test=develop

.gitignore

@@ -25,5 +25,6 @@ third_party/
 bazel-*
 third_party/
 
+build_*
 # clion workspace.
 cmake-build-*

CMakeLists.txt

@@ -72,6 +72,7 @@ option(WITH_INFERENCE    "Compile fluid inference library"              ON)
 option(WITH_INFERENCE_API_TEST   "Test fluid inference high-level api interface"  OFF)
 option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
+option(WITH_FAST_MATH   "Make use of fast math library, might affect the precision to some extent" ON)
 
 # PY_VERSION
 if(NOT PY_VERSION)

Dockerfile

@@ -24,6 +24,7 @@ COPY ./paddle/scripts/docker/root/ /root/
 
 RUN apt-get update && \
     apt-get install -y --allow-downgrades patchelf \
+    python3 python3-dev python3-pip \
     git python-pip python-dev python-opencv openssh-server bison \
     libnccl2=2.1.2-1+cuda8.0 libnccl-dev=2.1.2-1+cuda8.0 \
     wget unzip unrar tar xz-utils bzip2 gzip coreutils ntp \
@@ -70,24 +71,33 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
 # specify sphinx version as 1.5.6 and remove -U option for [pip install -U
 # sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
 # version(1.7.1 for now), which causes building documentation failed.
-RUN easy_install -U pip && \
+RUN pip3 install -U wheel && \
+    pip3 install -U docopt PyYAML sphinx==1.5.6 && \
+    pip3 install sphinx-rtd-theme==0.1.9 recommonmark && \
+    easy_install -U pip && \
     pip install -U wheel && \
     pip install -U docopt PyYAML sphinx==1.5.6 && \
     pip install sphinx-rtd-theme==0.1.9 recommonmark
 
-RUN pip install pre-commit 'ipython==5.3.0' && \
+RUN pip3 install pre-commit 'ipython==5.3.0' && \
+    pip3 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
+    pip3 install opencv-python && \
+    pip install pre-commit 'ipython==5.3.0' && \
     pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
     pip install opencv-python
 
 #For docstring checker
+RUN pip3 install pylint pytest astroid isort
 RUN pip install pylint pytest astroid isort LinkChecker
 
 COPY ./python/requirements.txt /root/
+RUN pip3 install -r /root/requirements.txt
 RUN pip install -r /root/requirements.txt
 
 # To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
 # the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
 RUN apt-get install -y libssl-dev libffi-dev
+RUN pip3 install certifi urllib3[secure]
 RUN pip install certifi urllib3[secure]
 
 

cmake/cblas.cmake

@@ -40,7 +40,7 @@ set(OPENBLAS_LIB_SEARCH_PATHS
         /usr/local/opt/openblas/lib)
 
 find_path(OPENBLAS_INC_DIR NAMES cblas.h
-  PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS})
+  PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS} NO_DEFAULT_PATH)
 find_path(OPENBLAS_LAPACKE_INC_DIR NAMES lapacke.h
   PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS})
 find_library(OPENBLAS_LIB NAMES openblas

cmake/cuda.cmake

@@ -175,7 +175,10 @@ list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
 list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
 endif(NOT WIN32)
 
-list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
+if(WITH_FAST_MATH)
+  # Make use of fast math library. https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html
+  list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
+endif()
 # in cuda9, suppress cuda warning on eigen 
 list(APPEND CUDA_NVCC_FLAGS "-w")
 # Set :expt-relaxed-constexpr to suppress Eigen warnings

cmake/external/eigen.cmake

@@ -3,6 +3,14 @@ INCLUDE(ExternalProject)
 SET(EIGEN_SOURCE_DIR ${THIRD_PARTY_PATH}/eigen3)
 SET(EIGEN_INCLUDE_DIR ${EIGEN_SOURCE_DIR}/src/extern_eigen3)
 INCLUDE_DIRECTORIES(${EIGEN_INCLUDE_DIR})
+if(NOT WITH_FAST_MATH)
+  # EIGEN_FAST_MATH: https://eigen.tuxfamily.org/dox/TopicPreprocessorDirectives.html
+  # enables some optimizations which might affect the accuracy of the result. 
+  # This currently enables the SSE vectorization of sin() and cos(), 
+  # and speedups sqrt() for single precision.
+  # Defined to 1 by default. Define it to 0 to disable.
+  add_definitions(-DEIGEN_FAST_MATH=0)
+endif()
 
 if(WITH_AMD_GPU)
     ExternalProject_Add(

cmake/external/openblas.cmake

@@ -27,7 +27,7 @@ IF(NOT ${CBLAS_FOUND})
 
     SET(CBLAS_SOURCES_DIR ${THIRD_PARTY_PATH}/openblas)
     SET(CBLAS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/openblas)
-    SET(CBLAS_INCLUDE_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
+    SET(CBLAS_INC_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
 
     SET(CBLAS_LIBRARIES
         "${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
@@ -96,7 +96,7 @@ IF(NOT ${CBLAS_FOUND})
     ENDIF(NOT WIN32)
     SET(CBLAS_PROVIDER openblas)
     IF(WITH_C_API)
-        INSTALL(DIRECTORY ${CBLAS_INCLUDE_DIR} DESTINATION third_party/openblas)
+        INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
         # Because libopenblas.a is a symbolic link of another library, thus need to
         # install the whole directory.
         IF(ANDROID)
@@ -117,8 +117,8 @@ IF(NOT ${CBLAS_FOUND})
 ENDIF(NOT ${CBLAS_FOUND})
 
 MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
-MESSAGE(STATUS "BLAS Include: ${CBLAS_INCLUDE_DIR}")
+MESSAGE(STATUS "BLAS Include: ${CBLAS_INC_DIR}")
-INCLUDE_DIRECTORIES(${CBLAS_INCLUDE_DIR})
+INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
 
 # FIXME(gangliao): generate cblas target to track all high performance
 # linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)

cmake/flags.cmake

@@ -157,6 +157,8 @@ if (APPLE)
         # On Mac OS X build fat binaries with x86_64 architectures by default.
         set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
     endif()
+    # On Mac OS X register class specifier is deprecated and will cause warning error on latest clang 10.0
+    set (COMMON_FLAGS -Wno-deprecated-register)
 endif(APPLE)
 
 if(LINUX)

paddle/fluid/API.spec

@@ -198,6 +198,9 @@ paddle.fluid.layers.argsort ArgSpec(args=['input', 'axis', 'name'], varargs=None
 paddle.fluid.layers.ones ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.has_inf ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.has_nan ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.isfinite ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None))
 paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))

paddle/fluid/framework/CMakeLists.txt

@@ -1,3 +1,4 @@
+
 # windows treat symbolic file as a real file, which is different with unix
 # We create a hidden file and compile it instead of origin source file.
 function(windows_symbolic TARGET)
@@ -9,11 +10,23 @@ function(windows_symbolic TARGET)
   if (NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc OR NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cu)
       message(FATAL " ${src}.cc and ${src}.cu must exsits, and ${src}.cu must be symbolic file.")
   endif()
-  add_custom_command(OUTPUT .${src}.cu 
+
+  # only copy the xx.cu to .xx.cu when the content are modified
+  set(copy_flag 1)
+  if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu)
+  file(READ ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc SOURCE_STR)
+  file(READ ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu TARGET_STR)
+  if (SOURCE_STR STREQUAL TARGET_STR)
+    set(copy_flag 0)
+  endif()
+  endif()
+  if (copy_flag)
+  add_custom_command(OUTPUT .${src}.cu
           COMMAND ${CMAKE_COMMAND} -E remove ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu
           COMMAND ${CMAKE_COMMAND} -E copy "${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc" "${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu"
           COMMENT "create hidden file of ${src}.cu")
-  add_custom_target(${TARGET} ALL DEPENDS .${src}.cu)  
+  endif(copy_flag)
+  add_custom_target(${TARGET} ALL DEPENDS .${src}.cu)
   endforeach()
 endfunction()
 
@@ -81,6 +94,8 @@ nv_test(data_device_transform_test SRCS data_device_transform_test.cu
 
 if(WITH_GPU)
   if (WIN32)
+    # windows treat symbolic file as a real file, which is different with unix
+    # We create a hidden file and compile it instead of origin source file.
       windows_symbolic(hidden_file SRCS data_type_transform.cu)
       nv_library(data_type_transform SRCS .data_type_transform.cu DEPS tensor)
       add_dependencies(data_type_transform hidden_file)
@@ -149,7 +164,7 @@ if(WITH_DISTRIBUTE)
   set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
 else()
   cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
-  cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass elementwise_add_op)
+  cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op)
 endif()
  
 if (NOT WIN32)

paddle/fluid/framework/data_type.h

@@ -17,7 +17,6 @@ limitations under the License. */
 #include <typeindex>
 #include "paddle/fluid/framework/framework.pb.h"
 #include "paddle/fluid/platform/enforce.h"
-
 #include "paddle/fluid/platform/float16.h"
 
 namespace paddle {

paddle/fluid/framework/details/reference_count_pass.cc

@@ -80,15 +80,15 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
       // This is weird but there is really some variables without var_desc
       // in computation_op
       if (var_desc == nullptr) {
-        if (compute_op->Node()->Op()->Block()->FindVar(var_name) == nullptr)
+        var_desc = compute_op->Node()->Op()->Block()->FindVar(var_name);
-          continue;
+        if (var_desc == nullptr) continue;
-      } else {
+      }
-        if (var_desc->Persistable()) continue;
+
-        auto var_type = var_desc->Proto()->type().type();
+      if (var_desc->Persistable()) continue;
-        if (var_type != proto::VarType::LOD_TENSOR &&
+      auto var_type = var_desc->Proto()->type().type();
-            var_type != proto::VarType::SELECTED_ROWS) {
+      if (var_type != proto::VarType::LOD_TENSOR &&
-          continue;
+          var_type != proto::VarType::SELECTED_ROWS) {
-        }
+        continue;
       }
 
       // compute op only runs in one device

paddle/fluid/framework/ir/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
 file(WRITE ${pass_file} "// Generated by the paddle/fluid/framework/ir/CMakeLists.txt.  DO NOT EDIT!\n\n")
+file(APPEND ${pass_file} "\#pragma once\n")
 file(APPEND ${pass_file} "\#include \"paddle/fluid/framework/ir/pass.h\"\n")
 
 
@@ -37,6 +38,7 @@ pass_library(fc_lstm_fuse_pass inference)
 pass_library(embedding_fc_lstm_fuse_pass inference)
 pass_library(fc_gru_fuse_pass inference)
 pass_library(seq_concat_fc_fuse_pass inference)
+pass_library(conv_bn_fuse_pass inference)
 
 cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
 

paddle/fluid/framework/ir/conv_bn_fuse_pass.h

@@ -0,0 +1,49 @@
+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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 "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+/*
+ * Fuse the Conv and BatchNorm to a ConvBNMKLDNNOp.
+ */
+class ConvBNFusePass : public FusePassBase {
+ public:
+  virtual ~ConvBNFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+  const std::string name_scope_{"conv_bn_fuse"};
+};
+
+class ConvEltwiseAddBNFusePass : public FusePassBase {
+ public:
+  virtual ~ConvEltwiseAddBNFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+  const std::string name_scope_{"conv_eltwiseadd_bn_fuse"};
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -626,6 +626,112 @@ bool VarLinksFromOp(Node *node, const std::string &op_type) {
   return false;
 }
 
+PDNode *patterns::ConvBN::operator()(paddle::framework::ir::PDNode *conv_input,
+                                     bool with_eltwise_add) {
+  // Create Operators
+  conv_input->assert_is_op_input("conv2d", "Input");
+  auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op("conv2d");
+
+  PDNode *eltwise_op = nullptr;
+  if (with_eltwise_add) {
+    eltwise_op =
+        pattern->NewNode(eltwise_repr())->assert_is_op("elementwise_add");
+  }
+  auto *batch_norm_op =
+      pattern->NewNode(batch_norm_repr())->assert_is_op("batch_norm");
+  // Create variables
+  // Conv Filter
+  auto *conv_weight_var = pattern->NewNode(conv_weight_repr())
+                              ->AsInput()
+                              ->assert_is_persistable_var()
+                              ->assert_is_op_input("conv2d", "Filter");
+
+  auto *conv_out_var = pattern->NewNode(conv_out_repr())
+                           ->AsIntermediate()
+                           ->assert_is_only_output_of_op("conv2d");
+
+  PDNode *eltwise_y_in_var = nullptr;
+  PDNode *eltwise_out_var = nullptr;
+  if (with_eltwise_add) {
+    // Conv output as Bias input
+    conv_out_var->assert_is_op_input("elementwise_add", "X");
+    // Bias
+    eltwise_y_in_var = pattern->NewNode(eltwise_y_in_repr())
+                           ->assert_is_op_input("elementwise_add", "Y")
+                           ->AsInput();
+    eltwise_out_var = pattern->NewNode(eltwise_out_repr())
+                          ->AsIntermediate()
+                          ->assert_is_only_output_of_op("elementwise_add");
+  } else {
+    // Conv output as BN input
+    conv_out_var->assert_is_op_input("batch_norm", "X");
+  }
+
+  // BN Scale
+  auto *bn_scale_var = pattern->NewNode(bn_scale_repr())
+                           ->AsInput()
+                           ->assert_is_persistable_var()
+                           ->assert_is_op_input("batch_norm", "Scale");
+  // BN Bias
+  auto *bn_bias_var = pattern->NewNode(bn_bias_repr())
+                          ->AsInput()
+                          ->assert_is_persistable_var()
+                          ->assert_is_op_input("batch_norm", "Bias");
+  // BN Mean
+  auto *bn_mean_var = pattern->NewNode(bn_mean_repr())
+                          ->AsInput()
+                          ->assert_is_persistable_var()
+                          ->assert_is_op_input("batch_norm", "Mean");
+  // BN Variance
+  auto *bn_variance_var = pattern->NewNode(bn_variance_repr())
+                              ->AsInput()
+                              ->assert_is_persistable_var()
+                              ->assert_is_op_input("batch_norm", "Variance");
+
+  // BN output
+  auto *bn_out_var = pattern->NewNode(bn_out_repr())
+                         ->AsOutput()
+                         ->assert_is_op_output("batch_norm");
+
+  auto *bn_mean_out_var = pattern->NewNode(bn_mean_out_repr())
+                              ->AsOutput()
+                              ->assert_is_op_output("batch_norm", "MeanOut");
+
+  auto *bn_variance_out_var =
+      pattern->NewNode(bn_variance_out_repr())
+          ->AsOutput()
+          ->assert_is_op_output("batch_norm", "VarianceOut");
+
+  auto *bn_saved_mean_var =
+      pattern->NewNode(bn_saved_mean_repr())
+          ->AsOutput()
+          ->assert_is_op_output("batch_norm", "SavedMean");
+
+  auto *bn_saved_variance_var =
+      pattern->NewNode(bn_saved_variance_repr())
+          ->AsOutput()
+          ->assert_is_op_output("batch_norm", "SavedVariance");
+
+  conv_op->LinksFrom({conv_input, conv_weight_var}).LinksTo({conv_out_var});
+
+  if (with_eltwise_add) {
+    eltwise_op->LinksFrom({conv_out_var, eltwise_y_in_var})
+        .LinksTo({eltwise_out_var});
+    batch_norm_op
+        ->LinksFrom({eltwise_out_var, bn_scale_var, bn_bias_var, bn_mean_var,
+                     bn_variance_var})
+        .LinksTo({bn_out_var, bn_mean_out_var, bn_variance_out_var,
+                  bn_saved_mean_var, bn_saved_variance_var});
+  } else {
+    batch_norm_op
+        ->LinksFrom({conv_out_var, bn_scale_var, bn_bias_var, bn_mean_var,
+                     bn_variance_var})
+        .LinksTo({bn_out_var, bn_mean_out_var, bn_variance_out_var,
+                  bn_saved_mean_var, bn_saved_variance_var});
+  }
+  return bn_out_var;
+}
+
 PDNode *patterns::ConvReLU::operator()(
     paddle::framework::ir::PDNode *conv_input) {
   // Create Operators

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -375,6 +375,44 @@ struct PatternBase {
   size_t id_;
 };
 
+// Conv with batch norm
+// op: conv + (elementwise_add +) batch_norm
+// named nodes:
+// conv_weight, conv_out, conv,
+// bn_x, bn_scale, bn_bias, bn_mean,  bn_variance,
+// bn_batch_norm, bn_y, bn_mean_out, bn_variance_out,
+// bn_saved_mean, bn_saved_variance
+struct ConvBN : public PatternBase {
+  ConvBN(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "conv_bn") {}
+
+  PDNode* operator()(PDNode* conv_input, bool with_eltwise_add);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(conv);
+  PATTERN_DECL_NODE(batch_norm);
+  PATTERN_DECL_NODE(eltwise);  // ELEMENTWISE_ADD
+  // CONV inputs
+  PATTERN_DECL_NODE(conv_weight);  // Filter
+  // CONV outputs
+  PATTERN_DECL_NODE(conv_out);  // tmp
+  // ELTWISE inputs
+  PATTERN_DECL_NODE(eltwise_y_in);
+  // ELTWISE outputs
+  PATTERN_DECL_NODE(eltwise_out);  // tmp
+  // BN inputs
+  PATTERN_DECL_NODE(bn_scale);
+  PATTERN_DECL_NODE(bn_bias);
+  PATTERN_DECL_NODE(bn_mean);
+  PATTERN_DECL_NODE(bn_variance);
+  // BN outputs
+  PATTERN_DECL_NODE(bn_out);  // Out
+  PATTERN_DECL_NODE(bn_mean_out);
+  PATTERN_DECL_NODE(bn_variance_out);
+  PATTERN_DECL_NODE(bn_saved_mean);
+  PATTERN_DECL_NODE(bn_saved_variance);
+};
+
 // CONV with ReLU
 // op: conv + relu
 // named nodes:

paddle/fluid/framework/naive_executor.cc

@@ -146,5 +146,22 @@ void NaiveExecutor::CleanFeedFetchOps() {
   ops_.swap(ops);
 }
 
+void NaiveExecutor::EnableMKLDNN(const ProgramDesc &program) {
+#ifdef PADDLE_WITH_MKLDNN
+  VLOG(3) << "use_mkldnn=True";
+  for (size_t block_id = 0; block_id < program.Size(); ++block_id) {
+    auto *block = const_cast<ProgramDesc &>(program).MutableBlock(block_id);
+    for (auto *op : block->AllOps()) {
+      if (op->HasAttr("use_mkldnn")) {
+        op->SetAttr("use_mkldnn", true);
+      }
+    }
+  }
+#else
+  LOG(WARNING)
+      << "'MKLDNN' is not supported, Please re-compile with WITH_MKLDNN option";
+#endif
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/naive_executor.h

@@ -14,6 +14,8 @@
 
 #pragma once
 
+#include <string>
+#include <vector>
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
@@ -46,6 +48,8 @@ class NaiveExecutor {
 
   void CleanFeedFetchOps();
 
+  void EnableMKLDNN(const ProgramDesc& program);
+
  protected:
   void CreateVariables(const ProgramDesc& desc, Scope* scope, int block_id);
 

paddle/fluid/framework/op_desc.cc

@@ -50,6 +50,27 @@ class CompileTimeInferShapeContext : public InferShapeContext {
   const std::vector<std::string> &Outputs(
       const std::string &name) const override;
 
+  void ShareDim(const std::string &in, const std::string &out, size_t i = 0,
+                size_t j = 0) override {
+    PADDLE_ENFORCE_LT(i, Inputs(in).size());
+    PADDLE_ENFORCE_LT(j, Outputs(out).size());
+    const std::string &input_n = Inputs(in)[i];
+    const std::string &output_n = Outputs(out)[j];
+
+    PADDLE_ENFORCE(input_n != framework::kEmptyVarName, "The %s[%d] is @EMPTY@",
+                   in, i);
+    PADDLE_ENFORCE(output_n != framework::kEmptyVarName,
+                   "The %s[%d] is @EMPTY@", out, j);
+
+    auto *in_var = block_.FindVarRecursive(input_n);
+    auto *out_var = block_.FindVarRecursive(output_n);
+
+    PADDLE_ENFORCE(in_var->GetType() == out_var->GetType(),
+                   "The type of %s and %s is not the same.", input_n, output_n);
+
+    SetDim(output_n, GetDim(input_n));
+  }
+
   void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
                 size_t j = 0) const override {
     PADDLE_ENFORCE_LT(i, Inputs(in).size());

paddle/fluid/framework/op_proto_maker.cc

@@ -132,9 +132,7 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
 
   AddAttr<std::string>(OpNamescopeAttrName(), "Operator name with namesope.")
       .SetDefault("");
-  AddAttr<std::vector<std::string>>(OpCreationCallstackAttrName(),
+
-                                    "Callstack for Op Creatation.")
-      .SetDefault({});
   Validate();
 }
 

paddle/fluid/framework/op_proto_maker.h

@@ -46,7 +46,6 @@ class OpProtoAndCheckerMaker {
   static const char *OpRoleAttrName() { return "op_role"; }
   static const char *OpRoleVarAttrName() { return "op_role_var"; }
   static const char *OpNamescopeAttrName() { return "op_namescope"; }
-  static const char *OpCreationCallstackAttrName() { return "op_callstack"; }
 
   void operator()(proto::OpProto *proto, OpAttrChecker *attr_checker);
 

paddle/fluid/framework/operator.cc

@@ -14,17 +14,15 @@ limitations under the License. */
 #define GLOG_NO_ABBREVIATED_SEVERITIES
 #define GOOGLE_GLOG_DLL_DECL
 
-#include "paddle/fluid/framework/operator.h"
 #include <gflags/gflags.h>
 #include <glog/logging.h>
+
 #include <algorithm>
-#include <sstream>
+
-#include <string>
-#include <vector>
 #include "paddle/fluid/framework/data_transform.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/lod_tensor.h"
-#include "paddle/fluid/framework/op_proto_maker.h"
+#include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/shape_inference.h"
 #include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -142,54 +140,19 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
 }
 
 void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
-  try {
+  VLOG(4) << place << " " << DebugStringEx(&scope);
-    if (VLOG_IS_ON(4)) {
+  if (platform::is_gpu_place(place)) {
-      VLOG(4) << place << " " << DebugStringEx(&scope);
-    }
-    if (platform::is_gpu_place(place)) {
 #ifndef PADDLE_WITH_CUDA
-      PADDLE_THROW("Cannot run operator on place %s", place);
+    PADDLE_THROW("Cannot run operator on place %s", place);
 #else
-      auto dev_id = boost::get<platform::CUDAPlace>(place).device;
+    auto dev_id = boost::get<platform::CUDAPlace>(place).device;
-      platform::SetDeviceId(dev_id);
+    platform::SetDeviceId(dev_id);
 #endif
-    }
-
-    if (platform::IsProfileEnabled()) {
-      platform::DeviceContextPool& pool =
-          platform::DeviceContextPool::Instance();
-      platform::RecordEvent record_event(Type(), pool.Get(place));
-    }
-
-    RunImpl(scope, place);
-
-    if (VLOG_IS_ON(3)) {
-      VLOG(3) << place << " " << DebugStringEx(&scope);
-    }
-  } catch (platform::EnforceNotMet exception) {
-    if (Attrs().count("sub_block") != 0) {
-      throw exception;
-    }
-
-    auto& callstack = Attr<std::vector<std::string>>(
-        OpProtoAndCheckerMaker::OpCreationCallstackAttrName());
-
-    if (callstack.empty()) {
-      throw exception;
-    }
-    std::ostringstream sout;
-    sout << "Invoke operator " << Type() << " error.\n";
-    sout << "Python Callstacks: \n";
-    for (auto& line : callstack) {
-      sout << line;
-    }
-    sout << "C++ Callstacks: \n";
-    sout << exception.err_str_;
-    exception.err_str_ = sout.str();
-    throw exception;
-  } catch (...) {
-    std::rethrow_exception(std::current_exception());
   }
+  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+  platform::RecordEvent record_event(Type(), pool.Get(place));
+  RunImpl(scope, place);
+  VLOG(3) << place << " " << DebugStringEx(&scope);
 }
 
 bool OperatorBase::HasInputs(const std::string& name) const {
@@ -217,7 +180,7 @@ const std::vector<std::string>& OperatorBase::Inputs(
 }
 
 bool OperatorBase::HasOutputs(const std::string& name) const {
-  if (outputs_.end() != outputs_.find(name)) {
+  if (outputs_.find(name) != outputs_.end()) {
     return true;
   } else {
     return false;
@@ -579,13 +542,45 @@ class RuntimeInferShapeContext : public InferShapeContext {
     return op_.Outputs(name);
   }
 
-  void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
+  void ShareDim(const std::string& in, const std::string& out, size_t i = 0,
-                size_t j = 0) const override {
+                size_t j = 0) override {
     PADDLE_ENFORCE_LT(i, Inputs(in).size());
     PADDLE_ENFORCE_LT(j, Outputs(out).size());
-    Variable* in_var = scope_.FindVar(Inputs(in)[i]);
+    const std::string& input_n = Inputs(in)[i];
-    Variable* out_var = scope_.FindVar(Outputs(out)[j]);
+    const std::string& output_n = Outputs(out)[j];
+
+    Variable* in_var = scope_.FindVar(input_n);
+    Variable* out_var = scope_.FindVar(output_n);
+    PADDLE_ENFORCE(in_var->Type() == out_var->Type(),
+                   "The type of %s and %s is not the same.", output_n,
+                   GetDim(input_n));
+
+    if (in_var->IsType<framework::SelectedRows>()) {
+      auto& in_sele_rows = in_var->Get<framework::SelectedRows>();
+      auto out_sele_rows = out_var->GetMutable<framework::SelectedRows>();
+      out_sele_rows->mutable_value()->Resize(in_sele_rows.value().dims());
+      out_sele_rows->set_rows(in_sele_rows.rows());
+      out_sele_rows->set_height(in_sele_rows.height());
+    } else if (in_var->IsType<framework::LoDTensor>()) {
+      auto& in_lod_tensor = in_var->Get<framework::LoDTensor>();
+      auto* out_lod_tensor = out_var->GetMutable<framework::LoDTensor>();
+      out_lod_tensor->Resize(in_lod_tensor.dims());
+    } else {
+      PADDLE_THROW(
+          "Currently, the input type of ShareDim only can be LoDTensor "
+          "or SelectedRows.");
+    }
+  }
+
+  void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
+                size_t j = 0) const override {
+    const std::vector<std::string>& inputs = Inputs(in);
+    const std::vector<std::string>& outputs = Outputs(out);
+    PADDLE_ENFORCE_LT(i, inputs.size());
+    PADDLE_ENFORCE_LT(j, outputs.size());
+    Variable* in_var = scope_.FindVar(inputs.at(i));
     if (!in_var->IsType<LoDTensor>()) return;
+    Variable* out_var = scope_.FindVar(outputs.at(j));
     PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
                    "The %d-th output of Output(%s) must be LoDTensor.", j, out);
     auto in_tensor = in_var->Get<LoDTensor>();
@@ -613,20 +608,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
       out_tensor->set_layout(in_tensor.layout());
   }
 
-  void ShareLayout(const std::string& in, const std::string& out, size_t i = 0,
-                   size_t j = 0) const {
-    PADDLE_ENFORCE_LT(i, Inputs(in).size());
-    PADDLE_ENFORCE_LT(j, Outputs(out).size());
-    Variable* in_var = scope_.FindVar(Inputs(in)[i]);
-    Variable* out_var = scope_.FindVar(Outputs(out)[j]);
-    if (!in_var->IsType<LoDTensor>()) return;
-    PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
-                   "The %d-th output of Output(%s) must be LoDTensor.", j, out);
-    auto in_tensor = in_var->Get<LoDTensor>();
-    auto* out_tensor = out_var->GetMutable<LoDTensor>();
-    out_tensor->set_layout(in_tensor.layout());
-  }
-
   bool IsRuntime() const override { return true; }
 
  protected:

paddle/fluid/framework/parallel_executor.cc

@@ -250,6 +250,13 @@ void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
 #ifdef PADDLE_WITH_CUDA
   if (!gcs_.empty()) {
     ResetReferenceCount();
+    for (auto &pair : cur_ref_cnts_) {
+      auto &name_map = *(pair.second);
+      for (auto &fetch_name : fetch_tensors) {
+        name_map.erase(fetch_name);
+      }
+      name_map.erase(fetched_var_name);
+    }
   }
 #endif
   auto fetch_data = member_->executor_->Run(fetch_tensors);

paddle/fluid/framework/rw_lock.h

@@ -46,6 +46,7 @@ struct RWLock {
  private:
   pthread_rwlock_t lock_;
 };
+// TODO(paddle-dev): Support RWLock for WIN32 for correctness.
 #else
 // https://stackoverflow.com/questions/7125250/making-pthread-rwlock-wrlock-recursive
 // In windows, rw_lock seems like a hack. Use empty object and do nothing.