Merge branch 'windows/build' into windows/online

test=develop

CMakeLists.txt

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

cmake/cuda.cmake

@@ -172,21 +172,18 @@ 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
+# -std=c++11 -fPIC not recoginize by msvc, -Xcompiler will be added by cmake.
 list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
-# 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")
+list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
 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(WITH_FAST_MATH)
-
+endif()
+# in cuda9, suppress cuda warning on eigen 
+list(APPEND CUDA_NVCC_FLAGS "-w")
 # Set :expt-relaxed-constexpr to suppress Eigen warnings
 list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
 

cmake/cudnn.cmake

@@ -53,6 +53,7 @@ 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()
@@ -87,7 +88,7 @@ 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

@@ -43,7 +43,7 @@ ExternalProject_Add(
     ${EXTERNAL_PROJECT_LOG_ARGS}
     DOWNLOAD_DIR          ${BOOST_DOWNLOAD_DIR}
     URL      ${BOOST_URL}
-    DOWNLOAD_NO_PROGRESS  0
+    DOWNLOAD_NO_PROGRESS  1
     PREFIX                ${BOOST_SOURCES_DIR}
     CONFIGURE_COMMAND     ""
     BUILD_COMMAND         ""

cmake/external/gflags.cmake

@@ -50,10 +50,6 @@ 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
@@ -61,6 +57,9 @@ 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

@@ -51,7 +51,6 @@ 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
@@ -71,5 +70,6 @@ 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/mkldnn.cmake

@@ -45,7 +45,7 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
 ELSE()
     MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
 ENDIF()
-SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result")
+SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result -Wno-error=array-bounds")
 SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
 SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
 SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
@@ -54,7 +54,7 @@ ExternalProject_Add(
     ${EXTERNAL_PROJECT_LOG_ARGS}
     DEPENDS             ${MKLDNN_DEPENDS}
     GIT_REPOSITORY      "https://github.com/01org/mkl-dnn.git"
-    GIT_TAG             "64e03a1939e0d526aa8e9f2e3f7dc0ad8d372944"
+    GIT_TAG             "21fb5f2af1dd14e132af4f1b79160977ee487818"
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}

cmake/external/openblas.cmake

@@ -147,7 +147,6 @@ 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

@@ -144,14 +144,11 @@ 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)
 
@@ -167,8 +164,8 @@ endif(APPLE)
 if(LINUX)
     set(GPU_COMMON_FLAGS
         -Wall
-        -Werror
         -Wextra
+        -Werror
         ${GPU_COMMON_FLAGS})
 endif(LINUX)
 

cmake/generic.cmake

@@ -238,7 +238,6 @@ 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 ${cc_library_SRCS})
@@ -351,11 +350,7 @@ function(cc_test TARGET_NAME)
     set(multiValueArgs SRCS DEPS ARGS)
     cmake_parse_arguments(cc_test "${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} ${cc_test_ARGS}
@@ -426,11 +421,7 @@ function(nv_test TARGET_NAME)
     set(multiValueArgs SRCS DEPS)
     cmake_parse_arguments(nv_test "${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

@@ -22,196 +22,196 @@ function(copy TARGET)
 
     list(LENGTH copy_lib_SRCS copy_lib_SRCS_len)
     list(LENGTH copy_lib_DSTS copy_lib_DSTS_len)
-    if(NOT ${copy_lib_SRCS_len} EQUAL ${copy_lib_DSTS_len})
+    if (NOT ${copy_lib_SRCS_len} EQUAL ${copy_lib_DSTS_len})
         message(FATAL_ERROR "${TARGET} source numbers are not equal to destination numbers")
-    endif()
+    endif ()
     math(EXPR len "${copy_lib_SRCS_len} - 1")
 
     add_custom_target(${TARGET} DEPENDS ${copy_lib_DEPS})
-    foreach(index RANGE ${len})
+    foreach (index RANGE ${len})
         list(GET copy_lib_SRCS ${index} src)
         list(GET copy_lib_DSTS ${index} dst)
         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")
-        file(GLOB static_lib_files ${src} "*.lib")
-        file(GLOB dll_lib_files ${src} "*.dll")
-        set(src_files ${header_files} ${static_lib_files} ${dll_lib_files})
-
-        if (NOT "${src_files}" STREQUAL "")
-        list(REMOVE_DUPLICATES src_files)
-        endif()
-        add_custom_command(TARGET ${TARGET} PRE_BUILD 
-          COMMAND ${CMAKE_COMMAND} -E make_directory  "${dst}"
-          )
-        foreach(src_file ${src_files})
-          add_custom_command(TARGET ${TARGET} PRE_BUILD 
-          COMMAND ${CMAKE_COMMAND} -E copy "${src_file}" "${dst}"
-          COMMENT "copying ${src_file} -> ${dst}")
-        endforeach()
-        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()
+            # windows cmd shell will not expand wildcard automatically.
+            # below expand the files,libs and copy them by rules.
+            file(GLOB header_files ${src} "*.h")
+            file(GLOB static_lib_files ${src} "*.lib")
+            file(GLOB dll_lib_files ${src} "*.dll")
+            set(src_files ${header_files} ${static_lib_files} ${dll_lib_files})
+
+            if (NOT "${src_files}" STREQUAL "")
+                list(REMOVE_DUPLICATES src_files)
+            endif ()
+            add_custom_command(TARGET ${TARGET} PRE_BUILD
+                    COMMAND ${CMAKE_COMMAND} -E make_directory "${dst}"
+                    )
+            foreach (src_file ${src_files})
+                add_custom_command(TARGET ${TARGET} PRE_BUILD
+                        COMMAND ${CMAKE_COMMAND} -E copy "${src_file}" "${dst}"
+                        COMMENT "copying ${src_file} -> ${dst}")
+            endforeach ()
+        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) # not windows
+    endforeach ()
 endfunction()
 
 # third party
 set(dst_dir "${FLUID_INSTALL_DIR}/third_party/eigen3")
 copy(eigen3_lib
-  SRCS ${EIGEN_INCLUDE_DIR}/Eigen/Core ${EIGEN_INCLUDE_DIR}/Eigen/src ${EIGEN_INCLUDE_DIR}/unsupported/Eigen
-  DSTS ${dst_dir}/Eigen ${dst_dir}/Eigen ${dst_dir}/unsupported
-  DEPS eigen3
-)
+        SRCS ${EIGEN_INCLUDE_DIR}/Eigen/Core ${EIGEN_INCLUDE_DIR}/Eigen/src ${EIGEN_INCLUDE_DIR}/unsupported/Eigen
+        DSTS ${dst_dir}/Eigen ${dst_dir}/Eigen ${dst_dir}/unsupported
+        DEPS eigen3
+        )
 
 set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/gflags")
 copy(gflags_lib
-  SRCS ${GFLAGS_INCLUDE_DIR} ${GFLAGS_LIBRARIES}
-  DSTS ${dst_dir} ${dst_dir}/lib
-  DEPS gflags
-)
+        SRCS ${GFLAGS_INCLUDE_DIR} ${GFLAGS_LIBRARIES}
+        DSTS ${dst_dir} ${dst_dir}/lib
+        DEPS gflags
+        )
 
 set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/glog")
 copy(glog_lib
-  SRCS ${GLOG_INCLUDE_DIR} ${GLOG_LIBRARIES}
-  DSTS ${dst_dir} ${dst_dir}/lib
-  DEPS glog
-)
+        SRCS ${GLOG_INCLUDE_DIR} ${GLOG_LIBRARIES}
+        DSTS ${dst_dir} ${dst_dir}/lib
+        DEPS glog
+        )
 
 set(dst_dir "${FLUID_INSTALL_DIR}/third_party/boost/")
 copy(boost_lib
-  SRCS ${BOOST_INCLUDE_DIR}/boost
-  DSTS ${dst_dir}
-  DEPS boost
-)
+        SRCS ${BOOST_INCLUDE_DIR}/boost
+        DSTS ${dst_dir}
+        DEPS boost
+        )
 
 set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/xxhash")
 copy(xxhash_lib
-  SRCS ${XXHASH_INCLUDE_DIR} ${XXHASH_LIBRARIES}
-  DSTS ${dst_dir} ${dst_dir}/lib
-  DEPS xxhash
-)
+        SRCS ${XXHASH_INCLUDE_DIR} ${XXHASH_LIBRARIES}
+        DSTS ${dst_dir} ${dst_dir}/lib
+        DEPS xxhash
+        )
 
-if(NOT PROTOBUF_FOUND)
+if (NOT PROTOBUF_FOUND)
     set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/protobuf")
     copy(protobuf_lib
-      SRCS ${PROTOBUF_INCLUDE_DIR} ${PROTOBUF_LIBRARY}
-      DSTS ${dst_dir} ${dst_dir}/lib
-      DEPS extern_protobuf
-    )
-endif()
+            SRCS ${PROTOBUF_INCLUDE_DIR} ${PROTOBUF_LIBRARY}
+            DSTS ${dst_dir} ${dst_dir}/lib
+            DEPS extern_protobuf
+            )
+endif ()
 
-if(NOT CBLAS_FOUND)
+if (NOT CBLAS_FOUND)
     set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/openblas")
     copy(openblas_lib
-      SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
-      DSTS ${dst_dir} ${dst_dir}
-      DEPS extern_openblas
-    )
+            SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
+            DSTS ${dst_dir} ${dst_dir}
+            DEPS extern_openblas
+            )
 elseif (WITH_MKLML)
     set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mklml")
     copy(mklml_lib
-      SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
-      DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
-      DEPS mklml
-    )
-endif()
-
-if(WITH_MKLDNN)
-  set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mkldnn")
-  copy(mkldnn_lib
-    SRCS ${MKLDNN_INC_DIR} ${MKLDNN_SHARED_LIB}
-    DSTS ${dst_dir} ${dst_dir}/lib
-    DEPS mkldnn
-  )
-endif()
+            SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
+            DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
+            DEPS mklml
+            )
+endif ()
+
+if (WITH_MKLDNN)
+    set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mkldnn")
+    copy(mkldnn_lib
+            SRCS ${MKLDNN_INC_DIR} ${MKLDNN_SHARED_LIB}
+            DSTS ${dst_dir} ${dst_dir}/lib
+            DEPS mkldnn
+            )
+endif ()
 
 if (NOT WIN32)
-if(NOT MOBILE_INFERENCE AND NOT RPI)
-  set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
-  copy(snappy_lib
-    SRCS ${SNAPPY_INCLUDE_DIR} ${SNAPPY_LIBRARIES}
-    DSTS ${dst_dir} ${dst_dir}/lib
-    DEPS snappy)
-
-  set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappystream")
-  copy(snappystream_lib
-    SRCS ${SNAPPYSTREAM_INCLUDE_DIR} ${SNAPPYSTREAM_LIBRARIES}
-    DSTS ${dst_dir} ${dst_dir}/lib
-    DEPS snappystream)
-
-  set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
-  copy(zlib_lib
-    SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
-    DSTS ${dst_dir} ${dst_dir}/lib
-    DEPS zlib)
-endif()
-endif(NOT WIN32)
+    if (NOT MOBILE_INFERENCE AND NOT RPI)
+        set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
+        copy(snappy_lib
+                SRCS ${SNAPPY_INCLUDE_DIR} ${SNAPPY_LIBRARIES}
+                DSTS ${dst_dir} ${dst_dir}/lib
+                DEPS snappy)
+
+        set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappystream")
+        copy(snappystream_lib
+                SRCS ${SNAPPYSTREAM_INCLUDE_DIR} ${SNAPPYSTREAM_LIBRARIES}
+                DSTS ${dst_dir} ${dst_dir}/lib
+                DEPS snappystream)
+
+        set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
+        copy(zlib_lib
+                SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
+                DSTS ${dst_dir} ${dst_dir}/lib
+                DEPS zlib)
+    endif ()
+endif (NOT WIN32)
 
 # paddle fluid module
 set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
 set(dst_dir "${FLUID_INSTALL_DIR}/paddle/fluid")
 set(module "framework")
 if (NOT WIN32)
-set(framework_lib_deps framework_py_proto)
-endif(NOT WIN32)
+    set(framework_lib_deps framework_py_proto)
+endif (NOT WIN32)
 copy(framework_lib DEPS ${framework_lib_deps}
-  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
-       ${src_dir}/${module}/ir/*.h
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module} ${dst_dir}/${module}/ir
-)
+        SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
+        ${src_dir}/${module}/ir/*.h
+        DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module} ${dst_dir}/${module}/ir
+        )
 
 set(module "memory")
 copy(memory_lib
-  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/detail/*.h
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/detail
-)
+        SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/detail/*.h
+        DSTS ${dst_dir}/${module} ${dst_dir}/${module}/detail
+        )
 
 set(inference_deps paddle_fluid_shared paddle_fluid)
 
 set(module "inference/api")
 if (WITH_ANAKIN AND WITH_MKL)
     copy(anakin_inference_lib DEPS paddle_inference_api inference_anakin_api
-        SRCS
-        ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libinference_anakin_api* # compiled anakin api
-        ${ANAKIN_INSTALL_DIR} # anakin release
-        DSTS ${FLUID_INSTALL_DIR}/third_party/install/anakin ${FLUID_INSTALL_DIR}/third_party/install/anakin)
-     list(APPEND inference_deps anakin_inference_lib)
-endif()
+            SRCS
+            ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libinference_anakin_api* # compiled anakin api
+            ${ANAKIN_INSTALL_DIR} # anakin release
+            DSTS ${FLUID_INSTALL_DIR}/third_party/install/anakin ${FLUID_INSTALL_DIR}/third_party/install/anakin)
+    list(APPEND inference_deps anakin_inference_lib)
+endif ()
 
 set(module "inference")
 copy(inference_lib DEPS ${inference_deps}
-  SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
-       ${src_dir}/${module}/api/paddle_inference_api.h
-       ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
-)
+        SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
+        ${src_dir}/${module}/api/paddle_inference_api.h
+        ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
+        DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
+        )
 
 set(module "platform")
 copy(platform_lib DEPS profiler_py_proto
-  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/dynload/*.h ${src_dir}/${module}/details/*.h
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/dynload ${dst_dir}/${module}/details
-)
+        SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/dynload/*.h ${src_dir}/${module}/details/*.h
+        DSTS ${dst_dir}/${module} ${dst_dir}/${module}/dynload ${dst_dir}/${module}/details
+        )
 
 set(module "string")
 copy(string_lib
-  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/tinyformat/*.h
-  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/tinyformat
-)
+        SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/tinyformat/*.h
+        DSTS ${dst_dir}/${module} ${dst_dir}/${module}/tinyformat
+        )
 
 set(module "pybind")
 copy(pybind_lib
-  SRCS ${CMAKE_CURRENT_BINARY_DIR}/paddle/fluid/${module}/pybind.h
-  DSTS ${dst_dir}/${module}
-)
+        SRCS ${CMAKE_CURRENT_BINARY_DIR}/paddle/fluid/${module}/pybind.h
+        DSTS ${dst_dir}/${module}
+        )
 
 # CMakeCache Info
 copy(cmake_cache
-  SRCS ${CMAKE_CURRENT_BINARY_DIR}/CMakeCache.txt
-  DSTS ${FLUID_INSTALL_DIR})
+        SRCS ${CMAKE_CURRENT_BINARY_DIR}/CMakeCache.txt
+        DSTS ${FLUID_INSTALL_DIR})
 
 # This command generates a complete fluid library for both train and inference
 add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep})
@@ -219,35 +219,35 @@ add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep})
 # Following commands generate a inference-only fluid library
 # third_party, version.txt and CMakeCache.txt are the same position with ${FLUID_INSTALL_DIR}
 copy(third_party DEPS fluid_lib_dist
-  SRCS ${FLUID_INSTALL_DIR}/third_party ${FLUID_INSTALL_DIR}/CMakeCache.txt
-  DSTS ${FLUID_INFERENCE_INSTALL_DIR} ${FLUID_INFERENCE_INSTALL_DIR}
-)
+        SRCS ${FLUID_INSTALL_DIR}/third_party ${FLUID_INSTALL_DIR}/CMakeCache.txt
+        DSTS ${FLUID_INFERENCE_INSTALL_DIR} ${FLUID_INFERENCE_INSTALL_DIR}
+        )
 
 # only need libpaddle_fluid.so/a and paddle_inference_api.h for inference-only library
 copy(inference_api_lib DEPS fluid_lib_dist
-  SRCS ${FLUID_INSTALL_DIR}/paddle/fluid/inference/libpaddle_fluid.*
-       ${FLUID_INSTALL_DIR}/paddle/fluid/inference/paddle_inference_api.h
-  DSTS ${FLUID_INFERENCE_INSTALL_DIR}/paddle/lib ${FLUID_INFERENCE_INSTALL_DIR}/paddle/include
-)
+        SRCS ${FLUID_INSTALL_DIR}/paddle/fluid/inference/libpaddle_fluid.*
+        ${FLUID_INSTALL_DIR}/paddle/fluid/inference/paddle_inference_api.h
+        DSTS ${FLUID_INFERENCE_INSTALL_DIR}/paddle/lib ${FLUID_INFERENCE_INSTALL_DIR}/paddle/include
+        )
 
 add_custom_target(inference_lib_dist DEPENDS third_party inference_api_lib)
 
 # paddle fluid version
 function(version version_file)
-  execute_process(
-    COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}
-    OUTPUT_VARIABLE PADDLE_GIT_COMMIT)
-  file(WRITE ${version_file}
-    "GIT COMMIT ID: ${PADDLE_GIT_COMMIT}\n"
-    "WITH_MKL: ${WITH_MKL}\n"
-    "WITH_MKLDNN: ${WITH_MKLDNN}\n"
-    "WITH_GPU: ${WITH_GPU}\n")
-  if(WITH_GPU)
-    file(APPEND ${version_file}
-      "CUDA version: ${CUDA_VERSION}\n"
-      "CUDNN version: v${CUDNN_MAJOR_VERSION}\n")
-  endif()
+    execute_process(
+            COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
+            WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}
+            OUTPUT_VARIABLE PADDLE_GIT_COMMIT)
+    file(WRITE ${version_file}
+            "GIT COMMIT ID: ${PADDLE_GIT_COMMIT}\n"
+            "WITH_MKL: ${WITH_MKL}\n"
+            "WITH_MKLDNN: ${WITH_MKLDNN}\n"
+            "WITH_GPU: ${WITH_GPU}\n")
+    if (WITH_GPU)
+        file(APPEND ${version_file}
+                "CUDA version: ${CUDA_VERSION}\n"
+                "CUDNN version: v${CUDNN_MAJOR_VERSION}\n")
+    endif ()
 endfunction()
 version(${FLUID_INSTALL_DIR}/version.txt)
 version(${FLUID_INFERENCE_INSTALL_DIR}/version.txt)

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

paddle/fluid/API.spec

@@ -118,9 +118,10 @@ paddle.fluid.layers.label_smooth ArgSpec(args=['label', 'prior_dist', 'epsilon',
 paddle.fluid.layers.roi_pool ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0))
 paddle.fluid.layers.roi_align ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None))
 paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,))
-paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR'))
+paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None))
 paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',))
-paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
+paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, None))
+paddle.fluid.layers.resize_nearest ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.layers.gather ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))

paddle/fluid/framework/executor.cc

@@ -12,8 +12,6 @@ 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"
@@ -48,7 +46,6 @@ ExecutorPrepareContext::~ExecutorPrepareContext() {
   VLOG(5) << "destroy ExecutorPrepareContext";
 }
 
-#ifndef _WIN32
 template <typename RefCntMap>
 static void DeleteUnusedTensors(const Scope& scope, const OperatorBase* op,
                                 GarbageCollector<Tensor>* gc,
@@ -83,7 +80,6 @@ static void DeleteUnusedTensors(const Scope& scope, const OperatorBase* op,
     gc->Add(erase_tensors);
   }
 }
-#endif
 
 Executor::Executor(const platform::Place& place) : place_(place) {}
 
@@ -371,7 +367,6 @@ 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
@@ -413,16 +408,6 @@ 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

@@ -17,14 +17,12 @@ 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/graph_pattern_detector.cc

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <algorithm>
 #include <array>
 #include <string>
 #include <vector>

paddle/fluid/framework/ir/node.cc

@@ -19,9 +19,9 @@ namespace framework {
 namespace ir {
 // msvc15 don't support constexpr in correct way.
 #if !defined(_WIN32)
-constexpr char Node::kControlDepVarName[];
+        constexpr char Node::kControlDepVarName[];
 #else
-const char Node::kControlDepVarName[] = "__control_var";
+        const char Node::kControlDepVarName[] = "__control_var";
 #endif
 int Node::count_ = 0;
 

paddle/fluid/framework/ir/node.h

@@ -56,9 +56,9 @@ class Node {
 
   enum class Type { kOperation, kVariable };
 #if !defined(_WIN32)  // msvc not support constexpr correctly.
-  static constexpr char kControlDepVarName[] = "__control_var";
+        static constexpr char kControlDepVarName[] = "__control_var";
 #else
-  static const char kControlDepVarName[];
+        static const char kControlDepVarName[];
 #endif
 
   Type NodeType() const { return type_; }

paddle/fluid/framework/ir/pass.h

@@ -21,7 +21,6 @@ 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 {
@@ -196,7 +195,6 @@ 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(                                \
@@ -219,30 +217,7 @@ 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/operator.cc

@@ -361,7 +361,7 @@ static bool VarIsTensor(const Variable& var) {
   return var.IsType<LoDTensor>() || var.IsType<SelectedRows>();
 }
 
-const Tensor* GetTensorFromVar(const Variable& var) {
+const Tensor* GetLoDTensorOrSelectedRowsValueFromVar(const Variable& var) {
   if (var.IsType<LoDTensor>()) {
     return static_cast<const Tensor*>(&(var.Get<LoDTensor>()));
   } else if (var.IsType<SelectedRows>()) {
@@ -372,7 +372,7 @@ const Tensor* GetTensorFromVar(const Variable& var) {
   }
 }
 
-static Tensor* GetMutableTensorFromVar(Variable* var) {
+Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var) {
   if (var->IsType<LoDTensor>()) {
     return var->GetMutable<LoDTensor>();
   } else if (var->IsType<SelectedRows>()) {
@@ -417,8 +417,7 @@ bool ExecutionContext::HasOutput(const std::string& name) const {
 
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
-  auto* var = InputVar(name);
-  return var == nullptr ? nullptr : GetTensorFromVar(*var);
+  return Input<LoDTensor>(name);
 }
 
 template <>
@@ -428,17 +427,21 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
   std::vector<const Tensor*> res;
   res.reserve(names.size());
   std::transform(names.begin(), names.end(), std::back_inserter(res),
-                 [&](const std::string& sub_name) {
+                 [&](const std::string& sub_name) -> const Tensor* {
                    auto var = scope_.FindVar(sub_name);
-                   return var == nullptr ? nullptr : GetTensorFromVar(*var);
+                   if (var == nullptr) return nullptr;
+                   PADDLE_ENFORCE(
+                       var->IsType<LoDTensor>(),
+                       "%s should be LoDTensor, but the received type is %s",
+                       sub_name, var->Type().name());
+                   return &(var->Get<LoDTensor>());
                  });
   return res;
 }
 
 template <>
 Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
-  auto var = OutputVar(name);
-  return var == nullptr ? nullptr : GetMutableTensorFromVar(var);
+  return Output<LoDTensor>(name);
 }
 
 template <>
@@ -448,10 +451,14 @@ std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
   std::vector<Tensor*> res;
   res.reserve(names.size());
   std::transform(names.begin(), names.end(), std::back_inserter(res),
-                 [&](const std::string& sub_name) {
+                 [&](const std::string& sub_name) -> Tensor* {
                    auto var = scope_.FindVar(sub_name);
-                   return var == nullptr ? nullptr
-                                         : GetMutableTensorFromVar(var);
+                   if (var == nullptr) return nullptr;
+                   PADDLE_ENFORCE(
+                       var->IsType<LoDTensor>(),
+                       "%s should be LoDTensor, but the received type is %s",
+                       sub_name, var->Type().name());
+                   return var->GetMutable<LoDTensor>();
                  });
   return res;
 }
@@ -771,11 +778,12 @@ void OperatorWithKernel::TransferInplaceVarsBack(
     const Scope& transfer_scope) const {
   for (auto& var_name : inplace_vars) {
     VLOG(3) << "share inplace var " + var_name + " back to it's original scope";
-    auto* original_tensor = GetMutableTensorFromVar(scope.FindVar(var_name));
+    auto* original_tensor =
+        GetMutableLoDTensorOrSelectedRowsValueFromVar(scope.FindVar(var_name));
     auto* var = transfer_scope.FindVar(var_name);
     PADDLE_ENFORCE(var != nullptr, "The var[%s] should not be nullptr",
                    var_name);
-    auto* transformed_tensor = GetTensorFromVar(*var);
+    auto* transformed_tensor = GetLoDTensorOrSelectedRowsValueFromVar(*var);
     original_tensor->ShareDataWith(*transformed_tensor);
   }
 }
@@ -792,7 +800,7 @@ Scope* OperatorWithKernel::TryTransferData(
         continue;
       }
 
-      auto* tensor_in = GetTensorFromVar(*var);
+      auto* tensor_in = GetLoDTensorOrSelectedRowsValueFromVar(*var);
       if (!tensor_in->IsInitialized()) {
         continue;
       }

paddle/fluid/framework/operator.h

@@ -54,6 +54,9 @@ constexpr char kGradVarSuffix[] = "@GRAD";
 /// Variables with this suffix are supposed to be filled up with zeros.
 constexpr char kZeroVarSuffix[] = "@ZERO";
 
+/// Variables with this suffix are the new Gradient.
+constexpr char kNewGradSuffix[] = "@NEWGRAD@";
+
 // define some kernel priority
 /* Define multiple kernel type fallback order*/
 extern std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority;
@@ -63,7 +66,8 @@ inline std::string GradVarName(const std::string& var_name) {
 }
 
 proto::VarType::Type GetDataTypeOfVar(const Variable* var);
-const Tensor* GetTensorFromVar(const Variable& var);
+const Tensor* GetLoDTensorOrSelectedRowsValueFromVar(const Variable& var);
+Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var);
 
 class OperatorBase;
 class ExecutionContext;
@@ -224,7 +228,7 @@ class ExecutionContext {
     std::vector<const T*> res;
     res.reserve(names.size());
     std::transform(names.begin(), names.end(), std::back_inserter(res),
-                   [&](const std::string& sub_name) {
+                   [&](const std::string& sub_name) -> const T* {
                      auto var = scope_.FindVar(sub_name);
                      return var == nullptr ? nullptr : &var->Get<T>();
                    });
@@ -237,7 +241,7 @@ class ExecutionContext {
     std::vector<T*> res;
     res.reserve(names.size());
     std::transform(names.begin(), names.end(), std::back_inserter(res),
-                   [&](const std::string& sub_name) {
+                   [&](const std::string& sub_name) -> T* {
                      auto var = scope_.FindVar(sub_name);
                      return var == nullptr ? nullptr : var->GetMutable<T>();
                    });

paddle/fluid/framework/tensor.h

@@ -20,11 +20,6 @@ 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/analysis/analyzer.cc

@@ -101,6 +101,7 @@ Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
 
 void Analyzer::Run(Argument* argument) {
   std::vector<std::string> passes;
+  passes.push_back("graph_viz_pass");  // add graphviz for debug.
 #ifdef PADDLE_WITH_MKLDNN
   if (use_mkldnn_) {
     VLOG(3) << "Adding MKL-DNN placement pass";
@@ -110,13 +111,13 @@ void Analyzer::Run(Argument* argument) {
   // infer_clean_graph_pass should be the first default pass
   // after mkldnn_placement_pass.
   passes.push_back("infer_clean_graph_pass");
+  passes.push_back("graph_viz_pass");  // add graphviz for debug.
   for (auto& pass : ir_passes_) {
     if (!disabled_ir_passes_.count(pass)) {
       passes.push_back(pass);
       passes.push_back("graph_viz_pass");  // add graphviz for debug.
     }
   }
-  passes.push_back("graph_viz_pass");
   argument->Set(kFluidToIrPassesAttr, new std::vector<std::string>(passes));
 
   for (auto& x : data_) {

paddle/fluid/inference/analysis/argument.h

@@ -26,7 +26,6 @@
 #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 {
@@ -103,6 +102,7 @@ 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

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <sys/stat.h>
 #include <cstdio>
 #include <fstream>
 #include <string>
@@ -145,6 +146,16 @@ 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

@@ -24,7 +24,6 @@ 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)
@@ -38,8 +37,8 @@ if(WITH_TESTING)
                       ARGS --word2vec_dirname=${WORD2VEC_MODEL_DIR} --book_dirname=${PYTHON_TESTS_DIR}/book)
   set_tests_properties(test_api_impl PROPERTIES DEPENDS test_image_classification)
 endif()
-cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps} paddle_inference_api
-        ARGS --dirname=${PYTHON_TESTS_DIR}/book)
+cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps}
+        ARGS --dirname=${WORD2VEC_MODEL_DIR})
 
 if(WITH_GPU AND TENSORRT_FOUND)
 cc_library(paddle_inference_tensorrt_subgraph_engine

paddle/fluid/inference/api/analysis_predictor.h

@@ -13,6 +13,8 @@
 // limitations under the License.
 
 #pragma once
+#include <algorithm>
+#include <map>
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/naive_executor.h"

paddle/fluid/inference/api/analysis_predictor_tester.cc

@@ -24,7 +24,7 @@ using contrib::AnalysisConfig;
 
 TEST(AnalysisPredictor, ZeroCopy) {
   AnalysisConfig config;
-  config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
+  config.model_dir = FLAGS_dirname;
   config.use_feed_fetch_ops = false;
 
   auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);

paddle/fluid/inference/api/api_impl.cc

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <algorithm>
-#include <fstream>
 #include <map>
 #include <set>
 #include <sstream>
@@ -25,7 +24,6 @@ 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"
 
@@ -33,6 +31,16 @@ 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()) {
@@ -55,6 +63,7 @@ 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";
@@ -82,21 +91,21 @@ 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;
@@ -126,7 +135,7 @@ NativePaddlePredictor::~NativePaddlePredictor() {
 bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
                                 std::vector<PaddleTensor> *output_data,
                                 int batch_size) {
-  using Timer = paddle::inference::Timer;
+  VLOG(3) << "Predictor::predict";
   Timer timer;
   timer.tic();
   // set feed variable
@@ -138,9 +147,11 @@ 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";
@@ -155,6 +166,7 @@ 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_)) {
@@ -172,6 +184,7 @@ 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();
@@ -231,6 +244,7 @@ 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"));
@@ -255,22 +269,25 @@ 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=" +
-                         std::to_string(config.fraction_of_gpu_memory);
+                         num2str<float>(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)
@@ -37,25 +37,26 @@ if(NOT DEFINED DEMO_NAME)
 endif()
 
 
-if(WITH_GPU) # default gpu path
+if(WITH_GPU)
   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("${PADDLE_LIB}/third_party/boost")
@@ -63,15 +64,15 @@ include_directories("${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("${PADDLE_LIB}/third_party/install/protobuf/lib")
@@ -85,7 +86,7 @@ add_executable(${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")
@@ -98,25 +99,25 @@ 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 will be deprecated.
-  set(DEPS ${DEPS} libcmt shlwapi)
+# NOTE(dzhwinter) shlwapi is deprecated.
+set(DEPS ${DEPS} libcmt shlwapi)
 endif(NOT WIN32)
 
 if(WITH_GPU)
@@ -128,8 +129,8 @@ 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

@@ -14,26 +14,42 @@
 
 #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 <iterator>
 #include <algorithm>
 #include <chrono>  // NOLINT
-#include <iterator>
 #include <numeric>
 #include <sstream>
 #include <string>
 #include <vector>
-#include "paddle/fluid/inference/api/timer.h"
-#include "paddle_inference_api.h"  //NOLINT
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/string/printf.h"
 
 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/inference/io.cc

@@ -59,7 +59,8 @@ void ReadBinaryFile(const std::string& filename, std::string* contents) {
 bool IsPersistable(const framework::VarDesc* var) {
   if (var->Persistable() &&
       var->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
-      var->GetType() != framework::proto::VarType::FETCH_LIST) {
+      var->GetType() != framework::proto::VarType::FETCH_LIST &&
+      var->GetType() != framework::proto::VarType::RAW) {
     return true;
   }
   return false;

paddle/fluid/inference/tensorrt/engine.h

@@ -134,7 +134,7 @@ class TensorRTEngine : public EngineBase {
   std::unordered_map<std::string /*name*/, std::unique_ptr<framework::Tensor>>
       weight_map;
 
-  // TODO: (NHZLX)
+  // TODO(NHZLX)
   // In the normal case, the paddle-trt exists bug when runing the googlenet.
   // When there are more than two convolutions of 1 * 1 with the same input, the
   // paddle-tensorrt will do the merging optimization, which fuse those conv

paddle/fluid/memory/detail/buddy_allocator.cc

@@ -11,8 +11,7 @@ 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

@@ -12,8 +12,6 @@ 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

@@ -12,7 +12,6 @@ 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/CMakeLists.txt

@@ -297,7 +297,6 @@ op_library(cos_sim_op DEPS cos_sim_functor)
 op_library(parallel_do_op DEPS executor)
 op_library(unsqueeze_op DEPS reshape_op)
 op_library(squeeze_op DEPS reshape_op)
-op_library(extract_rows_op DEPS memory)
 op_library(flatten_op DEPS reshape_op)
 op_library(sequence_pad_op DEPS sequence_padding)
 op_library(unstack_op DEPS stack_op)

paddle/fluid/operators/accuracy_op.h

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

paddle/fluid/operators/add_position_encoding_op.h

@@ -66,9 +66,10 @@ class AddPositionEncodingKernel : public framework::OpKernel<T> {
           x_lod.empty() ? max_seq_len : x_lod[0][i + 1] - x_lod[0][i];
       for (int j = 0; j < max_length; ++j) {
         for (int k = 0; k < half_size; ++k) {
-          const double val = (half_size > 1)
-                                 ? j / pow(10000.0, double(k) / (half_size - 1))
-                                 : j / 10000.0;
+          const double val =
+              (half_size > 1)
+                  ? j / pow(10000.0, static_cast<double>(k) / (half_size - 1))
+                  : j / 10000.0;
           dst_ptr[k] = src_ptr[k] * alpha + sin(val) * beta;
           dst_ptr[half_size + k] =
               src_ptr[half_size + k] * alpha + cos(val) * beta;

paddle/fluid/operators/bilinear_interp_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-   http://www.apache.org/licenses/LICENSE-2.0
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License. */
-
-#pragma once
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/math/math_function.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-
-template <typename T>
-class BilinearInterpKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* input_t = ctx.Input<Tensor>("X");      // float tensor
-    auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
-    auto out_dims = output_t->dims();
-    auto* input = input_t->data<T>();
-    int out_h = ctx.Attr<int>("out_h");
-    int out_w = ctx.Attr<int>("out_w");
-    auto out_size_t = ctx.Input<Tensor>("OutSize");
-    if (out_size_t != nullptr) {
-      auto out_size_data = out_size_t->data<int>();
-      out_h = out_size_data[0];
-      out_w = out_size_data[1];
-    }
-    auto* output = output_t->mutable_data<T>(
-        {out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
-    int batch_size = input_t->dims()[0];
-    int channels = input_t->dims()[1];
-    int in_h = input_t->dims()[2];
-    int in_w = input_t->dims()[3];
-
-    int in_hw = in_h * in_w;
-    int out_hw = out_h * out_w;
-    int in_chw = channels * in_hw;
-    int out_chw = channels * out_hw;
-
-    float ratio_h =
-        (out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
-    float ratio_w =
-        (out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
-
-    if (in_h == out_h && in_w == out_w) {
-      memcpy(output, input, input_t->numel() * sizeof(T));
-    } else {
-      for (int k = 0; k < batch_size; ++k) {  // loop for batches
-        for (int i = 0; i < out_h; ++i) {     // loop for images
-          int h = ratio_h * i;
-          int hid = (h < in_h - 1) ? 1 : 0;
-          float h1lambda = ratio_h * i - h;
-          float h2lambda = 1.f - h1lambda;
-
-          for (int j = 0; j < out_w; ++j) {
-            int w = ratio_w * j;
-            int wid = (w < in_w - 1) ? 1 : 0;
-            float w1lambda = ratio_w * j - w;
-            float w2lambda = 1.f - w1lambda;
-            // calculate four position for bilinear interpolation
-            const T* in_pos = &input[k * in_chw + h * in_w + w];
-            T* out_pos = &output[k * out_chw + i * out_w + j];
-
-            for (int c = 0; c < channels; ++c) {  // loop for channels
-              // bilinear interpolation
-              out_pos[0] = static_cast<T>(
-                  h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[wid]) +
-                  h1lambda * (w2lambda * in_pos[hid * in_w] +
-                              w1lambda * in_pos[hid * in_w + wid]));
-              in_pos += in_hw;
-              out_pos += out_hw;
-            }
-          }
-        }
-      }
-    }
-  }
-};
-
-template <typename T>
-class BilinearInterpGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* d_output = d_output_t->data<T>();
-    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
-    auto& device_ctx =
-        ctx.template device_context<platform::CPUDeviceContext>();
-    math::SetConstant<platform::CPUDeviceContext, T> zero;
-    zero(device_ctx, d_input_t, static_cast<T>(0.0));
-
-    int out_h = ctx.Attr<int>("out_h");
-    int out_w = ctx.Attr<int>("out_w");
-
-    auto out_size_t = ctx.Input<Tensor>("OutSize");
-    if (out_size_t != nullptr) {
-      auto out_size_data = out_size_t->data<int>();
-      out_h = out_size_data[0];
-      out_w = out_size_data[1];
-    }
-
-    int batch_size = d_input_t->dims()[0];
-    int channels = d_input_t->dims()[1];
-    int in_h = d_input_t->dims()[2];
-    int in_w = d_input_t->dims()[3];
-
-    int in_hw = in_h * in_w;
-    int out_hw = out_h * out_w;
-    int in_chw = channels * in_hw;
-    int out_chw = channels * out_hw;
-
-    float ratio_h =
-        (out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
-    float ratio_w =
-        (out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
-
-    if (in_h == out_h && in_w == out_w) {
-      memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
-    } else {
-      for (int k = 0; k < batch_size; ++k) {  // loop for batches
-        for (int i = 0; i < out_h; ++i) {     // loop for images
-          int h = ratio_h * i;
-          int hid = (h < in_h - 1) ? 1 : 0;
-          float h1lambda = ratio_h * i - h;
-          float h2lambda = 1 - h1lambda;
-
-          for (int j = 0; j < out_w; ++j) {
-            int w = ratio_w * j;
-            int wid = (w < in_w - 1) ? 1 : 0;
-            float w1lambda = ratio_w * j - w;
-            float w2lambda = 1 - w1lambda;
-            T* in_pos = &d_input[k * in_chw + h * in_w + w];
-            const T* out_pos = &d_output[k * out_chw + i * out_w + j];
-
-            for (int c = 0; c < channels; ++c) {  // loop for channels
-              in_pos[0] += static_cast<T>(h2lambda * w2lambda * out_pos[0]);
-              in_pos[wid] += static_cast<T>(h2lambda * w1lambda * out_pos[0]);
-              in_pos[hid * in_w] +=
-                  static_cast<T>(h1lambda * w2lambda * out_pos[0]);
-              in_pos[hid * in_w + wid] +=
-                  static_cast<T>(h1lambda * w1lambda * out_pos[0]);
-              in_pos += in_hw;
-              out_pos += out_hw;
-            }
-          }
-        }
-      }
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/fluid/operators/cast_op.h

@@ -54,7 +54,6 @@ 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/conv_cudnn_op.cu.cc

@@ -15,15 +15,22 @@ limitations under the License. */
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/memory.h"
+#include "paddle/fluid/operators/conv_cudnn_op_cache.h"
 #include "paddle/fluid/operators/conv_op.h"
 #include "paddle/fluid/platform/assert.h"
 #include "paddle/fluid/platform/cudnn_helper.h"
 #include "paddle/fluid/platform/float16.h"
+#include "paddle/fluid/platform/profiler.h"
 
 DEFINE_bool(cudnn_deterministic, false,
             "Whether allow using an autotuning algorithm for convolution "
             "operator. The autotuning algorithm may be non-deterministic. If "
             "true, the algorithm is deterministic.");
+DEFINE_uint64(conv_workspace_size_limit, 4096,
+              "cuDNN convolution workspace limit in MB unit.");
+DEFINE_bool(cudnn_exhaustive_search, false,
+            "Whether enable exhaustive search for cuDNN convolution or "
+            "not, defalut is False.");
 
 namespace paddle {
 namespace operators {
@@ -36,13 +43,25 @@ using DataLayout = platform::DataLayout;
 template <typename T>
 using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
 
+static constexpr char kCUDNNFwdAlgoCache[] = "kCUDNNFwdAlgoCache";
+static constexpr char kCUDNNBwdDataAlgoCache[] = "kCUDNNBwdDataAlgoCache";
+static constexpr char kCUDNNBwdFilterAlgoCache[] = "kCUDNNBwdFilterAlgoCache";
+
 static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
     static_cast<size_t>(1024) * 1024 * 1024;
 
+static constexpr size_t kNUM_CUDNN_FWD_ALGS =
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
+static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS =
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
+static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS =
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT;
+
 template <typename T>
 class CUDNNConvOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "It must use CUDAPlace.");
     auto* input = ctx.Input<Tensor>("Input");
@@ -55,6 +74,8 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     int groups = ctx.Attr<int>("groups");
     int64_t user_workspace_size =
         static_cast<size_t>(ctx.Attr<int>("workspace_size_MB"));
+    bool exhaustive_search =
+        FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
 
     const T* input_data = input->data<T>();
     const T* filter_data = filter->data<T>();
@@ -120,19 +141,19 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     // ------------------- cudnn conv workspace ---------------------
     size_t workspace_size_in_bytes;  // final workspace to allocate.
     size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
-    if (user_workspace_size > 0) {
-      workspace_size_limit = user_workspace_size * 1024 * 1024;
+    if (FLAGS_conv_workspace_size_limit > 0 || user_workspace_size > 0) {
+      int64_t max_user_size =
+          std::max(static_cast<int64_t>(FLAGS_conv_workspace_size_limit),
+                   user_workspace_size);
+      workspace_size_limit = max_user_size * 1024 * 1024;
     }
+
     // ------------------- cudnn conv algorithm ---------------------
     cudnnConvolutionFwdAlgo_t algo;
-    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
     auto handle = dev_ctx.cudnn_handle();
+    auto workspace_handle = dev_ctx.cudnn_workspace_handle();
 
-    CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
-        handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
-        cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-        workspace_size_limit, &algo));
-
+    bool half_float = false;
 #if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
     // Tensor core is supported since the volta GPU and
     // is only enabled when input and filter data are float16
@@ -143,6 +164,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
           cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
       // Currently tensor core is only enabled using this algo
       algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
+      half_float = true;
       VLOG(5) << "use cudnn_tensor_op_math";
     } else {
       CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
@@ -151,6 +173,57 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     }
 #endif
 
+    auto x_dims = framework::vectorize(input->dims());
+    auto f_dims = framework::vectorize(filter->dims());
+    if ((!exhaustive_search) && (!half_float)) {
+      CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+          handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
+          cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+          workspace_size_limit, &algo));
+      VLOG(3) << "cuDNN forward algo " << algo;
+    } else if (exhaustive_search && (!half_float)) {
+      AlgorithmsCache<cudnnConvolutionFwdAlgo_t>* algo_cache = nullptr;
+      if (ctx.scope().FindVar(kCUDNNFwdAlgoCache)) {
+        algo_cache =
+            ctx.scope()
+                .FindVar(kCUDNNFwdAlgoCache)
+                ->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
+      } else {
+        algo_cache =
+            const_cast<framework::Scope&>(ctx.scope())
+                .Var(kCUDNNFwdAlgoCache)
+                ->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
+      }
+      algo = algo_cache->GetAlgorithm(
+          x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
+            int returned_algo_count;
+            std::array<cudnnConvolutionFwdAlgoPerf_t, kNUM_CUDNN_FWD_ALGS>
+                fwd_perf_stat;
+            auto cudnn_find_func = [&](void* cudnn_workspace) {
+              CUDNN_ENFORCE(
+                  platform::dynload::cudnnFindConvolutionForwardAlgorithmEx(
+                      handle, cudnn_input_desc, input_data, cudnn_filter_desc,
+                      filter_data, cudnn_conv_desc, cudnn_output_desc,
+                      output_data, kNUM_CUDNN_FWD_ALGS, &returned_algo_count,
+                      fwd_perf_stat.data(), cudnn_workspace,
+                      workspace_size_limit));
+            };
+            workspace_handle.RunFunc(cudnn_find_func, workspace_size_limit);
+
+            VLOG(3) << "Perf result: (algo: stat, time, memory)";
+            for (int i = 0; i < returned_algo_count; ++i) {
+              const auto& stat = fwd_perf_stat[i];
+              VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
+                      << " " << stat.memory;
+            }
+            return fwd_perf_stat[0].algo;
+          });
+      VLOG(3) << "choose algo " << algo;
+    } else {
+      PADDLE_ENFORCE(half_float,
+                     "cuDNN exhaustive search doesn't support half float.");
+    }
+
     // get workspace size able to allocate
     CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
         handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
@@ -162,7 +235,6 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
 
     // ------------------- cudnn conv forward ---------------------
     ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
-    auto workspace_handle = dev_ctx.cudnn_workspace_handle();
     for (int i = 0; i < groups; i++) {
       auto cudnn_func = [&](void* cudnn_workspace) {
         CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
@@ -180,6 +252,7 @@ template <typename T>
 class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "It must use CUDAPlace.");
     auto input = ctx.Input<Tensor>("Input");
@@ -198,6 +271,13 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     int groups = ctx.Attr<int>("groups");
     int64_t user_workspace_size =
         static_cast<size_t>(ctx.Attr<int>("workspace_size_MB"));
+    bool exhaustive_search =
+        FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
+    if (exhaustive_search && FLAGS_cudnn_deterministic) {
+      PADDLE_THROW(
+          "Cann't set exhaustive_search True and "
+          "FLAGS_cudnn_deterministic True at same time.");
+    }
 
     // ------------------- cudnn descriptors ---------------------
     ScopedTensorDescriptor input_desc;
@@ -265,14 +345,66 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     cudnnConvolutionBwdFilterAlgo_t filter_algo;
     size_t workspace_size_in_bytes = 0, tmp_size = 0;
     size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
-    if (user_workspace_size > 0) {
-      workspace_size_limit = user_workspace_size * 1024 * 1024;
+    if (FLAGS_conv_workspace_size_limit > 0 || user_workspace_size > 0) {
+      int64_t max_user_size =
+          std::max(static_cast<int64_t>(FLAGS_conv_workspace_size_limit),
+                   user_workspace_size);
+      workspace_size_limit = max_user_size * 1024 * 1024;
     }
 
-    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    auto x_dims = framework::vectorize(input->dims());
+    auto f_dims = framework::vectorize(filter->dims());
     auto handle = dev_ctx.cudnn_handle();
+    auto workspace_handle = dev_ctx.cudnn_workspace_handle();
     if (input_grad) {
-      if (!FLAGS_cudnn_deterministic) {
+      T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
+      if (exhaustive_search) {
+        AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>* data_algo_cache;
+        if (ctx.scope().FindVar(kCUDNNBwdDataAlgoCache)) {
+          data_algo_cache =
+              ctx.scope()
+                  .FindVar(kCUDNNBwdDataAlgoCache)
+                  ->GetMutable<
+                      AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>();
+        } else {
+          data_algo_cache =
+              const_cast<framework::Scope&>(ctx.scope())
+                  .Var(kCUDNNBwdDataAlgoCache)
+                  ->GetMutable<
+                      AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>();
+        }
+        data_algo = data_algo_cache->GetAlgorithm(
+            x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
+              int returned_algo_count;
+              std::array<cudnnConvolutionBwdDataAlgoPerf_t,
+                         kNUM_CUDNN_BWD_DATA_ALGS>
+                  data_perf_stat;
+              auto cudnn_find_bd_data_func = [&](void* cudnn_workspace) {
+                CUDNN_ENFORCE(
+                    platform::dynload::
+                        cudnnFindConvolutionBackwardDataAlgorithmEx(
+                            handle, cudnn_filter_desc, filter_data,
+                            cudnn_output_grad_desc, output_grad_data,
+                            cudnn_conv_desc, cudnn_input_desc, input_grad_data,
+                            kNUM_CUDNN_BWD_DATA_ALGS, &returned_algo_count,
+                            data_perf_stat.data(), cudnn_workspace,
+                            workspace_size_limit));
+              };
+              workspace_handle.RunFunc(cudnn_find_bd_data_func,
+                                       workspace_size_limit);
+
+              VLOG(3) << "Perf result: (algo: stat, time, memory)";
+              for (int i = 0; i < returned_algo_count; ++i) {
+                const auto& stat = data_perf_stat[i];
+                VLOG(3) << stat.algo << ": " << stat.status << " " << stat.time
+                        << " " << stat.memory;
+              }
+              return data_perf_stat[0].algo;
+            });
+        VLOG(3) << "cuDNN backward data algo " << data_algo;
+      } else if (FLAGS_cudnn_deterministic) {
+        data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
+      } else {
         CUDNN_ENFORCE(
             platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
                 handle, cudnn_filter_desc,
@@ -285,10 +417,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
                 cudnn_input_desc,
                 CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
                 workspace_size_limit, &data_algo));
-      } else {
-        data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
       }
-
       CUDNN_ENFORCE(
           platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
               handle, cudnn_filter_desc, cudnn_output_grad_desc,
@@ -297,17 +426,54 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     }
 
     if (filter_grad) {
-      if (!FLAGS_cudnn_deterministic) {
+      T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
+      if (exhaustive_search) {
+        AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>* f_algo_cache;
+        if (ctx.scope().FindVar(kCUDNNBwdFilterAlgoCache)) {
+          f_algo_cache =
+              ctx.scope()
+                  .FindVar(kCUDNNBwdFilterAlgoCache)
+                  ->GetMutable<
+                      AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>();
+        } else {
+          f_algo_cache =
+              const_cast<framework::Scope&>(ctx.scope())
+                  .Var(kCUDNNBwdFilterAlgoCache)
+                  ->GetMutable<
+                      AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>();
+        }
+        filter_algo = f_algo_cache->GetAlgorithm(
+            x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
+              int returned_algo_count;
+              std::array<cudnnConvolutionBwdFilterAlgoPerf_t,
+                         kNUM_CUDNN_BWD_FILTER_ALGS>
+                  filter_perf_stat;
+              auto cudnn_find_bd_f_func = [&](void* cudnn_workspace) {
+                CUDNN_ENFORCE(
+                    platform::dynload::
+                        cudnnFindConvolutionBackwardFilterAlgorithmEx(
+                            handle, cudnn_input_desc, input_data,
+                            cudnn_output_grad_desc, output_grad_data,
+                            cudnn_conv_desc, cudnn_filter_desc,
+                            filter_grad_data, kNUM_CUDNN_BWD_FILTER_ALGS,
+                            &returned_algo_count, filter_perf_stat.data(),
+                            cudnn_workspace, workspace_size_limit));
+              };
+              workspace_handle.RunFunc(cudnn_find_bd_f_func,
+                                       workspace_size_limit);
+              return filter_perf_stat[0].algo;
+            });
+        VLOG(3) << "cuDNN backward filter algo " << filter_algo;
+      } else if (FLAGS_cudnn_deterministic) {
+        filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
+      } else {
         CUDNN_ENFORCE(
             platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
                 handle, cudnn_input_desc, cudnn_output_grad_desc,
                 cudnn_conv_desc, cudnn_filter_desc,
                 CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
                 workspace_size_limit, &filter_algo));
-      } else {
-        filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
       }
-
       CUDNN_ENFORCE(
           platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
               handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
@@ -317,7 +483,6 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
 
     // ------------------- cudnn conv backward data ---------------------
     ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
-    auto workspace_handle = dev_ctx.cudnn_workspace_handle();
     if (input_grad) {
       T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset input_grad.

paddle/fluid/operators/conv_cudnn_op_cache.h

+/* Copyright (c) 2016 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 <functional>
+#include <unordered_map>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+template <typename TAlgorithm>
+class AlgorithmsCache {
+ public:
+  // Caches the best algorithm for a given
+  // combination of tensor dimensions & compute data type.
+  TAlgorithm GetAlgorithm(
+      const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
+      const std::vector<int>& strides, const std::vector<int>& paddings,
+      const std::vector<int>& dilations,
+      int algorithmFlags,  // can set for different data type
+      std::function<TAlgorithm()> gen_func);
+
+ private:
+  std::unordered_map<int64_t, TAlgorithm> hash_;
+  std::mutex mutex_;
+};
+
+template <typename TAlgorithm>
+TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm(
+    const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
+    const std::vector<int>& strides, const std::vector<int>& paddings,
+    const std::vector<int>& dilations, int algorithmFlags,
+    std::function<TAlgorithm()> gen_func) {
+  std::lock_guard<std::mutex> lock(mutex_);
+  int64_t seed = 0;
+  // Hash all of the inputs, use to try and look up a previously
+  // discovered algorithm, or fall back to generating a new one.
+  std::hash<int64_t> hashFn;
+  // do hash like boost
+  // https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
+  for (const auto num : dims1) {
+    seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
+  }
+
+  for (const auto num : dims2) {
+    seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2) + 1;
+  }
+
+  for (const auto num : strides) {
+    seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
+            (seed >> 2) + 2;
+  }
+
+  for (const auto num : paddings) {
+    seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
+            (seed >> 2) + 3;
+  }
+
+  for (const auto num : dilations) {
+    seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
+            (seed >> 2) + 4;
+  }
+
+  seed ^= hashFn(static_cast<int64_t>(algorithmFlags)) + 0x9e3779b9 +
+          (seed << 6) + (seed >> 2) + 5;
+
+  if (seed == 0) return gen_func();
+
+  if (hash_.find(seed) == hash_.end()) {
+    TAlgorithm value = gen_func();
+    hash_[seed] = value;
+  }
+  return hash_[seed];
+}
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/conv_mkldnn_op.cc

@@ -375,8 +375,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     auto src_md = platform::MKLDNNMemDesc(
         src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
     auto weights_md = platform::MKLDNNMemDesc(
-        weights_tz, platform::MKLDNNGetDataType<T>(),
-        (g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw);
+        weights_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
     std::vector<int> bias_tz;  // TODO(mgallus): avoid empty vector creation.
                                // Currently used whenever bias is != nullptr.
     auto dst_md = platform::MKLDNNMemDesc(

paddle/fluid/operators/conv_op.cc

@@ -189,6 +189,11 @@ void Conv2DOpMaker::Make() {
                "workspace size can increase performance but also requires "
                "better hardware. This size should be chosen carefully.")
       .SetDefault(4096);
+  AddAttr<bool>("exhaustive_search",
+                "(bool, default false) cuDNN has many algorithm to calculation "
+                "convolution, whether enable exhaustive search ",
+                "for cuDNN convolution or not, defalut is False.")
+      .SetDefault(false);
   AddComment(R"DOC(
 Convolution Operator.
 
@@ -283,7 +288,11 @@ void Conv3DOpMaker::Make() {
                "workspace size can increase performance but also requires "
                "better hardware. This size should be chosen carefully.")
       .SetDefault(4096);
-
+  AddAttr<bool>("exhaustive_search",
+                "(bool, default false) cuDNN has many algorithm to calculation "
+                "convolution, whether enable exhaustive search ",
+                "for cuDNN convolution or not, defalut is False.")
+      .SetDefault(false);
   AddComment(R"DOC(
 Convolution3D Operator.
 

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) || fabsf(static_cast<float>(a - b)) < 1e-4;
+  return (a > b) || fabs(a - b) < 1e-4;
 }
 
 template <typename T>
 __device__ bool LT_E(T a, T b) {
-  return (a < b) || fabsf(static_cast<float>(a - b)) < 1e-4;
+  return (a < b) || fabs(a - b) < 1e-4;
 }
 
 template <typename T>

paddle/fluid/operators/elementwise_add_op.h

@@ -28,9 +28,9 @@ struct AddFunctor {
 };
 
 template <typename DeviceContext, typename T>
-void default_elementwise_add(const framework::ExecutionContext& ctx,
-                             const framework::Tensor* x,
-                             const framework::Tensor* y, framework::Tensor* z) {
+void default_elementwise_add(const framework::ExecutionContext &ctx,
+                             const framework::Tensor *x,
+                             const framework::Tensor *y, framework::Tensor *z) {
   int axis = ctx.Attr<int>("axis");
   ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
                                                         AddFunctor<T>(), z);
@@ -40,9 +40,9 @@ template <typename DeviceContext, typename T>
 typename std::enable_if<
     std::is_floating_point<T>::value &&
     std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
-elementwise_add(const framework::ExecutionContext& ctx,
-                const framework::Tensor* x, const framework::Tensor* y,
-                framework::Tensor* z) {
+elementwise_add(const framework::ExecutionContext &ctx,
+                const framework::Tensor *x, const framework::Tensor *y,
+                framework::Tensor *z) {
   auto eigen_x = framework::EigenVector<T>::Flatten(*x);
   auto eigen_y = framework::EigenVector<T>::Flatten(*y);
   auto eigen_z = framework::EigenVector<T>::Flatten(*z);
@@ -55,21 +55,20 @@ template <typename DeviceContext, typename T>
 typename std::enable_if<
     !std::is_floating_point<T>::value ||
     !std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
-elementwise_add(const framework::ExecutionContext& ctx,
-                const framework::Tensor* x, const framework::Tensor* y,
-                framework::Tensor* z) {
+elementwise_add(const framework::ExecutionContext &ctx,
+                const framework::Tensor *x, const framework::Tensor *y,
+                framework::Tensor *z) {
   default_elementwise_add<DeviceContext, T>(ctx, x, y, z);
 }
 
 template <typename DeviceContext, typename T>
 class ElementwiseAddKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *x = ctx.Input<framework::LoDTensor>("X");
+    auto *y = ctx.Input<framework::LoDTensor>("Y");
+    auto *z = ctx.Output<framework::LoDTensor>("Out");
 
-    const auto x = ctx.Input<Tensor>("X");
-    const auto y = ctx.Input<Tensor>("Y");
-    auto z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
 
     auto dims_equal = x->dims() == y->dims();
@@ -87,13 +86,13 @@ struct IdentityGrad {
 };
 
 template <typename DeviceContext, typename T>
-void default_elementwise_add_grad(const framework::ExecutionContext& ctx,
-                                  const framework::Tensor* x,
-                                  const framework::Tensor* y,
-                                  const framework::Tensor* out,
-                                  const framework::Tensor* dout,
-                                  framework::Tensor* dx,
-                                  framework::Tensor* dy) {
+void default_elementwise_add_grad(const framework::ExecutionContext &ctx,
+                                  const framework::Tensor *x,
+                                  const framework::Tensor *y,
+                                  const framework::Tensor *out,
+                                  const framework::Tensor *dout,
+                                  framework::Tensor *dx,
+                                  framework::Tensor *dy) {
   int axis = ctx.Attr<int>("axis");
 
   ElemwiseExplicitGradCompute<DeviceContext, T, IdentityGrad<T>,
@@ -106,11 +105,11 @@ template <typename DeviceContext, typename T>
 typename std::enable_if<
     std::is_floating_point<T>::value &&
     std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
-elementwise_add_grad(const framework::ExecutionContext& ctx,
-                     const framework::Tensor* x, const framework::Tensor* y,
-                     const framework::Tensor* out,
-                     const framework::Tensor* dout, framework::Tensor* dx,
-                     framework::Tensor* dy) {
+elementwise_add_grad(const framework::ExecutionContext &ctx,
+                     const framework::Tensor *x, const framework::Tensor *y,
+                     const framework::Tensor *out,
+                     const framework::Tensor *dout, framework::Tensor *dx,
+                     framework::Tensor *dy) {
   auto blas = math::GetBlas<DeviceContext, T>(ctx);
 
   if (dx) {
@@ -128,27 +127,27 @@ template <typename DeviceContext, typename T>
 typename std::enable_if<
     !std::is_floating_point<T>::value ||
     !std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
-elementwise_add_grad(const framework::ExecutionContext& ctx,
-                     const framework::Tensor* x, const framework::Tensor* y,
-                     const framework::Tensor* out,
-                     const framework::Tensor* dout, framework::Tensor* dx,
-                     framework::Tensor* dy) {
+elementwise_add_grad(const framework::ExecutionContext &ctx,
+                     const framework::Tensor *x, const framework::Tensor *y,
+                     const framework::Tensor *out,
+                     const framework::Tensor *dout, framework::Tensor *dx,
+                     framework::Tensor *dy) {
   default_elementwise_add_grad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
 }
 
 template <typename DeviceContext, typename T>
 class ElementwiseAddGradKernel : public ElemwiseGradKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
+  void Compute(const framework::ExecutionContext &ctx) const override {
     ElemwiseGradKernel<T>::Compute(ctx);
 
     using Tensor = framework::Tensor;
 
-    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    auto *dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto *dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
     // skip out, x, y
-    auto* out = dout;
+    auto *out = dout;
     auto *x = dout, *y = dout;
 
     if (platform::is_cpu_place(ctx.GetPlace()) && dx != nullptr &&

paddle/fluid/operators/elementwise_div_op.h

@@ -28,11 +28,10 @@ template <typename DeviceContext, typename T>
 class ElementwiseDivKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto* y = ctx.Input<framework::LoDTensor>("Y");
+    auto* z = ctx.Output<framework::LoDTensor>("Out");
 
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
     ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(ctx, x, y, axis,

paddle/fluid/operators/elementwise_max_op.h

@@ -29,11 +29,10 @@ template <typename DeviceContext, typename T>
 class ElementwiseMaxKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto* y = ctx.Input<framework::LoDTensor>("Y");
+    auto* z = ctx.Output<framework::LoDTensor>("Out");
 
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
     ElementwiseComputeEx<MaxFunctor<T>, DeviceContext, T>(ctx, x, y, axis,

paddle/fluid/operators/elementwise_min_op.h

@@ -28,11 +28,10 @@ template <typename DeviceContext, typename T>
 class ElementwiseMinKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto* y = ctx.Input<framework::LoDTensor>("Y");
+    auto* z = ctx.Output<framework::LoDTensor>("Out");
 
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
     ElementwiseComputeEx<MinFunctor<T>, DeviceContext, T>(ctx, x, y, axis,

paddle/fluid/operators/elementwise_mul_op.h

@@ -60,11 +60,10 @@ template <typename DeviceContext, typename T>
 class ElementwiseMulKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto* y = ctx.Input<framework::LoDTensor>("Y");
+    auto* z = ctx.Output<framework::LoDTensor>("Out");
 
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     if (x->numel() == y->numel()) {
       elementwise_mul<DeviceContext, T>(ctx, x, y, z);

paddle/fluid/operators/elementwise_op.h

@@ -13,10 +13,12 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+
 #include <string>
 #include "paddle/fluid/framework/data_layout.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
+
 #ifdef PADDLE_WITH_MKLDNN
 #include "paddle/fluid/platform/mkldnn_helper.h"
 #endif
@@ -29,7 +31,8 @@ class ElementwiseOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   using Tensor = framework::Tensor;
-  void InferShape(framework::InferShapeContext* ctx) const override {
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"),
                    "Input(X) of elementwise op should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Y"),
@@ -37,6 +40,17 @@ class ElementwiseOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of elementwise op should not be null.");
 
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("X").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("X").front(), ctx->GetInputsVarType("X").front());
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Y").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Y").front(), ctx->GetInputsVarType("Y").front());
+
     auto x_dim = ctx->GetInputDim("X");
     auto y_dim = ctx->GetInputDim("Y");
     PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
@@ -47,9 +61,8 @@ class ElementwiseOp : public framework::OperatorWithKernel {
   }
 
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    auto input_data_type =
-        framework::ToDataType(ctx.Input<Tensor>("X")->type());
+      const framework::ExecutionContext &ctx) const override {
+    auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("X"));
 
 #ifdef PADDLE_WITH_MKLDNN
     if (platform::CanMKLDNNBeUsed(ctx)) {
@@ -64,12 +77,12 @@ class ElementwiseOp : public framework::OperatorWithKernel {
 
 class ElementwiseOpInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
-                  framework::BlockDesc* block) const override {
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {
     auto x_name = op_desc.Input("X")[0];
     auto out_name = op_desc.Output("Out")[0];
-    auto& x = block->FindRecursiveOrCreateVar(x_name);
-    auto& out = block->FindRecursiveOrCreateVar(out_name);
+    auto &x = block->FindRecursiveOrCreateVar(x_name);
+    auto &out = block->FindRecursiveOrCreateVar(out_name);
     out.SetType(x.GetType());
     out.SetDataType(x.GetDataType());
   }
@@ -131,6 +144,7 @@ But the output only shares the LoD information with the input $X$.
 
  protected:
   virtual std::string GetName() const = 0;
+
   virtual std::string GetEquation() const = 0;
 };
 
@@ -139,7 +153,7 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
   using Tensor = framework::Tensor;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
+  void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
     PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
@@ -165,7 +179,7 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
   }
 
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
+      const framework::ExecutionContext &ctx) const override {
     auto input_data_type = framework::ToDataType(
         ctx.Input<Tensor>(framework::GradVarName("Out"))->type());
 
@@ -187,7 +201,7 @@ class ElementwiseOpExplicitGrad : public ElementwiseOpGrad {
   using operators::ElementwiseOpGrad::GetExpectedKernelType;
   using Tensor = framework::Tensor;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
+  void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@GRAD) should not be null");
 
@@ -209,11 +223,11 @@ class ElementwiseOpExplicitGrad : public ElementwiseOpGrad {
 template <typename T>
 class ElemwiseGradKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* dx =
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto *dx =
         context.Output<framework::LoDTensor>(framework::GradVarName("X"));
     if (dx != nullptr) {
-      auto& dout =
+      auto &dout =
           *context.Input<framework::LoDTensor>(framework::GradVarName("Out"));
       dx->set_lod(dout.lod());
     }
@@ -234,7 +248,7 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
                                                                              \
    protected:                                                                \
     std::unique_ptr<paddle::framework::OpDesc> Apply() const override {      \
-      auto* op = new paddle::framework::OpDesc();                            \
+      auto *op = new paddle::framework::OpDesc();                            \
       op->SetType(#kernel_type "_grad");                                     \
       op->SetInput("Y", Input("Y"));                                         \
       op->SetInput(::paddle::framework::GradVarName("Out"),                  \

paddle/fluid/operators/elementwise_op_function.h

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

paddle/fluid/operators/elementwise_sub_op.h

@@ -28,11 +28,10 @@ template <typename DeviceContext, typename T>
 class ElementwiseSubKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
+    auto* x = ctx.Input<framework::LoDTensor>("X");
+    auto* y = ctx.Input<framework::LoDTensor>("Y");
+    auto* z = ctx.Output<framework::LoDTensor>("Out");
 
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
     ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(ctx, x, y, axis,

paddle/fluid/operators/extract_rows_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 <string>
-#include <vector>
-#include "paddle/fluid/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-class ExtractRowsOpInferShape : public framework::InferShapeBase {
- public:
-  void operator()(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of ExtractRowsOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of ExtractRowsOp should not be null.");
-    PADDLE_ENFORCE_EQ(ctx->GetInputsVarType("X")[0],
-                      framework::proto::VarType::SELECTED_ROWS,
-                      "The type of input(X) must be SelectedRows.");
-    auto in_dims = ctx->GetInputDim("X");
-
-    ctx->SetOutputDim(
-        "Out", framework::make_ddim(std::vector<int64_t>{in_dims[0], 1}));
-  }
-};
-
-class ExtractRowsOp : public framework::OperatorBase {
- public:
-  ExtractRowsOp(const std::string &type,
-                const framework::VariableNameMap &inputs,
-                const framework::VariableNameMap &outputs,
-                const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>();
-    auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
-
-    auto &in_rows = in.rows();
-    auto out_dim = framework::make_ddim(
-        std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1});
-    auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place());
-
-    if (paddle::platform::is_gpu_place(in.place())) {
-#ifdef PADDLE_WITH_CUDA
-      platform::DeviceContextPool &pool =
-          platform::DeviceContextPool::Instance();
-      auto *dev_ctx = pool.Get(in.place());
-      auto src_ptr = in_rows.Data(in.place());
-      auto stream =
-          reinterpret_cast<const platform::CUDADeviceContext &>(*dev_ctx)
-              .stream();
-      memory::Copy(boost::get<platform::CUDAPlace>(out->place()), dst_ptr,
-                   boost::get<platform::CUDAPlace>(in.place()), src_ptr,
-                   in_rows.size() * sizeof(int64_t), stream);
-#else
-      PADDLE_THROW("Not compiled with CUDA.");
-#endif
-    } else {
-      memory::Copy(platform::CPUPlace(), dst_ptr, platform::CPUPlace(),
-                   in_rows.data(), in_rows.size() * sizeof(int64_t));
-    }
-  }
-};
-
-class ExtractRowsOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput("X",
-             "(SelectedRows). The input tensor of extract_rows operator,"
-             " and its type is SelectedRows.");
-    AddOutput("Out", "(Tensor). The the rows of input(X).");
-
-    AddComment(R"DOC(
-    ExtractRows Operator.
-
-The function of extract_rows_op is extracting the rows from the input(X)
-whose type is SelectedRows.
-
-    )DOC");
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OPERATOR(extract_rows, ops::ExtractRowsOp, ops::ExtractRowsOpMaker,
-                  ops::ExtractRowsOpInferShape);

paddle/fluid/operators/bilinear_interp_op.cc → paddle/fluid/operators/interpolate_op.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at
@@ -9,7 +9,8 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/fluid/operators/bilinear_interp_op.h"
+#include "paddle/fluid/operators/interpolate_op.h"
+#include <string>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 
@@ -18,27 +19,34 @@ namespace operators {
 
 using framework::Tensor;
 
-class BilinearInterpOp : public framework::OperatorWithKernel {
+class InterpolateOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
  protected:
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of BilinearInterOp should not be null.");
+                   "Input(X) of InterpolateOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of BilinearInterOp should not be null.");
+                   "Output(Out) of InterpolationOp should not be null.");
+
+    auto interp_method = ctx->Attrs().Get<std::string>("interp_method");
+    PADDLE_ENFORCE(
+        "bilinear" == interp_method || "nearest" == interp_method,
+        "Interpolation method can only be \"bilinear\" or \"nearest\".");
 
     auto dim_x = ctx->GetInputDim("X");  // NCHW format
     int out_h = ctx->Attrs().Get<int>("out_h");
     int out_w = ctx->Attrs().Get<int>("out_w");
     PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
 
-    if (ctx->HasInput("OutSize")) {
+    if (ctx->HasInput("OutSize") && ctx->IsRuntime()) {
       auto out_size_dim = ctx->GetInputDim("OutSize");
       PADDLE_ENFORCE_EQ(out_size_dim.size(), 1,
                         "OutSize's dimension size must be 1");
       PADDLE_ENFORCE_EQ(out_size_dim[0], 2, "OutSize's dim[0] must be 2");
+      ctx->ShareLoD("X", "Out");
+      return;
     }
     std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
     ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
@@ -52,35 +60,53 @@ class BilinearInterpOp : public framework::OperatorWithKernel {
   }
 };
 
-class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
+class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("X",
-             "The input tensor of bilinear interpolation, "
-             "This is a 4-D tensor with shape of (N x C x h x w)");
+             "The input tensor of interpolate operator, "
+             "This is a 4-D tensor with shape of [N,  C, H, w].");
     AddInput("OutSize",
-             "This is a 1-D tensor with two number. "
+             "This is a 1-D tensor with two numbers to specify output size. "
              "The first number is height and the second number is width.")
         .AsDispensable();
-    AddOutput("Out", "The dimension of output is (N x C x out_h x out_w)");
+    AddOutput("Out",
+              "The output tensor of interpolate operator, "
+              "This is a 4-D tensor with shape of [N, C, H, W].");
 
-    AddAttr<int>("out_h", "output height of bilinear interpolation op.");
-    AddAttr<int>("out_w", "output width of bilinear interpolation op.");
+    AddAttr<int>("out_h", "output height of interpolate op.");
+    AddAttr<int>("out_w", "output width of interpolate op.");
+    AddAttr<std::string>(
+        "interp_method",
+        "(string), interpolation method, can be \"bilinear\" for "
+        "bilinear interpolation and \"nearest\" for nearest "
+        "neighbor interpolation.");
     AddComment(R"DOC(
+          This operator samples input X to given output shape by using specified
+          interpolation method, the interpolation methods can be \"nearest\"
+          for nearest neighbor interpolation and \"bilinear\" for bilinear 
+          interpolation.
+
+          Nearest neighbor interpolation is to perform nearest neighbor interpolation
+          in both the 3rd dimention(in height direction) and the 4th dimention(in width 
+          direction) on input tensor.
+            
           Bilinear interpolation is an extension of linear interpolation for 
           interpolating functions of two variables (e.g. H-direction and 
-          W-direction in this op) on a rectilinear 2D grid. 
-          
-          The key idea is to perform linear interpolation first in one 
-          direction, and then again in the other direction.
-            
-          For details, please refer to Wikipedia: 
+          W-direction in this op) on a rectilinear 2D grid. The key idea is 
+          to perform linear interpolation first in one direction, and then 
+          again in the other direction.
+
+          For details of nearest neighbor interpolation, please refer to Wikipedia: 
+          https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
+
+          For details of bilinear interpolation, please refer to Wikipedia: 
           https://en.wikipedia.org/wiki/Bilinear_interpolation
          )DOC");
   }
 };
 
-class BilinearInterpOpGrad : public framework::OperatorWithKernel {
+class InterpolateOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
@@ -106,11 +132,11 @@ class BilinearInterpOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(bilinear_interp, ops::BilinearInterpOp,
-                  ops::BilinearInterpOpMaker,
+REGISTER_OPERATOR(interpolate, ops::InterpolateOp, ops::InterpolateOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(bilinear_interp_grad, ops::BilinearInterpOpGrad);
-REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>,
-                       ops::BilinearInterpKernel<uint8_t>);
-REGISTER_OP_CPU_KERNEL(bilinear_interp_grad,
-                       ops::BilinearInterpGradKernel<float>);
+REGISTER_OPERATOR(interpolate_grad, ops::InterpolateOpGrad);
+REGISTER_OP_CPU_KERNEL(interpolate, ops::InterpolateKernel<float>,
+                       ops::InterpolateKernel<double>,
+                       ops::InterpolateKernel<uint8_t>);
+REGISTER_OP_CPU_KERNEL(interpolate_grad, ops::InterpolateGradKernel<float>,
+                       ops::InterpolateGradKernel<double>);

paddle/fluid/operators/bilinear_interp_op.cu → paddle/fluid/operators/interpolate_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at
@@ -9,7 +9,8 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/fluid/operators/bilinear_interp_op.h"
+#include <string>
+#include "paddle/fluid/operators/interpolate_op.h"
 #include "paddle/fluid/platform/cuda_primitives.h"
 
 namespace paddle {
@@ -17,15 +18,72 @@ namespace operators {
 
 using framework::Tensor;
 
+template <typename T>
+__global__ void KeNearestNeighborInterpFw(
+    const T* in, const size_t in_img_h, const size_t in_img_w,
+    const size_t input_h, const size_t input_w, T* out, const size_t out_img_h,
+    const size_t out_img_w, const size_t output_h, const size_t output_w,
+    const size_t num_channels, const float ratio_h, const float ratio_w) {
+  int nthreads = output_h * output_w;
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  int stride = blockDim.x * gridDim.x;
+  for (; tid < nthreads; tid += stride) {
+    int out_id_h = tid / output_w;
+    int out_id_w = tid % output_w;
+    int in_img_size = input_w / num_channels;
+    int out_img_size = output_w / num_channels;
+    int channel_id = out_id_w / out_img_size;
+
+    int out_img_idy = (out_id_w % out_img_size) / out_img_w;
+    int in_img_idy = static_cast<int>(ratio_h * out_img_idy + 0.5);
+
+    int out_img_idx = tid % out_img_w;
+    int in_img_idx = static_cast<int>(ratio_w * out_img_idx + 0.5);
+
+    out[tid] = in[out_id_h * input_w + channel_id * in_img_size +
+                  in_img_idy * in_img_w + in_img_idx];
+  }
+}
+
+template <typename T>
+__global__ void KeNearestNeighborInterpBw(
+    T* in, const size_t in_img_h, const size_t in_img_w, const size_t input_h,
+    const size_t input_w, const T* out, const size_t out_img_h,
+    const size_t out_img_w, const size_t output_h, const size_t output_w,
+    const size_t num_channels, const float ratio_h, const float ratio_w) {
+  int nthreads = output_h * output_w;
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  int stride = blockDim.x * gridDim.x;
+  for (; tid < nthreads; tid += stride) {
+    int out_id_h = tid / output_w;
+    int out_id_w = tid % output_w;
+    int in_img_size = input_w / num_channels;
+    int out_img_size = output_w / num_channels;
+    int channel_id = out_id_w / out_img_size;
+
+    int out_img_idy = (out_id_w % out_img_size) / out_img_w;
+    int in_img_idy = static_cast<int>(ratio_h * out_img_idy + 0.5);
+
+    int out_img_idx = tid % out_img_w;
+    int in_img_idx = static_cast<int>(ratio_w * out_img_idx + 0.5);
+
+    T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
+                    in_img_idy * in_img_w + in_img_idx];
+    const T out_pos = out[out_id_h * output_w + out_id_w];
+    platform::CudaAtomicAdd(in_pos, out_pos);
+  }
+}
+
 template <typename T>
 __global__ void KeBilinearInterpFw(
     const T* in, const size_t in_img_h, const size_t in_img_w,
     const size_t input_h, const size_t input_w, T* out, const size_t out_img_h,
     const size_t out_img_w, const size_t output_h, const size_t output_w,
-    const size_t num_channels, const T ratio_h, const T ratioW) {
+    const size_t num_channels, const float ratio_h, const float ratio_w) {
   int nthreads = output_h * output_w;
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
-  if (tid < nthreads) {
+  int stride = blockDim.x * gridDim.x;
+  for (; tid < nthreads; tid += stride) {
     int out_id_h = tid / output_w;
     int out_id_w = tid % output_w;
     int in_img_size = input_w / num_channels;
@@ -39,9 +97,9 @@ __global__ void KeBilinearInterpFw(
     T h2lambda = 1.f - h1lambda;
 
     int out_img_idx = tid % out_img_w;
-    int in_img_idx = ratioW * out_img_idx;
+    int in_img_idx = ratio_w * out_img_idx;
     int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
-    T w1lambda = ratioW * out_img_idx - in_img_idx;
+    T w1lambda = ratio_w * out_img_idx - in_img_idx;
     T w2lambda = 1.f - w1lambda;
 
     const T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
@@ -60,10 +118,11 @@ __global__ void KeBilinearInterpBw(
     T* in, const size_t in_img_h, const size_t in_img_w, const size_t input_h,
     const size_t input_w, const T* out, const size_t out_img_h,
     const size_t out_img_w, const size_t output_h, const size_t output_w,
-    const size_t num_channels, const T ratio_h, const T ratioW) {
+    const size_t num_channels, const T ratio_h, const T ratio_w) {
   int nthreads = output_h * output_w;
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
-  if (tid < nthreads) {
+  int stride = blockDim.x * gridDim.x;
+  for (; tid < nthreads; tid += stride) {
     int out_id_h = tid / output_w;
     int out_id_w = tid % output_w;
     int in_img_size = input_w / num_channels;
@@ -77,122 +136,146 @@ __global__ void KeBilinearInterpBw(
     T h2lambda = 1.f - h1lambda;
 
     int out_img_idx = tid % out_img_w;
-    int in_img_idx = ratioW * out_img_idx;
+    int in_img_idx = ratio_w * out_img_idx;
     int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
-    T w1lambda = ratioW * out_img_idx - in_img_idx;
+    T w1lambda = ratio_w * out_img_idx - in_img_idx;
     T w2lambda = 1.f - w1lambda;
 
     T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
                     in_img_idy * in_img_w + in_img_idx];
     const T* out_pos = &out[out_id_h * output_w + out_id_w];
-    atomicAdd(&in_pos[0], h2lambda * w2lambda * out_pos[0]);
-    atomicAdd(&in_pos[w_id], h2lambda * w1lambda * out_pos[0]);
-    atomicAdd(&in_pos[h_id * in_img_w], h1lambda * w2lambda * out_pos[0]);
-    atomicAdd(&in_pos[h_id * in_img_w + w_id],
-              h1lambda * w1lambda * out_pos[0]);
+    platform::CudaAtomicAdd(&in_pos[0], h2lambda * w2lambda * out_pos[0]);
+    platform::CudaAtomicAdd(&in_pos[w_id], h2lambda * w1lambda * out_pos[0]);
+    platform::CudaAtomicAdd(&in_pos[h_id * in_img_w],
+                            h1lambda * w2lambda * out_pos[0]);
+    platform::CudaAtomicAdd(&in_pos[h_id * in_img_w + w_id],
+                            h1lambda * w1lambda * out_pos[0]);
   }
 }
 
 template <typename T>
-class BilinearInterpOpCUDAKernel : public framework::OpKernel<T> {
+class InterpolateOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "This kernel only runs on GPU device.");
-    auto* input_t = ctx.Input<Tensor>("X");      // float tensor
-    auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
-    auto* input = input_t->data<T>();
+    auto* input = ctx.Input<Tensor>("X");
+    auto* output = ctx.Output<Tensor>("Out");
+    auto* input_data = input->data<T>();
 
+    auto interp_method = ctx.Attr<std::string>("interp_method");
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
-    auto out_dims = output_t->dims();
-    auto out_size_t = ctx.Input<Tensor>("OutSize");
-    if (out_size_t != nullptr) {
+    auto out_size = ctx.Input<Tensor>("OutSize");
+    if (out_size != nullptr) {
       Tensor sizes;
-      framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
+      framework::TensorCopy(*out_size, platform::CPUPlace(), &sizes);
       auto size_data = sizes.data<int>();
       out_h = size_data[0];
       out_w = size_data[1];
     }
-    auto* output = output_t->mutable_data<T>(
-        {out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
 
-    int batch_size = input_t->dims()[0];
-    int channels = input_t->dims()[1];
-    int in_h = input_t->dims()[2];
-    int in_w = input_t->dims()[3];
+    int n = input->dims()[0];
+    int c = input->dims()[1];
+    int in_h = input->dims()[2];
+    int in_w = input->dims()[3];
+
+    auto* output_data =
+        output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace());
 
     int in_hw = in_h * in_w;
     int out_hw = out_h * out_w;
-    int in_chw = channels * in_hw;
-    int out_chw = channels * out_hw;
+    int in_chw = c * in_hw;
+    int out_chw = c * out_hw;
 
-    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
-    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
+    float ratio_h =
+        (out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
+    float ratio_w =
+        (out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
 
     if (in_h == out_h && in_w == out_w) {
-      memcpy(output, input, input_t->numel() * sizeof(T));
-    } else {
-      int threadNum = batch_size * out_chw;
-      int blocks = (threadNum + 1024 - 1) / 1024;
+      framework::TensorCopy(*input, ctx.GetPlace(), output);
+      return;
+    }
+
+    int pixelNum = n * out_chw;
+    int grid_dim = (pixelNum + 512 - 1) / 512;
+    grid_dim = grid_dim > 8 ? 8 : grid_dim;
 
+    if ("nearest" == interp_method) {
+      KeNearestNeighborInterpFw<
+          T><<<grid_dim, 512, 0, ctx.cuda_device_context().stream()>>>(
+          input_data, in_h, in_w, n, in_chw, output_data, out_h, out_w, n,
+          out_chw, c, ratio_h, ratio_w);
+    } else if ("bilinear" == interp_method) {
       KeBilinearInterpFw<
-          T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
-          input, in_h, in_w, batch_size, in_chw, output, out_h, out_w,
-          batch_size, out_chw, channels, ratio_h, ratio_w);
+          T><<<grid_dim, 512, 0, ctx.cuda_device_context().stream()>>>(
+          input_data, in_h, in_w, n, in_chw, output_data, out_h, out_w, n,
+          out_chw, c, ratio_h, ratio_w);
     }
   }
 };
 
 template <typename T>
-class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
+class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* d_output = d_output_t->data<T>();
-    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* output_grad_data = output_grad->data<T>();
+    auto* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
 
     auto& device_ctx =
         ctx.template device_context<platform::CUDADeviceContext>();
     math::SetConstant<platform::CUDADeviceContext, T> zero;
-    zero(device_ctx, d_input_t, static_cast<T>(0.0));
+    zero(device_ctx, input_grad, static_cast<T>(0.0));
 
+    auto interp_method = ctx.Attr<std::string>("interp_method");
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
-
-    auto out_size_t = ctx.Input<Tensor>("OutSize");
-    if (out_size_t != nullptr) {
+    auto out_size = ctx.Input<Tensor>("OutSize");
+    if (out_size != nullptr) {
       Tensor sizes;
-      framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
+      framework::TensorCopy(*out_size, platform::CPUPlace(), &sizes);
       auto size_data = sizes.data<int>();
       out_h = size_data[0];
       out_w = size_data[1];
     }
 
-    int batch_size = d_input_t->dims()[0];
-    int channels = d_input_t->dims()[1];
-    int in_h = d_input_t->dims()[2];
-    int in_w = d_input_t->dims()[3];
+    int n = input_grad->dims()[0];
+    int c = input_grad->dims()[1];
+    int in_h = input_grad->dims()[2];
+    int in_w = input_grad->dims()[3];
 
     int in_hw = in_h * in_w;
     int out_hw = out_h * out_w;
-    int in_chw = channels * in_hw;
-    int out_chw = channels * out_hw;
+    int in_chw = c * in_hw;
+    int out_chw = c * out_hw;
 
-    T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
-    T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
+    float ratio_h =
+        (out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
+    float ratio_w =
+        (out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
 
     if (in_h == out_h && in_w == out_w) {
-      memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
-    } else {
-      int threadNum = batch_size * out_chw;
-      int blocks = (threadNum + 1024 - 1) / 1024;
+      framework::TensorCopy(*output_grad, ctx.GetPlace(), input_grad);
+      return;
+    }
+
+    int pixelNum = n * out_chw;
+    int grid_dim = (pixelNum + 512 - 1) / 512;
+    grid_dim = grid_dim > 8 ? 8 : grid_dim;
 
+    if ("nearest" == interp_method) {
+      KeNearestNeighborInterpBw<
+          T><<<grid_dim, 512, 0, ctx.cuda_device_context().stream()>>>(
+          input_grad_data, in_h, in_w, n, in_chw, output_grad_data, out_h,
+          out_w, n, out_chw, c, ratio_h, ratio_w);
+    } else if ("bilinear" == interp_method) {
       KeBilinearInterpBw<
-          T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
-          d_input, in_h, in_w, batch_size, in_chw, d_output, out_h, out_w,
-          batch_size, out_chw, channels, ratio_h, ratio_w);
+          T><<<grid_dim, 512, 0, ctx.cuda_device_context().stream()>>>(
+          input_grad_data, in_h, in_w, n, in_chw, output_grad_data, out_h,
+          out_w, n, out_chw, c, ratio_h, ratio_w);
     }
   }
 };
@@ -201,7 +284,9 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(bilinear_interp,
-                        ops::BilinearInterpOpCUDAKernel<float>);
-REGISTER_OP_CUDA_KERNEL(bilinear_interp_grad,
-                        ops::BilinearInterpGradOpCUDAKernel<float>);
+REGISTER_OP_CUDA_KERNEL(interpolate, ops::InterpolateOpCUDAKernel<float>,
+                        ops::InterpolateOpCUDAKernel<double>,
+                        ops::InterpolateOpCUDAKernel<int>);
+REGISTER_OP_CUDA_KERNEL(interpolate_grad,
+                        ops::InterpolateGradOpCUDAKernel<float>,
+                        ops::InterpolateGradOpCUDAKernel<double>);

paddle/fluid/operators/interpolate_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+   http://www.apache.org/licenses/LICENSE-2.0
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+#include <string>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+using Tensor = framework::Tensor;
+
+template <typename T>
+static void NearestNeighborInterpolate(const Tensor& input, Tensor* output,
+                                       const float ratio_h, const float ratio_w,
+                                       const int n, const int c,
+                                       const int out_h, const int out_w) {
+  auto input_t = EigenTensor<T, 4>::From(input);
+  auto output_t = EigenTensor<T, 4>::From(*output);
+  for (int k = 0; k < out_h; k++) {  // loop for images
+    int in_k = static_cast<int>(ratio_h * k + 0.5);
+
+    for (int l = 0; l < out_w; l++) {
+      int in_l = static_cast<int>(ratio_w * l + 0.5);
+
+      for (int i = 0; i < n; i++) {    // loop for batches
+        for (int j = 0; j < c; j++) {  // loop for channels
+          output_t(i, j, k, l) = input_t(i, j, in_k, in_l);
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void BilinearInterpolation(const Tensor& input, Tensor* output,
+                                  const float ratio_h, const float ratio_w,
+                                  const int in_h, const int in_w, const int n,
+                                  const int c, const int out_h,
+                                  const int out_w) {
+  auto input_t = EigenTensor<T, 4>::From(input);
+  auto output_t = EigenTensor<T, 4>::From(*output);
+  for (int k = 0; k < out_h; k++) {  // loop for images
+    int y_n = static_cast<int>(ratio_h * k);
+    int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
+    float d_n = ratio_h * k - y_n;
+    float d_s = 1.f - d_n;
+
+    for (int l = 0; l < out_w; l++) {
+      int x_w = static_cast<int>(ratio_w * l);
+      int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
+      float d_w = ratio_w * l - x_w;
+      float d_e = 1.f - d_w;
+
+      for (int i = 0; i < n; i++) {    // loop for batches
+        for (int j = 0; j < c; j++) {  // loop for channels
+          // bilinear interpolation
+          output_t(i, j, k, l) = input_t(i, j, y_n, x_w) * d_s * d_e +
+                                 input_t(i, j, y_s, x_w) * d_n * d_e +
+                                 input_t(i, j, y_n, x_e) * d_s * d_w +
+                                 input_t(i, j, y_s, x_e) * d_n * d_w;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void NearestNeighborInterpolateGrad(const Tensor& output_grad,
+                                           Tensor* input_grad,
+                                           const float ratio_h,
+                                           const float ratio_w, const int n,
+                                           const int c, const int out_h,
+                                           const int out_w) {
+  auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
+  auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
+  for (int k = 0; k < out_h; k++) {  // loop for images
+    int in_k = static_cast<int>(ratio_h * k + 0.5);
+
+    for (int l = 0; l < out_w; l++) {
+      int in_l = static_cast<int>(ratio_w * l + 0.5);
+
+      for (int i = 0; i < n; i++) {    // loop for batches
+        for (int j = 0; j < c; j++) {  // loop for channels
+          input_grad_t(i, j, in_k, in_l) += output_grad_t(i, j, k, l);
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void BilinearInterpolationGrad(const Tensor& output_grad,
+                                      Tensor* input_grad, const float ratio_h,
+                                      const float ratio_w, const int in_h,
+                                      const int in_w, const int n, const int c,
+                                      const int out_h, const int out_w) {
+  auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
+  auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
+  for (int k = 0; k < out_h; k++) {  // loop for images
+    int y_n = static_cast<int>(ratio_h * k);
+    int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
+    float d_n = ratio_h * k - y_n;
+    float d_s = 1.f - d_n;
+
+    for (int l = 0; l < out_w; l++) {
+      int x_w = static_cast<int>(ratio_w * l);
+      int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
+      float d_w = ratio_w * l - x_w;
+      float d_e = 1.f - d_w;
+
+      for (int i = 0; i < n; i++) {    // loop for batches
+        for (int j = 0; j < c; j++) {  // loop for channels
+          // bilinear interpolation grad
+          const T grad = output_grad_t(i, j, k, l);
+          input_grad_t(i, j, y_n, x_w) += static_cast<T>(grad * d_s * d_e);
+          input_grad_t(i, j, y_s, x_w) += static_cast<T>(grad * d_n * d_e);
+          input_grad_t(i, j, y_n, x_e) += static_cast<T>(grad * d_s * d_w);
+          input_grad_t(i, j, y_s, x_e) += static_cast<T>(grad * d_n * d_w);
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+class InterpolateKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    auto* output = ctx.Output<Tensor>("Out");
+
+    std::string interp_method = ctx.Attr<std::string>("interp_method");
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    auto out_size = ctx.Input<Tensor>("OutSize");
+    if (out_size != nullptr) {
+      auto out_size_data = out_size->data<int>();
+      out_h = out_size_data[0];
+      out_w = out_size_data[1];
+    }
+
+    const int n = input->dims()[0];
+    const int c = input->dims()[1];
+    const int in_h = input->dims()[2];
+    const int in_w = input->dims()[3];
+
+    output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace());
+    auto& device_ctx =
+        ctx.template device_context<platform::CPUDeviceContext>();
+    math::SetConstant<platform::CPUDeviceContext, T> zero;
+    zero(device_ctx, output, static_cast<T>(0.0));
+
+    if (in_h == out_h && in_w == out_w) {
+      framework::TensorCopy(*input, ctx.GetPlace(), output);
+      return;
+    }
+
+    float ratio_h =
+        (out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
+    float ratio_w =
+        (out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
+
+    if ("bilinear" == interp_method) {
+      BilinearInterpolation<T>(*input, output, ratio_h, ratio_w, in_h, in_w, n,
+                               c, out_h, out_w);
+    } else if ("nearest" == interp_method) {
+      NearestNeighborInterpolate<T>(*input, output, ratio_h, ratio_w, n, c,
+                                    out_h, out_w);
+    }
+  }
+};
+
+template <typename T>
+class InterpolateGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+
+    std::string interp_method = ctx.Attr<std::string>("interp_method");
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    auto out_size = ctx.Input<Tensor>("OutSize");
+    if (out_size != nullptr) {
+      auto out_size_data = out_size->data<int>();
+      out_h = out_size_data[0];
+      out_w = out_size_data[1];
+    }
+
+    const int n = input->dims()[0];
+    const int c = input->dims()[1];
+    const int in_h = input->dims()[2];
+    const int in_w = input->dims()[3];
+
+    input_grad->mutable_data<T>({n, c, in_h, in_w}, ctx.GetPlace());
+    auto& device_ctx =
+        ctx.template device_context<platform::CPUDeviceContext>();
+    math::SetConstant<platform::CPUDeviceContext, T> zero;
+    zero(device_ctx, input_grad, static_cast<T>(0.0));
+
+    if (in_h == out_h && in_w == out_w) {
+      framework::TensorCopy(*output_grad, ctx.GetPlace(), input_grad);
+      return;
+    }
+
+    float ratio_h =
+        (out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
+    float ratio_w =
+        (out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
+
+    if ("bilinear" == interp_method) {
+      BilinearInterpolationGrad<T>(*output_grad, input_grad, ratio_h, ratio_w,
+                                   in_h, in_w, n, c, out_h, out_w);
+    } else if ("nearest" == interp_method) {
+      NearestNeighborInterpolateGrad<T>(*output_grad, input_grad, ratio_h,
+                                        ratio_w, n, c, out_h, out_w);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/load_combine_op.cc

@@ -12,7 +12,6 @@ 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"
@@ -33,15 +32,9 @@ 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");
-    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),
+
+    std::ifstream fin(filename);
+    PADDLE_ENFORCE(static_cast<bool>(fin),
                    "Cannot open file %s for load_combine op", filename);
 
     auto out_var_names = Outputs("Out");
@@ -61,11 +54,11 @@ 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 =
@@ -110,18 +103,6 @@ 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

@@ -12,7 +12,6 @@ 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"
@@ -35,15 +34,8 @@ 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");
-    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",
+    std::ifstream fin(filename);
+    PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
                    filename);
 
     auto out_var_name = Output("Out");
@@ -52,9 +44,9 @@ 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,
@@ -118,18 +110,6 @@ 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/cpu_vec.h

@@ -18,6 +18,10 @@ 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

@@ -15,10 +15,13 @@ 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_code.cc

@@ -24,21 +24,30 @@ namespace gen {
 
 using namespace platform::jit;  // NOLINT
 
-bool VVVJitCode::init(int d) {
+bool VXXJitCode::init(int d, int scalar_index) {
   // It's not necessary to use avx512 since it would slow down the frequency
   // and this kernel is not compute bound.
-  return MayIUse(avx);
+  return MayIUse(avx) && scalar_index >= 0 && scalar_index <= 2;
 }
 
-void VVVJitCode::generate() {
+void VXXJitCode::generate() {
   // do not need push stack, and do not need save avx512reg if do not use avx512
   int offset = 0;
   if (with_relu_) {
     vxorps(ymm_zero, ymm_zero, ymm_zero);
   }
+  if (scalar_index_ == 1) {
+    vbroadcastss(ymm_src1, ptr[param1]);
+  } else if (scalar_index_ == 2) {
+    vbroadcastss(ymm_src2, ptr[param2]);
+  }
   for (int i = 0; i < num_ / AVX_FLOAT_BLOCK; ++i) {
-    vmovups(ymm_src1, ptr[param1 + offset]);
-    vmovups(ymm_src2, ptr[param2 + offset]);
+    if (scalar_index_ != 1) {
+      vmovups(ymm_src1, ptr[param1 + offset]);
+    }
+    if (scalar_index_ != 2) {
+      vmovups(ymm_src2, ptr[param2 + offset]);
+    }
     if (type_ == operand_type::mul) {
       vmulps(ymm_dst, ymm_src1, ymm_src2);
     } else if (type_ == operand_type::add) {
@@ -52,8 +61,12 @@ void VVVJitCode::generate() {
   }
   int rest = num_ % AVX_FLOAT_BLOCK;
   if (rest >= 4) {
-    vmovups(xmm_src1, ptr[param1 + offset]);
-    vmovups(xmm_src2, ptr[param2 + offset]);
+    if (scalar_index_ != 1) {
+      vmovups(xmm_src1, ptr[param1 + offset]);
+    }
+    if (scalar_index_ != 2) {
+      vmovups(xmm_src2, ptr[param2 + offset]);
+    }
     if (type_ == operand_type::mul) {
       vmulps(xmm_dst, xmm_src1, xmm_src2);
     } else if (type_ == operand_type::add) {
@@ -67,8 +80,12 @@ void VVVJitCode::generate() {
     rest -= 4;
   }
   if (rest >= 2) {
-    vmovq(xmm_src1, ptr[param1 + offset]);
-    vmovq(xmm_src2, ptr[param2 + offset]);
+    if (scalar_index_ != 1) {
+      vmovups(xmm_src1, ptr[param1 + offset]);
+    }
+    if (scalar_index_ != 2) {
+      vmovups(xmm_src2, ptr[param2 + offset]);
+    }
     if (type_ == operand_type::mul) {
       vmulps(xmm_dst, xmm_src1, xmm_src2);
     } else if (type_ == operand_type::add) {
@@ -82,8 +99,12 @@ void VVVJitCode::generate() {
     rest -= 2;
   }
   if (rest > 0) {
-    vmovss(xmm_src1, ptr[param1 + offset]);
-    vmovss(xmm_src2, ptr[param2 + offset]);
+    if (scalar_index_ != 1) {
+      vmovups(xmm_src1, ptr[param1 + offset]);
+    }
+    if (scalar_index_ != 2) {
+      vmovups(xmm_src2, ptr[param2 + offset]);
+    }
     if (type_ == operand_type::mul) {
       vmulss(xmm_dst, xmm_src1, xmm_src2);
     } else if (type_ == operand_type::add) {
@@ -96,6 +117,7 @@ void VVVJitCode::generate() {
   }
   ret();
 }
+
 }  // namespace gen
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/jit_code.h

@@ -29,33 +29,46 @@ using ymm_t = const Xbyak::Ymm;
 using zmm_t = const Xbyak::Zmm;
 using Label = Xbyak::Label;
 
-// function: vec = Operand(vec, vec) (maybe with relu)
 typedef enum { mul = 0, add } operand_type;
 
-class VVVJitCode : public JitCode {
+// function: vec = Operand(vec(or scalar), vec(or scalar)) (maybe with relu)
+class VXXJitCode : public JitCode {
  public:
   const char* name() const override {
-    std::string base = "VVVJitCode";
+    std::string base = "VXXJitCode";
+    if (scalar_index_ == 1) {
+      base += "_Scalar";
+    } else {
+      base += "_Vec";
+    }
     if (type_ == operand_type::mul) {
       base += "_Mul";
     } else if (type_ == operand_type::add) {
       base += "_Add";
     }
-    base += (with_relu_ ? "_relu" : "");
+    if (scalar_index_ == 2) {
+      base += "_Scalar";
+    } else {
+      base += "_Vec";
+    }
+    base += (with_relu_ ? "_Relu" : "");
     return base.c_str();
   }
-  explicit VVVJitCode(int d, operand_type type, bool with_relu,
-                      size_t code_size = 256 * 1024, void* code_ptr = nullptr)
+  explicit VXXJitCode(int d, operand_type type, int scalar_index,
+                      bool with_relu, size_t code_size = 256 * 1024,
+                      void* code_ptr = nullptr)
       : JitCode(code_size, code_ptr),
         num_(d),
         type_(type),
+        scalar_index_(scalar_index),
         with_relu_(with_relu) {}
-  static bool init(int d);
+  static bool init(int d, int scalar_index = 0);
   void generate() override;
 
  private:
   int num_;
   operand_type type_;
+  int scalar_index_;
   bool with_relu_;
   reg64_t param1{abi_param1};
   reg64_t param2{abi_param2};
@@ -63,13 +76,13 @@ class VVVJitCode : public JitCode {
 
   xmm_t xmm_src1 = xmm_t(0);
   xmm_t xmm_src2 = xmm_t(1);
-  xmm_t xmm_dst = xmm_t(1);
-  xmm_t xmm_zero = xmm_t(2);
+  xmm_t xmm_dst = xmm_t(2);
+  xmm_t xmm_zero = xmm_t(3);
 
   ymm_t ymm_src1 = ymm_t(0);
   ymm_t ymm_src2 = ymm_t(1);
-  ymm_t ymm_dst = ymm_t(1);
-  ymm_t ymm_zero = ymm_t(2);
+  ymm_t ymm_dst = ymm_t(2);
+  ymm_t ymm_zero = ymm_t(3);
 };
 
 }  // namespace gen

paddle/fluid/operators/math/jit_kernel.h

@@ -83,14 +83,15 @@ class VAddReluKernel : public Kernel {
 template <typename T>
 class VScalKernel : public Kernel {
  public:
-  virtual void Compute(const T a, const T *x, T *y) const = 0;
-  virtual void Compute(const T a, T *x) const = 0;
+  // y = a.*x
+  void (*Compute)(const T *, const T *, T *, int);
 };
 
 template <typename T>
 class VAddBiasKernel : public Kernel {
  public:
-  virtual void Compute(const T a, const T *x, T *y) const = 0;
+  // y = a.+x
+  void (*Compute)(const T *, const T *, T *, int);
 };
 
 template <typename T>

paddle/fluid/operators/math/jit_kernel_blas.cc

@@ -25,6 +25,10 @@ limitations under the License. */
 #include "paddle/fluid/platform/dynload/mklml.h"
 #endif
 
+#ifdef __AVX__
+#include <immintrin.h>
+#endif
+
 namespace paddle {
 namespace operators {
 namespace math {
@@ -53,6 +57,20 @@ void VAddReluRefer(const T* x, const T* y, T* z, int n) {
   }
 }
 
+template <typename T>
+void VScalRefer(const T* a, const T* x, T* y, int n) {
+  for (int i = 0; i < n; ++i) {
+    y[i] = a[0] * x[i];
+  }
+}
+
+template <typename T>
+void VAddBiasRefer(const T* a, const T* x, T* y, int n) {
+  for (int i = 0; i < n; ++i) {
+    y[i] = a[0] + x[i];
+  }
+}
+
 #ifdef PADDLE_WITH_MKLML
 template <typename T>
 void VMulMKL(const T* x, const T* y, T* z, int n);
@@ -79,6 +97,28 @@ template <>
 void VAddMKL<double>(const double* x, const double* y, double* z, int n) {
   platform::dynload::vdAdd(n, x, y, z);
 }
+
+template <typename T>
+void VScalMKL(const T* a, const T* x, T* y, int n);
+
+template <>
+void VScalMKL<float>(const float* a, const float* x, float* y, int n) {
+  if (x == y) {
+    platform::dynload::cblas_sscal(n, *a, y, 1);
+  } else {
+    VScalRefer<float>(a, x, y, n);
+  }
+}
+
+template <>
+void VScalMKL<double>(const double* a, const double* x, double* y, int n) {
+  if (x == y) {
+    platform::dynload::cblas_dscal(n, *a, y, 1);
+  } else {
+    VScalRefer<double>(a, x, y, n);
+  }
+}
+
 #endif
 
 #define DECLARE_STATIC_FUNC                                 \
@@ -98,7 +138,7 @@ class VMulKernelImpl : public VMulKernel<T> {
     if (useJIT(d)) {
       // roughly estimate the size of code
       size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
-      jitcode_.reset(new gen::VVVJitCode(d, gen::operand_type::mul, false,
+      jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::mul, 0, false,
                                          sz > 4096 ? sz : 4096));
       this->Compute =
           jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
@@ -117,14 +157,14 @@ class VMulKernelImpl : public VMulKernel<T> {
 #ifdef PADDLE_WITH_XBYAK
 
  private:
-  std::unique_ptr<gen::VVVJitCode> jitcode_{nullptr};
+  std::unique_ptr<gen::VXXJitCode> jitcode_{nullptr};
 #endif
 };
 
 #ifdef PADDLE_WITH_XBYAK
 template <>
 bool VMulKernelImpl<float>::useJIT(int d) {
-  return gen::VVVJitCode::init(d);
+  return gen::VXXJitCode::init(d);
 }
 #endif
 
@@ -149,7 +189,7 @@ class VAddKernelImpl : public VAddKernel<T> {
 #ifdef PADDLE_WITH_XBYAK
     if (useJIT(d)) {
       size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
-      jitcode_.reset(new gen::VVVJitCode(d, gen::operand_type::add, false,
+      jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::add, 0, false,
                                          sz > 4096 ? sz : 4096));
       this->Compute =
           jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
@@ -167,14 +207,14 @@ class VAddKernelImpl : public VAddKernel<T> {
 #ifdef PADDLE_WITH_XBYAK
 
  private:
-  std::unique_ptr<gen::VVVJitCode> jitcode_{nullptr};
+  std::unique_ptr<gen::VXXJitCode> jitcode_{nullptr};
 #endif
 };
 
 #ifdef PADDLE_WITH_XBYAK
 template <>
 bool VAddKernelImpl<float>::useJIT(int d) {
-  return gen::VVVJitCode::init(d);
+  return gen::VXXJitCode::init(d);
 }
 #endif
 
@@ -199,7 +239,7 @@ class VAddReluKernelImpl : public VAddReluKernel<T> {
 #ifdef PADDLE_WITH_XBYAK
     if (useJIT(d)) {
       size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
-      jitcode_.reset(new gen::VVVJitCode(d, gen::operand_type::add, true,
+      jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::add, 0, true,
                                          sz > 4096 ? sz : 4096));
       this->Compute =
           jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
@@ -211,148 +251,106 @@ class VAddReluKernelImpl : public VAddReluKernel<T> {
 #ifdef PADDLE_WITH_XBYAK
 
  private:
-  std::unique_ptr<gen::VVVJitCode> jitcode_{nullptr};
+  std::unique_ptr<gen::VXXJitCode> jitcode_{nullptr};
 #endif
 };
 
 #ifdef PADDLE_WITH_XBYAK
 template <>
 bool VAddReluKernelImpl<float>::useJIT(int d) {
-  return gen::VVVJitCode::init(d);
+  return gen::VXXJitCode::init(d);
 }
 #endif
 
-#undef DECLARE_STATIC_FUNC
-
-REGISTER_JITKERNEL(vmul, VMulKernel);
-REGISTER_JITKERNEL(vadd, VAddKernel);
-REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);
-
-/* VSCAL JitKernel */
-template <typename T, platform::jit::cpu_isa_t isa, jit_block>
+/* VScal JitKernel */
+template <typename T>
 class VScalKernelImpl : public VScalKernel<T> {
  public:
-  explicit VScalKernelImpl(int d) : VScalKernel<T>() { this->num_ = d; }
-  void Compute(const T a, const T* x, T* y) const override {
-    for (int i = 0; i < this->num_; ++i) {
-      y[i] = a * x[i];
-    }
-  }
-  void Compute(const T a, T* x) const override {
-    for (int i = 0; i < this->num_; ++i) {
-      x[i] = a * x[i];
+  DECLARE_STATIC_FUNC;
+  explicit VScalKernelImpl(int d) : VScalKernel<T>() {
+#ifdef PADDLE_WITH_XBYAK
+    if (useJIT(d)) {
+      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
+      jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::mul, 1, false,
+                                         sz > 4096 ? sz : 4096));
+      this->Compute =
+          jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
+      return;
     }
-  }
-};
-
+#endif
 #ifdef PADDLE_WITH_MKLML
-#define MKL_FLOAT(isa, block)                                               \
-  template <>                                                               \
-  void VScalKernelImpl<float, isa, block>::Compute(const float a, float* x) \
-      const {                                                               \
-    platform::dynload::cblas_sscal(this->num_, a, x, 1);                    \
-  }
-
-#define MKL_DOUBLE(isa, block)                                                 \
-  template <>                                                                  \
-  void VScalKernelImpl<double, isa, block>::Compute(const double a, double* x) \
-      const {                                                                  \
-    platform::dynload::cblas_dscal(this->num_, a, x, 1);                       \
-  }
-
-FOR_EACH_ISA(MKL_FLOAT, kGT16);
-FOR_EACH_ISA_BLOCK(MKL_DOUBLE);
+    if (useMKL(d)) {
+      this->Compute = VScalMKL<T>;
+      return;
+    }
 #endif
-
-#define INTRI8_FLOAT(isa)                              \
-  template <>                                          \
-  void VScalKernelImpl<float, isa, kEQ8>::Compute(     \
-      const float a, const float* x, float* y) const { \
-    __m256 tmp;                                        \
-    __m256 scalar = _mm256_set1_ps(a);                 \
-    tmp = _mm256_loadu_ps(x);                          \
-    tmp = _mm256_mul_ps(tmp, scalar);                  \
-    _mm256_storeu_ps(y, tmp);                          \
-  }
-#define INTRI8_INPLACE_FLOAT(isa)                                          \
-  template <>                                                              \
-  void VScalKernelImpl<float, isa, kEQ8>::Compute(const float a, float* x) \
-      const {                                                              \
-    __m256 tmp;                                                            \
-    __m256 scalar = _mm256_set1_ps(a);                                     \
-    tmp = _mm256_loadu_ps(x);                                              \
-    tmp = _mm256_mul_ps(tmp, scalar);                                      \
-    _mm256_storeu_ps(x, tmp);                                              \
+    this->Compute = VScalRefer<T>;
   }
+#ifdef PADDLE_WITH_XBYAK
 
-#ifdef __AVX__
-INTRI8_FLOAT(jit::avx);
-INTRI8_INPLACE_FLOAT(jit::avx);
-#endif
-#ifdef __AVX2__
-INTRI8_FLOAT(jit::avx2);
-INTRI8_INPLACE_FLOAT(jit::avx2);
+ private:
+  std::unique_ptr<gen::VXXJitCode> jitcode_{nullptr};
 #endif
-#ifdef __AVX512F__
-INTRI8_FLOAT(jit::avx512f);
-INTRI8_INPLACE_FLOAT(jit::avx512f);
+};
+
+#ifdef PADDLE_WITH_XBYAK
+template <>
+bool VScalKernelImpl<float>::useJIT(int d) {
+  return gen::VXXJitCode::init(d, 1);
+}
 #endif
-// TODO(TJ): eq16 test and complete avx512
 
-#undef INTRI8_FLOAT
-#undef INTRI8_INPLACE_FLOAT
-#undef MKL_FLOAT
-#undef MKL_DOUBLE
+#ifdef PADDLE_WITH_MKLML
+template <>
+bool VScalKernelImpl<float>::useMKL(int d) {
+  return d > 512;
+}
+template <>
+bool VScalKernelImpl<double>::useMKL(int d) {
+  return true;
+}
+#endif
 
 /* VAddBias JitKernel */
-template <typename T, platform::jit::cpu_isa_t isa, jit_block>
+template <typename T>
 class VAddBiasKernelImpl : public VAddBiasKernel<T> {
  public:
-  explicit VAddBiasKernelImpl(int d) : VAddBiasKernel<T>() { this->num_ = d; }
-  void Compute(const T a, const T* x, T* y) const override {
-    for (int i = 0; i < this->num_; ++i) {
-      y[i] = x[i] + a;
+  DECLARE_STATIC_FUNC;
+  explicit VAddBiasKernelImpl(int d) : VAddBiasKernel<T>() {
+#ifdef PADDLE_WITH_XBYAK
+    if (useJIT(d)) {
+      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
+      jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::add, 1, false,
+                                         sz > 4096 ? sz : 4096));
+      this->Compute =
+          jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
+      return;
     }
-  }
-};
-
-#define INTRI8_FLOAT(isa)                              \
-  template <>                                          \
-  void VAddBiasKernelImpl<float, isa, kEQ8>::Compute(  \
-      const float a, const float* x, float* y) const { \
-    __m256 tmp = _mm256_loadu_ps(x);                   \
-    tmp = _mm256_add_ps(tmp, _mm256_set1_ps(a));       \
-    _mm256_storeu_ps(y, tmp);                          \
-  }
+#endif
 
-#define INTRI16_FLOAT(isa)                             \
-  template <>                                          \
-  void VAddBiasKernelImpl<float, isa, kEQ16>::Compute( \
-      const float a, const float* x, float* y) const { \
-    __m256 tmp0 = _mm256_loadu_ps(x);                  \
-    __m256 tmp1 = _mm256_loadu_ps(x + 8);              \
-    tmp0 = _mm256_add_ps(tmp0, _mm256_set1_ps(a));     \
-    tmp1 = _mm256_add_ps(tmp1, _mm256_set1_ps(a));     \
-    _mm256_storeu_ps(y, tmp0);                         \
-    _mm256_storeu_ps(y + 8, tmp1);                     \
+    this->Compute = VAddBiasRefer<T>;
   }
+#ifdef PADDLE_WITH_XBYAK
 
-#ifdef __AVX__
-INTRI8_FLOAT(jit::avx);
-INTRI16_FLOAT(jit::avx);
-#endif
-#ifdef __AVX2__
-INTRI8_FLOAT(jit::avx2);
-INTRI16_FLOAT(jit::avx2);
+ private:
+  std::unique_ptr<gen::VXXJitCode> jitcode_{nullptr};
 #endif
-#ifdef __AVX512F__
-INTRI8_FLOAT(jit::avx512f);
-INTRI16_FLOAT(jit::avx512f);
+};
+
+#ifdef PADDLE_WITH_XBYAK
+template <>
+bool VAddBiasKernelImpl<float>::useJIT(int d) {
+  return gen::VXXJitCode::init(d, 1);
+}
 #endif
-// TODO(TJ): eq16 test and complete avx512
 
-#undef INTRI8_FLOAT
-#undef INTRI16_FLOAT
+#undef DECLARE_STATIC_FUNC
+
+REGISTER_JITKERNEL(vmul, VMulKernel);
+REGISTER_JITKERNEL(vadd, VAddKernel);
+REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);
+REGISTER_JITKERNEL(vscal, VScalKernel);
+REGISTER_JITKERNEL(vaddbias, VAddBiasKernel);
 
 /* VRelu JitKernel */
 template <typename T, platform::jit::cpu_isa_t isa, jit_block>
@@ -463,8 +461,6 @@ class VIdentityKernelImpl : public VIdentityKernel<T> {
   void Compute(const T* x, T* y) const override {}
 };
 
-REGISTER_JITKERNEL_DEPRECATED(vscal, VScalKernel);
-REGISTER_JITKERNEL_DEPRECATED(vaddb, VAddBiasKernel);
 REGISTER_JITKERNEL_DEPRECATED(vrelu, VReluKernel);
 REGISTER_JITKERNEL_DEPRECATED(videntity, VIdentityKernel);
 

paddle/fluid/operators/math/jit_kernel_crf_decode.cc

@@ -16,6 +16,9 @@ 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 {
@@ -260,7 +263,6 @@ class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
     }                                                                          \
   }
 
-#ifndef _WIN32  // commented out crf decoding
 #ifdef __AVX__
 INTRIAVX_FLOAT(kEQ8);
 INTRIAVX_FLOAT(kGT8LT16);
@@ -273,7 +275,6 @@ 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

@@ -20,6 +20,10 @@ limitations under the License. */
 #include "paddle/fluid/platform/dynload/mklml.h"
 #endif
 
+#ifdef __AVX__
+#include <immintrin.h>
+#endif
+
 namespace paddle {
 namespace operators {
 namespace math {
@@ -62,18 +66,14 @@ 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 ALIGN32 _ps256_##Name[8] = {Val, Val, Val, Val, \
+  static const float _ps256_##Name[8] ALIGN32 = {Val, Val, Val, Val, \
                                                  Val, Val, Val, Val}
 
 #define _PI256_CONST(Name, Val)                                    \
-  static const int ALIGN32 _pi256_##Name[8] = {Val, Val, Val, Val, \
+  static const int _pi256_##Name[8] ALIGN32 = {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 ALIGN32 u;                \
+    imm_xmm_union u ALIGN32;                \
     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 ALIGN32 u;                \
+    imm_xmm_union u ALIGN32;                \
     u.xmm[0] = xmm0_;                       \
     u.xmm[1] = xmm1_;                       \
     imm_ = u.imm;                           \
@@ -409,10 +409,11 @@ class VTanhKernelImpl : public VTanhKernel<T> {
     vaddbias_ = KernelPool::Instance().template Get<VAddBiasKernel<T>>(d);
   }
   void Compute(const T* x, T* y) const override {
-    vscal_->Compute(static_cast<T>(2), x, y);
+    const T a = static_cast<T>(2), b = static_cast<T>(-1);
+    vscal_->Compute(&a, x, y, this->num_);
     vsigmoid_->Compute(y, y);
-    vscal_->Compute(static_cast<T>(2), y);
-    vaddbias_->Compute(static_cast<T>(-1), y, y);
+    vscal_->Compute(&a, y, y, this->num_);
+    vaddbias_->Compute(&b, y, y, this->num_);
   }
 
  private:
@@ -472,10 +473,11 @@ class VTanhKernelImpl : public VTanhKernel<T> {
     _mm256_storeu_ps(y, tmp);                                                 \
     x += AVX_FLOAT_BLOCK;                                                     \
     y += AVX_FLOAT_BLOCK;                                                     \
-    vscal_->Compute(2.f, x, y);                                               \
+    const float a = 2.f, b = -1.f;                                            \
+    vscal_->Compute(&a, x, y, this->num_);                                    \
     vsigmoid_->Compute(y, y);                                                 \
-    vscal_->Compute(2.f, y);                                                  \
-    vaddbias_->Compute(-1.f, y, y);                                           \
+    vscal_->Compute(&a, y, y, this->num_);                                    \
+    vaddbias_->Compute(&b, y, y, this->num_);                                 \
   }
 
 #define INTRI_GT16_FLOAT(isa, expisa)                                         \
@@ -502,20 +504,19 @@ class VTanhKernelImpl : public VTanhKernel<T> {
     }                                                                         \
     x += this->end_;                                                          \
     y += this->end_;                                                          \
-    vscal_->Compute(2.f, x, y);                                               \
+    const float a = 2.f, b = -1.f;                                            \
+    vscal_->Compute(&a, x, y, this->num_);                                    \
     vsigmoid_->Compute(y, y);                                                 \
-    vscal_->Compute(2.f, y);                                                  \
-    vaddbias_->Compute(-1.f, y, y);                                           \
+    vscal_->Compute(&a, y, y, this->num_);                                    \
+    vaddbias_->Compute(&b, y, y, this->num_);                                 \
   }
 
-#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  // AVX
-#endif  // WIN32
+#endif
 #ifdef __AVX2__
 INTRI8_FLOAT(jit::avx2, detail::ExpAVX2);
 INTRI16_FLOAT(jit::avx2, detail::ExpAVX2);

paddle/fluid/operators/math/jit_kernel_rnn.cc

@@ -18,6 +18,10 @@ 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/jit_kernel_test.cc

@@ -128,7 +128,7 @@ TEST(JitKernel, vaddbias) {
     auto trefe = GetCurrentUS();
     auto ttgts = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      ker->Compute(a, x_data, ztgt_data);
+      ker->Compute(&a, x_data, ztgt_data, d);
     }
     auto ttgte = GetCurrentUS();
 
@@ -281,10 +281,11 @@ void vtanh_better(
         const paddle::operators::math::jitkernel::VAddBiasKernel<float>>&
         vaddbias,
     const int n, const float* x, float* y) {
-  vscal->Compute(2.f, x, y);
+  const float a = 2.f, b = -1.f;
+  vscal->Compute(&a, x, y, n);
   vsigmoid->Compute(y, y);
-  vscal->Compute(2.f, y);
-  vaddbias->Compute(-1.f, y, y);
+  vscal->Compute(&a, y, y, n);
+  vaddbias->Compute(&b, y, y, n);
 }
 
 TEST(JitKernel, vtanh) {
@@ -531,12 +532,12 @@ TEST(JitKernel, vscal) {
 
     auto ttgts = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      ker->Compute(a, x_data, ztgt_data);
+      ker->Compute(&a, x_data, ztgt_data, d);
     }
     auto ttgte = GetCurrentUS();
     auto ttgts1 = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      ker->Compute(a, y_data);
+      ker->Compute(&a, y_data, y_data, d);
     }
     auto ttgte1 = GetCurrentUS();
     VLOG(3) << "Vec size " << d << ": refer takes: " << (trefe - trefs) / repeat

paddle/fluid/operators/math/selected_rows_functor.cu

@@ -16,7 +16,6 @@ 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

@@ -16,12 +16,13 @@ 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 {
 
+#define FLT_MAX __FLT_MAX__
+
 template <typename T>
 struct MaxPoolFunctor {
   HOSTDEVICE void operator()(const T* input, const size_t start,

paddle/fluid/operators/print_op.cc

@@ -13,7 +13,6 @@
    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

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #include <stdint.h>
 #include <fstream>
-#include <memory>
 #include <numeric>
 #include <sstream>
 #include "paddle/fluid/framework/data_type.h"
@@ -42,7 +41,6 @@ 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) {
@@ -51,14 +49,8 @@ class SaveCombineOp : public framework::OperatorBase {
     }
 
     MkDirRecursively(DirName(filename).c_str());
-    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",
+    std::ofstream fout(filename);
+    PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
                    filename);
 
     auto inp_var_names = Inputs("X");
@@ -94,11 +86,12 @@ 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();
   }
 };
 
@@ -131,18 +124,6 @@ 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

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #include <stdint.h>
 #include <fstream>
-#include <memory>
 #include <numeric>
 
 #include "paddle/fluid/framework/data_type.h"
@@ -65,7 +64,6 @@ 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",
@@ -82,14 +80,8 @@ 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::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",
+    std::ofstream fout(filename);
+    PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
                    filename);
 
     auto save_as_fp16 = Attr<bool>("save_as_fp16");
@@ -103,10 +95,11 @@ 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,
@@ -117,7 +110,6 @@ 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());
@@ -130,16 +122,11 @@ 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::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",
+    std::ofstream fout(filename);
+    PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
                    filename);
-    framework::SerializeToStream(*fout, selectedRows, dev_ctx);
+    framework::SerializeToStream(fout, selectedRows, dev_ctx);
+    fout.close();
   }
 };
 
@@ -167,18 +154,6 @@ 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/scale_op.h

@@ -24,19 +24,13 @@ class ScaleKernel : public framework::OpKernel<T> {
  public:
   virtual void Compute(const framework::ExecutionContext& ctx) const {
     auto* in_var = ctx.InputVar("X");
-    auto* in = ctx.Input<framework::Tensor>("X");
-
-    auto* out_var = ctx.OutputVar("Out");
-    auto* out = ctx.Output<framework::Tensor>("Out");
-    out->mutable_data<T>(in->place());
-
-    PADDLE_ENFORCE_EQ(in->dims(), out->dims(),
-                      "in and out should have the same dim");
+    auto* in = framework::GetLoDTensorOrSelectedRowsValueFromVar(*in_var);
 
     auto scale = static_cast<T>(ctx.Attr<float>("scale"));
     auto bias = static_cast<T>(ctx.Attr<float>("bias"));
     auto bias_after_scale = ctx.Attr<bool>("bias_after_scale");
 
+    auto* out_var = ctx.OutputVar("Out");
     if (in_var->IsType<framework::SelectedRows>() && in_var != out_var) {
       auto& in_slr = in_var->Get<framework::SelectedRows>();
       auto* out_slr = out_var->GetMutable<framework::SelectedRows>();
@@ -44,6 +38,13 @@ class ScaleKernel : public framework::OpKernel<T> {
       out_slr->set_height(in_slr.height());
     }
 
+    auto* out =
+        framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(out_var);
+    out->mutable_data<T>(in->place());
+
+    PADDLE_ENFORCE_EQ(in->dims(), out->dims(),
+                      "in and out should have the same dim");
+
     auto eigen_out = framework::EigenVector<T>::Flatten(*out);
     auto eigen_in = framework::EigenVector<T>::Flatten(*in);
     auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();

paddle/fluid/operators/split_ids_op.cc

@@ -64,8 +64,7 @@ class SplitIdsOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(
-            ctx.MultiInput<framework::Tensor>("Ids").front()->type()),
+        framework::GetDataTypeOfVar(ctx.MultiInputVar("Ids").front()),
         ctx.GetPlace());
   }
 };

paddle/fluid/operators/split_ids_op.h

@@ -113,6 +113,10 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
                  row_width * sizeof(T));
         }
       }
+    } else {
+      PADDLE_THROW(
+          "% should be LoDTensor or SelectedRows, but the received type is %s",
+          ctx.Inputs("Ids")[0], ids_var->Type().name());
     }
   }
 };

paddle/fluid/operators/split_lod_tensor_op.cc

@@ -15,7 +15,6 @@ 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/sum_op.cc

@@ -85,8 +85,8 @@ class SumOp : public framework::OperatorWithKernel {
       for (size_t idx = 0; idx < x_vars.size(); ++idx) {
         PADDLE_ENFORCE(x_vars[idx] != nullptr,
                        "Input var[%s] should not be nullptr", x_vars_name[idx]);
-        // FIXME(zcd): The input x_var may be SelectedRows or LoDTensor.
-        auto tensor = framework::GetTensorFromVar(*x_vars[idx]);
+        auto tensor =
+            framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_vars[idx]);
         if (tensor->numel() == 0) {
           continue;
         }

paddle/fluid/operators/tensorrt_engine_op.h

@@ -60,7 +60,7 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t>& shape) {
   return nvinfer1::DimsCHW(shape[1], 1, 1);
 }
 
-}  // NOLINT  // namespace
+}  // namespace
 
 using inference::Singleton;
 using inference::tensorrt::TRT_EngineManager;

paddle/fluid/platform/cpu_info.h

@@ -16,18 +16,6 @@ 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

@@ -59,7 +59,6 @@ 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;                                \
@@ -67,16 +66,6 @@ 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

@@ -55,6 +55,7 @@ DeviceContextPool::DeviceContextPool(
   for (auto& p : places) {
     set.insert(p);
   }
+
   for (auto& p : set) {
     if (platform::is_cpu_place(p)) {
 #ifdef PADDLE_WITH_MKLDNN
@@ -203,10 +204,11 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
                           << "." << (driver_version_ % 100) / 10
                           << ", Runtime Version: " << runtime_version_ / 1000
                           << "." << (runtime_version_ % 100) / 10;
-
-#ifndef _WIN32
+  size_t cudnn_dso_ver = dynload::cudnnGetVersion();
+  LOG_FIRST_N(WARNING, 1) << "device: " << place_.device
+                          << ", cuDNN Version: " << cudnn_dso_ver / 1000 << "."
+                          << (cudnn_dso_ver % 100) / 10 << ".";
   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"
-#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+#ifdef PADDLE_WITH_CUDA
 #include "paddle/fluid/platform/stream_callback_manager.h"
 #endif
 #include "unsupported/Eigen/CXX11/Tensor"
@@ -173,7 +173,6 @@ 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_);
@@ -184,16 +183,6 @@ 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_;
@@ -212,12 +201,10 @@ 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/dynload/cudnn.h

@@ -65,51 +65,54 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
  * include all needed cudnn functions in HPPL
  * different cudnn version has different interfaces
  **/
-#define CUDNN_DNN_ROUTINE_EACH(__macro)              \
-  __macro(cudnnSetTensor4dDescriptor);               \
-  __macro(cudnnSetTensor4dDescriptorEx);             \
-  __macro(cudnnSetTensorNdDescriptor);               \
-  __macro(cudnnGetTensorNdDescriptor);               \
-  __macro(cudnnGetConvolutionNdForwardOutputDim);    \
-  __macro(cudnnGetConvolutionForwardAlgorithm);      \
-  __macro(cudnnCreateTensorDescriptor);              \
-  __macro(cudnnDestroyTensorDescriptor);             \
-  __macro(cudnnCreateFilterDescriptor);              \
-  __macro(cudnnSetFilter4dDescriptor);               \
-  __macro(cudnnSetFilterNdDescriptor);               \
-  __macro(cudnnGetFilterNdDescriptor);               \
-  __macro(cudnnSetPooling2dDescriptor);              \
-  __macro(cudnnSetPoolingNdDescriptor);              \
-  __macro(cudnnGetPoolingNdDescriptor);              \
-  __macro(cudnnDestroyFilterDescriptor);             \
-  __macro(cudnnCreateConvolutionDescriptor);         \
-  __macro(cudnnCreatePoolingDescriptor);             \
-  __macro(cudnnDestroyPoolingDescriptor);            \
-  __macro(cudnnSetConvolution2dDescriptor);          \
-  __macro(cudnnDestroyConvolutionDescriptor);        \
-  __macro(cudnnSetConvolutionNdDescriptor);          \
-  __macro(cudnnGetConvolutionNdDescriptor);          \
-  __macro(cudnnDeriveBNTensorDescriptor);            \
-  __macro(cudnnCreateSpatialTransformerDescriptor);  \
-  __macro(cudnnSetSpatialTransformerNdDescriptor);   \
-  __macro(cudnnDestroySpatialTransformerDescriptor); \
-  __macro(cudnnSpatialTfGridGeneratorForward);       \
-  __macro(cudnnSpatialTfGridGeneratorBackward);      \
-  __macro(cudnnSpatialTfSamplerForward);             \
-  __macro(cudnnSpatialTfSamplerBackward);            \
-  __macro(cudnnCreate);                              \
-  __macro(cudnnDestroy);                             \
-  __macro(cudnnSetStream);                           \
-  __macro(cudnnActivationForward);                   \
-  __macro(cudnnConvolutionForward);                  \
-  __macro(cudnnConvolutionBackwardBias);             \
-  __macro(cudnnGetConvolutionForwardWorkspaceSize);  \
-  __macro(cudnnTransformTensor);                     \
-  __macro(cudnnPoolingForward);                      \
-  __macro(cudnnPoolingBackward);                     \
-  __macro(cudnnSoftmaxBackward);                     \
-  __macro(cudnnSoftmaxForward);                      \
-  __macro(cudnnGetVersion);                          \
+#define CUDNN_DNN_ROUTINE_EACH(__macro)                   \
+  __macro(cudnnSetTensor4dDescriptor);                    \
+  __macro(cudnnSetTensor4dDescriptorEx);                  \
+  __macro(cudnnSetTensorNdDescriptor);                    \
+  __macro(cudnnGetTensorNdDescriptor);                    \
+  __macro(cudnnGetConvolutionNdForwardOutputDim);         \
+  __macro(cudnnGetConvolutionForwardAlgorithm);           \
+  __macro(cudnnCreateTensorDescriptor);                   \
+  __macro(cudnnDestroyTensorDescriptor);                  \
+  __macro(cudnnCreateFilterDescriptor);                   \
+  __macro(cudnnSetFilter4dDescriptor);                    \
+  __macro(cudnnSetFilterNdDescriptor);                    \
+  __macro(cudnnGetFilterNdDescriptor);                    \
+  __macro(cudnnSetPooling2dDescriptor);                   \
+  __macro(cudnnSetPoolingNdDescriptor);                   \
+  __macro(cudnnGetPoolingNdDescriptor);                   \
+  __macro(cudnnDestroyFilterDescriptor);                  \
+  __macro(cudnnCreateConvolutionDescriptor);              \
+  __macro(cudnnCreatePoolingDescriptor);                  \
+  __macro(cudnnDestroyPoolingDescriptor);                 \
+  __macro(cudnnSetConvolution2dDescriptor);               \
+  __macro(cudnnDestroyConvolutionDescriptor);             \
+  __macro(cudnnSetConvolutionNdDescriptor);               \
+  __macro(cudnnGetConvolutionNdDescriptor);               \
+  __macro(cudnnDeriveBNTensorDescriptor);                 \
+  __macro(cudnnCreateSpatialTransformerDescriptor);       \
+  __macro(cudnnSetSpatialTransformerNdDescriptor);        \
+  __macro(cudnnDestroySpatialTransformerDescriptor);      \
+  __macro(cudnnSpatialTfGridGeneratorForward);            \
+  __macro(cudnnSpatialTfGridGeneratorBackward);           \
+  __macro(cudnnSpatialTfSamplerForward);                  \
+  __macro(cudnnSpatialTfSamplerBackward);                 \
+  __macro(cudnnCreate);                                   \
+  __macro(cudnnDestroy);                                  \
+  __macro(cudnnSetStream);                                \
+  __macro(cudnnActivationForward);                        \
+  __macro(cudnnConvolutionForward);                       \
+  __macro(cudnnConvolutionBackwardBias);                  \
+  __macro(cudnnGetConvolutionForwardWorkspaceSize);       \
+  __macro(cudnnTransformTensor);                          \
+  __macro(cudnnPoolingForward);                           \
+  __macro(cudnnPoolingBackward);                          \
+  __macro(cudnnSoftmaxBackward);                          \
+  __macro(cudnnSoftmaxForward);                           \
+  __macro(cudnnGetVersion);                               \
+  __macro(cudnnFindConvolutionForwardAlgorithmEx);        \
+  __macro(cudnnFindConvolutionBackwardFilterAlgorithmEx); \
+  __macro(cudnnFindConvolutionBackwardDataAlgorithmEx);   \
   __macro(cudnnGetErrorString);
 CUDNN_DNN_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
 

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()));
-#if !defined(_WIN32)
+#ifndef _WIN32
   google::InstallFailureSignalHandler();
 #endif
 }