或者对数据进行归一化处理来解决这类问题。
 
 5.  如何调用 infer 接口输出多个layer的预测结果
 -----------------------------------------------

paddle/framework/block_desc.cc

@@ -41,6 +41,19 @@ bool BlockDescBind::HasVar(const std::string &name) const {
   return vars_.find(name) != vars_.end();
 }
 
+VarDescBind *BlockDescBind::FindVarRecursive(const std::string &name) const {
+  auto it = vars_.find(name);
+  if (it == vars_.end()) {
+    return Parent() == kNoneBlockIndex ? nullptr
+                                       : ParentBlock()->FindVarRecursive(name);
+  }
+  return it->second.get();
+}
+
+bool BlockDescBind::HasVarRecursive(const std::string &name) const {
+  return FindVarRecursive(name) != nullptr;
+}
+
 std::vector<VarDescBind *> BlockDescBind::AllVars() const {
   std::vector<VarDescBind *> res;
   for (const auto &p : vars_) {
@@ -97,7 +110,7 @@ void BlockDescBind::Flush() {
 }
 
 BlockDescBind *BlockDescBind::ParentBlock() const {
-  if (this->desc_->parent_idx() == -1) {
+  if (this->desc_->parent_idx() == kNoneBlockIndex) {
     return nullptr;
   }
   return prog_->Block(static_cast<size_t>(this->desc_->parent_idx()));

paddle/framework/block_desc.h

@@ -21,6 +21,7 @@ limitations under the License. */
 #include <vector>
 
 #include "paddle/framework/op_desc.h"
+#include "paddle/framework/proto_desc.h"
 #include "paddle/framework/var_desc.h"
 #include "paddle/platform/macros.h"
 
@@ -56,6 +57,10 @@ class BlockDescBind {
 
   bool HasVar(const std::string &var_name) const;
 
+  VarDescBind *FindVarRecursive(const std::string &name_bytes) const;
+
+  bool HasVarRecursive(const std::string &var_name) const;
+
   std::set<std::string> LocalVarNames() const {
     std::set<std::string> var_names;
     for (auto &var : vars_) {

paddle/framework/op_info.h

@@ -87,11 +87,8 @@ class OpInfoMap {
     }
   }
 
-  template <typename Callback>
-  void IterAllInfo(Callback callback) {
-    for (auto& it : map_) {
-      callback(it.first, it.second);
-    }
+  const std::unordered_map<std::string, const OpInfo>& map() const {
+    return map_;
   }
 
  private:

paddle/framework/operator.h

@@ -327,37 +327,47 @@ class CompileTimeInferShapeContext : public InferShapeContext {
   bool HasInput(const std::string& name) const override {
     const std::vector<std::string>& input_names = op_.Input(name);
     auto length = input_names.size();
+    if (length == 0) {
+      return false;
+    }
     PADDLE_ENFORCE_EQ(length, 1UL,
                       "Input(%s) should have only one value, "
                       "but it have %d now",
                       name, length);
-    return block_.HasVar(input_names[0]);
+    return block_.HasVarRecursive(input_names[0]);
   }
 
   bool HasOutput(const std::string& name) const override {
     const std::vector<std::string>& output_names = op_.Output(name);
     auto length = output_names.size();
+    if (length == 0) {
+      return false;
+    }
     PADDLE_ENFORCE_EQ(length, 1UL,
                       "Output(%s) should have only one value, "
                       "but it have %d now",
                       name, length);
-    return block_.HasVar(output_names[0]);
+    return block_.HasVarRecursive(output_names[0]);
   }
 
   bool HasInputs(const std::string& name) const override {
     const std::vector<std::string>& input_names = op_.Input(name);
-    PADDLE_ENFORCE(!input_names.empty(), "Inputs(%s) length is 0", name);
+    if (input_names.empty()) {
+      return false;
+    }
     for (auto& input : input_names) {
-      if (!block_.HasVar(input)) return false;
+      if (!block_.HasVarRecursive(input)) return false;
     }
     return true;
   }
 
   bool HasOutputs(const std::string& name) const override {
     const std::vector<std::string>& output_names = op_.Output(name);
-    PADDLE_ENFORCE(!output_names.empty(), "Inputs(%s) length is 0", name);
+    if (output_names.empty()) {
+      return false;
+    }
     for (auto& output : output_names) {
-      if (!block_.HasVar(output)) return false;
+      if (!block_.HasVarRecursive(output)) return false;
     }
     return true;
   }
@@ -404,11 +414,11 @@ class CompileTimeInferShapeContext : public InferShapeContext {
 
  private:
   DDim GetDim(const std::string& name) const override {
-    return framework::make_ddim(block_.FindVar(name)->Shape());
+    return framework::make_ddim(block_.FindVarRecursive(name)->Shape());
   }
 
   void SetDim(const std::string& name, const DDim& dim) override {
-    block_.FindVar(name)->SetShape(framework::vectorize(dim));
+    block_.FindVarRecursive(name)->SetShape(framework::vectorize(dim));
   }
 
   const OpDescBind& op_;
@@ -421,13 +431,27 @@ class RuntimeInferShapeContext : public InferShapeContext {
       : op_(op), scope_(scope) {}
 
   bool HasInput(const std::string& name) const override {
-    auto ipt = op_.Input(name);
+    auto& ins = Inputs(name);
+    size_t length = ins.size();
+    if (length == 0) {
+      return false;
+    }
+    PADDLE_ENFORCE_EQ(length, 1UL, "Input %s should have more than one inputs",
+                      name);
+    auto ipt = ins[0];
     auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
     return var != nullptr;
   }
 
   bool HasOutput(const std::string& name) const override {
-    auto ipt = op_.Output(name);
+    auto& outs = Outputs(name);
+    size_t length = outs.size();
+    if (length == 0) {
+      return false;
+    }
+    PADDLE_ENFORCE_EQ(length, 1UL, "Output %s should have more than one inputs",
+                      name);
+    auto ipt = outs[0];
     auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
     return var != nullptr;
   }

paddle/framework/program_desc.cc

@@ -35,8 +35,8 @@ ProgramDesc *ProgramDescBind::Proto() {
 
 ProgramDescBind::ProgramDescBind() {
   auto *block = prog_.mutable_blocks()->Add();
-  block->set_idx(0);
-  block->set_parent_idx(-1);
+  block->set_idx(kRootBlockIndex);
+  block->set_parent_idx(kNoneBlockIndex);
   blocks_.emplace_back(new BlockDescBind(this, block));
 }
 

paddle/framework/program_desc.h

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <memory>
 #include <vector>
 #include "paddle/framework/framework.pb.h"
+#include "paddle/framework/proto_desc.h"
 #include "paddle/platform/macros.h"
 
 namespace paddle {

paddle/framework/proto_desc.h

@@ -0,0 +1,26 @@
+/* Copyright (c) 2016 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
+
+namespace paddle {
+namespace framework {
+
+// The Index of first Block in Program. also called root block.
+constexpr int kRootBlockIndex = 0;
+// The Parent Index of root Block, this block does not exist.
+constexpr int kNoneBlockIndex = -1;
+
+}  // namespace framework
+}  // namespace paddle

paddle/framework/var_desc.cc

@@ -18,6 +18,10 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+VarDesc::VarType VarDescBind::GetType() const { return desc_.type(); }
+
+void VarDescBind::SetType(VarDesc::VarType type) { desc_.set_type(type); }
+
 void VarDescBind::SetShape(const std::vector<int64_t> &dims) {
   VectorToRepeated(dims, mutable_tensor_desc()->mutable_dims());
 }

paddle/framework/var_desc.h

@@ -75,9 +75,9 @@ class VarDescBind {
 
   int32_t GetLodLevel() const;
 
-  VarDesc::VarType GetType() const { return desc_.type(); }
+  VarDesc::VarType GetType() const;
 
-  void SetType(VarDesc::VarType type) { desc_.set_type(type); }
+  void SetType(VarDesc::VarType type);
 
   bool Persistable() const { return desc_.persistable(); }
 

paddle/gserver/activations/MKLDNNActivation.cpp

@@ -126,7 +126,7 @@ void MKLDNNEltwiseActivation::resetFwd(Argument& act) {
   copyInVal_ = nullptr;
   if (act.grad && algo == algorithm::eltwise_tanh) {
     // tanh need save src input for backward
-    inVal_ = MKLDNNMatrix::create(nullptr, val_->getPrimitiveDesc());
+    inVal_ = MKLDNNMatrix::create(val_->getPrimitiveDesc());
     copyInVal_ = std::make_shared<mkldnn::reorder>(*val_, *inVal_);
     CHECK(copyInVal_) << "should not be emptry";
     pipelineFwd_.push_back(*copyInVal_);
@@ -145,7 +145,7 @@ void MKLDNNEltwiseActivation::resetBwd(Argument& act) {
   algorithm algo = getAlgo(this->getName());
   float alpha = getBwdAlpha();
   float beta = getBeta();
-  grad_ = MKLDNNMatrix::create(act.grad, val_->getPrimitiveDesc());
+  grad_ = MKLDNNMatrix::create(val_->getPrimitiveDesc(), act.grad);
   auto eng = CPUEngine::Instance().getEngine();
   auto bwdDesc = eltwise_bwd::desc(
       algo, grad_->getMemoryDesc(), val_->getMemoryDesc(), alpha, beta);
@@ -230,7 +230,7 @@ void MKLDNNActivation::resetFwd(Argument& act) {
     int ic = cnt_ / bs / ih / iw;
     CHECK_EQ(cnt_, (size_t)bs * ic * ih * iw);
     val_ = MKLDNNMatrix::create(
-        act.value, {bs, ic, ih, iw}, mkldnn::memory::format::nchw, *engine_);
+        {bs, ic, ih, iw}, mkldnn::memory::format::nchw, *engine_, act.value);
     CHECK(val_);
     val_->downSpatial();
   }

paddle/gserver/layers/MKLDNNBase.h

@@ -21,8 +21,8 @@ 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_FMTS = 2,   // format info of MKLDNN
+  MKLDNN_SIZES = 3,  // size info of MKLDNN
   MKLDNN_ALL = 4,    // show all info of MKLDNN
 } MKLDNN_LOG_LEVEL;
 

paddle/gserver/layers/MKLDNNConvLayer.h

@@ -48,17 +48,6 @@ protected:
   // save forward primitive_desc, which can be used backward
   std::shared_ptr<conv_fwd::primitive_desc> fwdPD_;
 
-  // MKLDNNMatrixPtr which should be created from CPU Device
-  MKLDNNMatrixPtr cpuInVal_;
-  MKLDNNMatrixPtr cpuInGrad_;
-  MKLDNNMatrixPtr cpuOutVal_;
-  MKLDNNMatrixPtr cpuOutGrad_;
-  // convert handle between CPU device and MKLDNN device
-  std::shared_ptr<mkldnn::reorder> cvtInVal_;
-  std::shared_ptr<mkldnn::reorder> cvtInGrad_;
-  std::shared_ptr<mkldnn::reorder> cvtOutVal_;
-  std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
-
   // whether the weight has been init
   bool hasInitedWgt_;
 
@@ -94,8 +83,6 @@ public:
                 MKLDNNMatrixPtr& bias,
                 MKLDNNMatrixPtr& out) override;
 
-  void updateInputData() override;
-
   void updateWeights(const UpdateCallback& callback) override;
 
   void convertWeightsFromPaddle() override;
@@ -109,26 +96,6 @@ public:
                        << ", sw: " << sw_ << ", dh: " << dh_ << ", dw: " << dw_;
   }
 
-  void printValueFormatFlow() override {
-    if (cpuInVal_) {
-      VLOG(MKLDNN_FMTS) << cpuInVal_->getFormat() << " >>>";
-    }
-    MKLDNNLayer::printValueFormatFlow();
-    if (cpuOutVal_) {
-      VLOG(MKLDNN_FMTS) << " >>> " << cpuOutVal_->getFormat();
-    }
-  }
-
-  void printGradFormatFlow() override {
-    if (cpuInGrad_) {
-      VLOG(MKLDNN_FMTS) << cpuInGrad_->getFormat() << " <<<";
-    }
-    MKLDNNLayer::printGradFormatFlow();
-    if (cpuOutGrad_) {
-      VLOG(MKLDNN_FMTS) << " <<< " << cpuOutGrad_->getFormat();
-    }
-  }
-
 protected:
   /**
    * load the dims settings of this conv
@@ -162,23 +129,6 @@ protected:
                         MKLDNNMatrixPtr& bias,
                         MKLDNNMatrixPtr& out);
 
-  /**
-   * reset MKLDNNMatrix of input value
-   */
-  void resetInValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
-                    MKLDNNMatrixPtr& in);
-  /**
-   * reset MKLDNNMatrix of weight and bias value
-   */
-  void resetWgtBiasValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
-                         MKLDNNMatrixPtr& wgt,
-                         MKLDNNMatrixPtr& bias);
-  /**
-   * reset MKLDNNMatrix of output value
-   */
-  void resetOutValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
-                     MKLDNNMatrixPtr& out);
-
   /**
    * reset the backward weight primitive descriptor.
    */
@@ -207,22 +157,6 @@ protected:
                         MKLDNNMatrixPtr& bias,
                         MKLDNNMatrixPtr& out);
 
-  /**
-   * reset MKLDNNMatrix of output grad
-   */
-  void resetOutGrad(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
-                    MKLDNNMatrixPtr& out);
-  /**
-   * reset MKLDNNMatrix of weight and bias grad
-   */
-  void resetWgtBiasGrad(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
-                        MKLDNNMatrixPtr& wgt,
-                        MKLDNNMatrixPtr& bias);
-  /**
-   * reset MKLDNNMatrix of input grad
-   */
-  void resetInGrad(std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
-                   MKLDNNMatrixPtr& in);
   /**
    * reset MKLDNNMatrix of weight value for backward data
    * since the primitive_desc would be different with wgtVal_

paddle/gserver/layers/MKLDNNFcLayer.cpp

@@ -62,7 +62,7 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
   CHECK(wgtVal_) << "should have been initialized";
   bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
   auto targetDim = wgtVal_->getDims();
-  auto srcFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo;
+  auto srcFmt = hasNoSpatial_ ? format::io : format::ihwo;
   wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim);
   hasInitedWgt_ = true;
 }
@@ -71,7 +71,7 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
   CHECK(wgtVal_) << "should have been initialized";
   bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
   auto targetDim = wgtVal_->getDims();
-  auto dstFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo;
+  auto dstFmt = hasNoSpatial_ ? format::io : format::ihwo;
   wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
 }
 
@@ -100,8 +100,6 @@ void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
   resetFwdPD(fwdPD_, in, wgt, bias, out);
 
   resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
-
-  printValueFormatFlow();
 }
 
 void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
@@ -119,12 +117,6 @@ void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
   resetBwdDataPD(bwdDataPD, in, out);
 
   resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
-
-  printGradFormatFlow();
-}
-
-void MKLDNNFcLayer::updateInputData() {
-  inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
 }
 
 void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
@@ -139,51 +131,30 @@ void MKLDNNFcLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
                                     MKLDNNMatrixPtr& bias,
                                     MKLDNNMatrixPtr& out) {
   resetInValue(in);
-
-  resetWgtBiasValue(wgt, bias);
-
-  resetOutValue(out);
-}
-
-void MKLDNNFcLayer::resetInValue(MKLDNNMatrixPtr& in) {
-  if (inputIsOnlyMKLDNN()) {
-    const MatrixPtr& dnnIn = getInputValue(0);
-    in = std::dynamic_pointer_cast<MKLDNNMatrix>(dnnIn);
-    CHECK(in) << "Input should be MKLDNNMatrix";
-  } else {
-    CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
-    const MatrixPtr& cpuIn = getInputValue(0, CPU_DEVICE);
-    in = MKLDNNMatrix::create(
-        cpuIn, {bs_, ic_, ih_, iw_}, format::nchw, engine_);
-  }
+  CHECK(in);
   in->downSpatial();
-}
 
-void MKLDNNFcLayer::resetWgtBiasValue(MKLDNNMatrixPtr& wgt,
-                                      MKLDNNMatrixPtr& bias) {
+  auto outPD =
+      MKLDNNMatrix::createPrimitiveDesc({bs_, oc_}, format::nc, engine_);
+  resetOutValue(out, outPD);
+
   format wgtFmt = format::oihw;
-  if (inVal_->getFormat() == format::nChw8c) {
+  if (in->getFormat() == format::nChw8c) {
     wgtFmt = format::oIhw8i;
-  } else if (inVal_->getFormat() == format::nChw16c) {
+  } else if (in->getFormat() == format::nChw16c) {
     wgtFmt = format::oIhw16i;
   }
-  wgt = MKLDNNMatrix::create(
-      weight_->getW(), {oc_, ic_, ih_, iw_}, wgtFmt, engine_);
+  auto wgtPD =
+      MKLDNNMatrix::createPrimitiveDesc({oc_, ic_, ih_, iw_}, wgtFmt, engine_);
+  resetWithMatrix(wgt, weight_->getW(), wgtPD);
   wgt->downSpatial();
-  VLOG(MKLDNN_FMTS) << "Weight value format: " << wgt->getFormat();
-
-  bias = (biases_ && biases_->getW())
-             ? MKLDNNMatrix::create(biases_->getW(), {oc_}, format::x, engine_)
-             : nullptr;
-}
 
-void MKLDNNFcLayer::resetOutValue(MKLDNNMatrixPtr& out) {
-  out = MKLDNNMatrix::create(output_.value, {bs_, oc_}, format::nc, engine_);
-  if (!outputIsOnlyMKLDNN()) {
-    // fc cpu output value do not need create convert, just share data
-    getOutput(CPU_DEVICE).value->setData(out->getData());
+  if (biases_ && biases_->getW()) {
+    auto biasPD = MKLDNNMatrix::createPrimitiveDesc({oc_}, format::x, engine_);
+    resetWithMatrix(bias, biases_->getW(), biasPD);
+  } else {
+    bias = nullptr;
   }
-  output_.value = std::dynamic_pointer_cast<Matrix>(out);
 }
 
 void MKLDNNFcLayer::resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
@@ -219,7 +190,6 @@ void MKLDNNFcLayer::resetFwdPipeline(
   } else {
     fwd_.reset(new fc_fwd(*pd, *in, *wgt, *out));
   }
-
   pipeline.push_back(*fwd_);
 }
 
@@ -227,44 +197,18 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
                                     MKLDNNMatrixPtr& wgt,
                                     MKLDNNMatrixPtr& bias,
                                     MKLDNNMatrixPtr& out) {
-  resetOutGrad(out);
-
-  resetWgtBiasGrad(wgt, bias);
-
-  resetInGrad(in);
-}
-
-void MKLDNNFcLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
-  CHECK(outVal_);
-  if (outputIsOnlyMKLDNN()) {
-    MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
-  } else {
-    const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
-    output_.grad->setData(cpuOut->getData());
-    out = MKLDNNMatrix::create(cpuOut, outVal_->getPrimitiveDesc());
-  }
-}
+  CHECK(inVal_ && outVal_);
+  resetOutGrad(out, outVal_->getPrimitiveDesc());
+  resetInGrad(in, inVal_->getPrimitiveDesc());
 
-void MKLDNNFcLayer::resetWgtBiasGrad(MKLDNNMatrixPtr& wgt,
-                                     MKLDNNMatrixPtr& bias) {
   CHECK(wgtVal_);
-  wgt = MKLDNNMatrix::create(weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
+  resetWithMatrix(wgt, weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
 
-  bias = nullptr;
-  if (biasVal_ == nullptr) {
-    return;
-  }
-  bias =
-      MKLDNNMatrix::create(biases_->getWGrad(), biasVal_->getPrimitiveDesc());
-}
-
-void MKLDNNFcLayer::resetInGrad(MKLDNNMatrixPtr& in) {
-  in = nullptr;
-  if (inputLayers_[0]->getOutput().grad == nullptr) {
-    return;
+  if (biasVal_) {
+    resetWithMatrix(bias, biases_->getWGrad(), biasVal_->getPrimitiveDesc());
+  } else {
+    bias = nullptr;
   }
-  CHECK(inVal_);
-  MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
 }
 
 void MKLDNNFcLayer::resetBwdWgtPD(

paddle/gserver/layers/MKLDNNFcLayer.h

@@ -66,8 +66,6 @@ public:
                 MKLDNNMatrixPtr& bias,
                 MKLDNNMatrixPtr& out) override;
 
-  void updateInputData() override;
-
   void updateWeights(const UpdateCallback& callback) override;
 
   void convertWeightsFromPaddle() override;
@@ -84,9 +82,6 @@ protected:
                        MKLDNNMatrixPtr& wgt,
                        MKLDNNMatrixPtr& bias,
                        MKLDNNMatrixPtr& out);
-  void resetInValue(MKLDNNMatrixPtr& in);
-  void resetWgtBiasValue(MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& bias);
-  void resetOutValue(MKLDNNMatrixPtr& out);
   void resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
                   MKLDNNMatrixPtr in,
                   MKLDNNMatrixPtr wgt,
@@ -109,9 +104,6 @@ protected:
                        MKLDNNMatrixPtr& wgt,
                        MKLDNNMatrixPtr& bias,
                        MKLDNNMatrixPtr& out);
-  void resetOutGrad(MKLDNNMatrixPtr& out);
-  void resetWgtBiasGrad(MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& bias);
-  void resetInGrad(MKLDNNMatrixPtr& in);
   void resetBwdWgtPD(std::shared_ptr<fc_bwdWgt::primitive_desc>& pd,
                      MKLDNNMatrixPtr& wgt,
                      MKLDNNMatrixPtr& bias,

paddle/gserver/layers/MKLDNNPoolLayer.cpp

@@ -85,8 +85,6 @@ void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline,
   resetFwdPD(fwdPD_, in, out);
 
   resetFwdPipeline(pipeline, fwdPD_, in, out);
-
-  printValueFormatFlow();
 }
 
 void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
@@ -101,65 +99,22 @@ void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
   resetBwdPD(pd, in, out);
 
   resetBwdPipeline(pipeline, pd, in, out);
-
-  printGradFormatFlow();
-}
-
-void MKLDNNPoolLayer::updateInputData() {
-  inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
 }
 
 void MKLDNNPoolLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
                                       MKLDNNMatrixPtr& out) {
   resetInValue(in);
 
-  resetOutValue(out);
-}
-
-void MKLDNNPoolLayer::resetInValue(MKLDNNMatrixPtr& in) {
-  if (inputIsOnlyMKLDNN()) {
-    const MatrixPtr& dnnIn = getInputValue(0);
-    in = std::dynamic_pointer_cast<MKLDNNMatrix>(dnnIn);
-    CHECK(in) << "Input should be MKLDNNMatrix";
-  } else {
-    CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
-    const MatrixPtr& cpuIn = getInputValue(0, CPU_DEVICE);
-    in = MKLDNNMatrix::create(
-        cpuIn, {bs_, ic_, ih_, iw_}, format::nchw, engine_);
-  }
-}
-
-void MKLDNNPoolLayer::resetOutValue(MKLDNNMatrixPtr& out) {
-  CHECK(inVal_) << "Should reset input value first";
   memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
-  out = MKLDNNMatrix::create(
-      output_.value, outDims, inVal_->getFormat(), engine_);
-
-  // create reorder if output value has cpu device and pd do not match
-  cpuOutVal_ = nullptr;
-  cvtOutVal_ = nullptr;
-  if (!outputIsOnlyMKLDNN()) {
-    const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
-    cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
-    if (cpuOutVal_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
-      out = MKLDNNMatrix::create(nullptr, out->getPrimitiveDesc());
-      cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
-      CHECK(cvtOutVal_) << "should not be emptry";
-    } else {
-      cpuOut->setData(output_.value->getData());
-      cpuOutVal_ = out;
-    }
-    output_.value = std::dynamic_pointer_cast<Matrix>(cpuOutVal_);
-    return;
-  }
-  output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
+  CHECK(in);
+  auto outPD =
+      MKLDNNMatrix::createPrimitiveDesc(outDims, in->getFormat(), engine_);
+  resetOutValue(out, outPD);
 }
 
 void MKLDNNPoolLayer::resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
                                  MKLDNNMatrixPtr in,
                                  MKLDNNMatrixPtr out) {
-  memory::dims inDims = memory::dims{bs_, ic_, ih_, iw_};
-  memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
   memory::dims kernels = memory::dims{fh_, fw_};
   memory::dims strides = memory::dims{sh_, sw_};
   memory::dims padL = memory::dims{ph_, pw_};
@@ -194,58 +149,26 @@ void MKLDNNPoolLayer::resetFwdPipeline(
              ? std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out, *workspace_))
              : std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out));
   pipeline.push_back(*fwd_);
-
-  if (cvtOutVal_) {
-    pipeline.push_back(*cvtOutVal_);
-  }
 }
 
 void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
                                       MKLDNNMatrixPtr& out) {
-  resetOutGrad(out);
-
-  resetInGrad(in);
-}
-void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
-  cpuOutGrad_ = nullptr;
-  cvtOutGrad_ = nullptr;
-  CHECK(outVal_);
-  if (outputIsOnlyMKLDNN()) {
-    MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
-  } else {
-    const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
-    // always share the same grad data of CPU output
-    // then the activation can get the right grad from output_.grad
-    output_.grad->setData(cpuOut->getData());
-    cpuOutGrad_ = MKLDNNMatrix::create(
-        cpuOut, memory::dims{bs_, oc_, oh_, ow_}, format::nchw, engine_);
-    if (cpuOutGrad_->getPrimitiveDesc() != outVal_->getPrimitiveDesc()) {
-      out = MKLDNNMatrix::create(nullptr, outVal_->getPrimitiveDesc());
-      cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
-      CHECK(cvtOutGrad_) << "should not be emptry";
-    } else {
-      out = cpuOutGrad_;
-    }
-  }
-}
-
-void MKLDNNPoolLayer::resetInGrad(MKLDNNMatrixPtr& in) {
-  in = nullptr;
-  if (inputLayers_[0]->getOutput().grad == nullptr) {
-    return;
-  }
-  CHECK(inVal_);
-  MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
+  CHECK(inVal_ && outVal_);
+  resetOutGrad(out, outVal_->getPrimitiveDesc());
+  resetInGrad(in, inVal_->getPrimitiveDesc());
 }
 
 void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
                                  MKLDNNMatrixPtr& in,
                                  MKLDNNMatrixPtr& out) {
+  pd = nullptr;
+  if (in == nullptr) {
+    return;
+  }
   memory::dims kernels = memory::dims{fh_, fw_};
   memory::dims strides = memory::dims{sh_, sw_};
   memory::dims padL = memory::dims{ph_, pw_};
   memory::dims padR = getPaddingR();
-  CHECK(in);
   CHECK(out);
   auto bwdDesc = pool_bwd::desc(poolAlgo_,
                                 in->getMemoryDesc(),
@@ -263,8 +186,8 @@ void MKLDNNPoolLayer::resetBwdPipeline(
     std::shared_ptr<pool_bwd::primitive_desc>& pd,
     MKLDNNMatrixPtr& in,
     MKLDNNMatrixPtr& out) {
-  if (cvtOutGrad_) {
-    pipeline.push_back(*cvtOutGrad_);
+  if (pd == nullptr) {
+    return;
   }
 
   bwdData_ =

paddle/gserver/layers/MKLDNNPoolLayer.h

@@ -38,13 +38,6 @@ protected:
   // pooling_avg or pooling_max
   mkldnn::algorithm poolAlgo_;
 
-  // MKLDNNMatrixPtr which should be created from CPU Device
-  MKLDNNMatrixPtr cpuOutVal_;
-  MKLDNNMatrixPtr cpuOutGrad_;
-  // convert handle between CPU device and MKLDNN device
-  std::shared_ptr<mkldnn::reorder> cvtOutVal_;
-  std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
-
   // save forward primitive_desc, which can be used backward
   std::shared_ptr<pool_fwd::primitive_desc> fwdPD_;
   // according to https://github.com/01org/mkl-dnn/blob/master/tests/gtests/
@@ -74,8 +67,6 @@ public:
                 MKLDNNMatrixPtr& bias,
                 MKLDNNMatrixPtr& out) override;
 
-  void updateInputData() override;
-
   void printSizeInfo() override {
     MKLDNNLayer::printSizeInfo();
     VLOG(MKLDNN_SIZES) << getName() << ": fh: " << fh_ << ", fw: " << fw_
@@ -90,8 +81,6 @@ protected:
    *                    reset pipeline.
    */
   void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
-  void resetInValue(MKLDNNMatrixPtr& in);
-  void resetOutValue(MKLDNNMatrixPtr& out);
   void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
                   MKLDNNMatrixPtr in,
                   MKLDNNMatrixPtr out);
@@ -106,8 +95,6 @@ protected:
    *                     reset pipeline.
    */
   void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
-  void resetOutGrad(MKLDNNMatrixPtr& out);
-  void resetInGrad(MKLDNNMatrixPtr& in);
   void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
                   MKLDNNMatrixPtr& in,
                   MKLDNNMatrixPtr& out);

paddle/gserver/tests/MKLDNNTester.cpp

@@ -97,7 +97,7 @@ void MKLDNNTester::randomWgtDatas() {
     parameters_[REF][i]->randomize();
     dnnValue->copyFrom(*refValue);
 
-    VLOG(lvl_) << "Random weight data " << parameters_[DNN][i]->getName();
+    VLOG(MKLDNN_TESTS) << "Random weight " << parameters_[DNN][i]->getName();
     printVector(dnnValue);
   }
 }
@@ -109,7 +109,7 @@ void MKLDNNTester::randomBotDatas() {
     dataLayers_[REF][i]->getOutputValue()->randomizeUniform();
     dataLayers_[DNN][i]->getOutputValue()->copyFrom(
         *(dataLayers_[REF][i]->getOutputValue()));
-    VLOG(lvl_) << "Input " << i << " data:";
+    VLOG(MKLDNN_TESTS) << "Random Foward, InputValue " << i;
     printMatrix(dataLayers_[REF][i]->getOutputValue());
   }
 }
@@ -118,12 +118,12 @@ void MKLDNNTester::randomTopDiffs() {
   refLayer_->getOutputGrad()->randomizeUniform();
   dnnLayer_->getOutput(CPU_DEVICE)
       .grad->copyFrom(*(refLayer_->getOutputGrad()));
-  VLOG(lvl_) << "Random Backward Input, TopDiff: ";
+  VLOG(MKLDNN_TESTS) << "Random Backward, OutputGrad";
   printMatrix(refLayer_->getOutputGrad());
 }
 
 void MKLDNNTester::checkForward() {
-  VLOG(MKLDNN_ALL) << "Check Forward";
+  VLOG(MKLDNN_TESTS) << "Check Forward";
   printTopDatas();
   double delta =
       compareMatrix(dnnLayer_->getOutputValue(), refLayer_->getOutputValue());
@@ -131,15 +131,15 @@ void MKLDNNTester::checkForward() {
 }
 
 void MKLDNNTester::checkBackwardData() {
-  VLOG(MKLDNN_ALL) << "Check Backward Data";
+  VLOG(MKLDNN_TESTS) << "Check Backward Data";
   // 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;
+    VLOG(MKLDNN_ALL) << "MKLDNN Backward Result: InputGrad " << i;
     printMatrix(dnnDiff);
-    VLOG(lvl_) << "Reference Backward Output BotDiff " << i;
+    VLOG(MKLDNN_ALL) << "Reference Backward Result: InputGrad " << i;
     printMatrix(refDiff);
 
     double delta = compareMatrix(dnnDiff, refDiff);
@@ -153,7 +153,7 @@ void MKLDNNTester::checkBackwardData() {
 }
 
 void MKLDNNTester::checkBackwardWgts() {
-  VLOG(MKLDNN_ALL) << "Check Backward Weight";
+  VLOG(MKLDNN_TESTS) << "Check Backward Weight";
   CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
   vector<VectorPtr> dnnWgts;  // used to temply save mkldnn weights
   saveWgt(parameters_[DNN], dnnWgts);
@@ -165,9 +165,11 @@ void MKLDNNTester::checkBackwardWgts() {
   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();
+    VLOG(MKLDNN_ALL) << "MKLDNN Result: weight value"
+                     << parameters_[DNN][i]->getName();
     printVector(dnn);
-    VLOG(lvl_) << "Reference Output weight " << parameters_[REF][i]->getName();
+    VLOG(MKLDNN_ALL) << "Reference Result: weight value "
+                     << parameters_[REF][i]->getName();
     printVector(ref);
 
     double delta = compareVector(dnn, ref);
@@ -240,7 +242,8 @@ void MKLDNNTester::printTopDatas() {
   }
 
   for (int n = 0; n < NUM; ++n) {
-    VLOG(lvl_) << testLayers_[n]->getType() << " forward output TopData: ";
+    VLOG(MKLDNN_ALL) << testLayers_[n]->getType()
+                     << " Forward Result: OutputValue";
     printMatrix(testLayers_[n]->getOutputValue());
   }
 }
@@ -252,7 +255,7 @@ void MKLDNNTester::printMatrix(const MatrixPtr& m) {
 
   std::ostringstream ostr;
   m->print(ostr);
-  VLOG(lvl_) << std::endl << ostr.str();
+  VLOG(MKLDNN_ALL) << std::endl << ostr.str();
 }
 
 void MKLDNNTester::printVector(const VectorPtr& v) {
@@ -262,7 +265,7 @@ void MKLDNNTester::printVector(const VectorPtr& v) {
 
   std::ostringstream ostr;
   v->print(ostr, v->getSize());
-  VLOG(lvl_) << std::endl << ostr.str();
+  VLOG(MKLDNN_ALL) << std::endl << ostr.str();
 }
 
 double MKLDNNTester::getDelta(const real* d1,
@@ -314,7 +317,7 @@ void MKLDNNTester::runOnce() {
   UpdateCallback updateCallback = [](Parameter* para) {
     auto& grad = para->getBuf(PARAMETER_GRADIENT);
     auto& value = para->getBuf(PARAMETER_VALUE);
-    real lr = 1e-3;
+    real lr = 1e-2;
     value->add(*grad, lr);
     grad->zeroMem();
   };
@@ -340,10 +343,9 @@ void MKLDNNTester::run(const TestConfig& dnn,
                        size_t batchSize,
                        size_t inputImgH,
                        size_t inputImgW,
+                       bool printDetails,
                        size_t iter,
-                       float epsilon,
-                       bool log,
-                       int level) {
+                       float epsilon) {
   CHECK(dnn.layerConfig.type().compare(0, 7, "mkldnn_") == 0 ||
         dnn.layerConfig.active_type().compare(0, 7, "mkldnn_") == 0)
       << "should be MKLDNN layer or MKLDNN activation";
@@ -359,10 +361,9 @@ void MKLDNNTester::run(const TestConfig& dnn,
 
   ih_ = inputImgH;
   iw_ = inputImgW;
+  log_ = printDetails;
   iter_ = iter;
   eps_ = epsilon;
-  log_ = log;
-  lvl_ = level;
 
   // Firstly test mkldnn init from PARAM_FORMAT_ORIGINAL weight
   reset(dnn, ref, batchSize);
@@ -531,9 +532,11 @@ void MKLDNNTester::getOutResult(const std::string& configPath,
 void MKLDNNTester::compareResult(DataOut& ref, DataOut& dnn, float eps) {
   CHECK_EQ(ref.outValues.size(), dnn.outValues.size());
   CHECK_EQ(ref.paraValues.size(), dnn.paraValues.size());
+  VLOG(MKLDNN_TESTS) << "compare value size: " << ref.outValues.size();
   for (size_t i = 0; i < ref.outValues.size(); i++) {
     EXPECT_LE(fabs(compareMatrix(ref.outValues[i], dnn.outValues[i])), eps);
   }
+  VLOG(MKLDNN_TESTS) << "compare param size: " << ref.outValues.size();
   for (size_t i = 0; i < ref.paraValues.size(); i++) {
     EXPECT_LE(fabs(compareVector(ref.paraValues[i], dnn.paraValues[i])), eps);
   }
@@ -544,9 +547,10 @@ void MKLDNNTester::runBranchesTest(const std::string& configPath,
                                    float eps) {
   DataIn in;
   initArgument(in, configPath, iter);
-
   DataOut outCpu, outDnn;
+  VLOG(MKLDNN_TESTS) << "runing cpu network";
   getOutResult(configPath, in, outCpu, false, iter);
+  VLOG(MKLDNN_TESTS) << "runing mkldnn network";
   getOutResult(configPath, in, outDnn, true, iter);
 
   compareResult(outCpu, outDnn, eps);

paddle/gserver/tests/MKLDNNTester.h

@@ -58,8 +58,6 @@ protected:
   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
@@ -70,7 +68,6 @@ public:
     iter_ = iter;
     eps_ = epsilon;
     log_ = false;
-    lvl_ = MKLDNN_ALL;
   }
 
   ~MKLDNNTester() {}
@@ -81,10 +78,9 @@ public:
            size_t batchSize,
            size_t inputImgH = 1,
            size_t inputImgW = 1,
+           bool printDetails = false,
            size_t iter = 3,
-           float epsilon = 1e-4,
-           bool log = false,
-           int level = MKLDNN_ALL);
+           float epsilon = 1e-4);
   static void runBranchesTest(const std::string& configPath,
                               size_t iter = 3,
                               float eps = 1e-4);