Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_cudnn_pool3d

CMakeLists.txt

@@ -36,8 +36,7 @@ include(simd)
 ################################ Configurations #######################################
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
-option(WITH_MKLDNN      "Compile PaddlePaddle with mkl-dnn support."    ${AVX_FOUND})
-option(WITH_MKLML       "Compile PaddlePaddle with mklml package."      ${AVX_FOUND})
+option(WITH_MKL         "Compile PaddlePaddle with MKL support."        ${AVX_FOUND})
 option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
 option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        ON)
 option(WITH_SWIG_PY     "Compile PaddlePaddle with inference api"       ON)
@@ -82,10 +81,8 @@ if(ANDROID OR IOS)
         "Disable PYTHON when cross-compiling for Android and iOS" FORCE)
     set(WITH_RDMA OFF CACHE STRING
         "Disable RDMA when cross-compiling for Android and iOS" FORCE)
-    set(WITH_MKLDNN OFF CACHE STRING
-        "Disable MKLDNN when cross-compiling for Android and iOS" FORCE)
-    set(WITH_MKLML OFF CACHE STRING
-        "Disable MKLML package when cross-compiling for Android and iOS" FORCE)
+    set(WITH_MKL OFF CACHE STRING
+        "Disable MKL when cross-compiling for Android and iOS" FORCE)
 
     # Compile PaddlePaddle mobile inference library
     if (NOT WITH_C_API)
@@ -111,6 +108,14 @@ else()
     set(THIRD_PARTY_BUILD_TYPE Release)
 endif()
 
+set(WITH_MKLML ${WITH_MKL})
+if (WITH_MKL AND ${AVX2_FOUND})
+    set(WITH_MKLDNN ON)
+else()
+    message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
+    set(WITH_MKLDNN OFF)
+endif()
+
 ########################################################################################
 
 include(external/mklml)     # download mklml package
@@ -158,14 +163,15 @@ set(EXTERNAL_LIBS
 )
 
 if(WITH_GPU)
-    list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
-    if(NOT WITH_DSO)
-        list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
-    endif(NOT WITH_DSO)
+  include(cuda)
 endif(WITH_GPU)
 
+if(WITH_MKLML)
+    list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
+endif()
+
 if(WITH_MKLDNN)
-    list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB} ${MKLDNN_IOMP_LIB})
+    list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB})
 endif()
 
 if(USE_NNPACK)

cmake/configure.cmake

@@ -76,27 +76,14 @@ else()
     include_directories(${CUDA_TOOLKIT_INCLUDE})
 endif(NOT WITH_GPU)
 
-if(WITH_MKLDNN)
-    add_definitions(-DPADDLE_USE_MKLDNN)
-    if (WITH_MKLML AND MKLDNN_IOMP_DIR)
-        message(STATUS "Enable Intel OpenMP at ${MKLDNN_IOMP_DIR}")
-        set(OPENMP_FLAGS "-fopenmp")
-        set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
-        set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
-    else()
-        find_package(OpenMP)
-        if(OPENMP_FOUND)
-            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
-            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
-        else()
-            message(WARNING "Can not find OpenMP."
-                 "Some performance features in MKLDNN may not be available")
-        endif()
-    endif()
-
-endif(WITH_MKLDNN)
+if (WITH_MKLML AND MKLML_IOMP_LIB)
+    message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
+    set(OPENMP_FLAGS "-fopenmp")
+    set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
+    set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
+endif()
 
 set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")

cmake/cross_compiling/ios.cmake

@@ -76,11 +76,9 @@ set(IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform")
 # Set the architecture for iOS
 if(NOT DEFINED IOS_ARCH)
   if(IOS_PLATFORM STREQUAL "OS")
-    # FIXME(liuyiqun): support "armv7;armv7s;arm64" future
-    set(IOS_ARCH "arm64")
+    set(IOS_ARCH "armv7;armv7s;arm64")
   elseif(IOS_PLATFORM STREQUAL "SIMULATOR")
-    # FIXME(liuyiqun): support "i386;x86_64" future
-    set(IOS_ARCH "x86_64")
+    set(IOS_ARCH "i386;x86_64")
   endif()
 endif()
 set(CMAKE_OSX_ARCHITECTURES ${IOS_ARCH} CACHE string  "Build architecture for iOS")
@@ -248,7 +246,7 @@ set(IOS_COMPILER_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} ${XCODE_IOS_BITCODE_
 
 # Hidden visibilty is required for cxx on iOS 
 set(CMAKE_C_FLAGS "${IOS_COMPILER_FLAGS} ${CMAKE_C_FLAGS}" CACHE STRING "C flags")
-set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
+set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
 
 set(IOS_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first")
 

cmake/cuda.cmake

+if(NOT WITH_GPU)
+    return()
+endif()
+
+set(paddle_known_gpu_archs "30 35 50 52 60 61 70")
+set(paddle_known_gpu_archs7 "30 35 50 52")
+set(paddle_known_gpu_archs8 "30 35 50 52 60 61")
+
+######################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   detect_installed_gpus(out_variable)
+function(detect_installed_gpus out_variable)
+  if(NOT CUDA_gpu_detect_output)
+    set(cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
+
+    file(WRITE ${cufile} ""
+      "#include <cstdio>\n"
+      "int main() {\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device) {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "-ccbin=${CUDA_HOST_COMPILER}"
+                    "--run" "${cufile}"
+                    WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
+                    RESULT_VARIABLE nvcc_res OUTPUT_VARIABLE nvcc_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    if(nvcc_res EQUAL 0)
+      # only keep the last line of nvcc_out
+      STRING(REGEX REPLACE ";" "\\\\;" nvcc_out "${nvcc_out}")
+      STRING(REGEX REPLACE "\n" ";" nvcc_out "${nvcc_out}")
+      list(GET nvcc_out -1 nvcc_out)
+      string(REPLACE "2.1" "2.1(2.0)" nvcc_out "${nvcc_out}")
+      set(CUDA_gpu_detect_output ${nvcc_out} CACHE INTERNAL "Returned GPU architetures from detect_installed_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_gpu_detect_output)
+    message(STATUS "Automatic GPU detection failed. Building for all known architectures.")
+    set(${out_variable} ${paddle_known_gpu_archs} PARENT_SCOPE)
+  else()
+    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+########################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
+# Usage:
+#   select_nvcc_arch_flags(out_variable)
+function(select_nvcc_arch_flags out_variable)
+  # List of arch names
+  set(archs_names "Kepler" "Maxwell" "Pascal" "All" "Manual")
+  set(archs_name_default "All")
+  if(NOT CMAKE_CROSSCOMPILING)
+    list(APPEND archs_names "Auto")
+  endif()
+
+  # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
+  set(CUDA_ARCH_NAME ${archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
+  set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${archs_names} )
+  mark_as_advanced(CUDA_ARCH_NAME)
+
+  # verify CUDA_ARCH_NAME value
+  if(NOT ";${archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
+    string(REPLACE ";" ", " archs_names "${archs_names}")
+    message(FATAL_ERROR "Only ${archs_names} architeture names are supported.")
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(CUDA_ARCH_BIN ${paddle_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
+    set(CUDA_ARCH_PTX "50"                     CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
+    mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
+  else()
+    unset(CUDA_ARCH_BIN CACHE)
+    unset(CUDA_ARCH_PTX CACHE)
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Kepler")
+    set(cuda_arch_bin "30 35")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
+    set(cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
+    set(cuda_arch_bin "60 61")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
+    set(cuda_arch_bin "70")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "All")
+    set(cuda_arch_bin ${paddle_known_gpu_archs})
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
+    detect_installed_gpus(cuda_arch_bin)
+  else()  # (${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(cuda_arch_bin ${CUDA_ARCH_BIN})
+  endif()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" cuda_arch_ptx "${CUDA_ARCH_PTX}")
+  string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+"   cuda_arch_ptx "${cuda_arch_ptx}")
+  list(REMOVE_DUPLICATES cuda_arch_bin)
+  list(REMOVE_DUPLICATES cuda_arch_ptx)
+
+  set(nvcc_flags "")
+  set(nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(arch ${cuda_arch_bin})
+    if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified PTX for the concrete BIN
+      list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
+      list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
+      list(APPEND nvcc_archs_readable sm_${arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(arch ${cuda_arch_ptx})
+    list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
+    list(APPEND nvcc_archs_readable compute_${arch})
+  endforeach()
+
+  string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
+  set(${out_variable}          ${nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
+
+message(STATUS "CUDA detected: " ${CUDA_VERSION})
+if (${CUDA_VERSION} LESS 7.0)
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs})
+elseif (${CUDA_VERSION} LESS 8.0) # CUDA 7.x
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs7})
+  list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
+  list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
+elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs8})
+  list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
+  list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
+  # CUDA 8 may complain that sm_20 is no longer supported. Suppress the
+  # warning for now.
+  list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
+endif()
+
+include_directories(${CUDA_INCLUDE_DIRS})
+list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
+if(NOT WITH_DSO)
+    list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
+endif(NOT WITH_DSO)
+
+# setting nvcc arch flags
+select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
+
+# Set C++11 support
+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.
+list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
+list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
+list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
+# Set :expt-relaxed-constexpr to suppress Eigen warnings
+list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
+
+if(CMAKE_BUILD_TYPE  STREQUAL "Debug")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_DEBUG})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "Release")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELEASE})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "RelWithDebInfo")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "MinSizeRel")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_MINSIZEREL})
+endif()
+
+mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
+mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)

cmake/external/mkldnn.cmake

@@ -40,10 +40,9 @@ INCLUDE_DIRECTORIES(${MKLDNN_INC_DIR})
 
 IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
     SET(MKLDNN_DEPENDS   ${MKLML_PROJECT})
-    SET(MKLDNN_MKLROOT   ${MKLML_ROOT})
-    SET(MKLDNN_IOMP_LIB  ${MKLML_IOMP_LIB})
-    SET(MKLDNN_IOMP_DIR  ${MKLML_LIB_DIR})
-    MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
+    MESSAGE(STATUS "Build MKLDNN with MKLML ${MKLML_ROOT}")
+ELSE()
+    MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
 ENDIF()
 
 SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} -Wno-error=strict-overflow")
@@ -57,15 +56,16 @@ ExternalProject_Add(
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
-    CMAKE_ARGS          -DMKLROOT=${MKLDNN_MKLROOT}
+    CMAKE_ARGS          -DMKLROOT=${MKLML_ROOT}
     CMAKE_ARGS          -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
     CMAKE_ARGS          -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
     CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-                        -DMKLROOT:PATH=${MKLDNN_MKLROOT}
+                        -DMKLROOT:PATH=${MKLML_ROOT}
 )
 
 ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
 ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
-MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIB}")
+MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
+add_definitions(-DPADDLE_USE_MKLDNN)
 LIST(APPEND external_project_dependencies mkldnn)

cmake/external/openblas.cmake

@@ -29,7 +29,7 @@ IF(NOT ${CBLAS_FOUND})
         "${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
         CACHE FILEPATH "openblas library." FORCE)
 
-    SET(OPENBLAS_CC "${CMAKE_C_COMPILER}")
+    SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
 
     IF(CMAKE_CROSSCOMPILING)
         SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER})
@@ -45,15 +45,14 @@ IF(NOT ${CBLAS_FOUND})
                 SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0)
             ENDIF()
         ELSEIF(IOS)
-            # FIXME(liuyiqun): support multiple architectures
-            SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
-            SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
-            IF(CMAKE_OSX_ARCHITECTURES MATCHES "armv7")
-                SET(OPENBLAS_CC "${OPENBLAS_CC} -arch armv7")
-                SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0)
-            ELSEIF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
+            IF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
+                SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
+                SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
                 SET(OPENBLAS_CC "${OPENBLAS_CC} -arch arm64")
                 SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0 CROSS_SUFFIX=${CROSS_SUFFIX})
+            ELSE()
+                MESSAGE(FATAL_ERROR "OpenBLAS only support arm64 architectures on iOS. "
+                       "You can set IOS_USE_VECLIB_FOR_BLAS=ON or USE_EIGEN_FOR_BLAS=ON to use other blas library instead.")
             ENDIF()
         ELSEIF(RPI)
             # use hardfp

cmake/external/warpctc.cmake

@@ -12,6 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+IF(MOBILE_INFERENCE)
+    return()
+ENDIF()
+
 INCLUDE(ExternalProject)
 
 SET(WARPCTC_SOURCES_DIR ${THIRD_PARTY_PATH}/warpctc)

cmake/flags.cmake

@@ -149,58 +149,3 @@ endforeach()
 foreach(flag ${GPU_COMMON_FLAGS})
     safe_set_nvflag(${flag})
 endforeach()
-
-
-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.
-LIST(APPEND CUDA_NVCC_FLAGS -std=c++11)
-LIST(APPEND CUDA_NVCC_FLAGS --use_fast_math)
-
-if(CMAKE_BUILD_TYPE  STREQUAL "Debug")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_DEBUG})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "Release")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELEASE})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "RelWithDebInfo")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "MinSizeRel")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_MINSIZEREL})
-endif()
-
-function(specify_cuda_arch cuda_version cuda_arch)
-    if(${cuda_version} VERSION_GREATER "8.0")
-        foreach(capability 61 62)
-          if(${cuda_arch} STREQUAL ${capability})
-            list(APPEND __arch_flags " -gencode arch=compute_${cuda_arch},code=sm_${cuda_arch}")
-          endif()
-        endforeach()
-    elseif(${cuda_version} VERSION_GREATER "7.0" and ${cuda_arch} STREQUAL "53")
-        list(APPEND __arch_flags " -gencode arch=compute_${cuda_arch},code=sm_${cuda_arch}")
-    endif()
-endfunction()
-
-# Common gpu architectures: Kepler, Maxwell
-foreach(capability 30 35 50)
-      list(APPEND __arch_flags " -gencode arch=compute_${capability},code=sm_${capability}")
-endforeach()
-
-if (CUDA_VERSION VERSION_GREATER "7.0" OR CUDA_VERSION VERSION_EQUAL "7.0")
-      list(APPEND __arch_flags " -gencode arch=compute_52,code=sm_52")
-endif()
-
-# Modern gpu architectures: Pascal
-if (CUDA_VERSION VERSION_GREATER "8.0" OR CUDA_VERSION VERSION_EQUAL "8.0")
-      list(APPEND __arch_flags " -gencode arch=compute_60,code=sm_60")
-      list(APPEND CUDA_NVCC_FLAGS --expt-relaxed-constexpr)
-endif()
-
-# Custom gpu architecture
-set(CUDA_ARCH)
-
-if(CUDA_ARCH)
-  specify_cuda_arch(${CUDA_VERSION} ${CUDA_ARCH})
-endif()
-
-set(CUDA_NVCC_FLAGS ${__arch_flags} ${CUDA_NVCC_FLAGS})
-

cmake/util.cmake

@@ -115,8 +115,8 @@ function(link_paddle_exe TARGET_NAME)
         target_link_libraries(${TARGET_NAME} log)
     endif(ANDROID)
 
-    if(WITH_MKLDNN AND WITH_MKLML AND MKLDNN_IOMP_DIR)
-      target_link_libraries(${TARGET_NAME} "-L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
+    if(WITH_MKLML AND MKLML_LIB_DIR AND MKLML_IOMP_LIB)
+      target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
     endif()
 
     add_dependencies(${TARGET_NAME} ${external_project_dependencies})

doc/api/v2/config/layer.rst

@@ -335,6 +335,16 @@ bilinear_interp
 ..  autoclass:: paddle.v2.layer.bilinear_interp
     :noindex:
 
+dot_prod
+---------
+.. autoclass:: paddle.v2.layer.dot_prod
+    :noindex:
+
+out_prod
+--------
+.. autoclass:: paddle.v2.layer.out_prod
+    :noindex:
+
 power
 -----
 ..  autoclass:: paddle.v2.layer.power

doc/design/mkldnn/README.MD

@@ -36,13 +36,13 @@ Figure 1. PaddlePaddle on IA.
 我们把集成方案大致分为了如下几个方面。
 
 ### CMake
-我们会在`CMakeLists.txt`中会添加`WITH_MKLDNN`的选项，当设置这个值为`ON`的时候会启用编译MKL-DNN功能。同时会自动开启OpenMP用于提高MKL-DNN的性能。
+我们会在`CMakeLists.txt`中会给用户添加一个`WITH_MKL`的开关，他是负责`WITH_MKLML`和`WITH_MKLDNN`的总开关。
 
-同时，我们会引入`WITH_MKLML`选项，用于选择是否使用MKL-DNN自带的MKLML安装包。这个安装包可以独立于MKL-DNN使用，但是建议在开启MKL-DNN的同时也打开MKLML的开关，这样才能发挥最好的性能。
+当打开`WITH_MKL`时，会开启MKLML的功能，作为PaddlePaddle的CBLAS和LAPACK库，同时会开启Intel OpenMP用于提高MKLML的性能。 如果系统支持AVX2指令集及以上，同时会开启MKL-DNN功能。
 
-所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
+当关闭`WITH_MKL`时，MKLML和MKL-DNN功能会同时关闭。
 
-**备注**：当`WITH_MKLML=ON`的时候，会优先使用这个包作为PaddlePaddle的CBLAS和LAPACK库，所以会稍微改动`cmake/cblas.cmake`中的逻辑。
+所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
 
 ### Layers
 所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在

doc/howto/dev/write_docs_cn.rst

@@ -34,7 +34,7 @@ PaddlePaddle的文档构建有两种方式。
     cd TO_YOUR_PADDLE_CLONE_PATH
     mkdir -p build
     cd build
-    cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
+    cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
     make gen_proto_py
     make paddle_docs paddle_docs_cn
 

doc/mobile/cross_compiling_for_android_cn.md

-# 构建Android平台上的PaddlePaddle库
+# Android平台编译指南
 
 用户可通过如下两种方式，交叉编译Android平台上适用的PaddlePaddle库：
 - 基于Docker容器的编译方式

doc/mobile/cross_compiling_for_ios_cn.md

-# 构建iOS平台上的PaddlePaddle库
+# iOS平台编译指南
 交叉编译iOS平台上适用的PaddlePaddle库，需要在MacOS系统上进行。本文的将介绍在MacOS上，从源码交叉编译iOS平台上适用的PaddlePaddle库。
 
 ## 准备交叉编译环境
@@ -25,7 +25,7 @@ iOS平台可选配置参数：
 - `IOS_PLATFORM`，可设置为`OS/SIMULATOR`，默认值为`OS`。
   - `OS`，构建目标为`arm`架构的iPhone或者iPad等物理设备。
   - `SIMULATOR`，构建目标为`x86`架构的模拟器平台。
-- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示：
+- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示，默认编译所有架构：
 
     <table class="docutils">
     <colgroup>
@@ -41,11 +41,11 @@ iOS平台可选配置参数：
     <tbody valign="top">
       <tr class="row-even">
       <td>OS</td>
-      <td>armv7, armv7s, arm64 (默认)</td>
+      <td>armv7, armv7s, arm64 </td>
     </tr>
     <tr class="row-odd">
       <td>SIMULATOR</td>
-      <td>i386, x86_64 (默认)</td>
+      <td>i386, x86_64 </td>
     </tr>
     </tbody>
     </table>
@@ -66,7 +66,7 @@ iOS平台可选配置参数：
 ```bash
 cmake -DCMAKE_SYSTEM_NAME=iOS \
       -DIOS_PLATFORM=OS \
-      -DIOS_ARCH="arm64" \
+      -DIOS_ARCH="armv7;arm64" \
       -DIOS_ENABLE_BITCODE=ON \
       -DIOS_USE_VECLIB_FOR_BLAS=ON \
       -DCMAKE_INSTALL_PREFIX=your/path/to/install \
@@ -112,6 +112,6 @@ $ make install
 - `lib`目录，其中包含PaddlePaddle的C-API静态库
 - `third_party`目录，其中包含所依赖的所有第三方库
 
-注意，不同架构的PaddlePaddle库建议安装到不同的目录下，然后使用`lipo`工具将多个静态库合并成一个支持多个架构的fat库。
+注意，如果PaddlePaddle库需要同时支持真机和模拟器，则需要分别编译真机和模拟器版本，然后使用`lipo`工具合并fat库。
 
 自此，PaddlePaddle库已经安装完成，用户可将合成的fat库用于深度学习相关的iOS App中，调用方法见C-API文档。

doc/mobile/cross_compiling_for_raspberry_cn.md

-# 构建Raspberry Pi平台上的PaddlePaddle库
+# Raspberry Pi平台编译指南
 
 通常有两个方法来构建基于 Rasspberry Pi 的版本：
 

paddle/cuda/include/hl_gpu.h

@@ -25,7 +25,9 @@ limitations under the License. */
 #include "hl_matrix.h"
 #include "hl_sequence.h"
 #include "hl_sparse.h"
+#ifndef PADDLE_MOBILE_INFERENCE
 #include "hl_warpctc_wrap.h"
+#endif
 
 #ifdef HPPL_STUB_FUNC
 #include "stub/hl_aggregate_stub.h"

paddle/framework/backward.cc

@@ -270,6 +270,19 @@ static bool AllGradInSet(const std::vector<std::string>& names,
       return false;
     }
   }
+  if (VLOG_IS_ON(10)) {
+    std::ostringstream sout;
+    sout << "All input {";
+    for (auto& name : names) {
+      sout << name << ",";
+    }
+    sout << "} is in {";
+    for (auto& name : set) {
+      sout << name << ",";
+    }
+    sout << "}";
+    VLOG(10) << sout.str();
+  }
   return true;
 }
 
@@ -290,14 +303,12 @@ static void CreateGradVarInBlock(
   auto ops = block_desc->AllOps();
   for (size_t op_index = grad_op_start_index; op_index < ops.size();
        ++op_index) {
-    bool need_infer_shape = false;
     std::unordered_set<std::string> new_vars;
     ForEachVarName(ops[op_index]->Outputs(),
                    [&](const std::string& grad_var_name) {
                      if (block_desc->HasVar(grad_var_name)) {
                        return false;
                      }
-                     need_infer_shape = true;
                      auto var = block_desc->Var(grad_var_name);
                      new_vars.insert(var->Name());
                      auto it = param_name_map.find(grad_var_name);
@@ -311,23 +322,21 @@ static void CreateGradVarInBlock(
                      grad_record.op_idx_ = static_cast<int>(op_index);
                      return false; /* not break */
                    });
-    if (need_infer_shape) {
-      ops[op_index]->InferVarType(block_desc);
-      for (auto& arg : ops[op_index]->OutputArgumentNames()) {
-        if (new_vars.find(arg) == new_vars.end()) {
-          continue;
-        }
-        auto pname = FwdName(arg);
-        auto* param = block_desc->FindVarRecursive(pname);
-        auto* grad = block_desc->FindVar(arg);
-        if (param == nullptr) {
-          grad->SetDataType(DataType::FP32);
-        } else {
-          grad->SetDataType(param->GetDataType());
-        }
+    ops[op_index]->InferVarType(block_desc);
+    for (auto& arg : ops[op_index]->OutputArgumentNames()) {
+      if (new_vars.find(arg) == new_vars.end()) {
+        continue;
+      }
+      auto pname = FwdName(arg);
+      auto* param = block_desc->FindVarRecursive(pname);
+      auto* grad = block_desc->FindVar(arg);
+      if (param == nullptr) {
+        grad->SetDataType(DataType::FP32);
+      } else {
+        grad->SetDataType(param->GetDataType());
       }
-      ops[op_index]->InferShape(*block_desc);
     }
+    ops[op_index]->InferShape(*block_desc);
   }
 }
 
@@ -387,6 +396,7 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
     ProgramDescBind& program_desc, int block_idx,
     std::unordered_set<std::string>* no_grad_vars,
     std::unordered_map<std::string, std::string>* grad_to_var) {
+  VLOG(5) << "MakeBlockBackward";
   BlockDescBind* cur_block = program_desc.MutableBlock(block_idx);
   std::vector<OpDescBind*> op_descs = cur_block->AllOps();
   std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
@@ -394,9 +404,10 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
   std::vector<std::unique_ptr<OpDescBind>> backward_descs;
 
   for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
+    VLOG(5) << "Making backward " << (*it)->Type() << " op";
     std::vector<std::unique_ptr<OpDescBind>> op_grads;
 
-    if ((*it)->Type() == "recurrent") {
+    if ((*it)->Type() == "recurrent" || (*it)->Type() == "while") {
       int step_block_idx = (*it)->GetBlockAttr("step_block");
       BlockDescBind* backward_block = CreateStepBlock(
           program_desc, no_grad_vars, grad_to_var, step_block_idx);
@@ -410,6 +421,15 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
       op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var);
     }
 
+    if (VLOG_IS_ON(10)) {
+      std::ostringstream sout;
+      sout << "Made ";
+      for (auto& op_grad : op_grads) {
+        sout << op_grad->Type() << " ";
+      }
+      VLOG(10) << sout.str();
+    }
+
     for (const auto& desc : op_grads) {
       for (const std::string& out_name : desc->OutputArgumentNames()) {
         if (out_name.find("@GRAD") == std::string::npos) {
@@ -425,6 +445,8 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
         op_grads.begin(), op_grads.end(), std::back_inserter(backward_descs),
         [](std::unique_ptr<OpDescBind>& ptr) { return std::move(ptr); });
   }
+
+  VLOG(5) << "Appending Sums";
   // Check whether some variables are written more than once
   std::list<std::pair<size_t, std::unique_ptr<OpDescBind>>> pending_sum_ops;
   for (const auto& dup : dup_out_ops) {
@@ -432,16 +454,22 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
     const std::vector<size_t> dup_op = dup.second;
     if (out_name != kEmptyVarName && dup_op.size() > 1) {
       std::vector<std::string> sum_op_inputs;
+      std::string next_g_name = out_name;
       for (size_t i = 0; i < dup_op.size(); ++i) {
+        VLOG(10) << backward_descs[dup_op[i]]->Type() << " has " << out_name
+                 << " duplicated";
         std::string new_name = out_name + "@RENAME@" + std::to_string(i);
-        backward_descs[dup_op[i]]->Rename(out_name, new_name);
+        backward_descs[dup_op[i]]->RenameOutput(out_name, new_name);
+        backward_descs[dup_op[i]]->RenameInput(out_name, next_g_name);
         sum_op_inputs.emplace_back(new_name);
+        next_g_name = sum_op_inputs.back();
       }
       std::unique_ptr<OpDescBind> sum_op(new OpDescBind(
           "sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {}));
       pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
     }
   }
+
   pending_sum_ops.sort(
       [](const std::pair<size_t, std::unique_ptr<OpDescBind>>& a,
          const std::pair<size_t, std::unique_ptr<OpDescBind>>& b) {
@@ -452,6 +480,8 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
                           std::move(p.second));
   }
 
+  VLOG(5) << "MakeBlockBackward Finished";
+
   return backward_descs;
 }
 

paddle/framework/data_type.h

@@ -29,6 +29,8 @@ inline DataType ToDataType(std::type_index type) {
     return DataType::INT32;
   } else if (typeid(int64_t).hash_code() == type.hash_code()) {
     return DataType::INT64;
+  } else if (typeid(bool).hash_code() == type.hash_code()) {
+    return DataType::BOOL;
   } else {
     PADDLE_THROW("Not supported");
   }

paddle/framework/ddim.cc

@@ -60,8 +60,7 @@ void make_ddim(DDim& ddim, const int64_t* dims, int n) {
       ddim = make_dim<9>(dims);
       break;
     default:
-      throw std::invalid_argument(
-          "Dynamic dimensions must have between [1, 9] dimensions.");
+      PADDLE_THROW("Dynamic dimensions must have between [1, 9] dimensions.");
   }
 }
 

paddle/framework/executor.cc

@@ -120,6 +120,7 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
 
   for (auto& op_desc : block.AllOps()) {
     auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
+    VLOG(10) << op->DebugString();
     op->Run(*local_scope, *device);
   }
   if (create_local_scope) {

paddle/framework/op_desc.cc

@@ -235,6 +235,23 @@ void OpDescBind::Rename(const std::string &old_name,
   need_update_ = true;
 }
 
+void OpDescBind::RenameOutput(const std::string &old_name,
+                              const std::string &new_name) {
+  for (auto &output : outputs_) {
+    std::replace(output.second.begin(), output.second.end(), old_name,
+                 new_name);
+  }
+  need_update_ = true;
+}
+
+void OpDescBind::RenameInput(const std::string &old_name,
+                             const std::string &new_name) {
+  for (auto &input : inputs_) {
+    std::replace(input.second.begin(), input.second.end(), old_name, new_name);
+  }
+  need_update_ = true;
+}
+
 struct SetAttrDescVisitor : public boost::static_visitor<void> {
   explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}
   mutable OpDesc::Attr *attr_;
@@ -448,7 +465,12 @@ const std::vector<std::string> &CompileTimeInferShapeContext::Outputs(
 DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
   auto var = block_.FindVarRecursive(name);
   PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
-  return framework::make_ddim(var->Shape());
+  try {
+    return framework::make_ddim(var->Shape());
+  } catch (...) {
+    VLOG(5) << "GetDim of variable " << name << " error";
+    std::rethrow_exception(std::current_exception());
+  }
 }
 
 void CompileTimeInferShapeContext::SetDim(const std::string &name,

paddle/framework/op_desc.h

@@ -73,6 +73,10 @@ class OpDescBind {
 
   void Rename(const std::string &old_name, const std::string &new_name);
 
+  void RenameOutput(const std::string &old_name, const std::string &new_name);
+
+  void RenameInput(const std::string &old_name, const std::string &new_name);
+
   // Only be used in C++
   const AttributeMap &GetAttrMap() const;
 

paddle/framework/operator.cc

@@ -403,19 +403,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
 
 void OperatorWithKernel::Run(const Scope& scope,
                              const platform::DeviceContext& dev_ctx) const {
-  if (VLOG_IS_ON(1)) {
-    auto inputs = this->InputVars();
-    auto outputs = this->OutputVars(true);
-    std::ostringstream sout;
-    sout << "Run operator " << this->Type() << " From [";
-    std::ostream_iterator<std::string> out_it(sout, ",");
-    std::copy(inputs.begin(), inputs.end(), out_it);
-    sout << "] to [";
-    std::copy(outputs.begin(), outputs.end(), out_it);
-    sout << "]";
-    VLOG(1) << sout.str();
-  }
-
   RuntimeInferShapeContext infer_shape_ctx(*this, scope);
   this->InferShape(&infer_shape_ctx);
 

paddle/framework/scope.cc

@@ -38,11 +38,12 @@ Scope& Scope::NewScope() const {
 Variable* Scope::Var(const std::string& name) {
   auto iter = vars_.find(name);
   if (iter != vars_.end()) {
+    VLOG(3) << "Get existing variable " << name;
     return iter->second;
   }
   Variable* v = new Variable();
   vars_[name] = v;
-  VLOG(3) << "Create variable " << name << " on scope";
+  VLOG(3) << "Create variable " << name;
   v->name_ = &(vars_.find(name)->first);
   return v;
 }

paddle/framework/shape_inference.h

@@ -53,6 +53,10 @@ class InferShapeContext {
 
   virtual bool IsRuntime() const = 0;
 
+  // Note: In while op, we need this to be public
+  void SetDims(const std::vector<std::string> &names,
+               const std::vector<framework::DDim> &dims);
+
  protected:
   virtual framework::DDim GetDim(const std::string &name) const = 0;
   virtual void SetDim(const std::string &name, const framework::DDim &dim) = 0;
@@ -60,9 +64,6 @@ class InferShapeContext {
   std::vector<framework::DDim> GetDims(
       const std::vector<std::string> &names) const;
 
-  void SetDims(const std::vector<std::string> &names,
-               const std::vector<framework::DDim> &dims);
-
   std::vector<VarDesc::VarType> GetVarTypes(
       const std::vector<std::string> &names) const;
 

paddle/gserver/CMakeLists.txt

@@ -73,7 +73,6 @@ if(MOBILE_INFERENCE)
     list(REMOVE_ITEM GSERVER_SOURCES
          dataproviders/DataProvider.cpp
          dataproviders/MultiDataProvider.cpp
-         dataproviders/ProtoDataProvider.cpp
          dataproviders/PyDataProvider2.cpp
          dataproviders/PyDataProvider.cpp)
 

paddle/gserver/dataproviders/DataProvider.cpp

@@ -16,8 +16,8 @@ limitations under the License. */
 
 #include <unistd.h>
 #include <algorithm>
-#include "ProtoDataProvider.h"
 #include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
 #include "paddle/utils/StringUtil.h"
 #include "paddle/utils/Util.h"
 
@@ -164,8 +164,6 @@ DataProvider* DataProvider::create(const DataConfig& config,
 
 REGISTER_DATA_PROVIDER(simple, SimpleDataProvider);
 REGISTER_DATA_PROVIDER(dummy, DummyDataProvider);
-REGISTER_DATA_PROVIDER(proto, ProtoDataProvider);
-REGISTER_DATA_PROVIDER(proto_sequence, ProtoSequenceDataProvider);
 
 int64_t DataProvider::getNextBatch(int64_t size, DataBatch* batch) {
   int64_t batchSize = doubleBuffer_ ? getNextBatchFromBuffer(size, batch)

paddle/gserver/dataproviders/ProtoDataProvider.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 <vector>
-
-#include "DataFormat.pb.h"
-#include "paddle/utils/Stat.h"
-
-#include "DataProvider.h"
-#include "ProtoReader.h"
-
-namespace paddle {
-
-/**
- * @brief Provider data from protobuf data file with each sample
- * specified by proto message
- *
- * DataSample defined in DataFormat.proto.
- *
- * The file format is
- *
- *    header
- *
- *    sample1
- *
- *    sample2
- *
- *    ...
- *
- *    sampleN
- *
- * @note: In the data file, each message is prefixed with its length.
- * The read/write of the protbuf are implemented in ProtoReader.h
- */
-class ProtoDataProvider : public DataProvider {
-public:
-  ProtoDataProvider(const DataConfig& config,
-                    bool useGpu,
-                    bool loadDataAll = true);
-  virtual void reset();
-
-  /**
-   * @note this size includes the sequences which are skipped because they
-   * are longer than the batch size.
-   */
-  virtual int64_t getSize() {
-    int64_t size = sampleNums_;
-    if (usageRatio_ < 1.0f) {
-      size = static_cast<int64_t>(size * usageRatio_);
-    }
-    return size;
-  }
-  virtual void shuffle();
-
-  void loadData(const std::vector<std::string>& fileList);
-
-  virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
-
-protected:
-  /**
-   * @brief load protobuf data from a list of file
-   * @param[in]  fileName  file name of a file which contains
-   * a list of file names
-   */
-  void loadData(const std::string& fileName);
-
-  /**
-   * @brief load protobuf data from file
-   * @param[in]  fileName   data file name
-   */
-  void loadDataFile(const std::string& fileName);
-  /** @brief check data header of each data sample
-   *  @param[in] header     data header read from protobuf data
-   */
-  void checkDataHeader(const DataHeader& header);
-  /**
-   * @brief fill protobuf data into slot_,
-   * slot_ is a vector of ProtoSlot in memory.
-   * @param[in]  sample     data sample read from protobuf data
-   */
-  void fillSlots(const DataSample& sample);
-
-  /**
-   * @brief return true if each sample is one sequence, i.e., independent
-   * of other samples.
-   */
-  inline bool iidData() const { return sequenceStartPositions_.empty(); }
-
-  /**
-   * @brief check that sample is consistent with header_
-   */
-  void checkSample(const DataSample& sample);
-
-  template <class Op>
-  int64_t sequenceLoop(Op op, int64_t size);
-
-  template <class Op>
-  int64_t sampleLoop(Op op, int64_t size);
-
-  template <class Op>
-  int64_t subSampleLoop(Op op, int64_t size, int slot);
-
-  void showDataStats();
-
-protected:
-  struct ProtoVarSlot {
-    std::vector<real> data;
-    std::vector<int> dims;
-  };
-
-  struct ProtoSlot {
-    SlotDef::SlotType type;
-    int dim;
-    std::vector<int> indexData;
-    std::vector<real> denseData;
-    std::vector<sparse_non_value_t> sparseNonValueData;
-    std::vector<sparse_float_value_t> sparseFloatValueData;
-    std::vector<int64_t> indices;
-    std::vector<int64_t> subIndices;
-
-    std::vector<ProtoVarSlot> varDenseData;
-    std::vector<std::vector<int>> varIndices;
-    std::vector<std::string> strData;
-  };
-  DataHeader header_;
-  int numVecSlots_;
-
-  std::vector<ProtoSlot> slots_;
-  size_t sampleNums_;
-
-  /**
-   * The starting position of each sequence in samples.
-   * The last element should be num of samples.
-   * If empty, each sample is one sequence.
-   */
-  std::vector<size_t> sequenceStartPositions_;
-
-  int64_t currentSequenceIndex_;
-
-  // The size should be the number of sequences.
-  std::vector<size_t> shuffledSequenceIds_;
-
-  ThreadLocalD<DataBatch> cpuBatch_;
-  ThreadLocalD<DataBatch> gpuBatch_;
-
-  RWLock lock_;
-  std::vector<StatPtr> nnzStats_;  // stats for number of none-zeros entries
-};
-
-/**
- * @brief Special use for Proto data: instances should contain sparse-non-value
- * slots
- * and label.
- *
- * @note ProtoSequenceDataProvider treats each SPARSE SLOT as a SEQUENCE
- */
-class ProtoSequenceDataProvider : public ProtoDataProvider {
-public:
-  ProtoSequenceDataProvider(const DataConfig& config,
-                            bool useGpu,
-                            bool loadDataAll = true);
-  ~ProtoSequenceDataProvider() {}
-  virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
-};
-
-}  // namespace paddle

paddle/gserver/layers/DotProdLayer.cpp

+/* 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. */
+
+#include "Layer.h"
+#include "paddle/math/Matrix.h"
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
+
+namespace paddle {
+
+/**
+ * @brief A layer for computing the dot product of two vectors.
+ * Input1: vector (batchSize * dim)
+ * Input2: vector (batchSize * dim)
+ * Output: a matrix: (batchSize * 1)
+ */
+
+class DotProdLayer : public Layer {
+public:
+  explicit DotProdLayer(const LayerConfig& config) : Layer(config) {}
+
+  ~DotProdLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback = nullptr) override;
+};
+
+REGISTER_LAYER(dot_prod, DotProdLayer);
+
+bool DotProdLayer::init(const LayerMap& layerMap,
+                        const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+
+  CHECK_EQ(inputLayers_.size(), 2U);
+  CHECK_EQ(1UL, getSize())
+      << "The output dimensionality of this layer should be fixed to 1.";
+
+  return true;
+}
+
+void DotProdLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  MatrixPtr inV0 = getInputValue(0);
+  MatrixPtr inV1 = getInputValue(1);
+
+  size_t batchSize = inV0->getHeight();
+  CHECK_EQ(inV1->getHeight(), batchSize);
+  CHECK_EQ(inV0->getWidth(), inV1->getWidth());
+
+  {
+    REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
+    reserveOutput(batchSize, 1);
+  }
+
+  MatrixPtr outV = getOutputValue();
+  {
+    REGISTER_TIMER_INFO("FwDotProdTimer", getName().c_str());
+    outV->sumOfProducts(*inV0, *inV1, 1, 0);
+  }
+}
+
+void DotProdLayer::backward(const UpdateCallback& callback) {
+  MatrixPtr inV0 = getInputValue(0);
+  MatrixPtr inV1 = getInputValue(1);
+  MatrixPtr outG = getOutputGrad();
+  MatrixPtr inG0 = getInputGrad(0);
+  MatrixPtr inG1 = getInputGrad(1);
+
+  {
+    REGISTER_TIMER_INFO("BwDotProdTimer", getName().c_str());
+
+    if (inG0) {
+      inG0->addRowScale(0, *inV1, *outG);
+    }
+
+    if (inG1) {
+      inG1->addRowScale(0, *inV0, *outG);
+    }
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNConcatLayer.cpp

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "MKLDNNConcatLayer.h"
+
+using namespace mkldnn;  // NOLINT
+typedef memory::format format;
+
+namespace paddle {
+
+REGISTER_LAYER(mkldnn_concat, MKLDNNConcatLayer);
+
+bool MKLDNNConcatLayer::init(const LayerMap& layerMap,
+                             const ParameterMap& parameterMap) {
+  if (!MKLDNNLayer::init(layerMap, parameterMap)) {
+    return false;
+  }
+  CHECK_GT(inputLayers_.size(), 1UL);
+  CHECK(!biasParameter_);
+  return true;
+}
+
+void MKLDNNConcatLayer::reshape(
+    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+  reshapeInput(bs, ih, iw);
+  ic = inputLayers_[0]->getSize() / ih / iw;
+  CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
+  CHECK_EQ(inputElemenCnt_, (size_t)bs * ic * ih * iw);
+  CHECK_GT(inputLayers_.size(), 1UL);
+  channels_.resize(inputLayers_.size());
+  channels_[0] = ic;
+  // need change the output channel, so use oc_ instead
+  // TODO(TJ): change API, use &oc
+  oc_ = ic;
+  for (size_t i = 1; i < inputLayers_.size(); i++) {
+    int batchsize, height, witdh;
+    reshapeInput(batchsize, height, witdh, i);
+    CHECK_EQ(bs, batchsize);
+    CHECK_EQ(ih, height);
+    CHECK_EQ(iw, witdh);
+
+    channels_[i] = inputLayers_[i]->getSize() / height / witdh;
+    CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize());
+    oc_ += channels_[i];
+  }
+  oh = ih;
+  ow = iw;
+  reshapeOutput(oh, ow);
+  resizeOutput(bs, oc_ * oh * ow);
+}
+
+void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline,
+                                 MKLDNNMatrixPtr& in,
+                                 MKLDNNMatrixPtr& wgt,
+                                 MKLDNNMatrixPtr& bias,
+                                 MKLDNNMatrixPtr& out) {
+  resetFwdBuffers(inVals_, out);
+  in = inVals_[0];
+
+  std::shared_ptr<concat::primitive_desc> fwdPD;
+  resetFwdPD(fwdPD, inVals_, out);
+
+  resetFwdPipeline(pipeline, fwdPD, inVals_, out);
+}
+
+void MKLDNNConcatLayer::resetBwd(std::vector<primitive>& pipeline,
+                                 MKLDNNMatrixPtr& in,
+                                 MKLDNNMatrixPtr& wgt,
+                                 MKLDNNMatrixPtr& bias,
+                                 MKLDNNMatrixPtr& out) {
+  resetBwdBuffers(inGrads_, out);
+  in = inGrads_[0];
+
+  resetBwdPipeline(pipeline, bwds_, inGrads_, out);
+}
+
+void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                                        MKLDNNMatrixPtr& out) {
+  inputs.resize(inputLayers_.size());
+  bool has8c = false, has16c = false, hasnc = false;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    // resetInValue will use ic_ so temporary change as current input's channel
+    // TODO(TJ): change ic_ as vector then can remove channels_
+    ic_ = channels_[i];
+    resetInValue(inputs[i], nullptr, i);
+    CHECK(inputs[i]);
+    auto dm = inputs[i]->getDims();
+    // inputs format can be different, but ndims must equal
+    CHECK(i == 0 || dm.size() == inputs[0]->getDims().size());
+    CHECK_EQ(bs_, dm[0]);
+    CHECK_EQ(channels_[i], dm[1]);
+    if (dm.size() > 2) {
+      CHECK_EQ(ih_, dm[2]);
+      CHECK_EQ(iw_, dm[3]);
+    }
+    if (inputs[i]->getFormat() == format::nc) {
+      hasnc = true;
+    }
+    if (inputs[i]->getFormat() == format::nChw8c) {
+      has8c = true;
+    }
+    if (inputs[i]->getFormat() == format::nChw16c) {
+      has16c = true;
+    }
+  }
+  // change back, ic_ always save the input 0 size
+  ic_ = channels_[0];
+
+  format outFmt;
+  if (has16c && oc_ % 16 == 0) {
+    outFmt = format::nChw16c;
+  } else if (has8c && oc_ % 8 == 0) {
+    outFmt = format::nChw8c;
+  } else if (hasnc) {
+    CHECK(oh_ == 1 && ow_ == 1);
+    outFmt = format::nc;
+  } else {
+    outFmt = format::nchw;
+  }
+  memory::dims outDims =
+      hasnc ? memory::dims{bs_, oc_} : memory::dims{bs_, oc_, oh_, ow_};
+  auto outPD = MKLDNNMatrix::createPrimitiveDesc(outDims, outFmt, engine_);
+  resetOutValue(out, outPD);
+}
+
+void MKLDNNConcatLayer::resetFwdPD(std::shared_ptr<concat::primitive_desc>& pd,
+                                   std::vector<MKLDNNMatrixPtr>& inputs,
+                                   MKLDNNMatrixPtr out) {
+  std::vector<memory::primitive_desc> srcPDs;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    srcPDs.push_back(inputs[i]->getPrimitiveDesc());
+  }
+  CHECK(out);
+  pd.reset(new concat::primitive_desc(out->getMemoryDesc(), axis_, srcPDs));
+  CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
+}
+
+void MKLDNNConcatLayer::resetFwdPipeline(
+    std::vector<primitive>& pipeline,
+    std::shared_ptr<concat::primitive_desc>& pd,
+    std::vector<MKLDNNMatrixPtr>& inputs,
+    MKLDNNMatrixPtr& out) {
+  std::vector<primitive::at> srcs;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    srcs.push_back(*(inputs[i]));
+  }
+  fwd_.reset(new concat(*pd, srcs, *out));
+  pipeline.push_back(*fwd_);
+}
+
+void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                                        MKLDNNMatrixPtr& out) {
+  CHECK(outVal_);
+  resetOutGrad(out, outVal_->getPrimitiveDesc());
+  CHECK(out);
+
+  inputs.resize(inputLayers_.size());
+  for (size_t i = 0; i < inputs.size(); i++) {
+    CHECK(inVals_[i]);
+    // resetInGrad will use inVal_
+    // TODO(TJ): change move inVals_ to MKLDNNLayer ans remove inVal_
+    inVal_ = inVals_[i];
+    resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
+    CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc());
+  }
+  // change back, inVal_ always save the input 0
+  inVal_ = inVals_[0];
+}
+
+void MKLDNNConcatLayer::resetBwdPipeline(
+    std::vector<mkldnn::primitive>& pipeline,
+    std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
+    std::vector<MKLDNNMatrixPtr>& inputs,
+    MKLDNNMatrixPtr& out) {
+  // reset the backward primitives
+  memory::dims offsets = {0, 0, 0, 0};
+  prims.resize(inputs.size());
+  CHECK_EQ(inputs.size(), channels_.size());
+  for (size_t i = 0; i < inputs.size(); i++) {
+    auto viewPD = view::primitive_desc(
+        out->getPrimitiveDesc(), inputs[i]->getDims(), offsets);
+    auto bwdPD = reorder::primitive_desc(viewPD.dst_primitive_desc(),
+                                         inputs[i]->getPrimitiveDesc());
+    prims[i].reset(new reorder(bwdPD, *out, *(inputs[i])));
+    offsets[axis_] += channels_[i];
+    // push to pipeline
+    pipeline.push_back(*prims[i]);
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNConcatLayer.h

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "MKLDNNLayer.h"
+#include "mkldnn.hpp"
+
+namespace paddle {
+
+/**
+ * @brief A subclass of MKLDNNLayer Concatenate layer.
+ *
+ * The config file api is mkldnn_concat
+ */
+class MKLDNNConcatLayer : public MKLDNNLayer {
+protected:
+  std::vector<MKLDNNMatrixPtr> inVals_;
+  std::vector<MKLDNNMatrixPtr> inGrads_;
+  std::vector<std::shared_ptr<mkldnn::primitive>> bwds_;
+  // input channel numbers
+  std::vector<int> channels_;
+
+  // concat_dimension in MKLDNN
+  // if axis_ == 0, concat batchsize
+  // if axis_ == 1, concat channel (default)
+  int axis_;
+
+public:
+  explicit MKLDNNConcatLayer(const LayerConfig& config)
+      : MKLDNNLayer(config), axis_(1) {}
+
+  ~MKLDNNConcatLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void reshape(
+      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+
+  void resetFwd(std::vector<mkldnn::primitive>& pipeline,
+                MKLDNNMatrixPtr& in,
+                MKLDNNMatrixPtr& wgt,
+                MKLDNNMatrixPtr& bias,
+                MKLDNNMatrixPtr& out) override;
+
+  void resetBwd(std::vector<mkldnn::primitive>& pipeline,
+                MKLDNNMatrixPtr& in,
+                MKLDNNMatrixPtr& wgt,
+                MKLDNNMatrixPtr& bias,
+                MKLDNNMatrixPtr& out) override;
+
+  void printSizeInfo() override {
+    CHECK_EQ(channels_.size(), inputLayers_.size());
+    for (size_t i = 0; i < channels_.size(); ++i) {
+      VLOG(MKLDNN_SIZES) << "Input " << i << ", " << inputLayers_[i]->getName()
+                         << ": " << bs_ << ", " << channels_[i] << ", " << ih_
+                         << ", " << iw_;
+    }
+    VLOG(MKLDNN_SIZES) << "Output: " << bs_ << ", " << oc_ << ", " << oh_
+                       << ", " << ow_;
+  }
+
+  void printValueFormat() override {
+    for (size_t i = 0; i < inVals_.size(); ++i) {
+      VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
+                        << ": " << inVals_[i]->getFormat() << " >>>";
+    }
+    if (outVal_) {
+      VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
+    }
+    if (extOutVal_) {
+      VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
+    }
+  }
+
+  void printGradFormat() override {
+    if (extOutGrad_) {
+      VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
+    }
+    if (outGrad_) {
+      VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
+    }
+    for (size_t i = 0; i < inGrads_.size(); ++i) {
+      VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
+                        << ": " << inGrads_[i]->getFormat() << "<<<";
+    }
+  }
+
+protected:
+  /**
+   * Forward functions: reset buffers(inputs, output, bias),
+   *                    reset primitive descriptor,
+   *                    reset pipeline.
+   */
+  void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                       MKLDNNMatrixPtr& out);
+  void resetFwdPD(std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
+                  std::vector<MKLDNNMatrixPtr>& inputs,
+                  MKLDNNMatrixPtr out);
+  void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
+                        std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
+                        std::vector<MKLDNNMatrixPtr>& inputs,
+                        MKLDNNMatrixPtr& out);
+
+  /**
+   * Backward functions: reset buffers(inputs, output, bias)
+   *                     reset primitives and pipeline
+   */
+  void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                       MKLDNNMatrixPtr& out);
+  void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
+                        std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
+                        std::vector<MKLDNNMatrixPtr>& inputs,
+                        MKLDNNMatrixPtr& out);
+};
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNLayer.cpp

@@ -21,8 +21,8 @@ namespace paddle {
 
 bool MKLDNNLayer::init(const LayerMap& layerMap,
                        const ParameterMap& parameterMap) {
-  CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
-                          << "Please set WITH_MKLDNN=ON "
+  CHECK(FLAGS_use_mkldnn) << "MKLDNNLayers only support use_mkldnn."
+                          << "Please set WITH_MKL=ON "
                           << "and set use_mkldnn=True";
   CHECK(!useGpu_) << "Do not support GPU yet";
 
@@ -138,8 +138,11 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
   }
 }
 
-void MKLDNNLayer::reshapeInput(int& batchsize, int& height, int& width) {
-  const Argument& input = inputLayers_[0]->getOutput();
+void MKLDNNLayer::reshapeInput(int& batchsize,
+                               int& height,
+                               int& width,
+                               size_t inputIdx) {
+  const Argument& input = inputLayers_[inputIdx]->getOutput();
   batchsize = input.getBatchSize();
   int h = input.getFrameHeight();
   int w = input.getFrameWidth();

paddle/gserver/layers/MKLDNNLayer.h

@@ -178,7 +178,10 @@ protected:
   /**
    * reshape the input image sizes and input batchsize
    */
-  void reshapeInput(int& batchsize, int& height, int& width);
+  void reshapeInput(int& batchsize,
+                    int& height,
+                    int& width,
+                    size_t inputIdx = 0);
 
   /**
    * reshape output image sizes

paddle/gserver/tests/CMakeLists.txt

@@ -29,7 +29,7 @@ gserver_test(test_KmaxSeqScore)
 gserver_test(test_Expand)
 gserver_test(test_MaxPoolingWithMaskOutput)
 
-########## test_Mkldnn layers and activations ##########
+########## test_MKLDNN layers and activations ##########
 if(WITH_MKLDNN)
     add_unittest_without_exec(test_MKLDNN
         test_MKLDNN.cpp
@@ -62,17 +62,6 @@ if(NOT WITH_DOUBLE AND NOT MOBILE_INFERENCE)
 endif()
 
 if(NOT MOBILE_INFERENCE)
-################### test_ProtoDataProvider ############
-    add_unittest_without_exec(test_ProtoDataProvider
-        test_ProtoDataProvider.cpp)
-
-    # test_ProtoDataProvider will mkdir as same name,
-    # so if WORKING_DIRECTORY is default directory, then
-    # mkdir will get error.
-    add_test(NAME test_ProtoDataProvider
-        COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider
-        WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
-
 ################## test_Evaluator #######################
     add_unittest(test_Evaluator
         test_Evaluator.cpp)
@@ -110,3 +99,24 @@ add_test(NAME test_PyDataProvider2
    COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
         WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
 )
+
+################# test_CompareSparse ##################
+add_unittest_without_exec(test_CompareSparse
+    test_CompareSparse.cpp)
+if(NOT ON_TRAVIS)
+  add_test(NAME test_CompareSparse
+    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+          ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+              ./.set_port.sh -p port -n 6
+                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
+endif()
+
+################ test_CompareTwoNets ######################
+add_unittest_without_exec(test_CompareTwoNets
+    test_CompareTwoNets.cpp)
+add_test(NAME test_CompareTwoNets
+  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+        ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)

paddle/gserver/tests/MKLDNNTester.h

@@ -23,7 +23,7 @@ limitations under the License. */
 namespace paddle {
 
 /**
- * @brief test the functionality of Mkldnnlayers
+ * @brief test the functionality of MKLDNNlayers and MKLDNNActivations
  * refer to paddle original function
  */
 class MKLDNNTester {

paddle/gserver/tests/proto_files.txt

-./test_ProtoDataProvider/data1.bin
-./test_ProtoDataProvider/data2.bin

paddle/gserver/tests/proto_files_compressed.txt

-./test_ProtoDataProvider/data1.bin.gz
-./test_ProtoDataProvider/data2.bin.gz

paddle/trainer/tests/sample_trainer_config_opt_a.conf → paddle/gserver/tests/sequence_lstm.conf

+#!/usr/bin/env python
 # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,27 +15,50 @@
 
 from paddle.trainer_config_helpers import *
 
-################################### Data Configuration ###################################
-TrainData(ProtoData(files = "trainer/tests/mnist.list"))
-################################### Algorithm Configuration ###################################
-settings(batch_size = 1000,
-         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
-################################### Network Configuration ###################################
-data = data_layer(name ="input", size=784)
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
 
-fc1 = fc_layer(input=data, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
 
-fc2 = fc_layer(input=fc1, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 256
+label_dim = 3
+sparse_update = get_config_arg("sparse_update", bool, False)
 
-output = fc_layer(input=[fc1, fc2], size=10,
-                  bias_attr=True,
-                  act=SoftmaxActivation())
+data = data_layer(name="word", size=dict_dim)
 
-lbl = data_layer(name ="label", size=1)
+emb = embedding_layer(
+    input=data,
+    size=word_dim,
+    param_attr=ParamAttr(sparse_update=sparse_update))
 
-cost = classification_cost(input=output, label=lbl)
-outputs(cost)
+with mixed_layer(size=hidden_dim * 4) as lstm_input:
+    lstm_input += full_matrix_projection(input=emb)
+
+lstm = lstmemory(
+    input=lstm_input,
+    act=TanhActivation(),
+    gate_act=SigmoidActivation(),
+    state_act=TanhActivation())
+
+lstm_last = last_seq(input=lstm)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=lstm_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/trainer/tests/sample_trainer_config_opt_b.conf → paddle/gserver/tests/sequence_recurrent.py

+#!/usr/bin/env python
 # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,27 +15,42 @@
 
 from paddle.trainer_config_helpers import *
 
-################################### Data Configuration ###################################
-TrainData(ProtoData(files = "trainer/tests/mnist.list"))
-################################### Algorithm Configuration ###################################
-settings(batch_size = 1000,
-         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
-################################### Network Configuration ###################################
-data = data_layer(name ="input", size=784)
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
 
-fc1 = fc_layer(input=data, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
 
-fc2 = fc_layer(input=fc1, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 128
+label_dim = 3
 
-output = fc_layer(input=[fc1, fc2], size=10,
-                  bias_attr=True,
-                  act=SoftmaxActivation())
+# This config is designed to be equivalent with sequence_recurrent_group.py
 
-lbl = data_layer(name ="label", size=1)
+data = data_layer(name="word", size=dict_dim)
 
-cost = classification_cost(input=output, label=lbl)
-outputs(cost)
+emb = embedding_layer(
+    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
+
+recurrent = recurrent_layer(input=emb, bias_attr=False, act=SoftmaxActivation())
+
+recurrent_last = last_seq(input=recurrent)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=recurrent_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/gserver/tests/sequence_recurrent_group.py

+#!/usr/bin/env python
+# 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.
+
+from paddle.trainer_config_helpers import *
+
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
+
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
+
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 128
+label_dim = 3
+
+# This config is designed to be equivalent with sequence_recurrent.py
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(
+    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
+
+
+def step(y):
+    mem = memory(name="rnn_state", size=hidden_dim)
+    with mixed_layer(
+            name="rnn_state",
+            size=hidden_dim,
+            bias_attr=False,
+            act=SoftmaxActivation()) as out:
+        out += identity_projection(input=y)
+        out += full_matrix_projection(
+            input=mem, param_attr=ParamAttr(name="___recurrent_layer_0__"))
+    return out
+
+
+recurrent = recurrent_group(name="rnn", step=step, input=emb)
+
+recurrent_last = last_seq(input=recurrent)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=recurrent_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/trainer/tests/test_CompareSparse.cpp → paddle/gserver/tests/test_CompareSparse.cpp

@@ -22,8 +22,7 @@ limitations under the License. */
 using namespace paddle;  // NOLINT
 using namespace std;     // NOLINT
 
-static const string& configFile1 =
-    "trainer/tests/sample_trainer_config_compare_sparse.conf";
+static const string& configFile1 = "gserver/tests/sequence_lstm.conf";
 
 DECLARE_bool(use_gpu);
 DECLARE_string(config);

paddle/trainer/tests/test_CompareTwoNets.cpp → paddle/gserver/tests/test_CompareTwoNets.cpp

@@ -30,8 +30,6 @@ DECLARE_bool(use_gpu);
 DECLARE_string(config);
 DECLARE_string(nics);
 
-DEFINE_string(config_file_a, "", "config of one network to compare");
-DEFINE_string(config_file_b, "", "config of another network to compare");
 DEFINE_bool(need_high_accuracy,
             false,
             "whether need to run in double accuracy");
@@ -42,6 +40,10 @@ DEFINE_double(
 DECLARE_bool(thread_local_rand_use_global_seed);
 DECLARE_int32(seed);
 
+static const string& config_file_a = "gserver/tests/sequence_recurrent.py";
+static const string& config_file_b =
+    "gserver/tests/sequence_recurrent_group.py";
+
 struct ComData {
   vector<Argument> outArgs;
   vector<ParameterPtr> parameters;
@@ -66,6 +68,7 @@ void calcGradient(ComData& data, const string configFile) {
   DataBatch dataBatch;
   int32_t batchSize = trainer.getConfig().opt_config().batch_size();
 
+  trainer.getDataProvider()->reset();
   trainer.getDataProvider()->setSkipShuffle();
   trainer.getDataProvider()->getNextBatch(batchSize, &dataBatch);
 
@@ -167,11 +170,11 @@ void compareGradient(ComData& comDataA, ComData& comDataB) {
 
 TEST(Trainer, create) {
   ComData dataA;
-  calcGradient(dataA, FLAGS_config_file_a);
+  calcGradient(dataA, config_file_a);
   LOG(INFO) << "\n\nforwardBackward of Network A is finished\n\n";
 
   ComData dataB;
-  calcGradient(dataB, FLAGS_config_file_b);
+  calcGradient(dataB, config_file_b);
   LOG(INFO) << "\n\nforwardBackward of the Network B is finished\n\n";
 
   compareGradient(dataA, dataB);

paddle/gserver/tests/test_LayerGrad.cpp

@@ -1081,6 +1081,21 @@ TEST(Layer, InterpolationLayer) {
   }
 }
 
+TEST(Layer, DotProdLayer) {
+  TestConfig config;
+  config.layerConfig.set_type("dot_prod");
+  config.layerConfig.set_size(1);
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+  config.layerConfig.add_inputs();
+  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "dot_prod", 10, false, useGpu);
+  }
+}
+
 TEST(Layer, OuterProdLayer) {
   TestConfig config;
   config.layerConfig.set_type("out_prod");

paddle/gserver/tests/test_MKLDNN.cpp

@@ -313,6 +313,47 @@ TEST(MKLDNNLayer, AddtoLayer) {
   testAddtoLayer({4, 12, 1, 1}, 3);
 }
 
+static void getMKLDNNConcatConfig(TestConfig& cfg,
+                                  const std::vector<testImageDesc>& inputs) {
+  CHECK_GE(inputs.size(), 2) << "at least two inputs";
+  int oc = inputs[0].ic;
+  for (size_t i = 1; i < inputs.size(); ++i) {
+    CHECK_EQ(inputs[i].bs, inputs[0].bs);
+    CHECK_EQ(inputs[i].ih, inputs[0].ih);
+    CHECK_EQ(inputs[i].iw, inputs[0].iw);
+    oc += inputs[i].ic;
+  }
+  cfg.biasSize = 0;
+  cfg.layerConfig.set_type("mkldnn_concat");
+  cfg.layerConfig.set_size(oc * inputs[0].ih * inputs[0].iw);
+  cfg.layerConfig.set_active_type("relu");
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    std::stringstream ss;
+    ss << "layer_" << i;
+    cfg.inputDefs.push_back(
+        {INPUT_DATA,
+         ss.str(),
+         (size_t)(inputs[i].ic) * inputs[i].ih * inputs[i].iw,
+         0});
+    LayerInputConfig* input = cfg.layerConfig.add_inputs();
+    ImageConfig* img_conf = input->mutable_image_conf();
+    img_conf->set_channels(inputs[i].ic);
+    img_conf->set_img_size_y(inputs[i].ih);
+    img_conf->set_img_size(inputs[i].iw);
+  }
+}
+
+void testConcatLayer(const std::vector<testImageDesc>& inputs) {
+  TestConfig dnnConfig;
+  getMKLDNNConcatConfig(dnnConfig, inputs);
+  RUN_MKLDNN_TEST_LAYER(dnnConfig, "concat", inputs[0])
+}
+
+TEST(MKLDNNLayer, ConcatLayer) {
+  testConcatLayer({{64, 128, 1, 1}, {64, 32, 1, 1}, {64, 64, 1, 1}});
+  testConcatLayer({{32, 100, 8, 8}, {32, 10, 8, 8}});
+}
+
 void testActivation(std::string actType, const testImageDesc& pm) {
   // TODO(TJ): remove me when paddle support elu activation
   if (actType == "mkldnn_elu") {

paddle/math/Storage.cpp

@@ -17,9 +17,13 @@ limitations under the License. */
 #include "paddle/utils/StringUtil.h"
 #include "paddle/utils/Util.h"
 
+#ifndef PADDLE_MOBILE_INFERENCE
 DEFINE_int32(pool_limit_size,
              536870912,
              "maximum memory size managed by a memory pool, default is 512M");
+#else
+DEFINE_int32(pool_limit_size, 0, "default is 0");
+#endif
 
 namespace paddle {
 

paddle/operators/CMakeLists.txt

@@ -61,6 +61,18 @@ function(op_library TARGET)
         set(pybind_flag 1)
     endif()
 
+    if ("${TARGET}" STREQUAL "compare_op")
+        set(pybind_flag 1)
+        file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
+    endif()
+
+    # conv_op contains several operators
+    if ("${TARGET}" STREQUAL "conv_op")
+        set(pybind_flag 1)
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
+    endif()
+
     # pool_op contains several operators
     if ("${TARGET}" STREQUAL "pool_op")
         set(pybind_flag 1)
@@ -68,9 +80,11 @@ function(op_library TARGET)
         file(APPEND ${pybind_file} "USE_OP(pool2d);\n")
     endif()
 
-    if ("${TARGET}" STREQUAL "compare_op")
+    # pool_cudnn_op contains several operators
+    if ("${TARGET}" STREQUAL "pool_cudnn_op")
         set(pybind_flag 1)
-        file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
     endif()
 
     # pool_with_index_op contains several operators
@@ -80,25 +94,18 @@ function(op_library TARGET)
         file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
     endif()
 
-    # conv_op contains several operators
-    if ("${TARGET}" STREQUAL "conv_op")
-        set(pybind_flag 1)
-        # It's enough to just adding one operator to pybind
-        file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
-    endif()
-
     # conv_transpose_op contains several operators
     if ("${TARGET}" STREQUAL "conv_transpose_op")
         set(pybind_flag 1)
         # It's enough to just adding one operator to pybind
         file(APPEND ${pybind_file} "USE_OP(conv2d_transpose);\n")
     endif()
-    
-    # pool_cudnn_op contains several operators
-    if ("${TARGET}" STREQUAL "pool_cudnn_op")
+
+    # conv_transpose_cudnn_op contains two operators
+    if ("${TARGET}" STREQUAL "conv_transpose_cudnn_op")
         set(pybind_flag 1)
         # It's enough to just adding one operator to pybind
-        file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
+        file(APPEND ${pybind_file} "USE_OP(conv2d_transpose_cudnn);\n")
     endif()
 
     # save_restore_op contains several operators

paddle/operators/array_operator.h

@@ -42,6 +42,7 @@ class ArrayOp : public framework::OperatorBase {
     } else {
       offset = static_cast<size_t>(*i_tensor.data<int64_t>());
     }
+    VLOG(10) << " Offset = " << offset;
     return offset;
   }
 };

paddle/operators/bilinear_tensor_product_op.h

@@ -174,7 +174,7 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
     // Caculate the gradient of Input(Bias).
     if (d_bias) {
       d_bias->mutable_data<T>(ctx.GetPlace());
-      auto d_bias_mat = EigenMatrix<T>::From(*d_bias);
+      auto d_bias_mat = framework::EigenVector<T>::Flatten(*d_bias);
       d_bias_mat.device(place) = d_out_mat.sum(Eigen::DSizes<int, 1>(0));
     }
   }

paddle/operators/conv_cudnn_op.cu.cc

@@ -226,9 +226,8 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
     T alpha = 1.0f, beta = 0.0f;
     if (input_grad) {
       T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
-      auto t = framework::EigenVector<T>::Flatten(*input_grad);
-      t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
-          t.constant(static_cast<T>(0));
+      // Because beta is zero, it is unnecessary to reset input_grad.
+
       for (int i = 0; i < groups; i++) {
         PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
             handle, &alpha, cudnn_filter_desc,
@@ -241,9 +240,8 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
     // ------------------- cudnn conv backward filter ---------------------
     if (filter_grad) {
       T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
-      auto t = framework::EigenVector<T>::Flatten(*filter_grad);
-      t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
-          t.constant(static_cast<T>(0));
+      // Because beta is zero, it is unnecessary to reset filter_grad.
+
       for (int i = 0; i < groups; i++) {
         PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
             handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,

paddle/operators/conv_op.cc

@@ -225,11 +225,15 @@ REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
             ops::ConvOpGrad);
 
 REGISTER_OP_CPU_KERNEL(conv2d,
-                       ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
+                       ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
+                       ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
-    conv2d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
+    conv2d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
 
 REGISTER_OP_CPU_KERNEL(conv3d,
-                       ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
+                       ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
+                       ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
-    conv3d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
+    conv3d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);

paddle/operators/conv_op.cu.cc

@@ -17,11 +17,15 @@
 namespace ops = paddle::operators;
 
 REGISTER_OP_GPU_KERNEL(conv2d,
-                       ops::GemmConvKernel<paddle::platform::GPUPlace, float>);
+                       ops::GemmConvKernel<paddle::platform::GPUPlace, float>,
+                       ops::GemmConvKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
-    conv2d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>);
+    conv2d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvGradKernel<paddle::platform::GPUPlace, double>);
 
 REGISTER_OP_GPU_KERNEL(conv3d,
-                       ops::GemmConvKernel<paddle::platform::GPUPlace, float>);
+                       ops::GemmConvKernel<paddle::platform::GPUPlace, float>,
+                       ops::GemmConvKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
-    conv3d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>);
+    conv3d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvGradKernel<paddle::platform::GPUPlace, double>);

paddle/operators/conv2d_transpose_cudnn_op.cc → paddle/operators/conv_transpose_cudnn_op.cc

@@ -23,7 +23,24 @@ class CudnnConv2DTransposeOpMaker : public Conv2DTransposeOpMaker {
                               framework::OpAttrChecker* op_checker)
       : Conv2DTransposeOpMaker(proto, op_checker) {
     AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
-        .SetDefault(std::vector<int>{1, 1});
+        .SetDefault({1, 1});
+    AddAttr<int>("workspace_size_MB",
+                 "workspace size for cudnn, in MB, "
+                 "workspace is a section of GPU memory which will be "
+                 "allocated/freed each time the operator runs, larger "
+                 "workspace size can increase performance but also requires "
+                 "better hardward. This size should be carefully setted.")
+        .SetDefault(4096);
+  }
+};
+
+class CudnnConv3DTransposeOpMaker : public Conv3DTransposeOpMaker {
+ public:
+  CudnnConv3DTransposeOpMaker(framework::OpProto* proto,
+                              framework::OpAttrChecker* op_checker)
+      : Conv3DTransposeOpMaker(proto, op_checker) {
+    AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
+        .SetDefault({1, 1, 1});
     AddAttr<int>("workspace_size_MB",
                  "workspace size for cudnn, in MB, "
                  "workspace is a section of GPU memory which will be "
@@ -48,3 +65,14 @@ REGISTER_OP_CPU_KERNEL(
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose_cudnn_grad,
     ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
+
+REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
+            ops::CudnnConv3DTransposeOpMaker, conv3d_transpose_cudnn_grad,
+            ops::ConvTransposeOpGrad);
+
+REGISTER_OP_CPU_KERNEL(
+    conv3d_transpose_cudnn,
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    conv3d_transpose_cudnn_grad,
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/conv2d_transpose_cudnn_op.cu.cc → paddle/operators/conv_transpose_cudnn_op.cu.cc

@@ -54,15 +54,21 @@ class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
     ScopedTensorDescriptor output_desc;
     ScopedFilterDescriptor filter_desc;
     ScopedConvolutionDescriptor conv_desc;
-    DataLayout layout = DataLayout::kNCHW;
+    DataLayout layout;
+
+    if (strides.size() == 2U) {
+      layout = DataLayout::kNCHW;
+    } else {
+      layout = DataLayout::kNCDHW;
+    }
 
-    // N, M, H, W
+    // (N, M, H, W) or (N, M, D, H, W)
     cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
         layout, framework::vectorize2int(input->dims()));
-    // N, C, O_h, O_w
+    // (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
     cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
         layout, framework::vectorize2int(output->dims()));
-    // M, C, K_h, K_w
+    // (M, C, K_h, K_w) or (M, C, K_d, K_h, K_w)
     cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
         layout, framework::vectorize2int(filter->dims()));
     cudnnConvolutionDescriptor_t cudnn_conv_desc =
@@ -136,13 +142,13 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
     ScopedConvolutionDescriptor conv_desc;
     DataLayout layout = DataLayout::kNCHW;
 
-    // Input: (N, M, H, W)
+    // Input: (N, M, H, W) or (N, M, D, H, W)
     cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
         layout, framework::vectorize2int(input->dims()));
-    // Output: (N, C, O_H, O_W)
+    // Output: (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
     cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
         layout, framework::vectorize2int(output_grad->dims()));
-    // Filter (M, C, K_H, K_W)
+    // Filter (M, C, K_h, K_w) or (M, C, K_d K_h, K_w)
     cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
         layout, framework::vectorize2int(filter->dims()));
 
@@ -200,8 +206,7 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
     T alpha = 1.0f, beta = 0.0f;
     if (input_grad) {
       T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
-      math::set_constant(ctx.device_context(), input_grad, 0);
-
+      // Because beta is zero, it is unnecessary to reset input_grad.
       PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
           handle, &alpha, cudnn_output_desc, output_grad_data,
           cudnn_filter_desc, filter_data, cudnn_conv_desc, data_algo,
@@ -212,8 +217,7 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
     // ------------------- cudnn conv backward filter ---------------------
     if (filter_grad) {
       T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
-      math::set_constant(ctx.device_context(), filter_grad, 0);
-
+      // Because beta is zero, it is unnecessary to reset filter_grad.
       // Gradient with respect to the filter
       PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
           handle, &alpha, cudnn_output_desc, output_grad_data, cudnn_input_desc,
@@ -234,3 +238,8 @@ REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn,
                        ops::CudnnConvTransposeOpKernel<float>);
 REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn_grad,
                        ops::CudnnConvTransposeGradOpKernel<float>);
+
+REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn,
+                       ops::CudnnConvTransposeOpKernel<float>);
+REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn_grad,
+                       ops::CudnnConvTransposeGradOpKernel<float>);

paddle/operators/conv_transpose_op.cc

@@ -30,11 +30,6 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
   std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
   std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
 
-  for (size_t i = 0; i < paddings.size(); ++i) {
-    PADDLE_ENFORCE_EQ(paddings[i], 0,
-                      "No Padding allowed in conv transpose op.");
-  }
-
   PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
                  "ConvTransposeOp intput should be 4-D or 5-D tensor.");
   PADDLE_ENFORCE_EQ(in_dims.size(), filter_dims.size(),
@@ -52,7 +47,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
 
   std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
   for (size_t i = 0; i < strides.size(); ++i) {
-    output_shape.push_back((in_dims[i + 2] - 1) * strides[i] +
+    output_shape.push_back((in_dims[i + 2] - 1) * strides[i] - 2 * paddings[i] +
                            filter_dims[i + 2]);
   }
   ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
@@ -190,17 +185,21 @@ REGISTER_OP(conv2d_transpose, ops::ConvTransposeOp, ops::Conv2DTransposeOpMaker,
 
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose,
-    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
     conv2d_transpose_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
 
 REGISTER_OP(conv3d_transpose, ops::ConvTransposeOp, ops::Conv3DTransposeOpMaker,
             conv3d_transpose_grad, ops::ConvTransposeOpGrad);
 
 REGISTER_OP_CPU_KERNEL(
     conv3d_transpose,
-    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
 REGISTER_OP_CPU_KERNEL(
     conv3d_transpose_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
+    ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);

paddle/operators/conv_transpose_op.cu.cc

@@ -18,14 +18,18 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_GPU_KERNEL(
     conv2d_transpose,
-    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
     conv2d_transpose_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, double>);
 
 REGISTER_OP_GPU_KERNEL(
     conv3d_transpose,
-    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvTransposeKernel<paddle::platform::GPUPlace, double>);
 REGISTER_OP_GPU_KERNEL(
     conv3d_transpose_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>);
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, float>,
+    ops::GemmConvTransposeGradKernel<paddle::platform::GPUPlace, double>);

paddle/operators/conv_transpose_op.h

@@ -62,7 +62,6 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
     Tensor* output = context.Output<Tensor>("Output");
 
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
-    // Actually, no paddings and groups allowed in conv transpose.
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     // TODO(Zhuoyuan): Paddings can be added in future.
     // groups will alway be disabled in conv2dtranspose.
@@ -148,8 +147,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
       } else if (filter_shape_vec.size() == 3) {
         // col2vol: col_matrix -> dy
         // from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
-        col2vol(context.device_context(), col, dilations, strides,
-                std::vector<int>{0, 0, 0}, &output_batch);
+        col2vol(context.device_context(), col, dilations, strides, paddings,
+                &output_batch);
       }
     }
   }
@@ -173,7 +172,6 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
     if ((!input_grad) && (!filter_grad)) return;
 
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
-    // Actually, no paddings and groups allowed in conv transpose.
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
     const int batch_size = static_cast<int>(input->dims()[0]);

paddle/operators/cos_sim_op.h

@@ -132,7 +132,7 @@ class CosSimGradKernel : public framework::OpKernel<T> {
       // compute dy
       if (out_grad_y) {
         out_grad_y->mutable_data<T>(context.GetPlace());
-        auto dy = EigenMatrix<T>::Reshape(*out_grad_y, 1);
+        auto dy = EigenVector<T>::Flatten(*out_grad_y);
         auto grad = x / norm_prod_bcast - z_bcast * y_bcast / y_snorm_bcast;
         dy.device(place) = (dz_bcast * grad).sum(Eigen::array<int, 1>({{0}}));
       }

paddle/operators/detail/safe_ref.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
+
+namespace paddle {
+namespace operators {
+namespace detail {
+/**
+ * Get Reference From Pointer with check. The error message is printf format,
+ * and passed by `args`
+ */
+template <typename T, typename... ARGS>
+inline T &Ref(T *ptr, ARGS &&... args) {
+  PADDLE_ENFORCE(ptr != nullptr, args...);
+  return *ptr;
+}
+}  // namespace detail
+}  // namespace operators
+}  // namespace paddle

paddle/operators/fill_constant_batch_size_like_op.cc

@@ -101,4 +101,7 @@ REGISTER_OPERATOR(fill_constant_batch_size_like,
 REGISTER_OP_CPU_KERNEL(
     fill_constant_batch_size_like,
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, float>,
-    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, double>);
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, double>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace, int>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUPlace,
+                                           int64_t>);

paddle/operators/fill_constant_batch_size_like_op.cu.cc

@@ -19,4 +19,7 @@ namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
     fill_constant_batch_size_like,
     ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, float>,
-    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, double>);
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, double>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace, int>,
+    ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::GPUPlace,
+                                           int64_t>);

paddle/operators/fill_zeros_like_op.cc

@@ -54,5 +54,8 @@ namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, ops::FillZerosLikeOp,
                              ops::FillZerosLikeOpMaker);
 REGISTER_OP_CPU_KERNEL(
-    fill_zeros_like,
-    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>);
+    fill_zeros_like, ops::FillZerosLikeKernel<paddle::platform::CPUPlace, int>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, int64_t>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, float>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, double>,
+    ops::FillZerosLikeKernel<paddle::platform::CPUPlace, bool>);

paddle/operators/fill_zeros_like_op.cu.cc

@@ -17,5 +17,8 @@
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
-    fill_zeros_like,
-    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);
+    fill_zeros_like, ops::FillZerosLikeKernel<paddle::platform::GPUPlace, int>,
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, int64_t>,
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, float>,
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, double>,
+    ops::FillZerosLikeKernel<paddle::platform::GPUPlace, bool>);

paddle/operators/gru_op.h

@@ -24,8 +24,17 @@
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
+using Tensor = framework::Tensor;
+
+template <typename Place, typename T>
+inline void ReorderInitState(const platform::DeviceContext& ctx,
+                             const framework::Tensor& src, const size_t* index,
+                             framework::Tensor* dst, bool indexed_src) {
+  math::CopyMatrixRowsFunctor<Place, T> row_shuffle;
+  dst->mutable_data<T>(src.dims(), ctx.GetPlace());
+  row_shuffle(ctx, src, index, *dst, indexed_src);
+}
 
 template <typename Place, typename T>
 class GRUKernel : public framework::OpKernel<T> {
@@ -33,7 +42,6 @@ class GRUKernel : public framework::OpKernel<T> {
   void BatchCompute(const framework::ExecutionContext& context) const {
     auto* input = context.Input<LoDTensor>("Input");
     auto* h0 = context.Input<Tensor>("H0");
-    const T* h0_data = h0 ? h0->data<T>() : nullptr;
     auto* weight = context.Input<Tensor>("Weight");
     const T* weight_data = weight->data<T>();
     auto* bias = context.Input<Tensor>("Bias");
@@ -66,7 +74,18 @@ class GRUKernel : public framework::OpKernel<T> {
     gru_value.gateWeight = const_cast<T*>(weight_data);
     gru_value.stateWeight =
         const_cast<T*>(weight_data + 2 * frame_size * frame_size);
-    gru_value.prevOutValue = const_cast<T*>(h0_data);
+    Tensor ordered_h0;
+    const size_t* order = batch_gate->lod()[2].data();
+    if (h0) {
+      // Since the batch computing for GRU reorders the input sequences
+      // according to their length. The initialized cell state also needs
+      // to reorder.
+      ReorderInitState<Place, T>(context.device_context(), *h0, order,
+                                 &ordered_h0, true);
+      gru_value.prevOutValue = ordered_h0.data<T>();
+    } else {
+      gru_value.prevOutValue = nullptr;
+    }
     auto batch_starts = batch_gate->lod()[0];
     size_t num_batch = batch_starts.size() - 1;
     for (size_t n = 0; n < num_batch; n++) {
@@ -102,7 +121,6 @@ class GRUGradKernel : public framework::OpKernel<T> {
  public:
   void BatchCompute(const framework::ExecutionContext& context) const {
     auto* h0 = context.Input<Tensor>("H0");
-    const T* h0_data = h0 ? h0->data<T>() : nullptr;
     auto* weight = context.Input<Tensor>("Weight");
     const T* weight_data = weight->data<T>();
     auto* batch_gate = context.Input<LoDTensor>("BatchGate");
@@ -135,6 +153,17 @@ class GRUGradKernel : public framework::OpKernel<T> {
     zero(dev_ctx, &batch_gate_grad, static_cast<T>(0.0));
     zero(dev_ctx, &batch_reset_hidden_prev_grad, static_cast<T>(0.0));
 
+    Tensor ordered_h0, ordered_h0_grad;
+    const size_t* order = batch_gate->lod()[2].data();
+    if (h0) {
+      ReorderInitState<Place, T>(context.device_context(), *h0, order,
+                                 &ordered_h0, true);
+    }
+    if (h0_grad) {
+      ordered_h0_grad.mutable_data<T>(h0_grad->dims(), context.GetPlace());
+      zero(context.device_context(), &ordered_h0_grad, static_cast<T>(0.0));
+    }
+
     bool is_reverse = context.Attr<bool>("is_reverse");
     batch_hidden_grad.set_lod(batch_hidden->lod());
     to_batch(dev_ctx, *hidden_grad, batch_hidden_grad, false, is_reverse);
@@ -176,14 +205,9 @@ class GRUGradKernel : public framework::OpKernel<T> {
           batch_reset_hidden_prev_grad.Slice(bstart, bend);
       gru_grad.resetOutputGrad = reset_hidden_prev_grad_t.data<T>();
       if (n == 0) {
-        gru_value.prevOutValue = const_cast<T*>(h0_data);
-        if (h0_grad) {
-          T* h0_grad_data = h0_grad->mutable_data<T>(context.GetPlace());
-          zero(dev_ctx, h0_grad, static_cast<T>(0.0));
-          gru_grad.prevOutGrad = h0_grad_data;
-        } else {
-          gru_grad.prevOutGrad = nullptr;
-        }
+        gru_value.prevOutValue = h0 ? ordered_h0.data<T>() : nullptr;
+        gru_grad.prevOutGrad =
+            h0 && h0_grad ? ordered_h0_grad.data<T>() : nullptr;
       } else {
         int bstart_pre = static_cast<int>(batch_starts[n - 1]);
         Tensor hidden_prev_t = batch_hidden->Slice(bstart_pre, bstart);
@@ -208,6 +232,10 @@ class GRUGradKernel : public framework::OpKernel<T> {
       math::ColwiseSum<Place, T> col_sum;
       col_sum(dev_ctx, batch_gate_grad, bias_grad);
     }
+    if (h0 && h0_grad) {
+      ReorderInitState<Place, T>(context.device_context(), ordered_h0_grad,
+                                 order, h0_grad, false);
+    }
   }
 
   void Compute(const framework::ExecutionContext& context) const override {

paddle/operators/is_empty_op.cc

+/* 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. */
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
+
+namespace paddle {
+namespace operators {
+
+constexpr char kInput[] = "X";
+constexpr char kOutput[] = "Out";
+
+class IsEmptyOp : public framework::OperatorBase {
+ public:
+  IsEmptyOp(const std::string &type, const framework::VariableNameMap &inputs,
+            const framework::VariableNameMap &outputs,
+            const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+  void Run(const framework::Scope &scope,
+           const platform::DeviceContext &dev_ctx) const override {
+    // get input
+    auto *var = scope.FindVar(Input(kInput));
+    PADDLE_ENFORCE_NOT_NULL(var);
+    auto &tensor = var->Get<framework::LoDTensor>();
+    // get output
+    auto *out = scope.FindVar(Output(kOutput));
+    PADDLE_ENFORCE_NOT_NULL(out);
+    auto *out_tensor = out->GetMutable<framework::LoDTensor>();
+
+    out_tensor->Resize({1});
+    out_tensor->mutable_data<bool>(platform::CPUPlace())[0] =
+        framework::product(tensor.dims()) == 0;
+  }
+};
+
+class IsEmptyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  IsEmptyOpProtoMaker(framework::OpProto *proto,
+                      framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput(kInput, "(Tensor) Tensor which is to be checked.");
+    AddOutput(kOutput, "(Tensor) a boolean Tensor that indicate empty or not.");
+    AddComment(R"DOC(
+IsEmpty Operator which checks whether a tensor is empty.
+
+It will just return product(tensor.ddims()) > 0;
+              )DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_WITHOUT_GRADIENT(is_empty, paddle::operators::IsEmptyOp,
+                             paddle::operators::IsEmptyOpProtoMaker);

paddle/operators/math/CMakeLists.txt

 add_subdirectory(detail)
 
 if(WITH_GPU)
-    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context)
+    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context framework_proto)
     nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function tensor)
     nv_library(selected_rows_functor SRCS selected_rows_functor.cc selected_rows_functor.cu DEPS selected_rows math_function)
     nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor)
@@ -15,7 +15,7 @@ if(WITH_GPU)
     nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
     nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
 else()
-    cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context)
+    cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto)
     cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
     cc_library(softmax SRCS softmax.cc DEPS device_context)
     cc_library(cross_entropy SRCS cross_entropy.cc DEPS device_context)

paddle/operators/math/im2col.cu

@@ -119,8 +119,8 @@ __global__ void col2im(int n, const T* data_col, int im_height, int im_width,
 
   if (index < n) {
     T val = 0;
-    int w = index % im_width;
-    int h = (index / im_width) % im_height;
+    int w = index % im_width + padding_width;
+    int h = (index / im_width) % im_height + padding_height;
     int c = index / (im_width * im_height);
 
     // compute the start and end of the output

paddle/operators/math/math_function.cc

@@ -250,6 +250,8 @@ void axpy<platform::CPUPlace, double>(const platform::DeviceContext& context,
 template struct SetConstant<platform::CPUPlace, float>;
 template struct SetConstant<platform::CPUPlace, double>;
 template struct SetConstant<platform::CPUPlace, int>;
+template struct SetConstant<platform::CPUPlace, int64_t>;
+template struct SetConstant<platform::CPUPlace, bool>;
 
 #define DEFINE_CPU_TRANS(RANK)                                \
   template struct Transpose<platform::CPUPlace, float, RANK>; \

paddle/operators/math/math_function.cu

@@ -256,6 +256,8 @@ void axpy<platform::GPUPlace, double>(const platform::DeviceContext& context,
 template struct SetConstant<platform::GPUPlace, float>;
 template struct SetConstant<platform::GPUPlace, double>;
 template struct SetConstant<platform::GPUPlace, int>;
+template struct SetConstant<platform::GPUPlace, int64_t>;
+template struct SetConstant<platform::GPUPlace, bool>;
 
 #define DEFINE_GPU_TRANS(RANK)                                \
   template struct Transpose<platform::GPUPlace, float, RANK>; \

paddle/operators/pool_cudnn_op.cu.cc

@@ -147,8 +147,7 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
 
     if (input_grad) {
       T *input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
-      math::SetConstant<paddle::platform::GPUPlace, T> set_zero;
-      set_zero(ctx.device_context(), input_grad, static_cast<T>(0));
+      // Because beta is zero, it is unnecessary to reset input_grad.
 
       PADDLE_ENFORCE(platform::dynload::cudnnPoolingBackward(
           handle, cudnn_pool_desc, &alpha, cudnn_output_desc, output_data,

paddle/operators/sum_op.cc

@@ -12,6 +12,7 @@ limitations under the License. */
 #include "paddle/operators/sum_op.h"
 #include <vector>
 #include "paddle/framework/var_type_inference.h"
+#include "paddle/operators/detail/safe_ref.h"
 
 namespace paddle {
 namespace operators {
@@ -59,13 +60,16 @@ class SumOp : public framework::OperatorWithKernel {
               x_vars[0]->Get<framework::SelectedRows>().value().type()),
           ctx.device_context());
     } else if (x_vars[0]->IsType<framework::LoDTensorArray>()) {
-      auto& array = x_vars[0]->Get<framework::LoDTensorArray>();
-      for (auto& each : array) {
-        if (each.numel() != 0) {
-          return framework::OpKernelType(framework::ToDataType(each.type()),
-                                         ctx.device_context());
+      for (auto& x_var : x_vars) {
+        auto& array = x_var->Get<framework::LoDTensorArray>();
+        for (auto& each : array) {
+          if (each.numel() != 0) {
+            return framework::OpKernelType(framework::ToDataType(each.type()),
+                                           ctx.device_context());
+          }
         }
       }
+      PADDLE_THROW("Cannot find the input data type by all input data");
     }
     PADDLE_THROW("Unexpected branch. Input type is %s",
                  x_vars[0]->Type().name());
@@ -96,6 +100,11 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
     auto& inputs = op_desc.Input("X");
     auto var_type = framework::VarDesc::SELECTED_ROWS;
 
+    for (auto& name : op_desc.Input("X")) {
+      VLOG(10) << name << " "
+               << block->FindRecursiveOrCreateVar(name)->GetType();
+    }
+
     bool any_input_is_lod_tensor = std::any_of(
         inputs.begin(), inputs.end(), [block](const std::string& name) {
           return block->FindRecursiveOrCreateVar(name)->GetType() ==
@@ -103,7 +112,7 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
         });
 
     auto is_tensor_array = [block](const std::string& name) {
-      return block->FindRecursiveOrCreateVar(name)->GetType() ==
+      return detail::Ref(block->FindRecursiveOrCreateVar(name)).GetType() ==
              framework::VarDesc::LOD_TENSOR_ARRAY;
     };
 
@@ -113,14 +122,26 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
         std::all_of(inputs.begin(), inputs.end(), is_tensor_array);
 
     if (any_input_is_tensor_array) {
-      PADDLE_ENFORCE(all_inputs_are_tensor_array);
+      if (!all_inputs_are_tensor_array) {
+        std::ostringstream os;
+        for (auto& each : inputs) {
+          os << "    " << each << " type is "
+             << detail::Ref(block->FindRecursiveOrCreateVar(each)).GetType()
+             << "\n";
+        }
+        PADDLE_ENFORCE(all_inputs_are_tensor_array,
+                       "Not all inputs are tensor array:\n%s", os.str());
+      }
       var_type = framework::VarDesc::LOD_TENSOR_ARRAY;
     } else if (any_input_is_lod_tensor) {
       var_type = framework::VarDesc::LOD_TENSOR;
     }
 
     auto out_var_name = op_desc.Output("Out").front();
-    block->FindRecursiveOrCreateVar(out_var_name)->SetType(var_type);
+    auto& out_var = detail::Ref(block->FindRecursiveOrCreateVar(out_var_name));
+    out_var.SetType(var_type);
+    auto& in_var = detail::Ref(block->FindVarRecursive(inputs.front()));
+    out_var.SetDataType(in_var.GetDataType());
   }
 };
 

paddle/operators/tensor_array_read_write_op.cc

@@ -12,7 +12,7 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 #include "paddle/operators/array_operator.h"
-
+#include "paddle/operators/detail/safe_ref.h"
 namespace paddle {
 namespace operators {
 
@@ -33,6 +33,8 @@ class WriteToArrayOp : public ArrayOp {
     auto *out =
         scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensorArray>();
     if (offset >= out->size()) {
+      VLOG(10) << "Resize " << Output("Out") << " from " << out->size()
+               << " to " << offset + 1;
       out->resize(offset + 1);
     }
     auto *out_tensor = &out->at(offset);
@@ -85,11 +87,15 @@ class WriteToArrayInferVarType : public framework::VarTypeInference {
  public:
   void operator()(const framework::OpDescBind &op_desc,
                   framework::BlockDescBind *block) const override {
-    for (auto &out_var : op_desc.OutputArgumentNames()) {
-      VLOG(10) << "Set Variable " << out_var << " as LOD_TENSOR_ARRAY";
-      block->FindRecursiveOrCreateVar(out_var)->SetType(
-          framework::VarDesc::LOD_TENSOR_ARRAY);
-    }
+    auto x_name = op_desc.Input("X")[0];
+    auto out_name = op_desc.Output("Out")[0];
+    VLOG(10) << "Set Variable " << out_name << " as LOD_TENSOR_ARRAY";
+    auto &out = detail::Ref(block->FindRecursiveOrCreateVar(out_name),
+                            "Cannot found %s", out_name);
+    out.SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
+    auto &x =
+        detail::Ref(block->FindVarRecursive(x_name), "Cannot found %s", x_name);
+    out.SetDataType(x.GetDataType());
   }
 };
 
@@ -107,11 +113,11 @@ class ReadFromArrayOp : public ArrayOp {
     auto &x_array = x->Get<framework::LoDTensorArray>();
     auto *out = scope.FindVar(Output("Out"));
     PADDLE_ENFORCE(out != nullptr, "Out must be set");
-    auto *out_tesnor = out->GetMutable<framework::LoDTensor>();
+    auto *out_tensor = out->GetMutable<framework::LoDTensor>();
     size_t offset = GetOffset(scope, dev_ctx);
     PADDLE_ENFORCE_LT(offset, x_array.size());
-    out_tesnor->CopyFrom(x_array[offset], dev_ctx.GetPlace(), dev_ctx);
-    out_tesnor->set_lod(x_array[offset].lod());
+    out_tensor->CopyFrom(x_array[offset], dev_ctx.GetPlace(), dev_ctx);
+    out_tensor->set_lod(x_array[offset].lod());
   }
 };
 

paddle/operators/while_op.cc

@@ -14,8 +14,10 @@
 
 #include <vector>
 #include "paddle/framework/executor.h"
+#include "paddle/framework/lod_tensor_array.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
+#include "paddle/operators/detail/safe_ref.h"
 
 namespace paddle {
 namespace operators {
@@ -26,8 +28,9 @@ using LoDTensor = framework::LoDTensor;
 constexpr char kStepBlock[] = "step_block";
 constexpr char kCondition[] = "Condition";
 constexpr char kStepScopes[] = "StepScopes";
-constexpr char kParamGrads[] = "X@Grad";
 constexpr char kParameters[] = "X";
+constexpr char kParamGrads[] = "X@GRAD";
+constexpr char kOutputs[] = "Out";
 
 class WhileOp : public framework::OperatorBase {
  public:
@@ -71,9 +74,9 @@ class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
         kCondition,
         "(Bool) An scalar. When it's False, the While Op will be terminated.")
         .AsDuplicable();
-    AddOutput("Out",
+    AddOutput(kOutputs,
               "A set of variables, which will be assigned with values "
-              "generated by perators inside the block of While Op.")
+              "generated by the operators inside the block of While Op.")
         .AsDuplicable();
     AddOutput(kStepScopes,
               "(StepScopeVar) A vector of local scope, which size equals the "
@@ -104,17 +107,64 @@ class WhileGradOp : public framework::OperatorBase {
     auto *step_scopes =
         scope.FindVar(Input(kStepScopes))->GetMutable<StepScopeVar>();
 
+    auto outside_og_names = Inputs(framework::GradVarName(kOutputs));
+    auto inside_og_names =
+        Attr<std::vector<std::string>>("original_output_grad");
+
+    PADDLE_ENFORCE_EQ(outside_og_names.size(), inside_og_names.size());
+
     for (auto cur_scope_iter = step_scopes->rbegin();
          cur_scope_iter != step_scopes->rend(); ++cur_scope_iter) {
+      VLOG(3) << "Start backward at time_step "
+              << cur_scope_iter - step_scopes->rbegin();
+      framework::Scope &cur_scope = **cur_scope_iter;
+      // Link OG from outside to inside
+      for (size_t i = 0; i < outside_og_names.size(); ++i) {
+        auto outside_og_name = outside_og_names[i];
+        auto inside_og_name = inside_og_names[i];
+        VLOG(10) << "Linking outside " << outside_og_name << " --> inside "
+                 << inside_og_name;
+        auto &og_outside = detail::Ref(scope.FindVar(outside_og_name));
+        auto &og_inside = detail::Ref(cur_scope.Var(inside_og_name));
+        if (og_outside.Type().hash_code() ==
+            typeid(framework::LoDTensor).hash_code()) {
+          auto &outside_tensor = og_outside.Get<framework::LoDTensor>();
+          auto &inside_tensor =
+              detail::Ref(og_inside.GetMutable<framework::LoDTensor>());
+          inside_tensor.set_lod(outside_tensor.lod());
+          inside_tensor.ShareDataWith(outside_tensor);
+        } else if (og_outside.Type().hash_code() ==
+                   typeid(framework::LoDTensorArray).hash_code()) {
+          auto &outside_array = og_outside.Get<framework::LoDTensorArray>();
+          auto &inside_array =
+              detail::Ref(og_inside.GetMutable<framework::LoDTensorArray>());
+          VLOG(10) << outside_og_name << " size = " << outside_array.size();
+          inside_array.resize(outside_array.size());
+
+          for (size_t j = 0; j < inside_array.size(); ++j) {
+            VLOG(10) << j << " " << outside_array[j].numel();
+            if (outside_array[j].numel() != 0) {
+              inside_array[j].set_lod(outside_array[j].lod());
+              inside_array[j].ShareDataWith(outside_array[j]);
+            } else {
+              PADDLE_ENFORCE_EQ(inside_array[j].numel(), 0);
+            }
+          }
+        }
+      }
+
       executor.Run(*program, *cur_scope_iter, block->ID(), false);
 
       auto &pg_names = Outputs(kParamGrads);
       auto &p_names = Inputs(kParameters);
       PADDLE_ENFORCE_EQ(pg_names.size(), p_names.size());
-      for (size_t prog_id = 0; prog_id < pg_names.size(); ++prog_id) {
-        auto inside_grad_name = framework::GradVarName(p_names[prog_id]);
+      for (size_t param_id = 0; param_id < pg_names.size(); ++param_id) {
+        if (pg_names[param_id] == framework::kEmptyVarName) {
+          continue;  // iterator doesn't have gradient
+        }
+        auto inside_grad_name = framework::GradVarName(p_names[param_id]);
 
-        //  // TODO(tonyyang-savil: Not sure we need the following
+        //  // TODO(tonyyang-svail): Not sure we need the following
         //  // If does not compute gradient of that variable inside rnn,
         //  just
         //  // continue
@@ -126,7 +176,7 @@ class WhileGradOp : public framework::OperatorBase {
         // zero gradient variable in step 0
         if (cur_scope_iter == step_scopes->rbegin()) {
           auto *var = (*cur_scope_iter)->FindVar(inside_grad_name);
-          PADDLE_ENFORCE_NOT_NULL(var);
+          PADDLE_ENFORCE_NOT_NULL(var, "Can not find var %s", inside_grad_name);
           if (var->IsType<LoDTensor>()) {
             auto &inside_tensor = var->Get<framework::LoDTensor>();
             framework::AttributeMap attrs;
@@ -135,27 +185,18 @@ class WhileGradOp : public framework::OperatorBase {
             attrs["value"] = 0.0f;
 
             auto zero_op = framework::OpRegistry::CreateOp(
-                "fill_constant", {}, {{"Out", {pg_names[prog_id]}}}, attrs);
+                "fill_constant", {}, {{"Out", {pg_names[param_id]}}}, attrs);
             zero_op->Run(scope, dev_ctx);
           }
         }
 
         // sum gradient
-        auto *outside_var = scope.FindVar(pg_names[prog_id]);
-        PADDLE_ENFORCE_NOT_NULL(outside_var);
-        auto &outside_tensor = *outside_var->GetMutable<framework::LoDTensor>();
-
-        std::string result_var_name;
-        auto *local_result_var = (*cur_scope_iter)->Var(&result_var_name);
-        auto &local_result_tensor =
-            *local_result_var->GetMutable<framework::LoDTensor>();
-
-        local_result_tensor.ShareDataWith(outside_tensor);
-
+        auto new_inside_name = cur_scope.Rename(inside_grad_name);
         auto sum_op = framework::OpRegistry::CreateOp(
-            "sum", {{"X", {result_var_name, inside_grad_name}}},
-            {{"Out", {result_var_name}}}, {});
-        sum_op->Run(**cur_scope_iter, dev_ctx);
+            "sum", {{"X", {pg_names[param_id], new_inside_name}}},
+            {{"Out", {pg_names[param_id]}}}, {});
+        sum_op->Run(cur_scope, dev_ctx);
+        cur_scope.Rename(new_inside_name, inside_grad_name);
       }
     }
   }
@@ -169,29 +210,110 @@ class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
   virtual std::unique_ptr<framework::OpDescBind> Apply() const {
     auto *grad = new framework::OpDescBind();
     grad->SetType("while_grad");
-    for (auto &input_param : this->InputNames()) {
-      grad->SetInput(input_param, this->Input(input_param));
-      grad->SetOutput(framework::GradVarName(input_param),
-                      this->InputGrad(input_param));
+    grad->SetInput(kParameters, Input(kParameters));
+    grad->SetOutput(
+        framework::GradVarName(kParameters),
+        InputGrad(kParameters, /*do not drop empty gradient*/ false));
+    grad->SetInput(kOutputs, Output(kOutputs));
+
+    // OG should be re-calculated by step blocks, since many outputs of while op
+    // do not need to calculate gradients.
+    std::unordered_set<std::string> block_ins;
+    {
+      for (auto &p : Input(kParameters)) {
+        block_ins.insert(p);
+      }
+      for (auto &o : Output(kOutputs)) {
+        block_ins.insert(o);
+      }
     }
+    std::unordered_set<std::string> extra_inputs;
+    for (size_t i = 0; i < grad_block_[0]->OpSize(); ++i) {
+      for (auto &input_name : grad_block_[0]->Op(i)->InputArgumentNames()) {
+        if (block_ins.find(input_name) != block_ins.end()) {
+          continue;
+        }
+        extra_inputs.insert(input_name);
+      }
 
-    for (auto &output_param : this->OutputNames()) {
-      grad->SetInput(output_param, this->Output(output_param));
-      if (output_param != kStepScopes) {
-        grad->SetInput(framework::GradVarName(output_param),
-                       this->OutputGrad(output_param));
+      for (auto &output_name : grad_block_[0]->Op(i)->OutputArgumentNames()) {
+        block_ins.insert(output_name);
       }
     }
+
+    std::vector<std::string> extra_inputs_list;
+    extra_inputs_list.resize(extra_inputs.size());
+    std::copy(extra_inputs.begin(), extra_inputs.end(),
+              extra_inputs_list.begin());
+    grad->SetInput(framework::GradVarName(kOutputs), extra_inputs_list);
+    grad->SetInput(kStepScopes, Output(kStepScopes));
     grad->SetAttrMap(this->Attrs());
     grad->SetBlockAttr(kStepBlock, *grad_block_[0]);
+    // record the original output gradient names, since the gradient name of
+    // while operator could be renamed.
+    grad->SetAttr("original_output_grad", extra_inputs_list);
 
     return std::unique_ptr<framework::OpDescBind>(grad);
   }
 };
 
+class WhileGradOpVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDescBind &op_desc,
+                  framework::BlockDescBind *block) const override {
+    auto p_names = op_desc.Input(kParameters);
+    auto pg_names = op_desc.Output(framework::GradVarName(kParameters));
+
+    for (size_t i = 0; i < p_names.size(); ++i) {
+      auto &p_var = detail::Ref(block->FindVarRecursive(p_names[i]));
+      auto *g_var = block->FindVarRecursive(pg_names[i]);
+      if (g_var != nullptr) {  // Gradient could be @EMPTY@
+        VLOG(5) << "Setting " << pg_names[i] << " following " << p_names[i]
+                << " type: " << p_var.GetType();
+        g_var->SetType(p_var.GetType());
+        g_var->SetDataType(p_var.GetDataType());
+      }
+    }
+  }
+};
+
+class WhileGradOpShapeInference : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    ctx->HasInputs(kParameters);
+    ctx->HasOutputs(framework::GradVarName(kParameters));
+    ctx->HasInputs(kOutputs);
+    ctx->HasInputs(framework::GradVarName(kOutputs));
+
+    auto p_names = ctx->Inputs(kParameters);
+    auto pg_names = ctx->Outputs(kParamGrads);
+    auto dims = ctx->GetInputsDim(kParameters);
+    auto var_types = ctx->GetInputsVarType(kParameters);
+    std::vector<std::string> names_to_set;
+    std::vector<framework::DDim> dims_to_set;
+    for (size_t i = 0; i < p_names.size(); ++i) {
+      if (pg_names[i] == framework::kEmptyVarName) {
+        continue;
+      }
+      if (var_types[i] == framework::VarDesc::LOD_TENSOR) {
+        names_to_set.push_back(pg_names[i]);
+        dims_to_set.push_back(dims[i]);
+      } else if (var_types[i] == framework::VarDesc::LOD_TENSOR_ARRAY) {
+        // not sure how to set the dim of LOD_TENSOR_ARRAY
+        names_to_set.push_back(pg_names[i]);
+        dims_to_set.push_back(dims[i]);
+      }
+    }
+    ctx->SetDims(names_to_set, dims_to_set);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 REGISTER_OPERATOR(while, paddle::operators::WhileOp,
                   paddle::operators::WhileOpMaker,
                   paddle::operators::WhileGradOpDescMaker);
+REGISTER_OPERATOR(while_grad, paddle::operators::WhileGradOp,
+                  paddle::operators::WhileGradOpShapeInference,
+                  paddle::operators::WhileGradOpVarTypeInference);

paddle/parameter/ParameterUpdateFunctions.cpp

@@ -30,7 +30,7 @@ void sgdUpdateCpu(real learningRate,
                   const real* grad,
                   real* momentumVec) {
   decayRate *= learningRate;
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_USE_MKLML
 #pragma omp parallel for
 #endif
   for (size_t i = 0; i < size; ++i) {

paddle/platform/cudnn_helper.h

@@ -180,9 +180,10 @@ class ScopedFilterDescriptor {
                                             const cudnnDataType_t type,
                                             const std::vector<int>& kernel,
                                             const int groups = 1) {
-    // filter layout: MCHW, where M is the number of
+    // filter layout: MCHW(MCDHW), where M is the number of
     // output image channels, C is the number of input image channels,
-    // H and W is height and width of filter.
+    // D is the depth of the filter, H is the height of the filter, and W is the
+    // width of the filter.
     std::vector<int> kernel_with_group(kernel.begin(), kernel.end());
     if (groups > 1) {
       // M /= groups

paddle/scripts/docker/README.md

@@ -57,8 +57,7 @@ Users can specify the following Docker build arguments with either "ON" or "OFF"
 | `WITH_GPU` | OFF | Generates NVIDIA CUDA GPU code and relies on CUDA libraries. |
 | `WITH_AVX` | OFF | Set to "ON" to enable AVX support. |
 | `WITH_TESTING` | ON | Build unit tests binaries. |
-| `WITH_MKLDNN` | ON | Build with [Intel® MKL DNN](https://github.com/01org/mkl-dnn) support. |
-| `WITH_MKLML` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) support. |
+| `WITH_MKL` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) and [Intel® MKL-DNN](https://github.com/01org/mkl-dnn) support. |
 | `WITH_GOLANG` | ON | Build fault-tolerant parameter server written in go. |
 | `WITH_SWIG_PY` | ON | Build with SWIG python API support. |
 | `WITH_C_API` | OFF | Build capi libraries for inference. |

paddle/scripts/docker/build.sh

@@ -34,8 +34,7 @@ function cmake_gen() {
         ${PYTHON_FLAGS}
         -DWITH_DOC=OFF
         -DWITH_GPU=${WITH_GPU:-OFF}
-        -DWITH_MKLDNN=${WITH_MKLDNN:-ON}
-        -DWITH_MKLML=${WITH_MKLML:-ON}
+        -DWITH_MKL=${WITH_MKL:-ON}
         -DWITH_AVX=${WITH_AVX:-OFF}
         -DWITH_GOLANG=${WITH_GOLANG:-ON}
         -DWITH_SWIG_PY=ON
@@ -56,8 +55,7 @@ EOF
         ${PYTHON_FLAGS} \
         -DWITH_DOC=OFF \
         -DWITH_GPU=${WITH_GPU:-OFF} \
-        -DWITH_MKLDNN=${WITH_MKLDNN:-ON} \
-        -DWITH_MKLML=${WITH_MKLML:-ON} \
+        -DWITH_MKL=${WITH_MKL:-ON} \
         -DWITH_AVX=${WITH_AVX:-OFF} \
         -DWITH_GOLANG=${WITH_GOLANG:-ON} \
         -DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \

paddle/scripts/submit_local.sh.in

@@ -18,8 +18,8 @@ function version(){
         echo "PaddlePaddle @PADDLE_VERSION@, compiled with"
         echo "    with_avx: @WITH_AVX@"
         echo "    with_gpu: @WITH_GPU@"
+        echo "    with_mkl: @WITH_MKL@"
         echo "    with_mkldnn: @WITH_MKLDNN@"
-        echo "    with_mklml: @WITH_MKLML@"
         echo "    with_double: @WITH_DOUBLE@"
         echo "    with_python: @WITH_PYTHON@"
         echo "    with_rdma: @WITH_RDMA@"
@@ -45,8 +45,8 @@ function ver2num() {
 
 function cpu_config() {
   # auto set KMP_AFFINITY and OMP_DYNAMIC from Hyper Threading Status
-  # only when MKLDNN or MKLML enabled
-  if [ "@WITH_MKLDNN@" == "OFF" ] && [ "@WITH_MKLML@" == "OFF"]; then
+  # only when MKL enabled
+  if [ "@WITH_MKL@" == "OFF" ]; then
     return 0
   fi
   ht=`lscpu |grep "per core"|awk -F':' '{print $2}'|xargs`
@@ -70,8 +70,8 @@ function cpu_config() {
 function threads_config() {
   # auto set OMP_NUM_THREADS and MKL_NUM_THREADS
   # according to trainer_count and total processors
-  # only when MKLDNN or MKLML enabled
-  if [ "@WITH_MKLDNN@" == "OFF" ] && [ "@WITH_MKLML@" == "OFF"]; then
+  # only when MKL enabled
+  if [ "@WITH_MKL@" == "OFF" ]; then
     return 0
   fi
   processors=`grep "processor" /proc/cpuinfo|sort -u|wc -l`

paddle/scripts/travis/build_doc.sh

@@ -6,7 +6,7 @@ mkdir -p $TRAVIS_BUILD_DIR/build
 cd $TRAVIS_BUILD_DIR/build
 
 # Compile Documentation only.
-cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
+cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
 make -j `nproc` gen_proto_py
 make -j `nproc` paddle_docs paddle_docs_cn
 

paddle/trainer/Trainer.cpp

@@ -137,6 +137,10 @@ void Trainer::init(const std::shared_ptr<TrainerConfigHelper>& config,
     }
   }
 
+  if (FLAGS_use_mkldnn) {
+    CHECK_EQ(FLAGS_trainer_count, 1UL) << "MKLDNN only need 1 trainer";
+  }
+
   if (testing) {
     LOG(INFO) << "trainer: in testing mode";
     if (config_->getOptConfig().use_sparse_remote_updater() ||

paddle/trainer/tests/CMakeLists.txt

@@ -28,35 +28,7 @@ if(WITH_PYTHON)
           ${PADDLE_SOURCE_DIR}/paddle/.set_port.sh -p port ${CMAKE_CURRENT_BINARY_DIR}/test_TrainerOnePass
       WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 endif()
-################ test_CompareTwoNets ######################
-add_unittest_without_exec(test_CompareTwoNets
-    test_CompareTwoNets.cpp)
-add_test(NAME test_CompareTwoNets
-  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
-            --config_file_a=trainer/tests/sample_trainer_config_qb_rnn.conf --config_file_b=trainer/tests/sample_trainer_config_rnn.conf
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
 
-############### test_CompareTwoOpts ###################
-add_unittest_without_exec(test_CompareTwoOpts
-    test_CompareTwoOpts.cpp)
-add_test(NAME test_CompareTwoOpts
-  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoOpts
-            --config_file_a=trainer/tests/sample_trainer_config_opt_a.conf --config_file_b=trainer/tests/sample_trainer_config_opt_b.conf
-            --num_passes=1 --need_high_accuracy=0
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
-
-################# test_CompareSparse ##################
-add_unittest_without_exec(test_CompareSparse
-    test_CompareSparse.cpp)
-if(NOT ON_TRAVIS)
-  add_test(NAME test_CompareSparse
-    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
-              ./.set_port.sh -p port -n 6
-                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
-    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
-endif()
 ################# test_recurrent_machine_generation ###############
 add_unittest_without_exec(test_recurrent_machine_generation
     test_recurrent_machine_generation.cpp)

paddle/trainer/tests/mnist.list

-trainer/tests/mnist_bin_part

paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.protolist

-./trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.proto_data

paddle/trainer/tests/sample_trainer_config_compare_sparse.conf

-#edit-mode: -*- python -*-
-# 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.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 999
-l1 = 0
-l2 = 0
-
-model_type("nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(        
-            type = "proto_sequence",
-            files = ('trainer/tests/train_sparse.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 32
-layer2_dim = 16
-layer3_dim = 16
-hidden_dim = 32
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        Layer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            type = "recurrent",
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            inputs = Input(slot_names[i] + "_embedding_" + network_name,
-                           parameter_name = "rnn1.w0")
-        )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )

paddle/trainer/tests/sample_trainer_config_qb_rnn.conf

-#edit-mode: -*- python -*-
-# 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.
-
-#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
-
-# Note: when making change to this file, please make sure
-# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
-# for comparing these two nets can pass (test_CompareTwoNets)
-
-default_initial_std(0.1)
-default_device(0)
-
-word_dim = 1451594
-l1 = 0
-l2 = 0
-
-model_type("nn")
-
-sparse_update = get_config_arg("sparse_update", bool, False)
-
-TrainData(ProtoData(        
-            type = "proto_sequence",
-            files = ('trainer/tests/train.list'), 
-            ))
-
-Settings(
-    algorithm='sgd',
-    batch_size=100,
-    learning_rate=0.0001,
-    learning_rate_decay_a=4e-08,
-    learning_rate_decay_b=0.0,
-    learning_rate_schedule='poly',
-)
-
-
-wordvec_dim = 128
-layer2_dim = 96
-layer3_dim = 96
-hidden_dim = 128
-
-slot_names = ["qb", "qw", "tb", "tw"]
-
-def ltr_network(network_name,
-                word_dim=word_dim,
-                wordvec_dim=wordvec_dim,
-                layer2_dim=layer2_dim,
-                layer3_dim=layer3_dim,
-                hidden_dim=hidden_dim,
-                slot_names=slot_names,
-                l1=l1,
-                l2=l2):
-
-    slotnum = len(slot_names)
-    for i in xrange(slotnum):
-        Inputs(slot_names[i] + network_name)
-    for i in xrange(slotnum):
-        Layer(
-            name = slot_names[i] + network_name,
-            type = "data",
-            size = word_dim,
-            device = -1,
-        )
-        Layer(
-            name = slot_names[i] + "_embedding_" + network_name,
-            type = "mixed",
-            size = wordvec_dim,
-            bias = False,
-            device = -1,
-            inputs = TableProjection(slot_names[i] + network_name,
-                                     parameter_name = "embedding.w0",
-                                     decay_rate_l1=l1,
-                                     sparse_remote_update = True,
-                                     sparse_update = sparse_update,
-                                     ),
-        )
-        Layer(
-            name = slot_names[i] + "_rnn1_" + network_name,
-            type = "recurrent",
-            active_type = "tanh",
-            bias = Bias(initial_std = 0,
-                        parameter_name = "rnn1.bias"),
-            inputs = Input(slot_names[i] + "_embedding_" + network_name,
-                           parameter_name = "rnn1.w0")
-        )
-        Layer(
-            name = slot_names[i] + "_rnnlast_" + network_name,
-            type = "seqlastins",
-            inputs = [
-                slot_names[i] + "_rnn1_" + network_name,
-            ],
-        )
-
-    Layer(
-        name = "layer2_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer2_dim,
-        bias = Bias(parameter_name = "layer2.bias"),
-        inputs = [Input(slot_name + "_rnnlast_" + network_name, 
-                        parameter_name = "_layer2_" + slot_name + ".w", 
-                        decay_rate = l2, 
-                        initial_smart = True) for slot_name in slot_names]
-    )
-    Layer(
-        name = "layer3_" + network_name,
-        type = "fc",
-        active_type = "tanh",
-        size = layer3_dim,
-        bias = Bias(parameter_name = "layer3.bias"),
-        inputs = [
-            Input("layer2_" + network_name, 
-                  parameter_name = "_layer3.w", 
-                  decay_rate = l2, 
-                  initial_smart = True),
-        ]
-    )
-    Layer(
-        name = "output_" + network_name,
-        type = "fc",
-        size = 1,
-        bias = False,
-        inputs = [
-                  Input("layer3_" + network_name,
-                       parameter_name = "_layerO.w"),
-                 ],
-        )
-
-
-ltr_network("left")
-ltr_network("right")
-Inputs("label")
-Layer(
-    name = "label",
-    type = "data",
-    size = 1,
-    )
-Outputs("cost", "qb_rnnlast_left")
-Layer(
-    name = "cost",
-    type = "rank-cost",
-    inputs = ["output_left", "output_right", "label"],
-    )

python/paddle/trainer_config_helpers/tests/configs/file_list.sh

@@ -10,6 +10,7 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la
 test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
 test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
 test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer
-test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer)
+test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer
+test_dot_prod_layer)
 
 export whole_configs=(test_split_datasource)

python/paddle/trainer_config_helpers/tests/configs/test_dot_prod_layer.py

+from paddle.trainer_config_helpers import *
+
+vec1 = data_layer(name='vector1', size=10)
+vec2 = data_layer(name='vector2', size=10)
+dot_product = dot_prod_layer(input1=vec1, input2=vec2)
+
+outputs(dot_product)