Merge branch 'develop' into windows/build

CMakeLists.txt

@@ -38,7 +38,6 @@ if(NOT CMAKE_CROSSCOMPILING)
 endif(NOT CMAKE_CROSSCOMPILING)
 find_package(Git REQUIRED)
 find_package(Threads REQUIRED)
-
 include(simd)
 
 ################################ Configurations #######################################

cmake/cuda.cmake

@@ -172,18 +172,21 @@ set(CUDA_PROPAGATE_HOST_FLAGS OFF)
 
 # Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
 # So, don't set these flags here.
+
 if (NOT WIN32) # windows msvc2015 support c++11 natively. 
-# -std=c++11 -fPIC not recoginize by msvc, -Xcompiler will be added by cmake.
+# -std=c++11 -fPIC not recoginize by msvc
 list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
-list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
+# in cuda9, suppress cuda warning on eigen with "-w"
+list(APPEND CUDA_NVCC_FLAGS "-w" "-Xcompiler -fPIC")
+else(NOT WIN32)
+list(APPEND CUDA_NVCC_FLAGS "-w" "-Xcompiler -fPIC" "-Xcompiler /w")
 endif(NOT WIN32)
 
 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")
+endif(WITH_FAST_MATH)
+
 # Set :expt-relaxed-constexpr to suppress Eigen warnings
 list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
 

cmake/cudnn.cmake

@@ -53,7 +53,6 @@ find_library(CUDNN_LIBRARY NAMES ${CUDNN_LIB_NAME} # libcudnn_static.a
           NO_DEFAULT_PATH
     DOC "Path to cuDNN library.")
 
-
 if(CUDNN_INCLUDE_DIR AND CUDNN_LIBRARY)
     set(CUDNN_FOUND ON)
 else()
@@ -88,7 +87,7 @@ if(CUDNN_FOUND)
 
         if(NOT CUDNN_MAJOR_VERSION)
             set(CUDNN_VERSION "???")
-        else()
+        else() 
             math(EXPR CUDNN_VERSION
                 "${CUDNN_MAJOR_VERSION} * 1000 +
                  ${CUDNN_MINOR_VERSION} * 100 + ${CUDNN_PATCHLEVEL_VERSION}")

cmake/external/boost.cmake

@@ -33,42 +33,23 @@ MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
 
 set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
 set(BOOST_DOWNLOAD_DIR  "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
-if (WIN32)
-    set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}" CACHE PATH "boost include directory." FORCE)
-else(WIN32)
-    set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
-endif (WIN32)
-set_directory_properties(PROPERTIES CLEAN_NO_CUSTOM 1)
 
+set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}" CACHE PATH "boost include directory." FORCE)
+set_directory_properties(PROPERTIES CLEAN_NO_CUSTOM 1)
 include_directories(${BOOST_INCLUDE_DIR})
-if (WIN32)
-    ExternalProject_Add(
-        ${BOOST_PROJECT}
-        ${EXTERNAL_PROJECT_LOG_ARGS}
-        DOWNLOAD_DIR          ${BOOST_DOWNLOAD_DIR}
-        URL      ${BOOST_URL}
-        DOWNLOAD_NO_PROGRESS  0
-        PREFIX                ${BOOST_SOURCES_DIR}
-        CONFIGURE_COMMAND     ""
-        BUILD_COMMAND         ""
-        INSTALL_COMMAND       ""
-        UPDATE_COMMAND        ""
-        )
-else()
-    ExternalProject_Add(
-        ${BOOST_PROJECT}
-        ${EXTERNAL_PROJECT_LOG_ARGS}
-        DOWNLOAD_DIR          ${BOOST_DOWNLOAD_DIR}
-        DOWNLOAD_COMMAND      "wget --no-check-certificate ${BOOST_URL} -c -q -O ${BOOST_TAR}.tar.gz 
-            && tar zxf ${BOOST_TAR}.tar.gz"
-        DOWNLOAD_NO_PROGRESS  0
-        PREFIX                ${BOOST_SOURCES_DIR}
-        CONFIGURE_COMMAND     ""
-        BUILD_COMMAND         ""
-        INSTALL_COMMAND       ""
-        UPDATE_COMMAND        ""
-        )
-endif ()
+
+ExternalProject_Add(
+    ${BOOST_PROJECT}
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    DOWNLOAD_DIR          ${BOOST_DOWNLOAD_DIR}
+    URL      ${BOOST_URL}
+    DOWNLOAD_NO_PROGRESS  0
+    PREFIX                ${BOOST_SOURCES_DIR}
+    CONFIGURE_COMMAND     ""
+    BUILD_COMMAND         ""
+    INSTALL_COMMAND       ""
+    UPDATE_COMMAND        ""
+    )
 
 if (${CMAKE_VERSION} VERSION_LESS "3.3.0" OR NOT WIN32)
     set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/boost_dummy.c)

cmake/external/gflags.cmake

@@ -51,6 +51,10 @@ ExternalProject_Add(
                      -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
                      -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
 )
+
+ADD_LIBRARY(gflags STATIC IMPORTED GLOBAL)
+SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES})
+ADD_DEPENDENCIES(gflags extern_gflags)
 IF(WIN32)
   IF(NOT EXISTS "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib")
     add_custom_command(TARGET extern_gflags POST_BUILD
@@ -58,9 +62,6 @@ IF(WIN32)
             )
   ENDIF()
 ENDIF(WIN32)
-ADD_LIBRARY(gflags STATIC IMPORTED GLOBAL)
-SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES})
-ADD_DEPENDENCIES(gflags extern_gflags)
 
 LIST(APPEND external_project_dependencies gflags)
 

cmake/external/gtest.cmake

@@ -52,6 +52,7 @@ IF(WITH_TESTING)
                         -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
                         -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
                         -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
+                        -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
                         -DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR}
                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON
                         -DBUILD_GMOCK=ON
@@ -71,6 +72,5 @@ IF(WITH_TESTING)
     ADD_LIBRARY(gtest_main STATIC IMPORTED GLOBAL)
     SET_PROPERTY(TARGET gtest_main PROPERTY IMPORTED_LOCATION ${GTEST_MAIN_LIBRARIES})
     ADD_DEPENDENCIES(gtest_main extern_gtest)
-
     LIST(APPEND external_project_dependencies gtest gtest_main)
 ENDIF(WITH_TESTING)

cmake/external/openblas.cmake

@@ -149,6 +149,7 @@ INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
 # linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
 SET(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cblas_dummy.c)
 FILE(WRITE ${dummyfile} "const char *dummy_cblas = \"${dummyfile}\";")
+
 ADD_LIBRARY(cblas STATIC ${dummyfile})
 
 IF("${CBLAS_PROVIDER}" STREQUAL "MKLML")

cmake/flags.cmake

@@ -144,11 +144,14 @@ set(GPU_COMMON_FLAGS
     -Wno-error=unused-function  # Warnings in Numpy Header.
     -Wno-error=array-bounds # Warnings in Eigen::array
 )
-
 else(NOT WIN32)
 set(COMMON_FLAGS
+    -fPIC
+    -fno-omit-frame-pointer
     "/w") #disable all warnings.
 set(GPU_COMMON_FLAGS
+    -fPIC
+    -fno-omit-frame-pointer
     "/w") #disable all warnings
 endif(NOT WIN32)
 
@@ -164,8 +167,8 @@ endif(APPLE)
 if(LINUX)
     set(GPU_COMMON_FLAGS
         -Wall
-        -Wextra
         -Werror
+        -Wextra
         ${GPU_COMMON_FLAGS})
 endif(LINUX)
 

cmake/generic.cmake

@@ -238,6 +238,7 @@ function(cc_library TARGET_NAME)
       # add libxxx.lib prefix in windows
       set(${TARGET_NAME}_LIB_NAME "${CMAKE_STATIC_LIBRARY_PREFIX}${TARGET_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}" CACHE STRING "output library name for target ${TARGET_NAME}")
   endif(WIN32)
+
   if(cc_library_SRCS)
     if(cc_library_SHARED OR cc_library_shared) # build *.so
       add_library(${TARGET_NAME} SHARED ${cc_library_SRCS})
@@ -350,7 +351,11 @@ function(cc_test TARGET_NAME)
     set(multiValueArgs SRCS DEPS ARGS)
     cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_executable(${TARGET_NAME} ${cc_test_SRCS})
+    if(WIN32) # in windows deps. shlwapi library.
+    target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog shlwapi)
+    else(WIN32)
     target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
+    endif(WIN32)
     add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
     add_test(NAME ${TARGET_NAME}
              COMMAND ${TARGET_NAME} ${cc_test_ARGS}
@@ -421,7 +426,11 @@ function(nv_test TARGET_NAME)
     set(multiValueArgs SRCS DEPS)
     cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     cuda_add_executable(${TARGET_NAME} ${nv_test_SRCS})
+    if(WIN32)
+    target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog shlwapi)
+    else(WIN32)
     target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
+    endif(WIN32)
     add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
     add_test(${TARGET_NAME} ${TARGET_NAME})
     if (nv_test_SERIAL)

cmake/inference_lib.cmake

@@ -31,8 +31,7 @@ function(copy TARGET)
     foreach(index RANGE ${len})
         list(GET copy_lib_SRCS ${index} src)
         list(GET copy_lib_DSTS ${index} dst)
-        
-        if (WIN32) 
+        if (WIN32)
         # windows cmd shell will not expand wildcard automatically.
         # below expand the files,libs and copy them by rules.
         file(GLOB header_files ${src} "*.h")
@@ -47,14 +46,14 @@ function(copy TARGET)
           COMMAND ${CMAKE_COMMAND} -E make_directory  "${dst}"
           )
         foreach(src_file ${src_files})
-          add_custom_command(TARGET ${TARGET} PRE_BUILD
+          add_custom_command(TARGET ${TARGET} PRE_BUILD 
           COMMAND ${CMAKE_COMMAND} -E copy "${src_file}" "${dst}"
           COMMENT "copying ${src_file} -> ${dst}")
         endforeach()
-        else() # not windows
-        add_custom_command(TARGET ${TARGET} PRE_BUILD 
-          COMMAND ${CMAKE_COMMAND} -E make_directory  "${dst}"
-          COMMAND ${CMAKE_COMMAND} -E copy "${src_files}" "${dst}"
+        else(WIN32) # not windows
+          add_custom_command(TARGET ${TARGET} PRE_BUILD 
+          COMMAND mkdir -p "${dst}"
+          COMMAND cp -r "${src}" "${dst}"
           COMMENT "copying ${src} -> ${dst}")
         endif(WIN32)
     endforeach()

cmake/version.cmake

@@ -44,5 +44,5 @@ while ("${PADDLE_VERSION}" STREQUAL "")
   endif()
 endwhile()
 
-add_definitions(-DPADDLE_VERSION=${PADDLE_VERSION})
+add_definitions(-DPADDLE_VERSION="${PADDLE_VERSION}")
 message(STATUS "Paddle version is ${PADDLE_VERSION}")

doc/fluid/dev/contribute_to_paddle_cn.md

+../../v2/dev/contribute_to_paddle_cn.md

doc/fluid/dev/contribute_to_paddle_en.md

+../../v2/dev/contribute_to_paddle_en.md

doc/fluid/new_docs/advanced_usage/development/contribute_to_paddle.md

+../../../dev/contribute_to_paddle_cn.md

doc/fluid/new_docs/advanced_usage/development/cpu_profiling_cn.md

+../../../howto/optimization/cpu_profiling_cn.md

doc/fluid/new_docs/advanced_usage/development/host_memory_profiling_cn.md

+../../../howto/optimization/host_memory_profiling_cn.md

doc/fluid/new_docs/advanced_usage/development/new_op.md

+../../../dev/new_op_cn.md

doc/fluid/new_docs/advanced_usage/development/timeline_cn.md

+../../../howto/optimization/timeline_cn.md

doc/v2/dev/contribute_to_paddle_en.md

-../../../CONTRIBUTING.md
\ No newline at end of file
+../../../CONTRIBUTING.md

paddle/fluid/API.spec

@@ -174,6 +174,7 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
 paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
 paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))

paddle/fluid/framework/details/broadcast_op_handle_test.h

@@ -37,8 +37,9 @@ struct TestBroadcastOpHandle {
   std::vector<Scope*> local_scopes_;
   std::vector<Scope*> param_scopes_;
   Scope g_scope_;
-  std::unique_ptr<OpHandleBase> op_handle_;
-  std::vector<std::unique_ptr<VarHandleBase>> vars_;
+  OpHandleBase* op_handle_;
+  std::vector<VarHandleBase*> vars_;
+  std::vector<std::unique_ptr<ir::Node>> nodes_;
   std::vector<p::Place> place_list_;
   bool use_gpu_;
 #ifdef PADDLE_WITH_CUDA
@@ -90,6 +91,7 @@ struct TestBroadcastOpHandle {
   }
 
   void InitBroadcastOp(size_t input_scope_idx) {
+    nodes_.clear();
     for (size_t j = 0; j < place_list_.size(); ++j) {
       local_scopes_.push_back(&(g_scope_.NewScope()));
       Scope& local_scope = local_scopes_.back()->NewScope();
@@ -101,39 +103,39 @@ struct TestBroadcastOpHandle {
     }
     param_scopes_[input_scope_idx]->Var("input");
 
-    std::unique_ptr<ir::Node> n =
-        ir::CreateNodeForTest("node0", ir::Node::Type::kOperation);
+    nodes_.emplace_back(
+        ir::CreateNodeForTest("node0", ir::Node::Type::kOperation));
     if (use_gpu_) {
 #ifdef PADDLE_WITH_CUDA
-      op_handle_.reset(new BroadcastOpHandle(n.get(), local_scopes_,
-                                             place_list_, nccl_ctxs_.get()));
+      op_handle_ = new BroadcastOpHandle(nodes_.back().get(), local_scopes_,
+                                         place_list_, nccl_ctxs_.get());
 #else
       PADDLE_THROW("CUDA is not support.");
 #endif
     } else {
 #ifdef PADDLE_WITH_CUDA
-      op_handle_.reset(new BroadcastOpHandle(n.get(), local_scopes_,
-                                             place_list_, nccl_ctxs_.get()));
+      op_handle_ = new BroadcastOpHandle(nodes_.back().get(), local_scopes_,
+                                         place_list_, nccl_ctxs_.get());
 #else
-      op_handle_.reset(
-          new BroadcastOpHandle(n.get(), local_scopes_, place_list_));
+      op_handle_ = new BroadcastOpHandle(nodes_.back().get(), local_scopes_,
+                                         place_list_);
 #endif
     }
 
-    std::unique_ptr<ir::Node> v =
-        ir::CreateNodeForTest("node1", ir::Node::Type::kVariable);
-    auto* in_var_handle = new VarHandle(v.get(), 1, input_scope_idx, "input",
-                                        place_list_[input_scope_idx]);
+    nodes_.emplace_back(
+        ir::CreateNodeForTest("node1", ir::Node::Type::kVariable));
+    auto* in_var_handle = new VarHandle(nodes_.back().get(), 1, input_scope_idx,
+                                        "input", place_list_[input_scope_idx]);
     vars_.emplace_back(in_var_handle);
     op_handle_->AddInput(in_var_handle);
 
     // add dummy var
 
-    std::unique_ptr<ir::Node> v2 =
-        ir::CreateNodeForTest("node2", ir::Node::Type::kVariable);
-    vars_.emplace_back(new DummyVarHandle(v2.get()));
+    nodes_.emplace_back(
+        ir::CreateNodeForTest("node2", ir::Node::Type::kVariable));
+    vars_.emplace_back(new DummyVarHandle(nodes_.back().get()));
     DummyVarHandle* dummy_var_handle =
-        static_cast<DummyVarHandle*>(vars_.back().get());
+        static_cast<DummyVarHandle*>(vars_.back());
     dummy_var_handle->ClearGeneratedOp();
     op_handle_->AddInput(dummy_var_handle);
 
@@ -141,20 +143,20 @@ struct TestBroadcastOpHandle {
       if (!use_gpu_) {
         op_handle_->SetDeviceContext(place_list_[j], ctxs_[j].get());
       }
-      std::unique_ptr<ir::Node> v3 =
-          ir::CreateNodeForTest("node3", ir::Node::Type::kVariable);
+      nodes_.emplace_back(
+          ir::CreateNodeForTest("node3", ir::Node::Type::kVariable));
       VarHandle* out_var_handle =
-          new VarHandle(v3.get(), 2, j, "out", place_list_[j]);
+          new VarHandle(nodes_.back().get(), 2, j, "out", place_list_[j]);
       vars_.emplace_back(out_var_handle);
       op_handle_->AddOutput(out_var_handle);
     }
 
     // add dummy var
-    std::unique_ptr<ir::Node> v4 =
-        ir::CreateNodeForTest("node4", ir::Node::Type::kVariable);
-    vars_.emplace_back(new DummyVarHandle(v4.get()));
+    nodes_.emplace_back(
+        ir::CreateNodeForTest("node4", ir::Node::Type::kVariable));
+    vars_.emplace_back(new DummyVarHandle(nodes_.back().get()));
     DummyVarHandle* out_dummy_var_handle =
-        static_cast<DummyVarHandle*>(vars_.back().get());
+        static_cast<DummyVarHandle*>(vars_.back());
     out_dummy_var_handle->ClearGeneratedOp();
     op_handle_->AddOutput(out_dummy_var_handle);
   }

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -16,6 +16,7 @@
 #include <vector>
 #include "paddle/fluid/framework/details/fetch_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -32,13 +33,11 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
       pool_(strategy.num_threads_ +
             1),  // add one more thread for generate op_deps
       fetch_ctxs_(places) {
-  auto &ops = graph_->Get<details::GraphOps>("ops");
-
-  for (auto &op : ops) {
+  for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
     int dep = static_cast<int>(op->NotReadyInputSize());
-    op_deps_.emplace(op.get(), dep);
+    op_deps_.emplace(op, dep);
     if (dep == 0) {
-      bootstrap_ops_.emplace_back(op.get());
+      bootstrap_ops_.emplace_back(op);
     }
   }
 
@@ -54,13 +53,13 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
   paddle::framework::FeedFetchList fetches;
   fetches.resize(fetch_tensors.size());
   std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
-  std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
+  std::vector<FetchOpHandle *> fetch_ops;
 
   for (auto &fetch_var_name : fetch_tensors) {
     for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
       auto it = var_map.find(fetch_var_name);
       if (it != var_map.end()) {
-        fetched_vars[fetch_var_name].push_back(it->second.rbegin()->get());
+        fetched_vars[fetch_var_name].push_back(*it->second.rbegin());
       }
     }
   }
@@ -110,7 +109,10 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
           complete_q->Pop();
         }
       }
-      exception_.ReThrow();
+      if (exception_.IsCaught()) {
+        ClearFetchOp(graph_.get(), &fetch_ops);
+        exception_.ReThrow();
+      }
     }
     num_complete += num_comp;
   }

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -28,11 +28,7 @@ FetchOpHandle::FetchOpHandle(ir::Node *node, FeedFetchList *data, size_t offset,
       offset_(offset),
       local_scopes_(local_scopes) {}
 
-FetchOpHandle::~FetchOpHandle() {
-  for (auto *input_var : inputs_) {
-    input_var->RemoveOutput(this, this->Node());
-  }
-}
+FetchOpHandle::~FetchOpHandle() {}
 
 void FetchOpHandle::RecordWaitEventOnCtx(platform::DeviceContext *waited_ctx) {
   PADDLE_THROW("Nobody should wait FetchOp. Unexpceted Error");

paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc

@@ -22,8 +22,10 @@ namespace details {
 
 struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
   std::vector<std::string> out_varnames_;
+  std::vector<std::unique_ptr<ir::Node>> nodes_;
 
   void InitFusedBroadcastOp(std::vector<size_t> input_scope_idxes) {
+    nodes_.clear();
     // initialize scope and var
     for (size_t i = 0; i < place_list_.size(); ++i) {
       local_scopes_.push_back(&(g_scope_.NewScope()));
@@ -39,41 +41,41 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
     }
 
     // create op handle node
-    std::unique_ptr<ir::Node> n =
-        ir::CreateNodeForTest("fused_broadcast", ir::Node::Type::kOperation);
+    nodes_.emplace_back(
+        ir::CreateNodeForTest("fused_broadcast", ir::Node::Type::kOperation));
     if (use_gpu_) {
 #ifdef PADDLE_WITH_CUDA
-      op_handle_.reset(new FusedBroadcastOpHandle(
-          n.get(), local_scopes_, place_list_, nccl_ctxs_.get()));
+      op_handle_ = new FusedBroadcastOpHandle(
+          nodes_.back().get(), local_scopes_, place_list_, nccl_ctxs_.get());
 #else
       PADDLE_THROW("CUDA is not supported.");
 #endif
     } else {
 #ifdef PADDLE_WITH_CUDA
-      op_handle_.reset(new FusedBroadcastOpHandle(
-          n.get(), local_scopes_, place_list_, nccl_ctxs_.get()));
+      op_handle_ = new FusedBroadcastOpHandle(
+          nodes_.back().get(), local_scopes_, place_list_, nccl_ctxs_.get());
 #else
-      op_handle_.reset(
-          new FusedBroadcastOpHandle(n.get(), local_scopes_, place_list_));
+      op_handle_ = new FusedBroadcastOpHandle(nodes_.back().get(),
+                                              local_scopes_, place_list_);
 #endif
     }
 
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
       // add input var handle
-      std::unique_ptr<ir::Node> in_node =
-          ir::CreateNodeForTest("in_node" + i, ir::Node::Type::kVariable);
+      nodes_.emplace_back(
+          ir::CreateNodeForTest("in_node" + i, ir::Node::Type::kVariable));
       VarHandle* in_var_handle =
-          new VarHandle(in_node.get(), 1, input_scope_idxes[i], "in_var" + i,
-                        place_list_[input_scope_idxes[i]]);
+          new VarHandle(nodes_.back().get(), 1, input_scope_idxes[i],
+                        "in_var" + i, place_list_[input_scope_idxes[i]]);
       vars_.emplace_back(in_var_handle);
       op_handle_->AddInput(in_var_handle);
 
       // add output var handle
       for (size_t j = 0; j < place_list_.size(); ++j) {
-        std::unique_ptr<ir::Node> out_node =
-            ir::CreateNodeForTest("out_node" + i, ir::Node::Type::kVariable);
-        VarHandle* out_var_handle =
-            new VarHandle(out_node.get(), 2, j, "out_var" + i, place_list_[j]);
+        nodes_.emplace_back(
+            ir::CreateNodeForTest("out_node" + i, ir::Node::Type::kVariable));
+        VarHandle* out_var_handle = new VarHandle(
+            nodes_.back().get(), 2, j, "out_var" + i, place_list_[j]);
         vars_.emplace_back(out_var_handle);
         op_handle_->AddOutput(out_var_handle);
       }

paddle/fluid/framework/details/gather_op_handle_test.cc

@@ -31,9 +31,10 @@ struct TestGatherOpHandle {
   std::vector<Scope*> local_scopes_;
   std::vector<Scope*> param_scopes_;
   Scope g_scope_;
-  std::unique_ptr<OpHandleBase> op_handle_;
-  std::vector<std::unique_ptr<VarHandleBase>> vars_;
+  OpHandleBase* op_handle_;
+  std::vector<VarHandleBase*> vars_;
   std::vector<p::Place> gpu_list_;
+  std::vector<std::unique_ptr<ir::Node>> nodes_;
 
   void WaitAll() {
     for (size_t j = 0; j < ctxs_.size(); ++j) {
@@ -70,7 +71,7 @@ struct TestGatherOpHandle {
   }
 
   void InitGatherOp(size_t input_scope_idx) {
-    std::vector<std::unique_ptr<ir::Node>> nodes;
+    nodes_.clear();
     for (size_t j = 0; j < gpu_list_.size(); ++j) {
       local_scopes_.push_back(&(g_scope_.NewScope()));
       Scope& local_scope = local_scopes_.back()->NewScope();
@@ -82,44 +83,45 @@ struct TestGatherOpHandle {
     }
     param_scopes_[input_scope_idx]->Var("out");
 
-    nodes.emplace_back(
+    nodes_.emplace_back(
         ir::CreateNodeForTest("node", ir::Node::Type::kOperation).release());
-    op_handle_.reset(
-        new GatherOpHandle(nodes.back().get(), local_scopes_, gpu_list_));
+    op_handle_ =
+        new GatherOpHandle(nodes_.back().get(), local_scopes_, gpu_list_);
     // add input
     for (size_t j = 0; j < gpu_list_.size(); ++j) {
       op_handle_->SetDeviceContext(gpu_list_[j], ctxs_[j].get());
-      nodes.emplace_back(
+      nodes_.emplace_back(
           ir::CreateNodeForTest("node1", ir::Node::Type::kVariable).release());
       auto* in_var_handle =
-          new VarHandle(nodes.back().get(), 1, j, "input", gpu_list_[j]);
+          new VarHandle(nodes_.back().get(), 1, j, "input", gpu_list_[j]);
       vars_.emplace_back(in_var_handle);
       op_handle_->AddInput(in_var_handle);
     }
 
     // add dummy var
-    nodes.emplace_back(
+    nodes_.emplace_back(
         ir::CreateNodeForTest("node2", ir::Node::Type::kVariable).release());
-    vars_.emplace_back(new DummyVarHandle(nodes.back().get()));
+    vars_.emplace_back(new DummyVarHandle(nodes_.back().get()));
     DummyVarHandle* in_dummy_var_handle =
-        static_cast<DummyVarHandle*>(vars_.back().get());
+        static_cast<DummyVarHandle*>(vars_.back());
     in_dummy_var_handle->ClearGeneratedOp();
     op_handle_->AddInput(in_dummy_var_handle);
 
     // add output
-    nodes.emplace_back(
+    nodes_.emplace_back(
         ir::CreateNodeForTest("node3", ir::Node::Type::kVariable).release());
-    auto* out_var_handle = new VarHandle(nodes.back().get(), 2, input_scope_idx,
-                                         "out", gpu_list_[input_scope_idx]);
+    auto* out_var_handle =
+        new VarHandle(nodes_.back().get(), 2, input_scope_idx, "out",
+                      gpu_list_[input_scope_idx]);
     vars_.emplace_back(out_var_handle);
     op_handle_->AddOutput(out_var_handle);
 
     // add dummy var
-    nodes.emplace_back(
+    nodes_.emplace_back(
         ir::CreateNodeForTest("node4", ir::Node::Type::kVariable).release());
-    vars_.emplace_back(new DummyVarHandle(nodes.back().get()));
+    vars_.emplace_back(new DummyVarHandle(nodes_.back().get()));
     DummyVarHandle* dummy_var_handle =
-        static_cast<DummyVarHandle*>(vars_.back().get());
+        static_cast<DummyVarHandle*>(vars_.back());
     op_handle_->AddOutput(dummy_var_handle);
   }
 

paddle/fluid/framework/details/modify_op_lock_and_record_event_pass.cc

@@ -16,6 +16,7 @@
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/details/op_graph_view.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -35,10 +36,10 @@ static bool IsLockAndRecordEventFreeComputationOpHandle(
 
 std::unique_ptr<ir::Graph> ModifyOpLockAndRecordEventPass::ApplyImpl(
     std::unique_ptr<ir::Graph> ir_graph) const {
-  auto &all_ops = ir_graph->Get<GraphOps>(kGraphOps);
+  auto all_ops = ir::FilterByNodeWrapper<OpHandleBase>(*ir_graph);
   OpGraphView graph_view(all_ops);
   for (auto &op : all_ops) {
-    auto *compute_op = dynamic_cast<ComputationOpHandle *>(op.get());
+    auto *compute_op = dynamic_cast<ComputationOpHandle *>(op);
     if (compute_op == nullptr) continue;
     bool is_lock_and_record_event_free =
         IsLockAndRecordEventFreeComputationOpHandle(compute_op, graph_view);

paddle/fluid/framework/details/multi_devices_graph_check_pass.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
 #include <string>
 #include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -36,20 +37,20 @@ bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
   for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
     for (auto &name_pair : var_map) {
       for (auto &version_pair : name_pair.second) {
-        insert_pending_var(version_pair.get());
+        insert_pending_var(version_pair);
       }
     }
   }
 
   for (auto &var : graph->Get<GraphDepVars>(kGraphDepVars)) {
-    insert_pending_var(var.get());
+    insert_pending_var(var);
   }
 
-  for (auto &op : graph->Get<GraphOps>(kGraphOps)) {
+  for (OpHandleBase *op : ir::FilterByNodeWrapper<OpHandleBase>(*graph)) {
     if (op->Inputs().empty()) {
-      ready_ops.insert(op.get());
+      ready_ops.insert(op);
     } else {
-      pending_ops.insert({op.get(), op.get()->NoDupInputSize()});
+      pending_ops.insert({op, op->NoDupInputSize()});
     }
   }
 
@@ -89,6 +90,4 @@ bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
 REGISTER_PASS(multi_devices_check_pass,
               paddle::framework::details::SSAGraghBuilderWithChecker)
     .RequireGraphAttr(paddle::framework::details::kGraphVars)
-    .RequireGraphAttr(paddle::framework::details::kGraphDepVars)
-    .RequireGraphAttr(paddle::framework::details::kGraphOps)
-    .RequireGraphAttr(paddle::framework::details::kShardedVarDevice);
+    .RequireGraphAttr(paddle::framework::details::kGraphDepVars);

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -34,7 +34,14 @@
 namespace paddle {
 namespace framework {
 namespace details {
+
 namespace {
+// TODO(panyx0718): Clean this up as well.
+// all operators. NOTE that even we use a vector here, the operators is
+// unordered.
+typedef std::vector<OpHandleBase *> GraphOps;
+const char kGraphOps[] = "ops";
+
 void PolishGraphToSupportDataHazards(ir::Graph *graph) {
   for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
     for (auto &name_pair : var_map) {
@@ -92,7 +99,7 @@ VarHandle *CreateOrGetLatestVarHandle(ir::Graph *graph, ir::Node *node,
     }
     var_holder.emplace_back(var);
   } else {
-    var = var_holder.rbegin()->get();
+    var = *var_holder.rbegin();
   }
   return var;
 }
@@ -154,7 +161,7 @@ void MultiDevSSAGraphBuilder::CreateOpHandleIOs(ir::Graph *result,
                                                 ir::Node *node,
                                                 size_t place_id) const {
   auto p = places_[place_id];
-  auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
+  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
   op_handle->SetDeviceContext(p,
                               platform::DeviceContextPool::Instance().Get(p));
 
@@ -303,7 +310,6 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
   result.Set(kGraphVars, new GraphVars(places_.size()));
   result.Set(kGraphDepVars, new GraphDepVars);
   result.Set(kGraphOps, new GraphOps);
-  result.Set(kShardedVarDevice, new ShardedVarDevice);
 
   // find send/recv vars so that we can place the distributed training
   // related op in the place 0
@@ -317,11 +323,13 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
   bool is_forwarding = true;
   bool is_dist_train = false;
 
+  std::unordered_map<std::string, int> sharded_var_device;
+
   for (ir::Node *node : sorted_ops) {
     if (boost::get<int>(
             node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
         static_cast<int>(OpRole::kRPC)) {
-      int op_dev_id = CreateRPCOp(&result, node);
+      int op_dev_id = CreateRPCOp(&result, node, &sharded_var_device);
       PADDLE_ENFORCE(op_dev_id != -1,
                      "Can not schedule the RPC operator to the right place.");
       if (node->Op()->Type() == "recv") {
@@ -337,7 +345,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
     } else if (boost::get<int>(node->Op()->GetAttr(
                    OpProtoAndCheckerMaker::OpRoleAttrName())) ==
                static_cast<int>(OpRole::kDist)) {
-      int op_dev_id = CreateDistTrainOp(&result, node);
+      int op_dev_id = CreateDistTrainOp(&result, node, &sharded_var_device);
       if (node->Op()->Type() == "concat") {
         auto origin_param_name = node->Op()->OutputArgumentNames()[0];
         bcast_var_name_set[op_dev_id].emplace(origin_param_name);
@@ -356,12 +364,11 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
       // the block.
       is_forwarding = false;
     } else {
-      int op_dev_id = GetOpDeviceID(result, node);
+      int op_dev_id = GetOpDeviceID(result, node, sharded_var_device);
       if (op_dev_id != -1) {  // This op only runs on one specific device.
         CreateComputationalOp(&result, node, op_dev_id);
         for (ir::Node *n : node->outputs) {
-          graph->Get<ShardedVarDevice>(kShardedVarDevice)
-              .emplace(n->Name(), op_dev_id);
+          sharded_var_device.emplace(n->Name(), op_dev_id);
         }
       } else {
         // This op runs on all devices, and its output may have parameter's
@@ -398,8 +405,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
                   case BuildStrategy::ReduceStrategy::kReduce:
                     cur_device_id = GetAppropriateDeviceID({g_name});
                     CreateReduceOp(&result, g_name, cur_device_id);
-                    graph->Get<ShardedVarDevice>(kShardedVarDevice)
-                        .emplace(g_name, cur_device_id);
+                    sharded_var_device.emplace(g_name, cur_device_id);
                     if (!is_dist_train) {
                       bcast_var_name_set[cur_device_id].emplace(p_name);
                     }
@@ -458,7 +464,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
    * Only variables should be the leaves of graph.
    */
   AddOutputToLeafOps(&result);
-  PADDLE_ENFORCE(!ir::HasCircle(result));
+  result.Erase<GraphOps>(kGraphOps);
   return graph;
 }
 
@@ -498,7 +504,7 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
   result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
 
   auto *in =
-      result->Get<GraphVars>(kGraphVars).at(src_dev_id).at(p_name).back().get();
+      result->Get<GraphVars>(kGraphVars).at(src_dev_id).at(p_name).back();
   op_handle->AddInput(in);
 
   for (size_t i = 0; i < places_.size(); ++i) {
@@ -535,7 +541,7 @@ void MultiDevSSAGraphBuilder::CreateFusedBroadcastOp(
   for (size_t dev_id = 0; dev_id < bcast_varnames.size(); ++dev_id) {
     for (auto &p_name : bcast_varnames[dev_id]) {
       auto *in =
-          result->Get<GraphVars>(kGraphVars).at(dev_id).at(p_name).back().get();
+          result->Get<GraphVars>(kGraphVars).at(dev_id).at(p_name).back();
       op_handle->AddInput(in);
       for (size_t out_dev_id = 0; out_dev_id < places_.size(); ++out_dev_id) {
         auto &p = places_[out_dev_id];
@@ -571,7 +577,7 @@ void MultiDevSSAGraphBuilder::InsertAllReduceOp(ir::Graph *result,
       result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
       local_scopes_, places_));
 #endif
-  auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
+  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
 
   for (size_t i = 0; i < places_.size(); ++i) {
     auto &p = places_[i];
@@ -579,7 +585,7 @@ void MultiDevSSAGraphBuilder::InsertAllReduceOp(ir::Graph *result,
     auto &vars = result->Get<GraphVars>(kGraphVars)[i][og];
     PADDLE_ENFORCE(!vars.empty());
     auto &prev_grad = vars.back();
-    op_handle->AddInput(prev_grad.get());
+    op_handle->AddInput(prev_grad);
 
     auto var =
         new VarHandle(result->CreateEmptyNode(og, ir::Node::Type::kVariable),
@@ -600,14 +606,14 @@ void MultiDevSSAGraphBuilder::InsertDataBalanceOp(
       result->CreateEmptyNode("data_balance", ir::Node::Type::kOperation),
       local_scopes_, places_));
 #endif
-  auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
+  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
   for (size_t i = 0; i < places_.size(); ++i) {
     auto &p = places_[i];
     SetCommunicationContext(op_handle, p);
     for (const std::string &d_name : datas) {
       auto &vars = result->Get<GraphVars>(kGraphVars)[i][d_name];
       PADDLE_ENFORCE(!vars.empty());
-      op_handle->AddInput(vars.back().get());
+      op_handle->AddInput(vars.back());
       auto var = new VarHandle(
           result->CreateEmptyNode(d_name, ir::Node::Type::kVariable),
           vars.size(), i, d_name, p);
@@ -617,8 +623,9 @@ void MultiDevSSAGraphBuilder::InsertDataBalanceOp(
   }
 }
 
-int MultiDevSSAGraphBuilder::GetOpDeviceID(const ir::Graph &graph,
-                                           ir::Node *node) const {
+int MultiDevSSAGraphBuilder::GetOpDeviceID(
+    const ir::Graph &graph, ir::Node *node,
+    const std::unordered_map<std::string, int> &sharded_var_device) const {
   if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
     return -1;
   }
@@ -631,15 +638,15 @@ int MultiDevSSAGraphBuilder::GetOpDeviceID(const ir::Graph &graph,
       node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
 
   PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
-  int dev_id = GetVarDeviceID(graph, param_grad[1]);
+  int dev_id = GetVarDeviceID(graph, param_grad[1], sharded_var_device);
   PADDLE_ENFORCE_NE(dev_id, -1, "dev_id should not be -1.[%s, %s, %s]",
                     node->Op()->Type(), param_grad[0], param_grad[1]);
   return dev_id;
 }
 
-int MultiDevSSAGraphBuilder::GetVarDeviceID(const ir::Graph &graph,
-                                            const std::string &varname) const {
-  auto &sharded_var_device = graph.Get<ShardedVarDevice>(kShardedVarDevice);
+int MultiDevSSAGraphBuilder::GetVarDeviceID(
+    const ir::Graph &graph, const std::string &varname,
+    const std::unordered_map<std::string, int> &sharded_var_device) const {
   auto got = sharded_var_device.find(varname);
   return got == sharded_var_device.end() ? -1 : got->second;
 }
@@ -690,7 +697,7 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
       result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
       local_scopes_, places_));
 #endif
-  auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
+  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
 
   for (size_t i = 0; i < places_.size(); ++i) {
     auto &p = places_[i];
@@ -698,7 +705,7 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
     auto &vars = result->Get<GraphVars>(kGraphVars)[i][og];
     PADDLE_ENFORCE(!vars.empty());
     auto &prev_grad = vars.back();
-    op_handle->AddInput(prev_grad.get());
+    op_handle->AddInput(prev_grad);
   }
   auto &vars = result->Get<GraphVars>(kGraphVars)[dst_dev_id][og];
   auto var =
@@ -709,8 +716,9 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
   return var;
 }
 
-int MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
-                                               ir::Node *node) const {
+int MultiDevSSAGraphBuilder::CreateDistTrainOp(
+    ir::Graph *result, ir::Node *node,
+    std::unordered_map<std::string, int> *sharded_var_device) const {
   int op_dev_id = -1;
   std::vector<std::string> input_var_names;
   std::vector<std::string> output_var_names;
@@ -725,23 +733,22 @@ int MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
       node->Op()->Type() == "split_selected_rows" ||
       node->Op()->Type() == "split_ids") {
     // TODO(paddle-dev): getting the first var is not safe.
-    op_dev_id = GetVarDeviceID(*result, input_var_names[0]);
+    op_dev_id =
+        GetVarDeviceID(*result, input_var_names[0], *sharded_var_device);
     if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
       op_dev_id = GetAppropriateDeviceID(input_var_names);
       for (auto &varname : input_var_names) {
-        result->Get<ShardedVarDevice>(kShardedVarDevice)
-            .emplace(varname, op_dev_id);
+        sharded_var_device->emplace(varname, op_dev_id);
       }
     }
     for (auto &varname : output_var_names) {
-      result->Get<ShardedVarDevice>(kShardedVarDevice)
-          .emplace(varname, op_dev_id);
+      sharded_var_device->emplace(varname, op_dev_id);
     }
   } else if (node->Op()->Type() == "concat") {
-    op_dev_id = GetVarDeviceID(*result, input_var_names[0]);
+    op_dev_id =
+        GetVarDeviceID(*result, input_var_names[0], *sharded_var_device);
     for (auto &varname : output_var_names) {
-      result->Get<ShardedVarDevice>(kShardedVarDevice)
-          .emplace(varname, op_dev_id);
+      sharded_var_device->emplace(varname, op_dev_id);
     }
   } else {
     LOG(ERROR) << "got unexpected dist op: " << node->Op()->Type();
@@ -759,14 +766,14 @@ int MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
 }
 
 void SetOpInputsAllPlaces(ir::Graph *result, ir::Node *node, int num_places) {
-  auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
+  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
   for (ir::Node *input : node->inputs) {
     VarHandle *var = nullptr;
     for (int place_offset = 0; place_offset < num_places; ++place_offset) {
       auto &var_holders = result->Get<GraphVars>(kGraphVars)[place_offset];
       auto &var_holder = var_holders[input->Name()];
       if (!var_holder.empty()) {
-        var = var_holder.rbegin()->get();
+        var = *var_holder.rbegin();
         op_handle->AddInput(var);
       }
     }
@@ -774,12 +781,14 @@ void SetOpInputsAllPlaces(ir::Graph *result, ir::Node *node, int num_places) {
 }
 
 // Create RPC related op handles that connects its in ops and out ops.
-int MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
-                                         ir::Node *node) const {
+int MultiDevSSAGraphBuilder::CreateRPCOp(
+    ir::Graph *result, ir::Node *node,
+    std::unordered_map<std::string, int> *sharded_var_device) const {
   int op_dev_id = -1;
   if (node->Op()->Type() == "send") {
     // TODO(paddle-dev): getting the first var is not safe.
-    op_dev_id = GetVarDeviceID(*result, node->inputs[0]->Name());
+    op_dev_id =
+        GetVarDeviceID(*result, node->inputs[0]->Name(), *sharded_var_device);
     PADDLE_ENFORCE(!ir::IsControlDepVar(*node->inputs[0]),
                    "This hack no longer holds, please fix.");
     // the variable name which contains .block means it was splited by
@@ -797,11 +806,9 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
       VLOG(10) << "send grad " << input_var_names[0] << " origin "
                << send_param_grad[1] << " place: " << op_dev_id;
       for (auto &varname : input_var_names) {
-        result->Get<ShardedVarDevice>(kShardedVarDevice)
-            .emplace(varname, op_dev_id);
+        sharded_var_device->emplace(varname, op_dev_id);
       }
-      result->Get<ShardedVarDevice>(kShardedVarDevice)
-          .emplace(send_param_grad[1], op_dev_id);
+      sharded_var_device->emplace(send_param_grad[1], op_dev_id);
     }
   } else if (node->Op()->Type() == "recv") {
     std::vector<std::string> output_var_names;
@@ -811,7 +818,8 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
     auto recv_param_grad = boost::get<std::vector<std::string>>(
         node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
     if (recv_param_grad.size() == 2U) {
-      op_dev_id = GetVarDeviceID(*result, recv_param_grad[1]);
+      op_dev_id =
+          GetVarDeviceID(*result, recv_param_grad[1], *sharded_var_device);
       VLOG(10) << "recv param " << recv_param_grad[0]
                << " get grad place: " << recv_param_grad[1]
                << " place: " << op_dev_id;
@@ -819,8 +827,7 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
       op_dev_id = GetAppropriateDeviceID(output_var_names);
     }
     for (auto &varname : output_var_names) {
-      result->Get<ShardedVarDevice>(kShardedVarDevice)
-          .emplace(varname, op_dev_id);
+      sharded_var_device->emplace(varname, op_dev_id);
     }
   } else {
     // send_barrier, fetch_barrier will run on place 0;
@@ -839,7 +846,7 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
     // send_barrier, recv, fetch_barrier's inputs are deps var, get them from
     // all places
     auto p = places_[op_dev_id];
-    auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
+    auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
     op_handle->SetDeviceContext(p,
                                 platform::DeviceContextPool::Instance().Get(p));
 
@@ -847,7 +854,8 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
     for (ir::Node *output : node->outputs) {
       int outvar_dev_id = op_dev_id;
       if (node->Op()->Type() == "fetch_barrier") {
-        outvar_dev_id = GetVarDeviceID(*result, output->Name());
+        outvar_dev_id =
+            GetVarDeviceID(*result, output->Name(), *sharded_var_device);
         PADDLE_ENFORCE_NE(outvar_dev_id, -1);
       }
       p = places_[outvar_dev_id];

paddle/fluid/framework/details/multi_devices_graph_pass.h

@@ -44,12 +44,18 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
   mutable platform::NCCLContextMap *nccl_ctxs_;
 #endif
 
-  int GetVarDeviceID(const ir::Graph &graph, const std::string &varname) const;
+  int GetVarDeviceID(
+      const ir::Graph &graph, const std::string &varname,
+      const std::unordered_map<std::string, int> &sharded_var_device) const;
 
   bool IsScaleLossOp(ir::Node *node) const;
 
-  int CreateRPCOp(ir::Graph *result, ir::Node *node) const;
-  int CreateDistTrainOp(ir::Graph *result, ir::Node *node) const;
+  int CreateRPCOp(
+      ir::Graph *result, ir::Node *node,
+      std::unordered_map<std::string, int> *sharded_var_device) const;
+  int CreateDistTrainOp(
+      ir::Graph *result, ir::Node *node,
+      std::unordered_map<std::string, int> *sharded_var_device) const;
 
   std::vector<std::string> FindDistTrainSendVars(
       const std::vector<ir::Node *> &nodes) const;
@@ -69,7 +75,9 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
   void CreateComputationalOp(ir::Graph *result, ir::Node *node,
                              int dev_id) const;
 
-  int GetOpDeviceID(const ir::Graph &graph, ir::Node *node) const;
+  int GetOpDeviceID(
+      const ir::Graph &graph, ir::Node *node,
+      const std::unordered_map<std::string, int> &sharded_var_device) const;
 
   void InsertAllReduceOp(ir::Graph *result, const std::string &og) const;
 

paddle/fluid/framework/details/multi_devices_graph_print_pass.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
 #include <string>
 #include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -62,7 +63,7 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
   });
 
   size_t op_id = 0;
-  for (auto &op : graph.Get<GraphOps>(kGraphOps)) {
+  for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(graph)) {
     std::string op_name = "op_" + std::to_string(op_id++);
     sout << op_name << " [label=\"" << op->Name() << "\", shape=rect]"
          << std::endl;

paddle/fluid/framework/details/multi_devices_helper.h

@@ -35,23 +35,14 @@ namespace details {
 // The outside vector is the device vector. Each element of this vector is a
 // map from variable name to variables. The variables, who have the same name,
 // will have a differsent version. The offset in the
-// `std::vector<std::unique_ptr<VarHandle>>` is the version of varaibles.
-typedef std::vector<
-    std::unordered_map<std::string, std::vector<std::unique_ptr<VarHandle>>>>
+// `std::vector<VarHandle*>` is the version of varaibles.
+typedef std::vector<std::unordered_map<std::string, std::vector<VarHandle*>>>
     GraphVars;
 const char kGraphVars[] = "vars";
 
 // aux variables to represent dependency. Useful to resolve data hazard.
-typedef std::unordered_set<std::unique_ptr<VarHandleBase>> GraphDepVars;
+typedef std::unordered_set<VarHandleBase*> GraphDepVars;
 const char kGraphDepVars[] = "dep_vars";
-
-// all operators. NOTE that even we use a vector here, the operators is
-// unordered.
-typedef std::vector<std::unique_ptr<OpHandleBase>> GraphOps;
-const char kGraphOps[] = "ops";
-
-typedef std::unordered_map<std::string, int> ShardedVarDevice;
-const char kShardedVarDevice[] = "sharded_var_device";
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/op_graph_view.cc

@@ -20,19 +20,16 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-OpGraphView::OpGraphView(
-    const std::vector<std::unique_ptr<OpHandleBase>> &ops) {
-  Build(ops);
-}
+OpGraphView::OpGraphView(const std::vector<OpHandleBase *> &ops) { Build(ops); }
 
-void OpGraphView::Build(const std::vector<std::unique_ptr<OpHandleBase>> &ops) {
+void OpGraphView::Build(const std::vector<OpHandleBase *> &ops) {
   for (auto &op : ops) {
-    preceding_ops_[op.get()];
-    pending_ops_[op.get()];
+    preceding_ops_[op];
+    pending_ops_[op];
     for (auto &var : op->Outputs()) {
       for (auto &pending_op : var->PendingOps()) {
-        preceding_ops_[pending_op].insert(op.get());
-        pending_ops_[op.get()].insert(pending_op);
+        preceding_ops_[pending_op].insert(op);
+        pending_ops_[op].insert(pending_op);
       }
     }
   }
@@ -41,8 +38,6 @@ void OpGraphView::Build(const std::vector<std::unique_ptr<OpHandleBase>> &ops) {
       "There are duplicate ops in graph.");
 }
 
-size_t OpGraphView::OpNumber() const { return preceding_ops_.size(); }
-
 std::unordered_set<OpHandleBase *> OpGraphView::AllOps() const {
   std::unordered_set<OpHandleBase *> ret;
   for (auto &pair : preceding_ops_) {
@@ -60,12 +55,6 @@ void OpGraphView::EnforceHasOp(OpHandleBase *op) const {
                  op == nullptr ? "nullptr" : op->DebugString());
 }
 
-const std::unordered_set<OpHandleBase *> &OpGraphView::PrecedingOps(
-    OpHandleBase *op) const {
-  EnforceHasOp(op);
-  return preceding_ops_.at(op);
-}
-
 const std::unordered_set<OpHandleBase *> &OpGraphView::PendingOps(
     OpHandleBase *op) const {
   EnforceHasOp(op);

paddle/fluid/framework/details/op_graph_view.h

@@ -26,21 +26,16 @@ namespace details {
 
 class OpGraphView {
  public:
-  explicit OpGraphView(const std::vector<std::unique_ptr<OpHandleBase>> &ops);
-
-  size_t OpNumber() const;
+  explicit OpGraphView(const std::vector<OpHandleBase *> &ops);
 
   std::unordered_set<OpHandleBase *> AllOps() const;
 
-  const std::unordered_set<OpHandleBase *> &PrecedingOps(
-      OpHandleBase *op) const;
-
   const std::unordered_set<OpHandleBase *> &PendingOps(OpHandleBase *op) const;
 
   bool HasOp(OpHandleBase *op) const;
 
  private:
-  void Build(const std::vector<std::unique_ptr<OpHandleBase>> &ops);
+  void Build(const std::vector<OpHandleBase *> &ops);
   void EnforceHasOp(OpHandleBase *op) const;
 
   std::unordered_map<OpHandleBase *, std::unordered_set<OpHandleBase *>>

paddle/fluid/framework/details/op_handle_base.h

@@ -31,7 +31,10 @@ constexpr char kLocalExecScopeName[] = "@LCOAL_SCOPE@";
 // It's responsible for populating necessary fields of ir::Node.
 class OpHandleBase {
  public:
-  explicit OpHandleBase(ir::Node *node) : node_(node) {}
+  // Owned by `node`. No need to be deleted explicitly.
+  explicit OpHandleBase(ir::Node *node) : node_(node) {
+    node_->WrappedBy(this);
+  }
 
   virtual ~OpHandleBase();
 

paddle/fluid/framework/details/reduce_op_handle_test.cc

@@ -30,8 +30,8 @@ struct TestReduceOpHandle {
   Scope g_scope_;
   std::vector<Scope *> local_scopes_;
   std::vector<Scope *> param_scopes_;
-  std::unique_ptr<OpHandleBase> op_handle_;
-  std::vector<std::unique_ptr<VarHandleBase>> vars_;
+  OpHandleBase *op_handle_;
+  std::vector<VarHandleBase *> vars_;
   std::vector<p::Place> gpu_list_;
   std::vector<std::unique_ptr<p::DeviceContext>> ctxs_;
 

paddle/fluid/framework/details/reference_count_pass.cc

@@ -19,6 +19,7 @@
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/details/reference_count_pass.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -71,14 +72,13 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
   // Step 2: Find all variables in non-computation ops which refers to variables
   // in computation ops
   std::unordered_set<std::string> names;
-  std::unordered_map<OpHandleBase *, std::unique_ptr<ReferenceCountOpHandle>>
+  std::unordered_map<OpHandleBase *, ReferenceCountOpHandle *>
       compute_ref_cnt_map;
 
   auto get_ref_cnts_from_compute_op = [&](
-      const std::unique_ptr<OpHandleBase> &op,
-      const std::vector<VarHandleBase *> &vars) {
+      OpHandleBase *op, const std::vector<VarHandleBase *> &vars) {
     std::vector<std::string> var_names_in_op;
-    auto *compute_op = dynamic_cast<ComputationOpHandle *>(op.get());
+    auto *compute_op = dynamic_cast<ComputationOpHandle *>(op);
     if (compute_op == nullptr ||
         !platform::is_gpu_place(compute_op->GetPlace()))
       return var_names_in_op;
@@ -121,9 +121,8 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
   };
 
   auto update_ref_cnts_from_non_compute_op = [&](
-      const std::unique_ptr<OpHandleBase> &op,
-      const std::vector<VarHandleBase *> &vars) {
-    if (dynamic_cast<ComputationOpHandle *>(op.get()) != nullptr) return;
+      OpHandleBase *op, const std::vector<VarHandleBase *> &vars) {
+    if (dynamic_cast<ComputationOpHandle *>(op) != nullptr) return;
     for (VarHandleBase *var_handle_base : vars) {
       auto *var_handle = dynamic_cast<VarHandle *>(var_handle_base);
       if (var_handle == nullptr || !var_handle->Node()->IsVar()) continue;
@@ -151,21 +150,21 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
                 ref_cnt_node, next_compute_op->GetScope(), place, {var_name},
                 gcs[place.device].get(), cur_ref_cnts[place.device].get());
             AddDependencyBetween(next_compute_op, ref_cnt_handle, graph.get());
-            compute_ref_cnt_map[next_compute_op].reset(ref_cnt_handle);
+            compute_ref_cnt_map[next_compute_op] = ref_cnt_handle;
           }
         }
       }
     }
   };
 
-  auto &all_ops = graph->Get<GraphOps>(kGraphOps);
+  auto all_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
   for (auto &op : all_ops) {
     auto in_var_names = get_ref_cnts_from_compute_op(op, op->Inputs());
     auto out_var_names = get_ref_cnts_from_compute_op(op, op->Outputs());
     if (in_var_names.empty() && out_var_names.empty()) continue;
     in_var_names.insert(in_var_names.end(), out_var_names.begin(),
                         out_var_names.end());
-    auto *compute_op = dynamic_cast<ComputationOpHandle *>(op.get());
+    auto *compute_op = dynamic_cast<ComputationOpHandle *>(op);
     auto place = boost::get<platform::CUDAPlace>(compute_op->GetPlace());
     ir::Node *ref_cnt_node =
         graph->CreateEmptyNode("reference_count", ir::Node::Type::kOperation);
@@ -173,7 +172,7 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
         ref_cnt_node, compute_op->GetScope(), place, in_var_names,
         gcs[place.device].get(), cur_ref_cnts[place.device].get());
     AddDependencyBetween(compute_op, ref_cnt_handle, graph.get());
-    compute_ref_cnt_map[compute_op].reset(ref_cnt_handle);
+    compute_ref_cnt_map[compute_op] = ref_cnt_handle;
   }
 
   for (auto &op : all_ops) {
@@ -181,11 +180,11 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
     update_ref_cnts_from_non_compute_op(op, op->Outputs());
   }
 
-  std::vector<std::unique_ptr<OpHandleBase>> new_all_ops;
+  std::vector<OpHandleBase *> new_all_ops;
   new_all_ops.reserve(compute_ref_cnt_map.size() + all_ops.size());
   for (auto &op : all_ops) {
     new_all_ops.emplace_back(std::move(op));
-    auto it = compute_ref_cnt_map.find(new_all_ops.back().get());
+    auto it = compute_ref_cnt_map.find(new_all_ops.back());
     if (it != compute_ref_cnt_map.end()) {
       // Add LeafNode to ReferenceCountOpHandle
       auto *dummy_leaf = new DummyVarHandle(graph->CreateControlDepVar());

paddle/fluid/framework/details/ssa_graph_executor.cc

@@ -19,14 +19,16 @@ namespace framework {
 namespace details {
 SSAGraphExecutor::~SSAGraphExecutor() {}
 
-void ClearFetchOp(ir::Graph* graph,
-                  std::vector<std::unique_ptr<FetchOpHandle>>* fetch_ops) {
+void ClearFetchOp(ir::Graph* graph, std::vector<FetchOpHandle*>* fetch_ops) {
   if (fetch_ops->empty()) return;
 
   for (auto& op : *fetch_ops) {
     for (auto& out_var : op->Node()->outputs) {
       graph->RemoveNode(out_var);
     }
+    for (auto& in_var : op->Inputs()) {
+      in_var->RemoveOutput(op, op->Node());
+    }
     graph->RemoveNode(op->Node());
   }
   fetch_ops->clear();

paddle/fluid/framework/details/ssa_graph_executor.h

@@ -38,8 +38,7 @@ class SSAGraphExecutor {
   virtual FeedFetchList Run(const std::vector<std::string>& fetch_tensors) = 0;
 };
 
-void ClearFetchOp(ir::Graph* graph,
-                  std::vector<std::unique_ptr<FetchOpHandle>>* fetch_ops);
+void ClearFetchOp(ir::Graph* graph, std::vector<FetchOpHandle*>* fetch_ops);
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
@@ -51,25 +52,25 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
     for (auto &name_pair : var_map) {
       for (auto &version_pair : name_pair.second) {
-        InsertPendingVar(&pending_vars, ready_vars.get(), version_pair.get());
+        InsertPendingVar(&pending_vars, ready_vars.get(), version_pair);
       }
     }
   }
   for (auto &var : graph_->Get<details::GraphDepVars>(details::kGraphDepVars)) {
-    InsertPendingVar(&pending_vars, ready_vars.get(), var.get());
+    InsertPendingVar(&pending_vars, ready_vars.get(), var);
   }
 
-  for (auto &op : graph_->Get<details::GraphOps>(details::kGraphOps)) {
+  for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
     if (op->Inputs().empty()) {  // Special case, Op has no input.
-      ready_ops.insert(op.get());
+      ready_ops.insert(op);
     } else {
-      InsertPendingOp(&pending_ops, op.get());
+      InsertPendingOp(&pending_ops, op);
     }
   }
 
   // Step 2. Insert FetchOps
-  std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
-  std::unordered_set<std::unique_ptr<VarHandleBase>> fetch_dependencies;
+  std::vector<FetchOpHandle *> fetch_ops;
+  std::unordered_set<VarHandleBase *> fetch_dependencies;
   FeedFetchList fetch_data(fetch_tensors.size());
 
   InsertFetchOps(fetch_tensors, &fetch_ops, &fetch_dependencies, &pending_ops,
@@ -109,6 +110,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
         for (auto &run_op_future : run_op_futures_) {
           run_op_future.wait();
         }
+        ClearFetchOp(graph_.get(), &fetch_ops);
         exception_holder_.ReThrow();
       } else {
         continue;
@@ -140,8 +142,8 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
 
 void ThreadedSSAGraphExecutor::InsertFetchOps(
     const std::vector<std::string> &fetch_tensors,
-    std::vector<std::unique_ptr<FetchOpHandle>> *fetch_ops,
-    std::unordered_set<std::unique_ptr<VarHandleBase>> *fetch_dependencies,
+    std::vector<FetchOpHandle *> *fetch_ops,
+    std::unordered_set<VarHandleBase *> *fetch_dependencies,
     std::unordered_map<OpHandleBase *, size_t> *pending_ops,
     std::unordered_set<VarHandleBase *> *pending_vars,
     BlockingQueue<VarHandleBase *> *ready_vars, FeedFetchList *fetch_data) {
@@ -151,7 +153,7 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
     for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
       auto it = var_map.find(fetch_var_name);
       if (it != var_map.end()) {
-        fetched_vars[fetch_var_name].push_back(it->second.rbegin()->get());
+        fetched_vars[fetch_var_name].push_back(*it->second.rbegin());
       }
     }
   }

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -70,13 +70,13 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
                         BlockingQueue<VarHandleBase *> *ready_vars,
                         VarHandleBase *var) const;
 
-  void InsertFetchOps(
-      const std::vector<std::string> &fetch_tensors,
-      std::vector<std::unique_ptr<FetchOpHandle>> *fetch_ops,
-      std::unordered_set<std::unique_ptr<VarHandleBase>> *fetch_dependencies,
-      std::unordered_map<OpHandleBase *, size_t> *pending_ops,
-      std::unordered_set<VarHandleBase *> *pending_vars,
-      BlockingQueue<VarHandleBase *> *ready_vars, FeedFetchList *fetch_data);
+  void InsertFetchOps(const std::vector<std::string> &fetch_tensors,
+                      std::vector<FetchOpHandle *> *fetch_ops,
+                      std::unordered_set<VarHandleBase *> *fetch_dependencies,
+                      std::unordered_map<OpHandleBase *, size_t> *pending_ops,
+                      std::unordered_set<VarHandleBase *> *pending_vars,
+                      BlockingQueue<VarHandleBase *> *ready_vars,
+                      FeedFetchList *fetch_data);
 
  private:
   ExecutionStrategy strategy_;

paddle/fluid/framework/details/var_handle.cc

@@ -20,6 +20,8 @@ namespace details {
 
 VarHandleBase::~VarHandleBase() {}
 
+VarHandle::~VarHandle() { VLOG(4) << "deleting var handle " << DebugString(); }
+
 std::string VarHandle::DebugString() const {
   std::stringstream ss;
   ss << name_ << ":" << place_;
@@ -27,6 +29,10 @@ std::string VarHandle::DebugString() const {
 }
 
 std::string DummyVarHandle::DebugString() const { return node_->Name(); }
+
+DummyVarHandle::~DummyVarHandle() {
+  VLOG(4) << "deleting dummy var handle " << DebugString();
+}
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/var_handle.h

@@ -35,7 +35,10 @@ class OpHandleBase;
 // A variable can only be generated by a single operator. i.e.
 // This is a single assignment graph.
 struct VarHandleBase {
-  explicit VarHandleBase(ir::Node* node) : node_(node) {}
+  // Owned by `node`. No need to be deleted explicitly.
+  explicit VarHandleBase(ir::Node* node) : node_(node) {
+    node_->WrappedBy(this);
+  }
 
   virtual ~VarHandleBase();
 
@@ -94,6 +97,8 @@ struct VarHandleBase {
 struct VarHandle : public VarHandleBase {
   explicit VarHandle(ir::Node* node) : VarHandleBase(node) {}
 
+  virtual ~VarHandle();
+
   std::string DebugString() const override;
 
   VarHandle(ir::Node* node, size_t version, size_t scope_index,
@@ -121,6 +126,8 @@ struct VarHandle : public VarHandleBase {
 struct DummyVarHandle : public VarHandleBase {
   explicit DummyVarHandle(ir::Node* node) : VarHandleBase(node) {}
 
+  virtual ~DummyVarHandle();
+
   std::string DebugString() const override;
 };
 

paddle/fluid/framework/executor.cc

@@ -12,6 +12,8 @@ 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 <algorithm>
+
 #include "paddle/fluid/framework/executor.h"
 
 #include "paddle/fluid/framework/feed_fetch_method.h"
@@ -46,6 +48,7 @@ ExecutorPrepareContext::~ExecutorPrepareContext() {
   VLOG(5) << "destroy ExecutorPrepareContext";
 }
 
+#ifndef _WIN32
 template <typename RefCntMap>
 static void DeleteUnusedTensors(const Scope& scope, const OperatorBase* op,
                                 GarbageCollector<Tensor>* gc,
@@ -80,6 +83,7 @@ static void DeleteUnusedTensors(const Scope& scope, const OperatorBase* op,
     gc->Add(erase_tensors);
   }
 }
+#endif
 
 Executor::Executor(const platform::Place& place) : place_(place) {}
 
@@ -367,6 +371,7 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
     CreateVariables(ctx->prog_, local_scope, ctx->block_id_);
   }
 
+#ifndef _WIN32
   int64_t max_memory_size = GetEagerDeletionThreshold();
   std::unique_ptr<GarbageCollector<Tensor>> gc;
   // WhileOp would set keep_kids to false
@@ -408,6 +413,16 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
   } else {
     platform::DeviceContextPool::Instance().Get(place_)->Wait();
   }
+#else   // WIN32
+  for (auto& op : ctx->ops_) {
+    op->Run(*local_scope, place_);
+    if (FLAGS_benchmark) {
+      VLOG(2) << "Memory used after operator " + op->Type() + " running: "
+              << memory::memory_usage(place_);
+    }
+  }
+  platform::DeviceContextPool::Instance().Get(place_)->Wait();
+#endif  // NOT WIN32
 
   if (local_scope != scope) {
     scope->DeleteScope(local_scope);

paddle/fluid/framework/executor.h

@@ -17,12 +17,14 @@ limitations under the License. */
 #include <map>
 #include <string>
 #include <vector>
-#include "paddle/fluid/framework/garbage_collector.h"
 #include "paddle/fluid/framework/op_info.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/platform/device_context.h"
+#ifndef _WIN32
+#include "paddle/fluid/framework/garbage_collector.h"
+#endif
 
 namespace paddle {
 namespace framework {

paddle/fluid/framework/ir/CMakeLists.txt

@@ -53,6 +53,7 @@ set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
 
 cc_library(pass_builder SRCS pass_builder.cc DEPS pass)
 
+cc_test(node_test SRCS node_test.cc DEPS node)
 cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
 cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
 cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)

paddle/fluid/framework/ir/graph.h

@@ -102,6 +102,15 @@ class Graph {
     attr_dels_[attr_name] = []() {};
   }
 
+  template <typename AttrType>
+  void Erase(const std::string &attr_name) {
+    PADDLE_ENFORCE(attrs_.count(attr_name) != 0, "%s not set in the graph",
+                   attr_name);
+    attr_dels_[attr_name]();
+    attrs_.erase(attr_name);
+    attr_dels_.erase(attr_name);
+  }
+
   const std::unordered_set<ir::Node *> &Nodes() const { return node_set_; }
 
   // Create a normal variable with non-null VarDesc.

paddle/fluid/framework/ir/graph_helper.h

@@ -37,6 +37,15 @@ std::vector<ir::Node *> TopologySortOperations(const Graph &graph);
 std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
     const Graph &graph);
 
+template <typename T>
+std::vector<T *> FilterByNodeWrapper(const Graph &graph) {
+  std::vector<T *> ret;
+  for (ir::Node *n : graph.Nodes()) {
+    if (n->IsWrappedBy<T>()) ret.push_back(&n->Wrapper<T>());
+  }
+  return ret;
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/node.cc

@@ -17,7 +17,12 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 namespace ir {
+// msvc15 don't support constexpr in correct way.
+#if !defined(_WIN32)
 constexpr char Node::kControlDepVarName[];
+#else
+const char Node::kControlDepVarName[] = "__control_var";
+#endif
 int Node::count_ = 0;
 
 std::unique_ptr<Node> CreateNodeForTest(const std::string& name,

paddle/fluid/framework/ir/node.h

@@ -15,7 +15,10 @@ limitations under the License. */
 #pragma once
 
 #include <string>
+#include <typeindex>
+#include <typeinfo>
 #include <vector>
+
 #include "paddle/fluid/framework/op_desc.h"
 #include "paddle/fluid/framework/var_desc.h"
 #include "paddle/fluid/platform/macros.h"
@@ -24,11 +27,39 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
-// Node should normally created by Graph::CreateXXXNode().
+// Node should only created by Graph::CreateXXXNode().
+// 1. Every Node should be part of a graph. No dangling Node exists.
+// 2. Node only contains members necessary for building graph structure.
+//    It doesn't contain other unrelated members, such as device, etc.
+//
+// Sometimes, for specific usages, Node needs to have additional members,
+// such as device_placement, version in order to be executed. It is suggested
+// to use composition pattern.
+//
+// class RunnableOp {
+//    RunnableOp(ir::Node* n) : n_(n) { n_.WrappedBy(this); }
+//
+//    int any_thing_;
+// }
+//
+// RunnableOp is owned by the ir::Node that composes it. In other words.
+// ir::Node will be responsible for deleting RunnableOp, say, when ir::Node
+// is deleted from the graph.
 class Node {
  public:
+  virtual ~Node() {
+    if (!wrapper_.empty()) {
+      VLOG(4) << "ir::Node deleting a wrapper node " << Name();
+      wrapper_deleter_();
+    }
+  }
+
   enum class Type { kOperation, kVariable };
-  static constexpr const char kControlDepVarName[] = "__control_var";
+#if !defined(_WIN32)  // msvc not support constexpr correctly.
+  static constexpr char kControlDepVarName[] = "__control_var";
+#else
+  static const char kControlDepVarName[];
+#endif
 
   Type NodeType() const { return type_; }
 
@@ -44,6 +75,29 @@ class Node {
     return op_desc_.get();
   }
 
+  // Set the `wrapper` that wraps the Node. `wrapper` is owned by Node.
+  template <typename T>
+  void WrappedBy(T* wrapper) {
+    if (!wrapper_.empty()) {
+      wrapper_deleter_();
+    }
+    wrapper_ = wrapper;
+    wrapper_deleter_ = [wrapper]() { delete wrapper; };
+    wrapper_type_ = std::type_index(typeid(T));
+  }
+
+  // Return a reference to the `wrapper`.
+  template <typename T>
+  T& Wrapper() {
+    return *boost::any_cast<T*>(wrapper_);
+  }
+
+  // Test if the Node is wrapped by type T.
+  template <typename T>
+  bool IsWrappedBy() {
+    return std::type_index(typeid(T)) == wrapper_type_;
+  }
+
   // Please don't use this API!
   int id() const { return id_; }
 
@@ -95,6 +149,11 @@ class Node {
   static int count_;
   // Please don't use this API or make this public.
   static void ResetId() { count_ = 0; }
+
+  boost::any wrapper_;
+  std::function<void(void)> wrapper_deleter_;
+  std::type_index wrapper_type_ = std::type_index(typeid(void));
+
   DISABLE_COPY_AND_ASSIGN(Node);
 };
 

paddle/fluid/framework/ir/node_test.cc

+/* 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. */
+
+#include <string>
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class RunnableOp {
+ public:
+  RunnableOp(Node* node, bool* alive) : node_(node), alive_(alive) {
+    node_->WrappedBy(this);
+  }
+
+  virtual ~RunnableOp() { *alive_ = false; }
+
+ private:
+  Node* node_;
+  bool* alive_;
+};
+
+class RunnableOp2 {
+ public:
+  RunnableOp2(Node* node, bool* alive) : node_(node), alive_(alive) {
+    node_->WrappedBy(this);
+  }
+
+  virtual ~RunnableOp2() { *alive_ = false; }
+
+ private:
+  Node* node_;
+  bool* alive_;
+};
+
+TEST(NodeTest, Basic) {
+  bool alive1 = true;
+  bool alive2 = true;
+  std::unique_ptr<Node> n1(CreateNodeForTest("n1", Node::Type::kVariable));
+  std::unique_ptr<Node> n2(CreateNodeForTest("n2", Node::Type::kVariable));
+
+  EXPECT_FALSE(n1->IsWrappedBy<RunnableOp>());
+  EXPECT_FALSE(n1->IsWrappedBy<RunnableOp2>());
+  EXPECT_FALSE(n2->IsWrappedBy<RunnableOp>());
+  EXPECT_FALSE(n2->IsWrappedBy<RunnableOp2>());
+
+  new RunnableOp(n1.get(), &alive1);
+  new RunnableOp2(n2.get(), &alive2);
+
+  EXPECT_TRUE(n1->IsWrappedBy<RunnableOp>());
+  EXPECT_FALSE(n1->IsWrappedBy<RunnableOp2>());
+  EXPECT_FALSE(n2->IsWrappedBy<RunnableOp>());
+  EXPECT_TRUE(n2->IsWrappedBy<RunnableOp2>());
+
+  EXPECT_TRUE(alive1);
+  EXPECT_TRUE(alive2);
+
+  n1.reset(nullptr);
+  n2.reset(nullptr);
+  EXPECT_FALSE(alive1);
+  EXPECT_FALSE(alive2);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/pass.h

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/platform/port.h"
 #include "paddle/fluid/platform/variant.h"
 
 namespace paddle {
@@ -195,6 +196,7 @@ struct PassRegistrar : public Registrar {
                              __test_global_namespace_##uniq_name##__>::value, \
                 msg)
 
+#if !defined(_WIN32)
 // Register a new pass that can be applied on the IR.
 #define REGISTER_PASS(pass_type, pass_class)                          \
   STATIC_ASSERT_PASS_GLOBAL_NAMESPACE(                                \
@@ -217,7 +219,30 @@ struct PassRegistrar : public Registrar {
   extern int TouchPassRegistrar_##pass_type();                        \
   static int use_pass_itself_##pass_type##_ __UNUSED__() = \
       TouchPassRegistrar_##pass_type()
+#else
+#define REGISTER_PASS(pass_type, pass_class)                        \
+  STATIC_ASSERT_PASS_GLOBAL_NAMESPACE(                              \
+      __reg_pass__##pass_type,                                      \
+      "REGISTER_PASS must be called in global namespace");          \
+  static ::paddle::framework::ir::PassRegistrar<pass_class>         \
+      __pass_registrar_##pass_type##__(#pass_type);                 \
+  int TouchPassRegistrar_##pass_type() {                            \
+    __pass_registrar_##pass_type##__.Touch();                       \
+    return 0;                                                       \
+  }                                                                 \
+  static ::paddle::framework::ir::PassRegistrar<pass_class> UNUSED( \
+      &__pass_tmp_registrar_##pass_type##__) =                      \
+      __pass_registrar_##pass_type##__
+
+#define USE_PASS(pass_type)                           \
+  STATIC_ASSERT_PASS_GLOBAL_NAMESPACE(                \
+      __use_pass_itself_##pass_type,                  \
+      "USE_PASS must be called in global namespace"); \
+  extern int TouchPassRegistrar_##pass_type();        \
+  static int UNUSED(use_pass_itself_##pass_type##_) = \
+      TouchPassRegistrar_##pass_type()
 
+#endif  // !_WIN32
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/tensor.h

@@ -20,6 +20,11 @@ limitations under the License. */
 #include <typeindex>
 #include <vector>
 
+#if defined(_WIN32)
+#define GLOG_NO_ABBREVIATED_SEVERITIES  // msvc conflict logging with windows.h
+#define GOOGLE_GLOG_DLL_DECL
+#endif
+
 #include "paddle/fluid/framework/data_layout.h"
 #include "paddle/fluid/framework/ddim.h"
 #include "paddle/fluid/memory/memory.h"

paddle/fluid/inference/CMakeLists.txt

@@ -17,6 +17,10 @@ cc_library(paddle_fluid_api
 
 get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
 get_property(cuda_modules GLOBAL PROPERTY CUDA_MODULES)
+get_property(fluid_third_partys GLOBAL PROPERTY FLUID_THRID_PARTYS)
+if (WIN32)
+list(APPEND fluid_third_partys gflags glog protobuf cblas)
+endif(WIN32)
 
 # paddle_fluid_origin exclude inference api interface
 if(WIN32)

paddle/fluid/inference/analysis/argument.h

@@ -26,6 +26,7 @@
 #include <string>
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
+#include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/variant.h"
 
 namespace paddle {
@@ -102,7 +103,6 @@ struct Argument {
   std::unordered_map<std::string, std::function<void()>> attr_deleters_;
 };
 
-#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
 #define ANALYSIS_ARGUMENT_CHECK_FIELD(field__)               \
   if (UNLIKELY(!(field__))) {                                \
     LOG(ERROR) << "field " << #field__ << " should be set."; \

paddle/fluid/inference/analysis/helper.h

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #pragma once
 
-#include <sys/stat.h>
 #include <cstdio>
 #include <fstream>
 #include <string>
@@ -26,6 +25,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/variable.h"
 #include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/port.h"
 
 namespace paddle {
 namespace inference {
@@ -124,24 +124,6 @@ T &GetFromScope(const framework::Scope &scope, const std::string &name) {
   return *var->GetMutable<T>();
 }
 
-static void ExecShellCommand(const std::string &cmd, std::string *message) {
-  char buffer[128];
-#if !defined(_WIN32)
-  std::shared_ptr<FILE> pipe(popen(cmd.c_str(), "r"), pclose);
-#else
-  std::shared_ptr<FILE> pipe(_popen(cmd.c_str(), "r"), _pclose);
-#endif  // _WIN32
-  if (!pipe) {
-    LOG(ERROR) << "error running command: " << cmd;
-    return;
-  }
-  while (!feof(pipe.get())) {
-    if (fgets(buffer, 128, pipe.get()) != nullptr) {
-      *message += buffer;
-    }
-  }
-}
-
 static framework::proto::ProgramDesc LoadProgramDesc(
     const std::string &model_path) {
   std::ifstream fin(model_path, std::ios::in | std::ios::binary);
@@ -163,16 +145,6 @@ static bool FileExists(const std::string &filepath) {
   return exists;
 }
 
-static bool PathExists(const std::string &path) {
-  struct stat statbuf;
-  if (stat(path.c_str(), &statbuf) != -1) {
-    if (S_ISDIR(statbuf.st_mode)) {
-      return true;
-    }
-  }
-  return false;
-}
-
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/api/CMakeLists.txt

@@ -24,6 +24,7 @@ if(WITH_GPU AND TENSORRT_FOUND)
 endif()
 
 cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
+cc_library(helper SRCS helper.cc DEPS reset_tensor_array lod_tensor scope)
 cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS reset_tensor_array lod_tensor scope)
 cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
 cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)

paddle/fluid/inference/api/api.cc

@@ -16,7 +16,6 @@
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/platform/enforce.h"
-#include "paddle_inference_api.h"
 
 namespace paddle {
 

paddle/fluid/inference/api/api_impl.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <algorithm>
+#include <fstream>
 #include <map>
 #include <set>
 #include <sstream>
@@ -24,6 +25,7 @@ limitations under the License. */
 #include "paddle/fluid/inference/api/api_impl.h"
 #include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/helper.h"
+#include "paddle/fluid/inference/api/timer.h"
 #include "paddle/fluid/platform/cpu_helper.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -31,16 +33,6 @@ DEFINE_bool(profile, false, "Turn on profiler for fluid");
 DECLARE_int32(paddle_num_threads);
 
 namespace paddle {
-namespace {
-using paddle::inference::Timer;
-
-template <class T>
-std::string num2str(T a) {
-  std::stringstream istr;
-  istr << a;
-  return istr.str();
-}
-}  // namespace
 
 void NativePaddlePredictor::PrepareFeedFetch() {
   for (auto *op : inference_program_->Block(0).AllOps()) {
@@ -63,7 +55,6 @@ void NativePaddlePredictor::PrepareFeedFetch() {
 
 bool NativePaddlePredictor::Init(
     std::shared_ptr<framework::Scope> parent_scope) {
-  VLOG(3) << "Predictor::init()";
 #if !defined(_WIN32)
   if (FLAGS_profile) {
     LOG(WARNING) << "Profiler is actived, might affect the performance";
@@ -91,21 +82,21 @@ bool NativePaddlePredictor::Init(
     paddle::framework::InitDevices(false);
     scope_.reset(new paddle::framework::Scope());
   }
-
   executor_.reset(new paddle::framework::Executor(place_));
-
   // Initialize the inference program
   if (!config_.model_dir.empty()) {
     // Parameters are saved in separate files sited in
     // the specified `dirname`.
     inference_program_ = paddle::inference::Load(executor_.get(), scope_.get(),
                                                  config_.model_dir);
+
   } else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
     // All parameters are saved in a single file.
     // The file names should be consistent with that used
     // in Python API `fluid.io.save_inference_model`.
     inference_program_ = paddle::inference::Load(
         executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
+
   } else {
     LOG(ERROR) << "fail to load inference model from " << config_.model_dir;
     return false;
@@ -135,7 +126,7 @@ NativePaddlePredictor::~NativePaddlePredictor() {
 bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
                                 std::vector<PaddleTensor> *output_data,
                                 int batch_size) {
-  VLOG(3) << "Predictor::predict";
+  using Timer = paddle::inference::Timer;
   Timer timer;
   timer.tic();
   // set feed variable
@@ -147,11 +138,9 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
   }
   // Run the inference program
   // if share variables, we need not create variables
-  VLOG(4) << "Run prepared context";
   executor_->RunPreparedContext(ctx_.get(), scope,
                                 false, /* don't create local scope each time*/
                                 false /* don't create variable each time */);
-  VLOG(4) << "Finish prepared context";
   // get fetch variable
   if (!GetFetch(output_data, scope)) {
     LOG(ERROR) << "fail to get fetches";
@@ -166,7 +155,6 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
 }
 
 std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
-  VLOG(3) << "Predictor::clone";
   std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
 
   if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(scope_)) {
@@ -184,7 +172,6 @@ std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
 
 bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
                                     framework::Scope *scope) {
-  VLOG(3) << "Predictor::set_feed";
   if (inputs.size() != feeds_.size()) {
     LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
                << inputs.size();
@@ -244,7 +231,6 @@ void NativePaddlePredictor::GetFetchOne(const framework::LoDTensor &fetch,
 
 bool NativePaddlePredictor::GetFetch(std::vector<PaddleTensor> *outputs,
                                      framework::Scope *scope) {
-  VLOG(3) << "Predictor::get_fetch";
   outputs->resize(fetchs_.size());
   for (size_t i = 0; i < fetchs_.size(); ++i) {
     int idx = boost::get<int>(fetchs_[i]->GetAttr("col"));
@@ -269,25 +255,22 @@ bool NativePaddlePredictor::GetFetch(std::vector<PaddleTensor> *outputs,
 template <>
 std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
     NativeConfig, PaddleEngineKind::kNative>(const NativeConfig &config) {
-  VLOG(3) << "create NativePaddlePredictor";
   if (config.use_gpu) {
     // 1. GPU memeroy
     PADDLE_ENFORCE_GT(
         config.fraction_of_gpu_memory, 0.f,
-        "fraction_of_gpu_memory in the config should be set to range (0., 1.]");
+        "fraction_of_gpu_memory in the config should be set to range (0.,1.]");
     PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
     std::vector<std::string> flags;
     if (config.fraction_of_gpu_memory >= 0.0f ||
         config.fraction_of_gpu_memory <= 0.95f) {
       flags.push_back("dummpy");
       std::string flag = "--fraction_of_gpu_memory_to_use=" +
-                         num2str<float>(config.fraction_of_gpu_memory);
+                         std::to_string(config.fraction_of_gpu_memory);
       flags.push_back(flag);
-      VLOG(3) << "set flag: " << flag;
       framework::InitGflags(flags);
     }
   }
-
   std::unique_ptr<PaddlePredictor> predictor(new NativePaddlePredictor(config));
   if (!dynamic_cast<NativePaddlePredictor *>(predictor.get())->Init(nullptr)) {
     return nullptr;

paddle/fluid/inference/api/api_impl.h

@@ -31,10 +31,10 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/naive_executor.h"
 #include "paddle/fluid/inference/api/details/reset_tensor_array.h"
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/inference/io.h"
 #include "paddle/fluid/platform/init.h"
 #include "paddle/fluid/platform/profiler.h"
+#include "paddle_inference_api.h"  // NOLINT
 
 namespace paddle {
 

paddle/fluid/inference/api/demo_ci/CMakeLists.txt

@@ -6,13 +6,13 @@ option(WITH_STATIC_LIB "Compile demo with static/shared library, default use sta
 option(USE_TENSORRT "Compile demo with TensorRT."   OFF)
 
 macro(safe_set_static_flag)
-    foreach(flag_var
-        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
-        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
-      if(${flag_var} MATCHES "/MD")
-        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
-      endif(${flag_var} MATCHES "/MD")
-    endforeach(flag_var)
+  foreach(flag_var
+      CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
+      CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
+    if(${flag_var} MATCHES "/MD")
+      string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+    endif(${flag_var} MATCHES "/MD")
+  endforeach(flag_var)
 endmacro()
 
 if (WIN32)
@@ -37,26 +37,25 @@ if(NOT DEFINED DEMO_NAME)
 endif()
 
 
-if(WITH_GPU)
+if(WITH_GPU) # default gpu path
   if(NOT WIN32)
     set(CUDA_LIB "/usr/local/cuda/lib64/" CACHE STRING "CUDA Library")
   else()
     if(CUDA_LIB STREQUAL "")
-    set(CUDA_LIB "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64")
+      set(CUDA_LIB "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64")
     endif()
   endif(NOT WIN32)
 endif()
 
-include_directories("D:/Paddle/")
 include_directories("${PADDLE_LIB}")
 include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
 include_directories("${PADDLE_LIB}/third_party/install/glog/include")
 include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
 include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
 if (NOT WIN32)
-include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
-include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
-include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
+  include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
+  include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
+  include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
 endif(NOT WIN32)
 
 include_directories("${PADDLE_LIB}/third_party/boost")
@@ -64,15 +63,15 @@ include_directories("${PADDLE_LIB}/third_party/eigen3")
 
 if (NOT WIN32)
   if (USE_TENSORRT AND WITH_GPU)
-      include_directories("${TENSORRT_INCLUDE_DIR}")
-      link_directories("${TENSORRT_LIB_DIR}")
+    include_directories("${TENSORRT_INCLUDE_DIR}")
+    link_directories("${TENSORRT_LIB_DIR}")
   endif()
 endif(NOT WIN32)
 
 if (NOT WIN32)
-link_directories("${PADDLE_LIB}/third_party/install/snappy/lib")
-link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
-link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
+  link_directories("${PADDLE_LIB}/third_party/install/snappy/lib")
+  link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
+  link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
 endif(NOT WIN32)
 
 link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
@@ -86,7 +85,7 @@ add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
 if(WITH_MKL)
   include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
   set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
-               ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+    ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
   set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
   if(EXISTS ${MKLDNN_PATH})
     include_directories("${MKLDNN_PATH}/include")
@@ -99,25 +98,25 @@ endif()
 # Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
 if(WITH_STATIC_LIB)
   set(DEPS
-      ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
+    ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
 else()
   set(DEPS
-      ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
+    ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
 endif()
 
 if (NOT WIN32)
-set(EXTERNAL_LIB "-lrt -ldl -lpthread")
-set(DEPS ${DEPS}
+  set(EXTERNAL_LIB "-lrt -ldl -lpthread")
+  set(DEPS ${DEPS}
     ${MATH_LIB} ${MKLDNN_LIB}
     glog gflags protobuf snappystream snappy z xxhash
     ${EXTERNAL_LIB})
 else()
-set(DEPS ${DEPS}
+  set(DEPS ${DEPS}
     ${MATH_LIB} ${MKLDNN_LIB}
     ${CMAKE_STATIC_LIBRARY_PREFIX}glog  ${CMAKE_STATIC_LIBRARY_PREFIX}gflags  ${CMAKE_STATIC_LIBRARY_PREFIX}protobuf
     ${EXTERNAL_LIB})
-# NOTE(dzhwinter) shlwapi is deprecated.
-set(DEPS ${DEPS} libcmt shlwapi)
+  # NOTE(dzhwinter) shlwapi will be deprecated.
+  set(DEPS ${DEPS} libcmt shlwapi)
 endif(NOT WIN32)
 
 if(WITH_GPU)
@@ -129,8 +128,8 @@ if(WITH_GPU)
     set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
   else()
     set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} )
-  set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
-  set(DEPS ${DEPS} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDA_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX} )
   endif()
 endif()
 

paddle/fluid/inference/api/demo_ci/inference_icnet.cc

+// 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.
+#define GOOGLE_GLOG_DLL_DECL
+#include <gflags/gflags.h>
+#include <glog/logging.h>
+#include <chrono>  // NOLINT
+#include <fstream>
+#include <iostream>
+#include <thread>  // NOLINT
+#include <utility>
+#include "paddle/fluid/inference/paddle_inference_api.h"
+
+namespace paddle {
+
+NativeConfig GetConfig() {
+  NativeConfig config;
+  config.prog_file = "hs_lb_without_bn_cudnn/__model__";
+  config.param_file = "hs_lb_without_bn_cudnn/__params__";
+  config.fraction_of_gpu_memory = 0.0;
+  config.use_gpu = true;
+  config.device = 0;
+  return config;
+}
+
+using Time = decltype(std::chrono::high_resolution_clock::now());
+Time TimeNow() { return std::chrono::high_resolution_clock::now(); }
+double TimeDiff(Time t1, Time t2) {
+  typedef std::chrono::microseconds ms;
+  auto diff = t2 - t1;
+  ms counter = std::chrono::duration_cast<ms>(diff);
+  return counter.count() / 1000.0;
+}
+
+std::vector<PaddleTensor> PrepareData() {
+  int height = 449;
+  int width = 581;
+  std::vector<float> data;
+  for (int i = 0; i < 3 * height * width; ++i) {
+    data.push_back(0.0);
+  }
+  PaddleTensor tensor;
+  tensor.shape = std::vector<int>({batch_size, 3, height, width});
+  tensor.data.Resize(sizeof(float) * batch_size * 3 * height * width);
+  std::copy(data.begin(), data.end(), static_cast<float*>(tensor.data.data()));
+  tensor.dtype = PaddleDType::FLOAT32;
+  std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
+  return std::move(paddle_tensor_feeds);
+}
+
+void TestNaive(int batch_size, int thread_num) {
+  NativeConfig config = GetConfig();
+
+  int num_jobs = thread_num;   // parallel jobs.
+  constexpr int epoches = 10;  // each job run epoches.
+  std::vector<std::thread> threads;
+  std::vector<std::unique_ptr<PaddlePredictor>> predictors;
+  for (int tid = 0; tid < num_jobs; ++tid) {
+    auto& pred = CreatePaddlePredictor<NativeConfig>(config);
+    predictors.emplace_back(std::move(pred));
+  }
+
+  auto time1 = TimeNow();
+  for (int tid = 0; tid < num_jobs; ++tid) {
+    threads.emplace_back([&, tid]() {
+      auto& predictor = predictors[tid];
+      PaddleTensor tensor_out;
+      std::vector<PaddleTensor> outputs(1, tensor_out);
+      for (size_t i = 0; i < epoches; i++) {
+        ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
+        VLOG(3) << "tid : " << tid << " run: " << i << "finished";
+        ASSERT_EQ(outputs.size(), 1UL);
+      }
+    });
+  }
+  for (int i = 0; i < num_jobs; ++i) {
+    threads[i].join();
+  }
+  auto time2 = TimeNow();
+  VLOG(3) << "Thread num " << thread_num << "total time cost"
+          << (time2 - time1);
+}
+}  // namespace paddle
+
+int main(int argc, char** argv) {
+  paddle::TestNaive(1, 1);  // single thread.
+  paddle::TestNaive(1, 5);  // 5 threads.
+  return 0;
+}

paddle/fluid/inference/api/helper.h

@@ -14,40 +14,26 @@
 
 #pragma once
 
+#define GLOG_NO_ABBREVIATED_SEVERITIES
+#define GOOGLE_GLOG_DLL_DECL
 #include <glog/logging.h>
 #if !defined(_WIN32)
 #include <sys/time.h>
 #else
 #endif
+#include <algorithm>
 #include <chrono>  // NOLINT
 #include <iterator>
 #include <numeric>
 #include <sstream>
 #include <string>
 #include <vector>
-#include "paddle/fluid/string/printf.h"
-#include "paddle_inference_api.h"
+#include "paddle/fluid/inference/api/timer.h"
+#include "paddle_inference_api.h"  //NOLINT
 
 namespace paddle {
 namespace inference {
 
-// Timer for timer
-class Timer {
- public:
-  std::chrono::high_resolution_clock::time_point start;
-  std::chrono::high_resolution_clock::time_point startu;
-
-  void tic() { start = std::chrono::high_resolution_clock::now(); }
-  double toc() {
-    startu = std::chrono::high_resolution_clock::now();
-    std::chrono::duration<double> time_span =
-        std::chrono::duration_cast<std::chrono::duration<double>>(startu -
-                                                                  start);
-    double used_time_ms = static_cast<double>(time_span.count()) * 1000.0;
-    return used_time_ms;
-  }
-};
-
 static void split(const std::string &str, char sep,
                   std::vector<std::string> *pieces) {
   pieces->clear();

paddle/fluid/inference/api/timer.h

+// 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 <chrono>  // NOLINT
+
+namespace paddle {
+namespace inference {
+
+// Timer for timer
+class Timer {
+ public:
+  std::chrono::high_resolution_clock::time_point start;
+  std::chrono::high_resolution_clock::time_point startu;
+
+  void tic() { start = std::chrono::high_resolution_clock::now(); }
+  double toc() {
+    startu = std::chrono::high_resolution_clock::now();
+    std::chrono::duration<double> time_span =
+        std::chrono::duration_cast<std::chrono::duration<double>>(startu -
+                                                                  start);
+    double used_time_ms = static_cast<double>(time_span.count()) * 1000.0;
+    return used_time_ms;
+  }
+};
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/memory/detail/buddy_allocator.cc

@@ -11,7 +11,8 @@ 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. */
-
+#define GLOG_NO_ABBREVIATED_SEVERITIES
+#define GOOGLE_GLOG_DLL_DECL
 #include "paddle/fluid/memory/detail/buddy_allocator.h"
 #include "glog/logging.h"
 

paddle/fluid/memory/detail/meta_cache.cc

@@ -12,6 +12,8 @@ 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. */
 
+#define GLOG_NO_ABBREVIATED_SEVERITIES
+#define GOOGLE_GLOG_DLL_DECL
 #include "glog/logging.h"
 #include "paddle/fluid/memory/detail/memory_block.h"
 #include "paddle/fluid/platform/assert.h"

paddle/fluid/memory/detail/system_allocator.cc

@@ -12,6 +12,7 @@ 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. */
 #define GLOG_NO_ABBREVIATED_SEVERITIES
+#define GOOGLE_GLOG_DLL_DECL
 
 #include "paddle/fluid/memory/detail/system_allocator.h"
 

paddle/fluid/operators/accuracy_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 #include <algorithm>
+
 #include "paddle/fluid/framework/op_registry.h"
 
 namespace paddle {

paddle/fluid/operators/cast_op.h

@@ -54,6 +54,7 @@ class CastOpKernel : public framework::OpKernel<InT> {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* in = context.Input<framework::Tensor>("X");
     auto* out = context.Output<framework::Tensor>("Out");
+
     framework::VisitDataType(
         static_cast<framework::proto::VarType::Type>(
             context.Attr<int>("out_dtype")),

paddle/fluid/operators/detection/roi_perspective_transform_op.cu

@@ -35,12 +35,12 @@ namespace operators {
 
 template <typename T>
 __device__ bool GT_E(T a, T b) {
-  return (a > b) || Eigen::numext::abs(a - b) < 1e-4;
+  return (a > b) || fabsf(static_cast<float>(a - b)) < 1e-4;
 }
 
 template <typename T>
 __device__ bool LT_E(T a, T b) {
-  return (a < b) || Eigen::numext::abs(a - b) < 1e-4;
+  return (a < b) || fabsf(static_cast<float>(a - b)) < 1e-4;
 }
 
 template <typename T>

paddle/fluid/operators/elementwise_op_function.h

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #pragma once
 
-#include <glog/logging.h>
 #include <algorithm>
 #include <iterator>
 #include <vector>

paddle/fluid/operators/load_combine_op.cc

@@ -12,6 +12,7 @@ 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 <fstream>
+#include <memory>
 #include "paddle/fluid/framework/data_type_transform.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/platform/device_context.h"
@@ -32,9 +33,15 @@ class LoadCombineOp : public framework::OperatorBase {
                const platform::Place &place) const override {
     auto filename = Attr<std::string>("file_path");
     auto load_as_fp16 = Attr<bool>("load_as_fp16");
-
-    std::ifstream fin(filename);
-    PADDLE_ENFORCE(static_cast<bool>(fin),
+    auto format = Attr<std::string>("format");
+    std::unique_ptr<std::ifstream> fin;
+    if (format == "windows") {
+      fin.reset(new std::ifstream(filename,
+                                  std::ios_base::in | std::ios_base::binary));
+    } else {
+      fin.reset(new std::ifstream(filename));
+    }
+    PADDLE_ENFORCE(static_cast<bool>(*fin),
                    "Cannot open file %s for load_combine op", filename);
 
     auto out_var_names = Outputs("Out");
@@ -54,11 +61,11 @@ class LoadCombineOp : public framework::OperatorBase {
       auto *tensor = out_var->GetMutable<framework::LoDTensor>();
 
       // Error checking
-      PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot read more from file %s",
+      PADDLE_ENFORCE(static_cast<bool>(*fin), "Cannot read more from file %s",
                      filename);
 
       // Get data from fin to tensor
-      DeserializeFromStream(fin, tensor, dev_ctx);
+      DeserializeFromStream(*fin, tensor, dev_ctx);
 
       auto in_dtype = framework::ToDataType(tensor->type());
       auto out_dtype =
@@ -103,6 +110,18 @@ class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
                          "LoDTensors will be loaded from \"file_path\".")
         .AddCustomChecker(
             [](const std::string &path) { return !path.empty(); });
+    AddAttr<std::string>("format",
+                         R"DOC((windows|linux)" "saved model file format
+                         windows and linux file newline symbol is
+different. windows(newline is \n\r) or linux(newline is \r)
+So if you set attribute format to windows, then we saved model file in binary.
+It can be used both linux and windows. If you set format to linux,
+it will save file in normal file, newline symbol is \r. Need to note
+that these two format is not inter-compatible.)DOC")
+        .SetDefault("linux")
+        .AddCustomChecker([](const std::string &s) {
+          return s == "windows" || s == "linux";
+        });
     AddComment(R"DOC(
 LoadCombine Operator.
 

paddle/fluid/operators/load_op.cc

@@ -12,6 +12,7 @@ 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 <fstream>
+#include <memory>
 
 #include "paddle/fluid/framework/data_type_transform.h"
 #include "paddle/fluid/framework/op_registry.h"
@@ -34,8 +35,15 @@ class LoadOp : public framework::OperatorBase {
     // FIXME(yuyang18): We save variable to local file now, but we should change
     // it to save an output stream.
     auto filename = Attr<std::string>("file_path");
-    std::ifstream fin(filename);
-    PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
+    auto format = Attr<std::string>("format");
+    std::unique_ptr<std::ifstream> fin;
+    if (format == "windows") {
+      fin.reset(new std::ifstream(filename,
+                                  std::ios_base::in | std::ios_base::binary));
+    } else {
+      fin.reset(new std::ifstream(filename));
+    }
+    PADDLE_ENFORCE(static_cast<bool>(*fin), "Cannot open file %s for load op",
                    filename);
 
     auto out_var_name = Output("Out");
@@ -44,9 +52,9 @@ class LoadOp : public framework::OperatorBase {
                    out_var_name);
 
     if (out_var->IsType<framework::LoDTensor>()) {
-      LoadLodTensor(fin, place, out_var);
+      LoadLodTensor(*fin, place, out_var);
     } else if (out_var->IsType<framework::SelectedRows>()) {
-      LoadSelectedRows(fin, place, out_var);
+      LoadSelectedRows(*fin, place, out_var);
     } else {
       PADDLE_ENFORCE(
           false,
@@ -110,6 +118,18 @@ class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
                          R"(Variable will be loaded from "file_path")")
         .AddCustomChecker(
             [](const std::string &path) { return !path.empty(); });
+    AddAttr<std::string>("format",
+                         R"DOC((windows|linux)" "saved model file format
+                         windows and linux file newline symbol is
+different. windows(newline is \n\r) or linux(newline is \r)
+So if you set attribute format to windows, then we saved model file in binary.
+It can be used both linux and windows. If you set format to linux,
+it will save file in normal file, newline symbol is \r. Need to note
+that these two format is not inter-compatible.)DOC")
+        .SetDefault("linux")
+        .AddCustomChecker([](const std::string &s) {
+          return s == "windows" || s == "linux";
+        });
     AddComment(
         "Load operator will load a LoDTensor / SelectedRows variable from disk "
         "file.");

paddle/fluid/operators/lstm_unit_op.h

@@ -4,7 +4,7 @@ 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
+   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,

paddle/fluid/operators/math/CMakeLists.txt

@@ -57,9 +57,6 @@ math_library(sequence_padding)
 math_library(sequence_pooling DEPS math_function)
 math_library(sequence_scale)
 math_library(softmax DEPS math_function)
-if (NOT WIN32)
-    math_library(matrix_bit_code)
-endif (NOT WIN32)
 math_library(unpooling)
 math_library(vol2col)
 
@@ -76,12 +73,11 @@ endif()
 cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
 cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
 if (NOT WIN32)
+    math_library(matrix_bit_code)
     set(JIT_KERNEL_SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc)
     set(JIT_KERNEL_DEPS cpu_info cblas gflags enforce)
     if(WITH_XBYAK)
         list(APPEND JIT_KERNEL_SRCS jit_gen.cc jit_code.cc)
         list(APPEND JIT_KERNEL_DEPS xbyak)
     endif()
-    cc_library(jit_kernel SRCS ${JIT_KERNEL_SRCS} DEPS ${JIT_KERNEL_DEPS})
-    cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
-endif()
+endif (NOT WIN32)

paddle/fluid/operators/math/cpu_vec.h

@@ -18,10 +18,6 @@ limitations under the License. */
 #include <string>
 #include "paddle/fluid/platform/cpu_info.h"
 #include "paddle/fluid/platform/enforce.h"
-#ifdef __AVX__
-#include <immintrin.h>
-#endif
-
 #ifdef PADDLE_WITH_MKLML
 #include "paddle/fluid/platform/dynload/mklml.h"
 #endif

paddle/fluid/operators/math/detail/activation_functions.h

@@ -15,13 +15,10 @@ limitations under the License. */
 #pragma once
 #include <math.h>
 #include <string>
+#include "paddle/fluid/platform/cpu_info.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/hostdevice.h"
 
-#ifdef __AVX__
-#include <immintrin.h>
-#endif
-
 namespace paddle {
 namespace operators {
 namespace math {

paddle/fluid/operators/math/jit_kernel_blas.cc

@@ -25,10 +25,6 @@ limitations under the License. */
 #include "paddle/fluid/platform/dynload/mklml.h"
 #endif
 
-#ifdef __AVX__
-#include <immintrin.h>
-#endif
-
 namespace paddle {
 namespace operators {
 namespace math {

paddle/fluid/operators/math/jit_kernel_crf_decode.cc

@@ -16,9 +16,6 @@ limitations under the License. */
 #include <limits>
 #include <string>
 #include "paddle/fluid/operators/math/jit_kernel_macro.h"
-#ifdef __AVX__
-#include <immintrin.h>
-#endif
 
 namespace paddle {
 namespace operators {
@@ -263,6 +260,7 @@ class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
     }                                                                          \
   }
 
+#ifndef _WIN32  // commented out crf decoding
 #ifdef __AVX__
 INTRIAVX_FLOAT(kEQ8);
 INTRIAVX_FLOAT(kGT8LT16);
@@ -275,6 +273,7 @@ INTRIAVX2_FLOAT(jit::avx2, kGT8LT16);
 INTRIAVX2_FLOAT(jit::avx2, kEQ16);
 INTRIAVX2_FLOAT(jit::avx2, kGT16);
 #endif
+#endif  // WIN32
 #ifdef __AVX512F__
 INTRIAVX2_FLOAT(jit::avx512f, kEQ8);
 INTRIAVX2_FLOAT(jit::avx512f, kGT8LT16);

paddle/fluid/operators/math/jit_kernel_exp.cc

@@ -20,10 +20,6 @@ limitations under the License. */
 #include "paddle/fluid/platform/dynload/mklml.h"
 #endif
 
-#ifdef __AVX__
-#include <immintrin.h>
-#endif
-
 namespace paddle {
 namespace operators {
 namespace math {
@@ -66,14 +62,18 @@ namespace detail {
 
 #ifdef __AVX__
 
+#if defined(_WIN32)
+#define ALIGN32 __declspec(align(32))
+#else
 #define ALIGN32 __attribute__((aligned(32)))
+#endif  // _WIN32
 
 #define _PS256_CONST(Name, Val)                                      \
-  static const float _ps256_##Name[8] ALIGN32 = {Val, Val, Val, Val, \
+  static const float ALIGN32 _ps256_##Name[8] = {Val, Val, Val, Val, \
                                                  Val, Val, Val, Val}
 
 #define _PI256_CONST(Name, Val)                                    \
-  static const int _pi256_##Name[8] ALIGN32 = {Val, Val, Val, Val, \
+  static const int ALIGN32 _pi256_##Name[8] = {Val, Val, Val, Val, \
                                                Val, Val, Val, Val}
 
 _PI256_CONST(0x7f, 0x7f);
@@ -98,7 +98,7 @@ typedef union imm_xmm_union {
 
 #define COPY_IMM_TO_XMM(imm_, xmm0_, xmm1_) \
   {                                         \
-    imm_xmm_union u ALIGN32;                \
+    imm_xmm_union ALIGN32 u;                \
     u.imm = imm_;                           \
     xmm0_ = u.xmm[0];                       \
     xmm1_ = u.xmm[1];                       \
@@ -106,7 +106,7 @@ typedef union imm_xmm_union {
 
 #define COPY_XMM_TO_IMM(xmm0_, xmm1_, imm_) \
   {                                         \
-    imm_xmm_union u ALIGN32;                \
+    imm_xmm_union ALIGN32 u;                \
     u.xmm[0] = xmm0_;                       \
     u.xmm[1] = xmm1_;                       \
     imm_ = u.imm;                           \
@@ -508,12 +508,14 @@ class VTanhKernelImpl : public VTanhKernel<T> {
     vaddbias_->Compute(-1.f, y, y);                                           \
   }
 
+#ifndef __WIN32
 #ifdef __AVX__
 INTRI8_FLOAT(jit::avx, detail::ExpAVX);
 INTRI16_FLOAT(jit::avx, detail::ExpAVX);
 INTRI_GT8LT16_FLOAT(jit::avx, detail::ExpAVX);
 INTRI_GT16_FLOAT(jit::avx, detail::ExpAVX);
-#endif
+#endif  // AVX
+#endif  // WIN32
 #ifdef __AVX2__
 INTRI8_FLOAT(jit::avx2, detail::ExpAVX2);
 INTRI16_FLOAT(jit::avx2, detail::ExpAVX2);

paddle/fluid/operators/math/jit_kernel_rnn.cc

@@ -18,10 +18,6 @@ limitations under the License. */
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/macros.h"
 
-#ifdef __AVX__
-#include <immintrin.h>
-#endif
-
 namespace paddle {
 namespace operators {
 namespace math {

paddle/fluid/operators/math/selected_rows_functor.cu

@@ -16,6 +16,7 @@ limitations under the License. */
 #include <vector>
 
 #include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/math/math_function_impl.h"
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
 #include "paddle/fluid/platform/float16.h"

paddle/fluid/operators/math/sequence_pooling.cu

@@ -16,18 +16,12 @@ limitations under the License. */
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/operators/math/sequence_pooling.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/macros.h"
 
 namespace paddle {
 namespace operators {
 namespace math {
 
-#if defined(__FLT_MAX__)
-#define FLT_MAX __FLT_MAX__
-#else
-#include <float.h>
-#include <limits>
-#endif
-
 template <typename T>
 struct MaxPoolFunctor {
   HOSTDEVICE void operator()(const T* input, const size_t start,

paddle/fluid/operators/print_op.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include <algorithm>
+#include <iostream>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/var_type.h"
 

paddle/fluid/operators/save_combine_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include <stdint.h>
 #include <fstream>
+#include <memory>
 #include <numeric>
 #include <sstream>
 #include "paddle/fluid/framework/data_type.h"
@@ -41,6 +42,7 @@ class SaveCombineOp : public framework::OperatorBase {
     auto filename = Attr<std::string>("file_path");
     auto overwrite = Attr<bool>("overwrite");
     auto save_as_fp16 = Attr<bool>("save_as_fp16");
+    auto format = Attr<std::string>("format");
 
     bool is_present = FileExists(filename);
     if (is_present && !overwrite) {
@@ -49,8 +51,14 @@ class SaveCombineOp : public framework::OperatorBase {
     }
 
     MkDirRecursively(DirName(filename).c_str());
-    std::ofstream fout(filename);
-    PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
+    std::unique_ptr<std::ofstream> fout;
+    if (format == "windows") {
+      fout.reset(new std::ofstream(filename,
+                                   std::ios_base::out | std::ios_base::binary));
+    } else {
+      fout.reset(new std::ofstream(filename));
+    }
+    PADDLE_ENFORCE(static_cast<bool>(*fout), "Cannot open %s to write",
                    filename);
 
     auto inp_var_names = Inputs("X");
@@ -86,12 +94,11 @@ class SaveCombineOp : public framework::OperatorBase {
         // copy LoD info to the new tensor
         out.set_lod(tensor.lod());
         framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
-        framework::SerializeToStream(fout, out, dev_ctx);
+        framework::SerializeToStream(*fout, out, dev_ctx);
       } else {
-        framework::SerializeToStream(fout, tensor, dev_ctx);
+        framework::SerializeToStream(*fout, tensor, dev_ctx);
       }
     }
-    fout.close();
   }
 };
 
@@ -124,6 +131,18 @@ to a file on disk.
         "The \"file_path\" where the LoDTensor variables will be saved.")
         .AddCustomChecker(
             [](const std::string &path) { return !path.empty(); });
+    AddAttr<std::string>("format",
+                         R"DOC((windows|linux)" "saved model file format
+                         windows and linux file newline symbol is
+different. windows(newline is \n\r) or linux(newline is \r)
+So if you set attribute format to windows, then we saved model file in binary.
+It can be used both linux and windows. If you set format to linux,
+it will save file in normal file, newline symbol is \r. Need to note
+that these two format is not inter-compatible.)DOC")
+        .SetDefault("linux")
+        .AddCustomChecker([](const std::string &s) {
+          return s == "windows" || s == "linux";
+        });
   }
 };
 

paddle/fluid/operators/save_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include <stdint.h>
 #include <fstream>
+#include <memory>
 #include <numeric>
 
 #include "paddle/fluid/framework/data_type.h"
@@ -64,6 +65,7 @@ class SaveOp : public framework::OperatorBase {
                      framework::Variable *var) const {
     auto filename = Attr<std::string>("file_path");
     auto overwrite = Attr<bool>("overwrite");
+    auto format = Attr<std::string>("format");
 
     if (FileExists(filename) && !overwrite) {
       PADDLE_THROW("%s is existed, cannot save to it when overwrite=false",
@@ -80,8 +82,14 @@ class SaveOp : public framework::OperatorBase {
 
     // FIXME(yuyang18): We save variable to local file now, but we should change
     // it to save an output stream.
-    std::ofstream fout(filename);
-    PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
+    std::unique_ptr<std::ofstream> fout;
+    if (format == "windows") {
+      fout.reset(new std::ofstream(filename,
+                                   std::ios_base::out | std::ios_base::binary));
+    } else {
+      fout.reset(new std::ofstream(filename));
+    }
+    PADDLE_ENFORCE(static_cast<bool>(*fout), "Cannot open %s to write",
                    filename);
 
     auto save_as_fp16 = Attr<bool>("save_as_fp16");
@@ -95,11 +103,10 @@ class SaveOp : public framework::OperatorBase {
       framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
       // copy LoD info to the new tensor
       out.set_lod(tensor.lod());
-      framework::SerializeToStream(fout, out, dev_ctx);
+      framework::SerializeToStream(*fout, out, dev_ctx);
     } else {
-      framework::SerializeToStream(fout, tensor, dev_ctx);
+      framework::SerializeToStream(*fout, tensor, dev_ctx);
     }
-    fout.close();
   }
 
   void SaveSelectedRows(const framework::Scope &scope,
@@ -110,6 +117,7 @@ class SaveOp : public framework::OperatorBase {
         lt_var != nullptr,
         "Can not find variable kLookupTablePath for SaveSelectedRows");
     std::string filename = lt_var->data();
+    auto format = Attr<std::string>("format");
     VLOG(4) << "SaveSelectedRows get File name: " << filename;
 
     MkDirRecursively(DirName(filename).c_str());
@@ -122,11 +130,16 @@ class SaveOp : public framework::OperatorBase {
 
     // FIXME(yuyang18): We save variable to local file now, but we should change
     // it to save an output stream.
-    std::ofstream fout(filename);
-    PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
+    std::unique_ptr<std::ofstream> fout;
+    if (format == "windows") {
+      fout.reset(new std::ofstream(filename,
+                                   std::ios_base::out | std::ios_base::binary));
+    } else {
+      fout.reset(new std::ofstream(filename));
+    }
+    PADDLE_ENFORCE(static_cast<bool>(*fout), "Cannot open %s to write",
                    filename);
-    framework::SerializeToStream(fout, selectedRows, dev_ctx);
-    fout.close();
+    framework::SerializeToStream(*fout, selectedRows, dev_ctx);
   }
 };
 
@@ -154,6 +167,18 @@ This operator will serialize and write LoDTensor / SelectedRows variable to file
                          "The \"file_path\" where the variable will be saved.")
         .AddCustomChecker(
             [](const std::string &path) { return !path.empty(); });
+    AddAttr<std::string>("format",
+                         R"DOC((windows|linux)" "saved model file format
+                         windows and linux file newline symbol is
+different. windows(newline is \n\r) or linux(newline is \r)
+So if you set attribute format to windows, then we saved model file in binary.
+It can be used both linux and windows. If you set format to linux,
+it will save file in normal file, newline symbol is \r. Need to note
+that these two format is not inter-compatible.)DOC")
+        .SetDefault("linux")
+        .AddCustomChecker([](const std::string &s) {
+          return s == "windows" || s == "linux";
+        });
   }
 };
 

paddle/fluid/operators/space_to_depth_op.cc

+/* 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. */
+
+#include "paddle/fluid/operators/space_to_depth_op.h"
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+class SpaceToDepthOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SpaceToDepthOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of SpaceToDepthOp should not be null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_EQ(x_dims.size(), 4, "input should be a 4D tensor");
+    auto blocksize = ctx->Attrs().Get<int64_t>("blocksize");
+
+    PADDLE_ENFORCE_GT(blocksize, 1, "The blocksize should be Greater than 1");
+    PADDLE_ENFORCE_GT(x_dims[1], 0, "input channel should be Greater than 0");
+    PADDLE_ENFORCE_GT(x_dims[2], 0, "input Height should be Greater than 0");
+    PADDLE_ENFORCE_GT(x_dims[3], 0, "input Width should be Greater than 0");
+
+    PADDLE_ENFORCE_EQ(x_dims[1] % (blocksize * blocksize), 0,
+                      "input channel should be divisible of the square of "
+                      "SpaceToDepthOp blocksize");
+    PADDLE_ENFORCE_EQ(x_dims[2] % (blocksize), 0,
+                      "input Height should be divisible of the square of "
+                      "SpaceToDepthOp blocksize");
+    PADDLE_ENFORCE_EQ(x_dims[3] % (blocksize), 0,
+                      "input Width should be divisible of the square of "
+                      "SpaceToDepthOp blocksize");
+
+    VLOG(3) << "SpaceToDepthOp operator x.shape=" << x_dims
+            << "Attribute blocksize" << blocksize << std::endl;
+
+    std::vector<int64_t> output_shape(4, 0);  // [B,C,H,W]
+    output_shape[0] = x_dims[0];
+    output_shape[1] = x_dims[1] * blocksize * blocksize;
+    output_shape[2] = x_dims[2] / blocksize;
+    output_shape[3] = x_dims[3] / blocksize;
+
+    auto out_dims = framework::make_ddim(output_shape);
+
+    ctx->SetOutputDim("Out", out_dims);
+
+    if (x_dims[0] == out_dims[0]) {
+      // Only pass LoD when the first dimension of output and Input(X)
+      // are the same.
+      ctx->ShareLoD("X", /*->*/ "Out");
+    }
+  }
+};
+
+class SpaceToDepthOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor). The input should be a 4D tensor B * C * W * H of "
+             "SpaceToDepthOp "
+             "operator.");
+    AddOutput("Out",
+              "(Tensor), The output should be a 4D tensor B * C2 * W2 * H2 of "
+              "SpaceToDepthOp operator.");
+    AddAttr<int64_t>(
+        "blocksize",
+        "(int64_t, default 2) blocksize used to do change Space To Depth.")
+        .SetDefault(2)
+        .GreaterThan(1);
+    AddComment(R"DOC(
+        reorg operator used in Yolo v2.
+        The equation is: C2 = C1/blocksize * blocksize, W2 = W1 ∗ blocksize + offset % blocksize, H2 = H1 ∗ blocksize + offset / blocksize, 
+
+        Reshape Input(X) into the shape according to Attr(blocksize). The
+        data in Input(X) are unchanged.
+
+        Examples:
+
+            1. Given a 4-D tensor Input(X) with a shape [128, 2048, 26, 26], and the blocksize is 2, the reorg operator will transform Input(X)
+            into a 4-D tensor with shape [128, 2048, 13, 13] and leaving Input(X)'s data unchanged.
+
+    )DOC");
+  }
+};
+
+class SpaceToDepthGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) shouldn't be null.");
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(space_to_depth, ops::SpaceToDepthOp, ops::SpaceToDepthOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(space_to_depth_grad, ops::SpaceToDepthGradOp);
+REGISTER_OP_CPU_KERNEL(
+    space_to_depth,
+    ops::SpaceToDepthKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SpaceToDepthKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::SpaceToDepthKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    space_to_depth_grad,
+    ops::SpaceToDepthGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SpaceToDepthGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::SpaceToDepthGradKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/space_to_depth_op.cu

+// 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.
+
+#include "paddle/fluid/operators/space_to_depth_op.h"
+
+namespace plat = paddle::platform;
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    space_to_depth,
+    ops::SpaceToDepthKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SpaceToDepthKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::SpaceToDepthKernel<paddle::platform::CUDADeviceContext, int64_t>);
+
+REGISTER_OP_CUDA_KERNEL(
+    space_to_depth_grad,
+    ops::SpaceToDepthGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SpaceToDepthGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::SpaceToDepthGradKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/space_to_depth_op.h

+/* 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. */
+#ifndef PADDLE_FLUID_OPERATORS_SPACE_TO_DEPTH_OP_H_
+#define PADDLE_FLUID_OPERATORS_SPACE_TO_DEPTH_OP_H_
+#endif  // PADDLE_FLUID_OPERATORS_SPACE_TO_DEPTH_OP_H_
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/platform/for_range.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class space_to_depth_compute {
+ public:
+  HOSTDEVICE space_to_depth_compute(const T *x, int64_t w, int64_t h, int64_t c,
+                                    int64_t batch, int64_t blocksize,
+                                    int64_t forward, T *out)
+      : x_(x),
+        w_(w),
+        h_(h),
+        c_(c),
+        batch_(batch),
+        blocksize_(blocksize),
+        forward_(forward),
+        out_(out) {}
+
+  HOSTDEVICE void operator()(int64_t in_index) {
+    int64_t out_c = c_ / (blocksize_ * blocksize_);
+    // calculate each dim position with index of tensor
+    int64_t b = in_index / (c_ * h_ * w_);
+    int64_t k = (in_index % (c_ * h_ * w_)) / (h_ * w_);
+    int64_t j = ((in_index % (c_ * h_ * w_)) % (h_ * w_)) / w_;
+    int64_t i = ((in_index % (c_ * h_ * w_)) % (h_ * w_)) % w_;
+
+    int64_t c2 = k % out_c;
+    int64_t offset = k / out_c;
+    int64_t w2 = i * blocksize_ + offset % blocksize_;
+    int64_t h2 = j * blocksize_ + offset / blocksize_;
+    int64_t out_index =
+        w2 + w_ * blocksize_ * (h2 + h_ * blocksize_ * (c2 + out_c * b));
+    if (forward_)
+      out_[out_index] = x_[in_index];
+    else
+      out_[in_index] = x_[out_index];
+  }
+
+ private:
+  const T *x_;
+  int64_t w_, h_, c_, batch_, blocksize_, forward_;
+  T *out_;
+};
+
+template <typename DeviceContext, typename T>
+class SpaceToDepthKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto *out = context.Output<framework::LoDTensor>("Out");
+    auto *x = context.Input<framework::LoDTensor>("X");
+    auto blocksize = context.Attr<int64_t>("blocksize");
+    auto in_dims = x->dims();
+    out->mutable_data(context.GetPlace(), x->type());
+
+    auto out_dims = out->dims();
+    auto B = in_dims[0];
+    auto C = in_dims[1];
+    auto H = in_dims[2];
+    auto W = in_dims[3];
+    platform::ForRange<DeviceContext> for_range(
+        context.template device_context<DeviceContext>(),
+        static_cast<size_t>(x->numel()));
+
+    auto *x_data = x->data<T>();
+    auto *out_data = out->data<T>();
+    paddle::operators::space_to_depth_compute<T> computer(
+        x_data, W, H, C, B, blocksize, 1, out_data);
+    for_range(computer);
+
+    out->Resize(out_dims);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class SpaceToDepthGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto *d_out =
+        context.Input<framework::LoDTensor>(framework::GradVarName("Out"));
+    auto *d_x =
+        context.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto blocksize = context.Attr<int64_t>("blocksize");
+    auto in_dims = d_x->dims();
+    d_x->mutable_data(context.GetPlace(), d_out->type());
+
+    auto B = in_dims[0];
+    auto C = in_dims[1];
+    auto H = in_dims[2];
+    auto W = in_dims[3];
+
+    platform::ForRange<DeviceContext> for_range(
+        context.template device_context<DeviceContext>(),
+        static_cast<size_t>(d_x->numel()));
+
+    auto *dx_data = d_x->data<T>();
+    auto *dout_data = d_out->data<T>();
+
+    paddle::operators::space_to_depth_compute<T> computer(
+        dout_data, W, H, C, B, blocksize, 0, dx_data);
+    for_range(computer);
+
+    d_x->Resize(in_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/split_lod_tensor_op.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/port.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/tensorrt_engine_op.h

@@ -34,7 +34,7 @@ namespace operators {
 using FluidDT = framework::proto::VarType_Type;
 using TRT_DT = nvinfer1::DataType;
 
-namespace {
+namespace {  // NOLINT
 
 TRT_DT FluidDataType2TRT(FluidDT type) {
   switch (type) {
@@ -60,7 +60,7 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t>& shape) {
   return nvinfer1::DimsCHW(shape[1], 1, 1);
 }
 
-}  // namespace
+}  // NOLINT  // namespace
 
 using inference::Singleton;
 using inference::tensorrt::TRT_EngineManager;

paddle/fluid/platform/cpu_info.h

@@ -16,6 +16,18 @@ limitations under the License. */
 
 #include <stddef.h>
 
+#ifdef _WIN32
+#if defined(__AVX2__)
+#include <immintrin.h>  //avx2
+#elif defined(__AVX__)
+#include <intrin.h>  //avx
+#endif               // AVX
+#else                // WIN32
+#ifdef __AVX__
+#include <immintrin.h>
+#endif
+#endif  // WIN32
+
 namespace paddle {
 namespace platform {
 

paddle/fluid/platform/cudnn_helper.h

@@ -59,6 +59,7 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
 #define CUDNN_VERSION_MIN(major, minor, patch) \
   (CUDNN_VERSION >= ((major)*1000 + (minor)*100 + (patch)))
 
+#if !defined(_WIN32)
 #define CUDNN_ENFORCE(condition)                                     \
   do {                                                               \
     cudnnStatus_t status = condition;                                \
@@ -66,6 +67,16 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
       PADDLE_THROW(::paddle::platform::cudnnGetErrorString(status)); \
     }                                                                \
   } while (false)
+#else
+// windows
+#define CUDNN_ENFORCE(condition)                                    \
+  do {                                                              \
+    cudnnStatus_t status = condition;                               \
+    if (status != CUDNN_STATUS_SUCCESS) {                           \
+      std::cerr << ::paddle::platform::cudnnGetErrorString(status); \
+    }                                                               \
+  } while (false)
+#endif
 
 enum class DataLayout {  // Not use
   kNHWC,

paddle/fluid/platform/device_context.cc

@@ -55,7 +55,6 @@ DeviceContextPool::DeviceContextPool(
   for (auto& p : places) {
     set.insert(p);
   }
-
   for (auto& p : set) {
     if (platform::is_cpu_place(p)) {
 #ifdef PADDLE_WITH_MKLDNN
@@ -205,7 +204,9 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
                           << ", Runtime Version: " << runtime_version_ / 1000
                           << "." << (runtime_version_ % 100) / 10;
 
+#ifndef _WIN32
   callback_manager_.reset(new StreamCallbackManager(stream_));
+#endif  // NOT WIN32
 }
 
 CUDADeviceContext::~CUDADeviceContext() {

paddle/fluid/platform/device_context.h

@@ -32,7 +32,7 @@ limitations under the License. */
 #include "glog/logging.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/place.h"
-#ifdef PADDLE_WITH_CUDA && !defined(_WIN32)
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
 #include "paddle/fluid/platform/stream_callback_manager.h"
 #endif
 #include "unsupported/Eigen/CXX11/Tensor"
@@ -173,6 +173,7 @@ class CUDADeviceContext : public DeviceContext {
     PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
   }
 
+#ifndef _WIN32
   template <typename Callback>
   void AddStreamCallback(Callback&& callback) const {
     std::lock_guard<std::mutex> guard(callback_mtx_);
@@ -183,6 +184,16 @@ class CUDADeviceContext : public DeviceContext {
     std::lock_guard<std::mutex> guard(callback_mtx_);
     callback_manager_->Wait();
   }
+#else
+  template <typename Callback>
+  void AddStreamCallback(Callback&& callback) const {
+    // ugly empty functor.
+  }
+
+  void WaitStreamCallback() const {
+    // ugly empty functor.
+  }
+#endif
 
  private:
   CUDAPlace place_;
@@ -201,10 +212,12 @@ class CUDADeviceContext : public DeviceContext {
 
   mutable std::mutex mtx_;
 
+#ifndef _WIN32
   // This lock is only used by callback
   // If we use mtx_ for StreamCallbackManager, deadlock may occur sometimes
   mutable std::mutex callback_mtx_;
   std::unique_ptr<StreamCallbackManager> callback_manager_;
+#endif
 };
 
 template <>

paddle/fluid/platform/enforce.h

@@ -127,7 +127,7 @@ struct EOFException : public std::exception {
 #define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
 #else
 // there is no equivalent intrinsics in msvc.
-#define UNLIKELY(condition) (condition == 0)
+#define UNLIKELY(condition) ((condition) == 0)
 #endif
 
 #if !defined(_WIN32)

paddle/fluid/platform/init.cc

@@ -175,7 +175,7 @@ void InitGLOG(const std::string &prog_name) {
   // glog will not hold the ARGV[0] inside.
   // Use strdup to alloc a new string.
   google::InitGoogleLogging(strdup(prog_name.c_str()));
-#ifndef _WIN32
+#if !defined(_WIN32)
   google::InstallFailureSignalHandler();
 #endif
 }

paddle/fluid/platform/macros.h

@@ -28,3 +28,16 @@ limitations under the License. */
 #if defined(__FLT_MAX__)
 #define FLT_MAX __FLT_MAX__
 #endif  // __FLT_MAX__
+
+#ifdef _WIN32
+#if defined(PADDLE_COMPILE)
+// by default, msvc has predefined macro _LIB for static library
+// only shared library need to export and import symbols
+// static library export all symbols by default.
+#define PADDLE_DLL __declspec(dllexport)
+#else
+#define PADDLE_DLL __declspec(dllimport)
+#endif
+#else
+#define PADDLE_DLL
+#endif

paddle/fluid/platform/port.h

@@ -15,12 +15,13 @@
 #pragma once
 
 #include <cstdio>
-#include <stdexcept>
-
 #include <memory>
+#include <memory>  // NOLINT
+#include <stdexcept>
 #include <string>
 
 #define GLOG_NO_ABBREVIATED_SEVERITIES  // msvc conflict logging with windows.h
+#define GOOGLE_GLOG_DLL_DECL
 #include "glog/logging.h"
 
 #if !defined(_WIN32)
@@ -61,7 +62,6 @@ static void *dlopen(const char *filename, int flag) {
   }
   return reinterpret_cast<void *>(hModule);
 }
-
 #endif  // !_WIN32
 
 static void ExecShellCommand(const std::string &cmd, std::string *message) {

python/paddle/fluid/layers/nn.py

@@ -152,6 +152,7 @@ __all__ = [
     'mul',
     'sigmoid_cross_entropy_with_logits',
     'maxout',
+    'space_to_depth',
     'affine_grid',
     'sequence_reverse',
     'affine_channel',
@@ -3064,7 +3065,7 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
             x = fluid.layers.data(name='y', shape=[10, 5],
                              dtype='float32', lod_level=1)
             pad_value = fluid.layers.assign(
-                input=numpy.array([0], dtype=numpy.float32))
+                input=numpy.array([0.0], dtype=numpy.float32))
             out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
     """
 
@@ -7679,6 +7680,66 @@ def maxout(x, groups, name=None):
     return out
 
 
+def space_to_depth(x, blocksize, name=None):
+    """
+    Gives a blocksize to space_to_depth the input LoDtensor with Layout: [batch, channel, height, width]
+    
+    This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the 
+    input LoDtensor where values from the height and width dimensions are moved to the channel dimension. 
+    The attr blocksize indicates the input block size.
+    
+    space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] according 
+    to blocksize to construct output with shape [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize]:
+    
+    space_to_depth is used to This operation is useful for resizing the activations between convolutions 
+    (but keeping all data)
+
+    - Non-overlapping blocks of size block_size x block size are rearranged into depth at each location.
+    - The depth of the output tensor is block_size * block_size * input channel 
+    - The Y, X coordinates within each block of the input become the high order component of the output channel index
+    - channel should be divisible by square of blocksize
+    - height, width should be divsible by blocksize
+
+
+    Args:
+        x(variable): The input LoDtensor.
+        blocksize(variable): The blocksize to select the element on each feature map should be > 2
+
+    Returns:
+        Variable: The output LoDtensor.
+
+    Raises:
+        TypeError: blocksize type must be a long.
+
+    Examples:
+        .. code-block:: python
+
+            data = fluid.layers.data(
+                name='data', shape=[1, 4, 2, 2], dtype='float32')
+            space_to_depthed = fluid.layers.space_to_depth(
+                x=data, blocksize=2)
+    """
+
+    helper = LayerHelper("space_to_depth", **locals())
+
+    if not (isinstance(blocksize, int)):
+        raise ValueError("blocksize must be a python Int")
+
+    if name is None:
+        out = helper.create_variable_for_type_inference(
+            dtype=x.dtype)  #fix create
+    else:
+        out = helper.create_variable(
+            name=name, dtype=x.dtype, persistable=False)
+
+    helper.append_op(
+        type="space_to_depth",
+        inputs={"X": x},
+        attrs={"blocksize": blocksize},
+        outputs={"Out": out})
+    return out
+
+
 @templatedoc()
 def sequence_reverse(x, name=None):
     """ 

python/paddle/fluid/op.py

@@ -108,6 +108,8 @@ class OpDescCreationMethod(object):
                     new_attr.i = user_defined_attr
                 elif attr.type == framework_pb2.FLOAT:
                     new_attr.f = user_defined_attr
+                elif attr.type == framework_pb2.LONG:
+                    new_attr.l = user_defined_attr
                 elif attr.type == framework_pb2.STRING:
                     new_attr.s = user_defined_attr
                 elif attr.type == framework_pb2.BOOLEAN: