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

test=develop

CMakeLists.txt

@@ -55,6 +55,7 @@ option(WITH_DOUBLE      "Compile PaddlePaddle with double precision"    OFF)
 option(WITH_RDMA        "Compile PaddlePaddle with RDMA support"        OFF)
 option(WITH_TIMER       "Compile PaddlePaddle with stats timer"         OFF)
 option(WITH_PROFILER    "Compile PaddlePaddle with GPU profiler and gperftools"        OFF)
+option(WITH_JEMALLOC    "Compile PaddlePaddle with jemalloc"            OFF)
 option(WITH_DOC         "Compile PaddlePaddle with documentation"       OFF)
 option(WITH_COVERAGE    "Compile PaddlePaddle with code coverage"       OFF)
 option(COVERALLS_UPLOAD "Package code coverage data to coveralls"       OFF)
@@ -261,6 +262,12 @@ if (WITH_PROFILER)
     add_definitions(-DWITH_GPERFTOOLS)
 endif()
 
+if (WITH_JEMALLOC)
+    find_package(JeMalloc REQUIRED)
+    include_directories(${JEMALLOC_INCLUDE_DIR})
+    add_definitions(-DWITH_JEMALLOC)
+endif()
+
 include(generic)            # simplify cmake module
 include(package)            # set paddle packages
 include(ccache)             # set ccache for compilation
@@ -290,7 +297,7 @@ if(WITH_PSLIB)
     list(APPEND EXTERNAL_LIBS pslib_brpc)
     list(APPEND EXTERNAL_LIBS libmct)
 endif(WITH_PSLIB)
-    
+
 if(WITH_AMD_GPU)
     find_package(HIP)
     include(hip)

cmake/FindJeMalloc.cmake

+# - Find JeMalloc library
+# Find the native JeMalloc includes and library
+#
+# JEMALLOC_INCLUDE_DIR - where to find jemalloc.h, etc.
+# JEMALLOC_LIBRARIES - List of libraries when using jemalloc.
+# JEMALLOC_FOUND - True if jemalloc found.
+
+find_path(JEMALLOC_INCLUDE_DIR
+  NAMES jemalloc/jemalloc.h
+  HINTS ${JEMALLOC_ROOT_DIR}/include)
+
+find_library(JEMALLOC_LIBRARIES
+  NAMES jemalloc
+  HINTS ${JEMALLOC_ROOT_DIR}/lib)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(jemalloc DEFAULT_MSG JEMALLOC_LIBRARIES JEMALLOC_INCLUDE_DIR)
+
+mark_as_advanced(
+  JEMALLOC_LIBRARIES
+  JEMALLOC_INCLUDE_DIR)
+
+if (JEMALLOC_FOUND)
+  add_library(jemalloc::jemalloc UNKNOWN IMPORTED)
+  set_target_properties(jemalloc::jemalloc PROPERTIES
+    IMPORTED_LOCATION ${JEMALLOC_LIBRARIES}
+    INTERFACE_INCLUDE_DIRECTORIES "${JEMALLOC_INCLUDE_DIR}")
+endif()

cmake/configure.cmake

@@ -134,6 +134,7 @@ if(WITH_GPU)
             message(WARNING "Anakin needs CUDNN >= 7.0 to compile. Force WITH_ANAKIN=OFF")
             set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when CUDNN >= 7.0." FORCE)
         endif()
+        add_definitions(-DWITH_ANAKIN)
     endif()
     if(WITH_ANAKIN)
         # NOTICE(minqiyang): the end slash is important because $CUDNN_INCLUDE_DIR

cmake/cuda.cmake

@@ -5,6 +5,8 @@ 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")
+set(paddle_known_gpu_archs9 "30 35 50 52 60 61 70")
+set(paddle_known_gpu_archs10 "30 35 50 52 60 61 70 75")
 
 ######################################################################################
 # A function for automatic detection of GPUs installed  (if autodetection is enabled)
@@ -59,7 +61,7 @@ endfunction()
 #   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_names "Kepler" "Maxwell" "Pascal" "Volta" "Turing" "All" "Manual")
   set(archs_name_default "All")
   if(NOT CMAKE_CROSSCOMPILING)
     list(APPEND archs_names "Auto")
@@ -93,6 +95,8 @@ function(select_nvcc_arch_flags out_variable)
     set(cuda_arch_bin "60 61")
   elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
     set(cuda_arch_bin "70")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Turing")
+    set(cuda_arch_bin "75")
   elseif(${CUDA_ARCH_NAME} STREQUAL "All")
     set(cuda_arch_bin ${paddle_known_gpu_archs})
   elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
@@ -153,6 +157,16 @@ elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
   # warning for now.
   list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
   add_definitions("-DPADDLE_CUDA_BINVER=\"80\"")
+elseif (${CUDA_VERSION} LESS 10.0) # CUDA 9.x
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs9})
+  list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
+  list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
+  add_definitions("-DPADDLE_CUDA_BINVER=\"90\"")
+elseif (${CUDA_VERSION} LESS 11.0) # CUDA 10.x
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs10})
+  list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
+  list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
+  add_definitions("-DPADDLE_CUDA_BINVER=\"100\"")
 endif()
 
 include_directories(${CUDA_INCLUDE_DIRS})

cmake/external/boost.cmake

@@ -23,11 +23,8 @@ set(BOOST_PROJECT       "extern_boost")
 # checked that the devtools package of CentOS 6 installs boost 1.41.0.
 # So we use 1.41.0 here.
 set(BOOST_VER           "1.41.0")
-if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
-    message(STATUS "use pre defined download url")
-    set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
-    set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
-endif()
+set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
+set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
 
 MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
 

cmake/external/mkldnn.cmake

@@ -55,7 +55,7 @@ ExternalProject_Add(
     ${MKLDNN_PROJECT}
     ${EXTERNAL_PROJECT_LOG_ARGS}
     DEPENDS             ${MKLDNN_DEPENDS}
-    GIT_REPOSITORY      "https://github.com/01org/mkl-dnn.git"
+    GIT_REPOSITORY      "https://github.com/intel/mkl-dnn.git"
     GIT_TAG             "830a10059a018cd2634d94195140cf2d8790a75a"
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""

cmake/external/mklml.cmake

@@ -16,6 +16,12 @@ IF(NOT ${WITH_MKLML})
   return()
 ENDIF(NOT ${WITH_MKLML})
 
+IF(APPLE)
+    MESSAGE(WARNING "Mac is not supported with MKLML in Paddle yet. Force WITH_MKLML=OFF.")
+    SET(WITH_MKLML OFF CACHE STRING "Disable MKLML package in MacOS" FORCE)
+    return()
+ENDIF()
+
 INCLUDE(ExternalProject)
 SET(MKLML_DST_DIR       "mklml")
 SET(MKLML_INSTALL_ROOT  "${THIRD_PARTY_PATH}/install")
@@ -23,32 +29,24 @@ SET(MKLML_INSTALL_DIR   ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
 SET(MKLML_ROOT          ${MKLML_INSTALL_DIR})
 SET(MKLML_INC_DIR       ${MKLML_ROOT}/include)
 SET(MKLML_LIB_DIR       ${MKLML_ROOT}/lib)
-if(WIN32)
+SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
+
+SET(TIME_VERSION "2019.0.1.20181227")
+IF(WIN32)
+    SET(MKLML_VER "mklml_win_${TIME_VERSION}" CACHE STRING "" FORCE)
+    SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
     SET(MKLML_LIB                 ${MKLML_LIB_DIR}/mklml.lib)
     SET(MKLML_IOMP_LIB            ${MKLML_LIB_DIR}/libiomp5md.lib)
     SET(MKLML_SHARED_LIB          ${MKLML_LIB_DIR}/mklml.dll)
     SET(MKLML_SHARED_IOMP_LIB     ${MKLML_LIB_DIR}/libiomp5md.dll)
-else()
+ELSE()  
+    SET(MKLML_VER "mklml_lnx_${TIME_VERSION}" CACHE STRING "" FORCE)
+    SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
     SET(MKLML_LIB                 ${MKLML_LIB_DIR}/libmklml_intel.so)
     SET(MKLML_IOMP_LIB            ${MKLML_LIB_DIR}/libiomp5.so)
     SET(MKLML_SHARED_LIB          ${MKLML_LIB_DIR}/libmklml_intel.so)
     SET(MKLML_SHARED_IOMP_LIB     ${MKLML_LIB_DIR}/libiomp5.so)
-endif()
-SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
-
-IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
-    MESSAGE(STATUS "use pre defined download url")
-    if(WIN32)
-        SET(MKLML_VER "mklml_win_2019.0.1.20180928" CACHE STRING "" FORCE)
-        SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
-    elseif(APPLE)
-        SET(MKLML_VER "mklml_mac_2019.0.1.20180928" CACHE STRING "" FORCE)
-        SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
-    else()
-        SET(MKLML_VER "mklml_lnx_2019.0.1.20180928" CACHE STRING "" FORCE)
-        SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
-    ENDIF()
-endif()
+ENDIF()
 
 SET(MKLML_PROJECT       "extern_mklml")
 MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")

cmake/external/ngraph.cmake

@@ -37,14 +37,18 @@ INCLUDE(GNUInstallDirs)
 INCLUDE(ExternalProject)
 
 SET(NGRAPH_PROJECT         "extern_ngraph")
-SET(NGRAPH_GIT_TAG         "v0.10.1")
+SET(NGRAPH_GIT_TAG         "20bd8bbc79ae3a81c57313846a2be7313e5d1dab")
 SET(NGRAPH_SOURCES_DIR     ${THIRD_PARTY_PATH}/ngraph)
 SET(NGRAPH_INSTALL_DIR     ${THIRD_PARTY_PATH}/install/ngraph)
 SET(NGRAPH_INC_DIR         ${NGRAPH_INSTALL_DIR}/include)
 SET(NGRAPH_LIB_DIR         ${NGRAPH_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR})
 SET(NGRAPH_SHARED_LIB_NAME libngraph.so)
 SET(NGRAPH_CPU_LIB_NAME    libcpu_backend.so)
-SET(NGRAPH_TBB_LIB_NAME    libtbb.so.2)
+if(CMAKE_BUILD_TYPE STREQUAL "Debug")
+    SET(NGRAPH_TBB_LIB_NAME    libtbb_debug.so.2)
+else()
+    SET(NGRAPH_TBB_LIB_NAME    libtbb.so.2)
+endif()
 SET(NGRAPH_GIT_REPO        "https://github.com/NervanaSystems/ngraph.git")
 SET(NGRAPH_SHARED_LIB      ${NGRAPH_LIB_DIR}/${NGRAPH_SHARED_LIB_NAME})
 SET(NGRAPH_CPU_LIB         ${NGRAPH_LIB_DIR}/${NGRAPH_CPU_LIB_NAME})
@@ -66,16 +70,7 @@ ExternalProject_Add(
     CMAKE_ARGS          -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
     CMAKE_ARGS          -DMKLDNN_INCLUDE_DIR=${MKLDNN_INC_DIR}
     CMAKE_ARGS          -DMKLDNN_LIB_DIR=${MKLDNN_INSTALL_DIR}/lib
-)
-
-# Workaround for nGraph expecting mklml to be in mkldnn install directory.
-ExternalProject_Add_Step(
-    ${NGRAPH_PROJECT}
-    PrepareMKL
-    COMMAND ${CMAKE_COMMAND} -E create_symlink ${MKLML_LIB} ${MKLDNN_INSTALL_DIR}/lib/libmklml_intel.so
-    COMMAND ${CMAKE_COMMAND} -E create_symlink ${MKLML_IOMP_LIB} ${MKLDNN_INSTALL_DIR}/lib/libiomp5.so
-    DEPENDEES download
-    DEPENDERS configure
+    CMAKE_ARGS          -DMKLML_LIB_DIR=${MKLML_INSTALL_DIR}/lib
 )
 
 add_dependencies(ngraph ${NGRAPH_PROJECT})

cmake/generic.cmake

@@ -115,6 +115,10 @@ function(common_link TARGET_NAME)
   if (WITH_PROFILER)
     target_link_libraries(${TARGET_NAME} gperftools::profiler)
   endif()
+
+  if (WITH_JEMALLOC)
+    target_link_libraries(${TARGET_NAME} jemalloc::jemalloc)
+  endif()
 endfunction()
 
 
@@ -228,7 +232,7 @@ function(merge_static_libs TARGET_NAME)
       # Get the file names of the libraries to be merged
       set(libfiles ${libfiles} $<TARGET_FILE:${lib}>)
     endforeach()
-    # msvc will put libarary in directory of "/Release/xxxlib" by default 
+    # msvc will put libarary in directory of "/Release/xxxlib" by default
     #       COMMAND cmake -E remove "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/${TARGET_NAME}.lib"
     add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
       COMMAND cmake -E make_directory "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}"

paddle/fluid/API.spec

@@ -405,28 +405,50 @@ paddle.fluid.nets.glu ArgSpec(args=['input', 'dim'], varargs=None, keywords=None
 paddle.fluid.nets.scaled_dot_product_attention ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0))
 paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True))
 paddle.fluid.optimizer.SGDOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.optimizer.SGDOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.SGDOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.SGDOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
+paddle.fluid.optimizer.MomentumOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.MomentumOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, None, None))
+paddle.fluid.optimizer.AdagradOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.AdagradOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False))
+paddle.fluid.optimizer.AdamOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.AdamOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
+paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.AdamaxOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None))
+paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.DecayedAdagradOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.FtrlOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None))
+paddle.fluid.optimizer.FtrlOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.FtrlOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.FtrlOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None))
+paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.RMSPropOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.RMSPropOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdadeltaOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None))
+paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.AdadeltaOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None))
 paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.optimizer.ModelAverage.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.ModelAverage.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.ModelAverage.restore ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None))
+paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.LarsMomentumOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.LarsMomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.backward.append_backward ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.regularizer.L1DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))

paddle/fluid/framework/CMakeLists.txt

@@ -184,7 +184,7 @@ endif()
 target_link_libraries(executor garbage_collector)
 
 cc_library(parallel_executor SRCS parallel_executor.cc DEPS
-        threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
+        threaded_ssa_graph_executor scope_buffered_ssa_graph_executor parallel_ssa_graph_executor
         graph build_strategy
         fast_threaded_ssa_graph_executor variable_helper)
 

paddle/fluid/framework/details/CMakeLists.txt

@@ -77,6 +77,8 @@ cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUT
 cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
         simple_threadpool device_context)
 
+cc_library(parallel_ssa_graph_executor SRCS parallel_ssa_graph_executor.cc DEPS threaded_ssa_graph_executor)
+
 cc_test(broadcast_op_test SRCS broadcast_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
         device_context broadcast_op_handle)
 cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
@@ -92,4 +94,4 @@ cc_library(build_strategy SRCS build_strategy.cc DEPS
         graph_viz_pass multi_devices_graph_pass
         multi_devices_graph_print_pass multi_devices_graph_check_pass
         fuse_elewise_add_act_pass multi_batch_merge_pass
-        memory_optimize_pass)
+        memory_optimize_pass lock_free_optimize_pass)

paddle/fluid/framework/details/all_reduce_op_handle.cc

@@ -19,6 +19,13 @@
 #include "paddle/fluid/framework/details/variable_visitor.h"
 #include "paddle/fluid/platform/profiler.h"
 
+// asynchronous nccl allreduce or synchronous issue:
+// https://github.com/PaddlePaddle/Paddle/issues/15049
+DEFINE_bool(
+    sync_nccl_allreduce, false,
+    "If set true, will call `cudaStreamSynchronize(nccl_stream)`"
+    "after allreduce, this mode can get better performance in some scenarios.");
+
 namespace paddle {
 namespace framework {
 namespace details {
@@ -48,100 +55,104 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
 void AllReduceOpHandle::RunImpl() {
   platform::RecordEvent record_event(Name(), dev_ctxes_.cbegin()->second);
 
-// FIXME(typhoonzero): If scope0(global scope) have NCCL_ID_VAR,
-// this is a distributed or inter-process call, find a better way.
+  WaitInputVarGenerated();
+  auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
+  auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
+  PADDLE_ENFORCE_EQ(
+      in_var_handles.size(), places_.size(),
+      "The NoDummyInputSize should be equal to the number of places.");
+  PADDLE_ENFORCE_EQ(
+      in_var_handles.size(), out_var_handles.size(),
+      "The NoDummyInputSize and NoDummyOutputSize should be equal.");
+
+  std::vector<const LoDTensor *> lod_tensors;
+  for (size_t i = 0; i < local_scopes_.size(); ++i) {
+    auto *s = local_scopes_[i];
+    auto &local_scope = *s->FindVar(kLocalExecScopeName)->Get<Scope *>();
+    auto &lod_tensor =
+        local_scope.FindVar(in_var_handles[i]->name_)->Get<LoDTensor>();
+    lod_tensors.emplace_back(&lod_tensor);
+    PADDLE_ENFORCE_EQ(in_var_handles[i]->name_, out_var_handles[i]->name_,
+                      "The name of input and output should be equal.");
+  }
+
+  if (platform::is_gpu_place(lod_tensors[0]->place())) {
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-  if (NoDummyInputSize() == 1 &&
-      local_scopes_[0]->FindLocalVar(NCCL_ID_VARNAME) == nullptr) {
-#else
-  if (NoDummyInputSize() == 1) {
-#endif
-    return;  // No need to all reduce when GPU count = 1;
-  } else {
-    // Wait input done
-    WaitInputVarGenerated();
-    auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
-    auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
-    PADDLE_ENFORCE_EQ(
-        in_var_handles.size(), places_.size(),
-        "The NoDummyInputSize should be equal to the number of places.");
-    PADDLE_ENFORCE_EQ(
-        in_var_handles.size(), out_var_handles.size(),
-        "The NoDummyInputSize and NoDummyOutputSize should be equal.");
-
-    std::vector<const LoDTensor *> lod_tensors;
+    PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
+    int dtype = -1;
+    size_t numel = 0;
+    std::vector<std::function<void()>> all_reduce_calls;
     for (size_t i = 0; i < local_scopes_.size(); ++i) {
-      auto *s = local_scopes_[i];
-      auto &local_scope = *s->FindVar(kLocalExecScopeName)->Get<Scope *>();
-      auto &lod_tensor =
-          local_scope.FindVar(in_var_handles[i]->name_)->Get<LoDTensor>();
-      lod_tensors.emplace_back(&lod_tensor);
-      PADDLE_ENFORCE_EQ(in_var_handles[i]->name_, out_var_handles[i]->name_,
-                        "The name of input and output should be equal.");
-    }
+      auto &p = places_[i];
+      auto &lod_tensor = *lod_tensors[i];
+      void *buffer = const_cast<void *>(lod_tensor.data<void>());
 
-    if (platform::is_gpu_place(lod_tensors[0]->place())) {
-#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-      PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
-      int dtype = -1;
-      size_t numel = 0;
-      std::vector<std::function<void()>> all_reduce_calls;
-      for (size_t i = 0; i < local_scopes_.size(); ++i) {
-        auto &p = places_[i];
-        auto &lod_tensor = *lod_tensors[i];
-        void *buffer = const_cast<void *>(lod_tensor.data<void>());
-
-        if (dtype == -1) {
-          dtype = platform::ToNCCLDataType(lod_tensor.type());
-        }
+      if (dtype == -1) {
+        dtype = platform::ToNCCLDataType(lod_tensor.type());
+      }
+
+      if (numel == 0) {
+        numel = static_cast<size_t>(lod_tensor.numel());
+      }
 
-        if (numel == 0) {
-          numel = static_cast<size_t>(lod_tensor.numel());
+      int dev_id = boost::get<platform::CUDAPlace>(p).device;
+      auto &nccl_ctx = nccl_ctxs_->at(dev_id);
+      auto stream = nccl_ctx.stream();
+      auto comm = nccl_ctx.comm_;
+      all_reduce_calls.emplace_back([=] {
+        PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
+            buffer, buffer, numel, static_cast<ncclDataType_t>(dtype), ncclSum,
+            comm, stream));
+      });
+    }
+
+    this->RunAndRecordEvent([&] {
+      if (all_reduce_calls.size() == 1UL) {
+        // Do not use NCCLGroup when manage NCCL by per thread per device
+        all_reduce_calls[0]();
+      } else {
+        platform::NCCLGroupGuard guard;
+        for (auto &call : all_reduce_calls) {
+          call();
         }
+      }
+    });
 
+    if (FLAGS_sync_nccl_allreduce) {
+      for (auto &p : places_) {
         int dev_id = boost::get<platform::CUDAPlace>(p).device;
         auto &nccl_ctx = nccl_ctxs_->at(dev_id);
         auto stream = nccl_ctx.stream();
-        auto comm = nccl_ctx.comm_;
-        all_reduce_calls.emplace_back([=] {
-          PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
-              buffer, buffer, numel, static_cast<ncclDataType_t>(dtype),
-              ncclSum, comm, stream));
-        });
+        cudaStreamSynchronize(stream);
       }
-      this->RunAndRecordEvent([&] {
-        platform::NCCLGroupGuard guard;
-        for (auto &call : all_reduce_calls) {
-          call();
-        }
-      });
+    }
+
 #else
-      PADDLE_THROW("Not compiled with CUDA");
+    PADDLE_THROW("Not compiled with CUDA");
 #endif
-    } else {  // Special handle CPU only Operator's gradient. Like CRF
-      auto &trg = *this->local_scopes_[0]
-                       ->FindVar(kLocalExecScopeName)
-                       ->Get<Scope *>()
-                       ->FindVar(out_var_handles[0]->name_)
-                       ->GetMutable<framework::LoDTensor>();
-
-      // Reduce All Tensor to trg in CPU
-      ReduceLoDTensor func(lod_tensors, &trg);
-      VisitDataType(lod_tensors[0]->type(), func);
-
-      for (size_t i = 1; i < local_scopes_.size(); ++i) {
-        auto &scope =
-            *local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
-        auto &p = places_[i];
-        auto *var = scope.FindVar(out_var_handles[i]->name_);
-        auto *dev_ctx = dev_ctxes_.at(p);
-
-        RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
-          auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();
-          auto &tensor_cpu = trg;
-          TensorCopy(tensor_cpu, p, *dev_ctx, &tensor_gpu);
-        });
-      }
+  } else {  // Special handle CPU only Operator's gradient. Like CRF
+    auto &trg = *this->local_scopes_[0]
+                     ->FindVar(kLocalExecScopeName)
+                     ->Get<Scope *>()
+                     ->FindVar(out_var_handles[0]->name_)
+                     ->GetMutable<framework::LoDTensor>();
+
+    // Reduce All Tensor to trg in CPU
+    ReduceLoDTensor func(lod_tensors, &trg);
+    VisitDataType(lod_tensors[0]->type(), func);
+
+    for (size_t i = 1; i < local_scopes_.size(); ++i) {
+      auto &scope =
+          *local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
+      auto &p = places_[i];
+      auto *var = scope.FindVar(out_var_handles[i]->name_);
+      auto *dev_ctx = dev_ctxes_.at(p);
+
+      RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
+        auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();
+        auto &tensor_cpu = trg;
+        TensorCopy(tensor_cpu, p, *dev_ctx, &tensor_gpu);
+      });
     }
   }
 }

paddle/fluid/framework/details/build_strategy.cc

@@ -18,7 +18,7 @@ limitations under the License. */
 #include <memory>
 
 #include "paddle/fluid/framework/details/memory_reuse_types.h"
-#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
+#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/sequential_execution_pass.h"
@@ -31,7 +31,11 @@ namespace framework {
 namespace details {
 
 static inline bool SeqOnlyAllReduceOps(const BuildStrategy &strategy) {
-  return (!strategy.enable_sequential_execution_ && strategy.num_trainers_ > 1);
+  // Should fix the allreduce op order if scheduling
+  // them in multiple threads or processes to avoid hang.
+  return (!strategy.enable_sequential_execution_ &&
+          strategy.num_trainers_ > 1) ||
+         strategy.enable_parallel_graph_;
 }
 
 class ParallelExecutorPassBuilder : public ir::PassBuilder {
@@ -82,12 +86,8 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     if (strategy.memory_optimize_) {
       auto analysis_var_pass = AppendPass("analysis_var_pass");
     }
-    // Convert graph to run on multi-devices.
-    auto multi_devices_pass = AppendPass("multi_devices_pass");
-    multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
-                                                         &strategy_);
-    multi_devices_pass->Set<int>("num_trainers",
-                                 new int(strategy_.num_trainers_));
+
+    AppendMultiDevPass(strategy);
 
     // Add a graph print pass to record a graph with device info.
     if (!strategy_.debug_graphviz_path_.empty()) {
@@ -113,6 +113,25 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     }
   }
 
+  // Convert graph to run on multi-devices.
+  void AppendMultiDevPass(const BuildStrategy &strategy) {
+    ir::Pass *multi_devices_pass;
+    if (strategy_.is_distribution_) {
+      multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
+    } else {
+      if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
+        multi_devices_pass =
+            AppendPass("allreduce_mode_multi_devices_pass").get();
+      } else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
+        multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get();
+      } else {
+        PADDLE_THROW("Unknown reduce strategy.");
+      }
+    }
+    multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
+                                                         &strategy_);
+  }
+
  private:
   BuildStrategy strategy_;
 };
@@ -129,9 +148,14 @@ std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy(
   return pass_builder_;
 }
 
+bool BuildStrategy::IsMultiDevPass(const std::string &pass_name) const {
+  return framework::details::MultiDevSSAGraphBuilder().count(pass_name) > 0;
+}
+
 std::unique_ptr<ir::Graph> BuildStrategy::Apply(
     const ProgramDesc &main_program, const std::vector<platform::Place> &places,
     const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
+    const size_t &nranks,
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
     const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
 #else
@@ -142,19 +166,23 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
 
   std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
   for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
-    if (pass->Type() == "multi_devices_pass") {
-      pass->Erase("places");
-      pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
-      pass->Erase("loss_var_name");
-      pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
-      pass->Erase("local_scopes");
-      pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
+    if (IsMultiDevPass(pass->Type())) {
+      pass->Erase(kPlaces);
+      pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
+      pass->Erase(kLossVarName);
+      pass->SetNotOwned<const std::string>(kLossVarName, &loss_var_name);
+      pass->Erase(kLocalScopes);
+      pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
                                                     &local_scopes);
+      pass->Erase(kNRanks);
+      pass->Set<size_t>(kNRanks, new size_t(nranks));
+
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
       platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
       pass->Erase("nccl_ctxs");
       pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
 #endif
+
     } else if (pass->Type() == "analysis_var_pass") {
       const std::vector<OpDesc *> *all_op_descs =
           new std::vector<OpDesc *>(main_program.Block(0).AllOps());
@@ -195,10 +223,13 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
 USE_PASS(fuse_elewise_add_act_pass);
 USE_PASS(graph_viz_pass);
 USE_PASS(multi_batch_merge_pass);
-USE_PASS(multi_devices_pass);
+USE_PASS(reduce_mode_multi_devices_pass);
+USE_PASS(allreduce_mode_multi_devices_pass);
+USE_PASS(dist_multi_devices_pass);
 USE_PASS(multi_devices_check_pass);
 USE_PASS(multi_devices_print_pass);
 USE_PASS(analysis_var_pass);
 USE_PASS(sequential_execution_pass);
 USE_PASS(all_reduce_deps_pass);
 USE_PASS(modify_op_lock_and_record_event_pass);
+USE_PASS(lock_free_optimize_pass);

paddle/fluid/framework/details/build_strategy.h

@@ -74,8 +74,6 @@ struct BuildStrategy {
 
   bool fuse_elewise_add_act_ops_{false};
 
-  bool enable_data_balance_{false};
-
   bool memory_optimize_{false};
 
   bool memory_early_delete_{false};
@@ -84,6 +82,10 @@ struct BuildStrategy {
 
   bool fuse_broadcast_op_{false};
 
+  // FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
+  // num_trainers is 1, so the current fields of build_strategy doesn't tell if
+  // it's distributed model.
+  bool is_distribution_{false};
   int num_trainers_{1};
   int trainer_id_{0};
   std::vector<std::string> trainers_endpoints_;
@@ -104,12 +106,15 @@ struct BuildStrategy {
 
   bool IsFinalized() const { return is_finalized_; }
 
+  bool IsMultiDevPass(const std::string &pass_name) const;
+
   // Apply the passes built by the pass_builder_. The passes will be
   // applied to the Program and output an ir::Graph.
   std::unique_ptr<ir::Graph> Apply(const ProgramDesc &main_program,
                                    const std::vector<platform::Place> &places,
                                    const std::string &loss_var_name,
                                    const std::vector<Scope *> &local_scopes,
+                                   const size_t &nranks,
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
                                    const bool use_cuda,
                                    platform::NCCLContextMap *nccl_ctxs) const;
@@ -117,6 +122,13 @@ struct BuildStrategy {
                                    const bool use_cuda) const;
 #endif
 
+  // If set true, ParallelExecutor would build the main_program into multiple
+  // graphs,
+  // each of the graphs would run with one device. This approach can achieve
+  // better performance
+  // on some scenarios.
+  mutable bool enable_parallel_graph_ = false;
+
  private:
   mutable bool is_finalized_ = false;
   mutable std::shared_ptr<ir::PassBuilder> pass_builder_;

paddle/fluid/framework/details/multi_devices_graph_check_pass.cc

@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
 #include <string>
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_helper.h"
 
@@ -21,68 +21,78 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
-  std::unordered_map<OpHandleBase *, size_t> pending_ops;
-  std::unordered_set<VarHandleBase *> pending_vars;
-  std::unordered_set<VarHandleBase *> ready_vars;
-  std::unordered_set<OpHandleBase *> ready_ops;
+class SSAGraghBuilderWithChecker : public ir::Pass {
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(
+      std::unique_ptr<ir::Graph> graph) const override {
+    PADDLE_ENFORCE(IsValidGraph(graph.get()));
+    return graph;
+  }
 
-  auto insert_pending_var = [&](VarHandleBase *var) {
-    pending_vars.insert(var);
-    if (var->GeneratedOp() == nullptr) {
-      ready_vars.emplace(var);
-    }
-  };
+  bool IsValidGraph(const ir::Graph *graph) const {
+    std::unordered_map<OpHandleBase *, size_t> pending_ops;
+    std::unordered_set<VarHandleBase *> pending_vars;
+    std::unordered_set<VarHandleBase *> ready_vars;
+    std::unordered_set<OpHandleBase *> ready_ops;
 
-  for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
-    for (auto &name_pair : var_map) {
-      for (auto &version_pair : name_pair.second) {
-        insert_pending_var(version_pair);
+    auto insert_pending_var = [&](VarHandleBase *var) {
+      pending_vars.insert(var);
+      if (var->GeneratedOp() == nullptr) {
+        ready_vars.emplace(var);
       }
-    }
-  }
+    };
 
-  for (auto &var : graph->Get<GraphDepVars>(kGraphDepVars)) {
-    insert_pending_var(var);
-  }
+    for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
+      for (auto &name_pair : var_map) {
+        for (auto &version_pair : name_pair.second) {
+          insert_pending_var(version_pair);
+        }
+      }
+    }
 
-  for (OpHandleBase *op : ir::FilterByNodeWrapper<OpHandleBase>(*graph)) {
-    if (op->Inputs().empty()) {
-      ready_ops.insert(op);
-    } else {
-      pending_ops.insert({op, op->NoDupInputSize()});
+    for (auto &var : graph->Get<GraphDepVars>(kGraphDepVars)) {
+      insert_pending_var(var);
     }
-  }
 
-  auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
-    for (auto *op : set) {
-      for (auto out : op->Outputs()) {
-        ready_vars.emplace(out);
+    for (OpHandleBase *op : ir::FilterByNodeWrapper<OpHandleBase>(*graph)) {
+      if (op->Inputs().empty()) {
+        ready_ops.insert(op);
+      } else {
+        pending_ops.insert({op, op->NoDupInputSize()});
       }
     }
-    set.clear();
-  };
 
-  while (!pending_vars.empty()) {
-    run_all_ops(ready_ops);
+    auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
+      for (auto *op : set) {
+        for (auto out : op->Outputs()) {
+          ready_vars.emplace(out);
+        }
+      }
+      set.clear();
+    };
 
-    if (ready_vars.empty()) {
-      return false;
-    }
+    while (!pending_vars.empty()) {
+      run_all_ops(ready_ops);
 
-    for (auto ready_var : ready_vars) {
-      pending_vars.erase(ready_var);
-      for (auto *op : ready_var->PendingOps()) {
-        auto &deps = --pending_ops[op];
-        if (deps == 0) {
-          ready_ops.insert(op);
+      if (ready_vars.empty()) {
+        return false;
+      }
+
+      for (auto ready_var : ready_vars) {
+        pending_vars.erase(ready_var);
+        for (auto *op : ready_var->PendingOps()) {
+          auto &deps = --pending_ops[op];
+          if (deps == 0) {
+            ready_ops.insert(op);
+          }
         }
       }
+      ready_vars.clear();
     }
-    ready_vars.clear();
+    return true;
   }
-  return true;
-}
+};
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/multi_devices_graph_pass.h

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #pragma once
+
 #include <string>
 #include <utility>
 #include <vector>
@@ -30,78 +31,70 @@ namespace framework {
 class Scope;
 namespace details {
 
-class MultiDevSSAGraphBuilder : public ir::Pass {
+constexpr char kLossVarName[] = "loss_var_name";
+constexpr char kPlaces[] = "places";
+constexpr char kLocalScopes[] = "local_scopes";
+constexpr char kStrategy[] = "strategy";
+constexpr char kNRanks[] = "nranks";
+
+class MultiDevSSAGraphBuilderBase : public ir::Pass {
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(
       std::unique_ptr<ir::Graph> graph) const override;
 
- private:
-  void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
-                         size_t device_id) const;
-  void Init() const;
+  virtual void Init() const;
 
-#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-  mutable platform::NCCLContextMap *nccl_ctxs_;
-#endif
+  virtual std::vector<ir::Node *> SortOperations(const ir::Graph &graph) const;
 
-  int GetVarDeviceID(
-      const std::string &varname,
-      const std::unordered_map<std::string, int> &sharded_var_device) const;
+  virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
+                                  const std::string &g_name) const = 0;
 
-  bool IsScaleLossOp(ir::Node *node) const;
+  virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const = 0;
+
+  virtual void InsertPostprocessOps(ir::Graph *result) const = 0;
 
-  int CreateRPCOp(
-      ir::Graph *result, ir::Node *node,
-      std::unordered_map<std::string, int> *sharded_var_device) const;
-  int CreateDistTrainOp(
-      ir::Graph *result, ir::Node *node,
-      std::unordered_map<std::string, int> *sharded_var_device) const;
+  bool UseGPU() const;
+
+  bool NeedCollectiveOps() const;
+
+  bool IsScaleLossOp(ir::Node *node) const;
 
   void CreateComputationalOps(ir::Graph *result, ir::Node *node,
                               size_t num_places) const;
 
   void CreateScaleLossGradOp(ir::Graph *result,
                              const std::string &loss_grad_name,
-                             ir::Node *out_var_node,
+                             ir::Node *out_var_node, size_t loss_scale,
                              proto::VarType::Type dtype) const;
 
   VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
                             int dst_dev_id) const;
+
   void CreateComputationalOp(ir::Graph *result, ir::Node *node,
                              int dev_id) const;
 
-  int GetOpDeviceID(
-      ir::Node *node,
-      const std::unordered_map<std::string, int> &sharded_var_device) const;
-
-  void InsertAllReduceOp(ir::Graph *result, const std::string &og) const;
+  bool IsSparseGradient(const std::string &og) const;
 
-  void InsertDataBalanceOp(ir::Graph *result,
-                           const std::vector<std::string> &datas) const;
+  void CreateAllReduceOp(ir::Graph *result, const std::string &og) const;
 
   void CreateBroadcastOp(ir::Graph *result, const std::string &p_name,
                          size_t src_dev_id) const;
 
+  void InsertScaleLossGradOp(ir::Graph *result, const ir::Node *node) const;
+
   void CreateFusedBroadcastOp(
       ir::Graph *result,
       const std::vector<std::unordered_set<std::string>> &bcast_varnames) const;
 
-  bool IsSparseGradient(const std::string &og) const;
-
-  size_t GetAppropriateDeviceID(
-      const std::vector<std::string> &var_names) const;
-
   void SetCommunicationContext(OpHandleBase *op_handle,
                                const platform::Place &p) const;
 
-  std::vector<ir::Node *> SortForReduceMode(
-      const std::vector<ir::Node *> &) const;
+  void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
+                         size_t device_id) const;
 
-  int GetOpDeviceID(
-      ir::Node *node,
-      const std::unordered_map<std::string, int> &shared_var_device,
-      std::unordered_map<std::string, std::vector<ir::Node *>> *delay_ops)
-      const;
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+  mutable platform::NCCLContextMap *nccl_ctxs_;
+#endif
 
   mutable std::string loss_var_name_;
   mutable std::vector<platform::Place> places_;
@@ -109,8 +102,83 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
 
   mutable BuildStrategy strategy_;
   mutable std::unordered_map<std::string, VarDesc *> all_vars_;
+};
+
+class AllReduceSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
+ protected:
+  virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
+                                  const std::string &g_name) const;
+
+  virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const {
+    return false;
+  }
+
+  virtual void InsertPostprocessOps(ir::Graph *result) const {}
+};
+
+class BalanceVarSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
+ protected:
+  int GetVarDeviceID(const std::string &varname) const;
+
+  int GetOpDeviceID(ir::Node *node) const;
+
+  size_t GetAppropriateDeviceID(
+      const std::vector<std::string> &var_names) const;
+
+  virtual void ResetState() const;
+
+  mutable std::unordered_map<std::string, int> sharded_var_device_;
   mutable std::vector<int64_t> balance_vars_;
 };
+
+class ReduceSSAGraphBuilder : public BalanceVarSSAGraphBuilder {
+ protected:
+  virtual void Init() const;
+
+  virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
+                                  const std::string &g_name) const;
+
+  virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const;
+
+  virtual void InsertPostprocessOps(ir::Graph *result) const;
+
+  virtual std::vector<ir::Node *> SortOperations(const ir::Graph &graph) const;
+
+  virtual void ResetState() const;
+
+  int GetOpDeviceID(ir::Node *node,
+                    std::unordered_map<std::string, std::vector<ir::Node *>>
+                        *delay_ops) const;
+
+  std::vector<ir::Node *> SortForReduceMode(
+      const std::vector<ir::Node *> &topo_ops) const;
+
+  mutable std::vector<std::unordered_set<std::string>> bcast_var_name_set_;
+};
+
+class DistSSAGraphBuilder : public BalanceVarSSAGraphBuilder {
+ protected:
+  virtual void Init() const;
+
+  virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const;
+
+  virtual void InsertPostprocessOps(ir::Graph *result) const;
+
+  virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
+                                  const std::string &g_name) const;
+
+  virtual void ResetState() const;
+
+  int CreateRPCOp(ir::Graph *result, ir::Node *node) const;
+
+  int CreateDistTrainOp(ir::Graph *result, ir::Node *node) const;
+
+  mutable std::vector<std::unordered_set<std::string>> bcast_var_name_set_;
+  mutable bool need_broadcast_var_{false};
+};
+
+std::unordered_set<std::string> &MultiDevSSAGraphBuilder();
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/parallel_ssa_graph_executor.cc

+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/details/parallel_ssa_graph_executor.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
+    const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
+    const std::vector<platform::Place> &places,
+    std::vector<std::unique_ptr<ir::Graph>> &&graphs)
+    : strategy_(std::move(strategy)),
+      local_scopes_(std::move(local_scopes)),
+      pool_(places.size() >= 2 ? new ::ThreadPool(places.size()) : nullptr),
+      places_(std::move(places)),
+      graphs_(std::move(graphs)) {
+  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
+
+  // set the correct size of thread pool to each device.
+  strategy_.num_threads_ = strategy_.num_threads_ < places_.size()
+                               ? 1UL
+                               : strategy_.num_threads_ / places_.size();
+  VLOG(1) << "set num_threads: " << strategy_.num_threads_
+          << " to run the operators of the graph on each device.";
+  for (size_t i = 0; i < places.size(); ++i) {
+    executors_.emplace_back(new details::ThreadedSSAGraphExecutor(
+        strategy_, {local_scopes_[i]}, {places_[i]}, std::move(graphs_[i])));
+  }
+}
+
+FeedFetchList ParallelSSAGraphExecutor::Run(
+    const std::vector<std::string> &fetch_tensors) {
+  std::vector<std::future<FeedFetchList>> run_futures;
+
+  std::vector<FeedFetchList> fetch_data;
+  FeedFetchList ret;
+
+  fetch_data.reserve(places_.size());
+  ret.reserve(fetch_tensors.size());
+  exception_holder_.Clear();
+
+  for (size_t i = 0; i < places_.size(); ++i) {
+    auto call = [this, i, &fetch_tensors]() -> FeedFetchList {
+      try {
+        return executors_[i]->Run(fetch_tensors);
+      } catch (...) {
+        exception_holder_.Catch(std::current_exception());
+      }
+      return FeedFetchList();
+    };
+
+    if (pool_) {
+      run_futures.emplace_back(pool_->enqueue(std::move(call)));
+    } else {
+      fetch_data.emplace_back(std::move(call()));
+    }
+  }
+
+  if (pool_) {
+    for (auto &f : run_futures) {
+      if (exception_holder_.IsCaught()) {
+        f.wait();
+      } else {
+        fetch_data.emplace_back(std::move(f.get()));
+      }
+    }
+  }
+  if (exception_holder_.IsCaught()) {
+    exception_holder_.ReThrow();
+  }
+
+  for (size_t fetch_idx = 0; fetch_idx < fetch_tensors.size(); ++fetch_idx) {
+    std::vector<const LoDTensor *> lodtensor_ptrs;
+    lodtensor_ptrs.reserve(local_scopes_.size());
+    for (size_t scope_idx = 0; scope_idx < local_scopes_.size(); ++scope_idx) {
+      lodtensor_ptrs.push_back(&fetch_data.at(scope_idx).at(fetch_idx));
+    }
+    ret.emplace_back();
+    ret.back().MergeLoDTensor(lodtensor_ptrs, platform::CPUPlace());
+  }
+  return ret;
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/parallel_ssa_graph_executor.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include <vector>
+
+#include "ThreadPool.h"
+#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+class ParallelSSAGraphExecutor : public SSAGraphExecutor {
+ public:
+  ParallelSSAGraphExecutor(const ExecutionStrategy &strategy,
+                           const std::vector<Scope *> &local_scopes,
+                           const std::vector<platform::Place> &places,
+                           std::vector<std::unique_ptr<ir::Graph>> &&graphs);
+  ~ParallelSSAGraphExecutor() final = default;
+  const ir::Graph &Graph() const override { return *graphs_[0]; }
+
+  FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
+
+ private:
+  ExecutionStrategy strategy_;
+  std::vector<Scope *> local_scopes_;
+  std::unique_ptr<::ThreadPool> pool_{nullptr};
+  std::vector<platform::Place> places_;
+  std::vector<std::unique_ptr<ir::Graph>> graphs_;
+
+  std::vector<std::unique_ptr<details::ThreadedSSAGraphExecutor>> executors_;
+  ExceptionHolder exception_holder_;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc

@@ -56,7 +56,7 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
     }
   }
   std::vector<framework::LoDTensor> fetch_data;
-  std::exception_ptr eptr;
+  std::exception_ptr eptr = nullptr;
   try {
     fetch_data = underlying_executor_->Run(fetch_tensors);
   } catch (...) {

paddle/fluid/framework/ir/CMakeLists.txt

@@ -31,6 +31,7 @@ cc_library(fuse_pass_base SRCS fuse_pass_base.cc DEPS pass)
 
 pass_library(graph_to_program_pass base)
 pass_library(graph_viz_pass base)
+pass_library(lock_free_optimize_pass base)
 pass_library(fc_fuse_pass inference)
 pass_library(attention_lstm_fuse_pass inference)
 pass_library(infer_clean_graph_pass inference)
@@ -41,6 +42,7 @@ pass_library(seq_concat_fc_fuse_pass inference)
 pass_library(multi_batch_merge_pass base)
 pass_library(conv_bn_fuse_pass inference)
 pass_library(seqconv_eltadd_relu_fuse_pass inference)
+pass_library(seqpool_concat_fuse_pass inference)
 pass_library(is_test_pass base)
 pass_library(conv_elementwise_add_act_fuse_pass inference)
 pass_library(conv_elementwise_add2_act_fuse_pass inference)
@@ -67,6 +69,7 @@ cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_r
 cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
 cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
 cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
+cc_test(test_seqpool_concat_fuse_pass SRCS seqpool_concat_fuse_pass_tester.cc DEPS seqpool_concat_fuse_pass framework_proto)
 cc_test(test_is_test_pass SRCS is_test_pass_tester.cc DEPS is_test_pass)
 if (WITH_MKLDNN)
     cc_test(test_depthwise_conv_mkldnn_pass SRCS depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass)

paddle/fluid/framework/ir/graph.h

@@ -109,7 +109,6 @@ class Graph {
     attr_dels_[attr_name] = []() {};
   }
 
-  template <typename AttrType>
   void Erase(const std::string &attr_name) {
     PADDLE_ENFORCE(attrs_.count(attr_name) != 0, "%s not set in the graph",
                    attr_name);

paddle/fluid/framework/ir/lock_free_optimize_pass.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/ir/lock_free_optimize_pass.h"
+
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+#include "paddle/fluid/framework/ir/node.h"
+#include "paddle/fluid/framework/op_proto_maker.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+const char kSumGradOpName[] = "sum";
+// TODO(minqiyang): only support sgd at current time, please add
+// other optimizers later.
+const char kOptimizerType[] = "sgd";
+
+std::unique_ptr<ir::Graph> LockFreeOptimizePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  PADDLE_ENFORCE(graph.get());
+
+  // We could collect all weights' name from SGD, where
+  // W1 <- SGD(W0, Grad0)
+  std::unordered_set<std::string> weight_var_set;
+  for (auto* node : graph->Nodes()) {
+    if (IsOpNamed(node, kOptimizerType)) {
+      auto& param_out_vars = node->Op()->Output("ParamOut");
+      PADDLE_ENFORCE(param_out_vars.size() == 1u);
+      weight_var_set.insert(param_out_vars[0]);
+    }
+  }
+
+  // find all grad's merge op via weight name, where
+  // Grad0 <- SUM(Grad1, Grad2, Grad3 ...)
+  std::unordered_set<ir::Node*> grad_sum_op_set;
+  for (ir::Node* node : graph->Nodes()) {
+    if (IsOpNamed(node, kSumGradOpName)) {
+      for (ir::Node* output : node->outputs) {
+        // strip the last grad suffix @GRAD
+        std::string var_name = output->Name();
+        const std::string suffix(kGradVarSuffix);
+        if (var_name != suffix && var_name.size() > suffix.size() &&
+            var_name.substr(var_name.size() - suffix.size()) == suffix) {
+          // if so then strip them off
+          var_name = var_name.substr(0, var_name.size() - suffix.size());
+          if (weight_var_set.find(var_name) != weight_var_set.end()) {
+            grad_sum_op_set.insert(node);
+            break;
+          }
+        }
+      }
+    }
+  }
+
+  // get the forward op and backward op pairs, where
+  // out <- forward(X, W)
+  // Grad1 <- backward(out, X')
+  // Grad0 <- SUM(Grad1, Grad2, Grad3 ...)
+  // W0 <- SGD(W1, Grad0)
+  for (ir::Node* node : grad_sum_op_set) {
+    for (ir::Node* merged_grad_var : node->outputs) {
+      // find the optimizers connected with sum op
+      if (IsVarNameEndsWith(merged_grad_var, kGradVarSuffix) &&
+          merged_grad_var->outputs.size() == 1u) {
+        ir::Node* opt_node = merged_grad_var->outputs[0];
+        VLOG(3) << "Found opt node " << opt_node->Name();
+
+        // find the backward op connected with sum op
+        for (ir::Node* unmerged_grad_var : node->inputs) {
+          if (IsVarNameContains(unmerged_grad_var, kGradVarSuffix) &&
+              unmerged_grad_var->inputs.size() == 1u) {
+            ir::Node* backward_op = unmerged_grad_var->inputs[0];
+
+            VLOG(3) << "Found backward_op " << backward_op->Name();
+
+            // find the forward op related to the backward op
+            ir::Node* forward_op =
+                FindForwardOpViaBackwardOp(graph.get(), backward_op);
+
+            VLOG(3) << "Found forward_op " << forward_op->Name();
+
+            PADDLE_ENFORCE(forward_op);
+
+            Node* new_optimizer_node = CreateNewSGDNode(
+                graph.get(), forward_op, backward_op, node, opt_node);
+
+            PADDLE_ENFORCE(new_optimizer_node);
+          }
+        }
+      }
+    }
+  }
+
+  // Remove the sum_op and its' outputs and connected Optimizers
+  for (Node* sum_op : grad_sum_op_set) {
+    for (Node* sum_op_output : sum_op->outputs) {
+      for (Node* optimize_op : sum_op_output->outputs) {
+        if (optimize_op->NodeType() == Node::Type::kOperation &&
+            optimize_op->Name() == kOptimizerType) {
+          VLOG(3) << "remove optimize_op: " << optimize_op->Name() << "_"
+                  << optimize_op->id();
+          graph->RemoveNode(optimize_op);
+        }
+      }
+      VLOG(3) << "remove sum_op_output: " << sum_op_output->Name() << "_"
+              << sum_op_output->id();
+      graph->RemoveNode(sum_op_output);
+    }
+    VLOG(3) << "remove sum_op: " << sum_op->Name() << "_" << sum_op->id();
+    graph->RemoveNode(sum_op);
+  }
+
+  for (auto* node : graph->Nodes()) {
+    for (Node* output_node : node->outputs) {
+      if (output_node->Name() == "sgd") {
+        VLOG(3) << "Node link to SGD: " << node->Name() << "_" << node->id()
+                << " --> " << output_node->Name() << "_" << output_node->id();
+        for (Node* input_node : node->inputs) {
+          VLOG(3) << "SGD Input link: " << input_node->Name() << "_"
+                  << input_node->id() << " --> " << node->Name() << "_"
+                  << node->id();
+        }
+      }
+    }
+  }
+
+  return graph;
+}
+
+ir::Node* LockFreeOptimizePass::CreateNewSGDNode(
+    ir::Graph* graph, ir::Node* forward_node, ir::Node* backward_node,
+    ir::Node* grad_sum_node, ir::Node* optimize_node) const {
+  PADDLE_ENFORCE(graph);
+  PADDLE_ENFORCE(forward_node);
+  PADDLE_ENFORCE(backward_node);
+  PADDLE_ENFORCE(grad_sum_node);
+  PADDLE_ENFORCE(optimize_node);
+
+  // find the grad var node between the grad sum node and backward_node
+  std::vector<ir::Node*> grad_vars =
+      FindConnectedNode(backward_node, grad_sum_node);
+  ir::Node* grad_node = nullptr;
+  for (ir::Node* node : grad_vars) {
+    if (!ir::IsControlDepVar(*node)) {
+      grad_node = node;
+    }
+  }
+  PADDLE_ENFORCE(grad_node);
+
+  // create a new SGD node
+  OpDesc* old_desc = optimize_node->Op();
+  // keep with the same block between new optimizer and the old one
+  OpDesc new_desc(*old_desc, old_desc->Block());
+  new_desc.SetInput("Param", old_desc->Input("Param"));
+  new_desc.SetInput("LearningRate", old_desc->Input("LearningRate"));
+  new_desc.SetInput("Grad", std::vector<std::string>({grad_node->Name()}));
+  new_desc.SetOutput("ParamOut", old_desc->Output("ParamOut"));
+
+  std::vector<std::string> op_role_vars = boost::get<std::vector<std::string>>(
+      new_desc.GetAttr(framework::OpProtoAndCheckerMaker::OpRoleVarAttrName()));
+  // replace the second op role var, because the grad name was
+  // changed in new optimizer
+  op_role_vars.pop_back();
+  op_role_vars.push_back(grad_node->Name());
+  new_desc.SetAttr(framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(),
+                   op_role_vars);
+  new_desc.SetType(kOptimizerType);
+
+  // set backward op's op role var, this will be used to
+  // set device_id in multi_device_pass
+  backward_node->Op()->SetAttr(
+      framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(), op_role_vars);
+  // backward_node->Op()->SetAttr(
+  // framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(), {});
+
+  // keep with the same output nodes between new optimizer and the
+  // old one
+  Node* sgd_node = graph->CreateOpNode(&new_desc);
+
+  // change all outputs of the optimize_node to the new one
+  ReplaceAllDownstreamNode(optimize_node, sgd_node);
+
+  // find connected node between forward node and optimize node
+  // and replace the optimize node to new sgd node
+  std::vector<ir::Node*> forward_opt_connected_nodes =
+      FindConnectedNode(forward_node, optimize_node);
+  for (ir::Node* node : forward_opt_connected_nodes) {
+    ReplaceUpstreamNode(node, optimize_node, sgd_node);
+  }
+
+  // find connected node between backward node and optimize node
+  // and replace the optimize node to new sgd node
+  std::vector<ir::Node*> backward_opt_connected_nodes =
+      FindConnectedNode(backward_node, optimize_node);
+  for (ir::Node* node : backward_opt_connected_nodes) {
+    ReplaceUpstreamNode(node, optimize_node, sgd_node);
+  }
+
+  // SGD must have only one param and LR in
+  PADDLE_ENFORCE(old_desc->Input("LearningRate").size() == 1u);
+  PADDLE_ENFORCE(old_desc->Input("Param").size() == 1u);
+
+  // LR and weight nodes should be copied
+  for (Node* upstream_node : optimize_node->inputs) {
+    if (upstream_node->Name() == old_desc->Input("LearningRate")[0] ||
+        upstream_node->Name() == old_desc->Input("Param")[0]) {
+      ReplaceUpstreamNode(upstream_node, optimize_node, sgd_node);
+    }
+  }
+
+  VLOG(3) << "Create new opt node" << sgd_node->Name() << "_" << sgd_node->id();
+
+  return sgd_node;
+}
+
+std::vector<ir::Node*> LockFreeOptimizePass::FindConnectedNode(
+    ir::Node* upstream_node, ir::Node* downstream_node) const {
+  std::vector<ir::Node*> result;
+  for (ir::Node* out_node : upstream_node->outputs) {
+    for (ir::Node* in_node : downstream_node->inputs) {
+      if (in_node == out_node) {
+        result.push_back(in_node);
+      }
+    }
+  }
+
+  return result;
+}
+
+void LockFreeOptimizePass::ReplaceUpstreamNode(
+    ir::Node* upstream_node, ir::Node* old_optimizer_node,
+    ir::Node* new_optimizer_node) const {
+  PADDLE_ENFORCE(upstream_node);
+  PADDLE_ENFORCE(old_optimizer_node);
+  PADDLE_ENFORCE(new_optimizer_node);
+
+  // Remove the old_optimizer_node from upstream_node's outputs vector
+  auto& output_node_vec = upstream_node->outputs;
+  for (auto output_node_iter = output_node_vec.begin();
+       output_node_iter != output_node_vec.end();) {
+    if (*output_node_iter == old_optimizer_node) {
+      output_node_vec.erase(output_node_iter);
+      break;
+    } else {
+      ++output_node_iter;
+    }
+  }
+
+  // Add the new_optimizer_node to upstream_node's outputs vector
+  output_node_vec.emplace_back(new_optimizer_node);
+  new_optimizer_node->inputs.emplace_back(upstream_node);
+}
+
+void LockFreeOptimizePass::ReplaceAllDownstreamNode(
+    ir::Node* old_optimizer_node, ir::Node* new_optimizer_node) const {
+  PADDLE_ENFORCE(old_optimizer_node);
+  PADDLE_ENFORCE(new_optimizer_node);
+
+  for (ir::Node* downstream_node : old_optimizer_node->outputs) {
+    // Remove the old_optimizer_node from downstream_node's inputs vector
+    auto& input_node_vec = downstream_node->inputs;
+    for (auto input_node_iter = input_node_vec.begin();
+         input_node_iter != input_node_vec.end();) {
+      if (*input_node_iter == old_optimizer_node) {
+        input_node_vec.erase(input_node_iter);
+        break;
+      } else {
+        ++input_node_iter;
+      }
+    }
+
+    // Add the new_optimizer_node to downstream_node's inputs vector
+    input_node_vec.emplace_back(new_optimizer_node);
+    new_optimizer_node->outputs.emplace_back(downstream_node);
+  }
+}
+
+ir::Node* LockFreeOptimizePass::FindForwardOpViaBackwardOp(
+    ir::Graph* graph, ir::Node* backward_node) const {
+  PADDLE_ENFORCE(graph);
+  PADDLE_ENFORCE(backward_node);
+
+  // strip the suffix _grad of backward_node's name
+  std::string forward_op_name = backward_node->Name();
+  const std::string suffix("_grad");
+  if (forward_op_name != suffix && forward_op_name.size() > suffix.size() &&
+      forward_op_name.substr(forward_op_name.size() - suffix.size()) ==
+          suffix) {
+    // if so then strip them off
+    forward_op_name =
+        forward_op_name.substr(0, forward_op_name.size() - suffix.size());
+  } else {
+    LOG(WARNING) << "Illegal backward node's name " << backward_node->Name()
+                 << " id " << backward_node->id();
+
+    return nullptr;
+  }
+
+  for (ir::Node* node : graph->Nodes()) {
+    if (node->Name() == forward_op_name) {
+      if (node->outputs.size() == 0u) {
+        // if forward_node has no output, then it has NO grad op
+        continue;
+      }
+
+      // check whether all inputs of the backward_op that ends_with @GRAD
+      // comes from the output of forward_op is the input of the backward_op
+      bool is_related_forward_node = true;
+      for (ir::Node* backward_input : backward_node->inputs) {
+        if (IsVarNameEndsWith(backward_input, kGradVarSuffix)) {
+          bool meets_correct_output = false;
+          for (ir::Node* forward_output : node->outputs) {
+            if (forward_output->Name() + kGradVarSuffix ==
+                backward_input->Name()) {
+              meets_correct_output = true;
+              break;
+            }
+          }
+
+          if (!meets_correct_output) {
+            is_related_forward_node = false;
+            break;
+          }
+        }
+      }
+
+      if (is_related_forward_node) {
+        return node;
+      }
+    }
+  }
+
+  return nullptr;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(lock_free_optimize_pass,
+              paddle::framework::ir::LockFreeOptimizePass);

paddle/fluid/framework/ir/lock_free_optimize_pass.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_
+#define PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_
+
+#include <string>
+#include <vector>
+
+#include <boost/algorithm/string/predicate.hpp>
+
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class Node;
+
+/*
+* Remove the sum op of all gradients of the backward op.
+* And remove the dependecies of the optimizer related to the
+* same backward op.
+*
+* Before this pass:
+*
+* forward_op1 forward_op2
+*     |            |
+*  grad_op1    grad_op2
+*        \      /
+*          \  /
+*         sum_op
+*           |
+*         sgd_op
+*
+* After this pass:
+* forward_op1 forward_op2
+*     |            |
+*  grad_op1    grad_op2
+*     |            |
+*  sgd_op1      sgd_op2
+*
+* sgd_op1 and sgd_op2 will update the same weight which holds the same
+* memory, so we could benefits from the acceleration
+*/
+class LockFreeOptimizePass : public Pass {
+ public:
+  virtual ~LockFreeOptimizePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+
+ private:
+  // Create a new sgd node via current optimizer node
+  ir::Node* CreateNewSGDNode(ir::Graph* graph, ir::Node* forward_node,
+                             ir::Node* backward_node, ir::Node* grad_sum_node,
+                             ir::Node* optimize_node) const;
+
+  // Replace the input weight's optimizers
+  void ReplaceUpstreamNode(ir::Node* upstream_node,
+                           ir::Node* old_optimizer_node,
+                           ir::Node* new_optimizer_node) const;
+
+  // Replace the output weight's optimizers
+  void ReplaceAllDownstreamNode(ir::Node* old_optimizer_node,
+                                ir::Node* new_optimizer_node) const;
+
+  // Find all weight variables in graph
+  bool FindAllWeightVars(ir::Graph* graph) const;
+
+  // Find the forward_op node via the backward_op node
+  ir::Node* FindForwardOpViaBackwardOp(ir::Graph* graph,
+                                       ir::Node* backward_node) const;
+
+  std::vector<ir::Node*> FindConnectedNode(ir::Node* upstream_node,
+                                           ir::Node* downstream_node) const;
+
+  inline bool IsOpNamed(ir::Node* node, const std::string& name) const {
+    PADDLE_ENFORCE(node);
+
+    return node->NodeType() == Node::Type::kOperation && node->Name() == name;
+  }
+
+  inline bool IsVarNamed(ir::Node* node, const std::string& name) const {
+    PADDLE_ENFORCE(node);
+
+    return node->NodeType() == Node::Type::kVariable && node->Name() == name;
+  }
+
+  inline bool IsVarNameEndsWith(ir::Node* node, const std::string& name) const {
+    PADDLE_ENFORCE(node);
+
+    return node->NodeType() == Node::Type::kVariable &&
+           boost::algorithm::ends_with(node->Name(), name);
+  }
+
+  inline bool IsVarNameContains(ir::Node* node, const std::string& name) const {
+    PADDLE_ENFORCE(node);
+
+    return node->NodeType() == Node::Type::kVariable &&
+           node->Name().find(name) != std::string::npos;
+  }
+
+  inline bool IsControlDepFrom(ir::Node* ctrl_dep_node, ir::Node* node) const {
+    PADDLE_ENFORCE(ctrl_dep_node);
+    PADDLE_ENFORCE(node);
+
+    return IsControlDepVar(*ctrl_dep_node) &&
+           ctrl_dep_node->inputs.size() >= 1u &&
+           ctrl_dep_node->inputs[0] == node;
+  }
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+#endif  // PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_

paddle/fluid/framework/ir/seqpool_concat_fuse_pass.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/framework/ir/seqpool_concat_fuse_pass.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/lod_tensor.h"
+
+#define MAX_CONCAT_INPUTS 200
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+PDNode* BuildSeqPoolConcatPattern(PDPattern* pattern,
+                                  const std::string& name_scope,
+                                  int num_inputs) {
+  auto is_concat_op_with_inputs = [](Node* x, int num) -> bool {
+    return x && x->IsOp() && x->Op()->Type() == "concat" &&
+           x->Op()->Input("X").size() == static_cast<size_t>(num);
+  };
+
+  auto is_nth_input_var_of_concat = [=](Node* x, int idx) -> bool {
+    return x && x->IsVar() && VarLinksToOp(x, "concat") &&
+           x->outputs.size() == 1 && IsNthInput(x, x->outputs[0], "X", idx) &&
+           is_concat_op_with_inputs(x->outputs[0], num_inputs);
+  };
+
+  auto is_seqpool_op_with_pootype_of_nth_input_of_concat = [=](
+      Node* x, const std::string& type, int idx) -> bool {
+    bool this_is_seqpool_op =
+        x && x->IsOp() && x->Op()->Type() == "sequence_pool" &&
+        x->Op()->HasAttr("pooltype") &&
+        boost::get<std::string>(x->Op()->GetAttr("pooltype")) == type &&
+        x->outputs.size() == 2;  // seqpool should only have 2 outputs
+    bool satisfied_all = this_is_seqpool_op;
+    if (this_is_seqpool_op) {
+      // Only one output of seqpool_op is nth_input_var of concat,
+      // the other one should be unused empty var.
+      if (is_nth_input_var_of_concat(x->outputs[0], idx)) {
+        satisfied_all = satisfied_all && x->outputs[1]->IsVar() &&
+                        x->outputs[1]->outputs.size() == 0;
+      } else {
+        satisfied_all =
+            satisfied_all && is_nth_input_var_of_concat(x->outputs[1], idx) &&
+            x->outputs[0]->IsVar() && x->outputs[0]->outputs.size() == 0;
+      }
+    }
+    return satisfied_all;
+  };
+
+  auto* concat_op = pattern->NewNode(
+      [=](Node* x) { return is_concat_op_with_inputs(x, num_inputs); },
+      name_scope + "/concat_op");
+  concat_op->assert_op_attr<int>("axis", 1);
+
+  auto* concat_out_var = pattern->NewNode(
+      [=](Node* x) {
+        return x && x->IsVar() && VarLinksFromOp(x, "concat") &&
+               x->inputs.size() == 1 &&
+               is_concat_op_with_inputs(x->inputs[0], num_inputs);
+      },
+      name_scope + "/concat_out_var");
+  concat_out_var->assert_is_only_output_of_op("concat");
+
+  std::vector<PDNode*> seqpool_ops_input_var(num_inputs);
+  std::vector<PDNode*> seqpool_ops_output_var(num_inputs);
+  std::vector<PDNode*> seqpool_ops_output_unused_var(num_inputs);
+  std::vector<PDNode*> seqpool_ops(num_inputs);
+
+  for (int i = 0; i < num_inputs; ++i) {
+    seqpool_ops_output_var[i] = pattern->NewNode(
+        [=](Node* x) {
+          return x && x->IsVar() && is_nth_input_var_of_concat(x, i) &&
+                 x->inputs.size() == 1 &&
+                 is_seqpool_op_with_pootype_of_nth_input_of_concat(x->inputs[0],
+                                                                   "SUM", i);
+        },
+        name_scope + "/sequence_pool_out_" + std::to_string(i));
+
+    seqpool_ops_output_unused_var[i] = pattern->NewNode(
+        [=](Node* x) {
+          return x && x->IsVar() && x->inputs.size() == 1 &&
+                 x->outputs.size() == 0 &&
+                 is_seqpool_op_with_pootype_of_nth_input_of_concat(x->inputs[0],
+                                                                   "SUM", i);
+        },
+        name_scope + "/sequence_pool_unused_out_" + std::to_string(i));
+
+    seqpool_ops[i] = pattern->NewNode(
+        [=](Node* x) {
+          return x && x->IsOp() &&
+                 is_seqpool_op_with_pootype_of_nth_input_of_concat(x, "SUM", i);
+        },
+        name_scope + "/sequence_pool_op_" + std::to_string(i));
+
+    seqpool_ops_input_var[i] = pattern->NewNode(
+        [=](Node* x) {
+          bool basic = x && x->IsVar() && x->outputs.size() >= 1;
+          bool next_is_fine = false;
+          for (auto* o : x->outputs) {
+            if (is_seqpool_op_with_pootype_of_nth_input_of_concat(o, "SUM",
+                                                                  i)) {
+              next_is_fine = true;
+              break;
+            }
+          }
+          return basic && next_is_fine;
+        },
+        name_scope + "/sequence_pool_in_" + std::to_string(i));
+
+    // Links
+    seqpool_ops[i]
+        ->LinksFrom({seqpool_ops_input_var[i]})
+        .LinksTo({seqpool_ops_output_var[i], seqpool_ops_output_unused_var[i]});
+  }
+  concat_op->LinksFrom(seqpool_ops_output_var).LinksTo({concat_out_var});
+  return concat_out_var;
+}
+
+int BuildFusion(Graph* graph, const std::string& name_scope, int num_inputs) {
+  GraphPatternDetector gpd;
+  auto* pattern = gpd.mutable_pattern();
+  BuildSeqPoolConcatPattern(pattern, name_scope, num_inputs);
+
+  auto retrieve_node = [](const std::string& name,
+                          const GraphPatternDetector::subgraph_t& subgraph,
+                          const PDPattern& pat) -> Node* {
+    PADDLE_ENFORCE(subgraph.count(pat.RetrieveNode(name)),
+                   "pattern has no Node called %s", name.c_str());
+    Node* p = subgraph.at(pat.RetrieveNode(name));
+    PADDLE_ENFORCE_NOT_NULL(p, "subgraph has no node %s", name.c_str());
+    return p;
+  };
+
+  int fusion_count{0};
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    VLOG(4) << "handle SeqPool Concat fuse";
+    std::vector<std::string> input_names(num_inputs);
+    std::vector<Node*> input_vars(num_inputs);
+    auto& fused_pattern = gpd.pattern();
+    for (int i = 0; i < num_inputs; ++i) {
+      input_vars[i] =
+          retrieve_node(name_scope + "/sequence_pool_in_" + std::to_string(i),
+                        subgraph, fused_pattern);
+      input_names[i] = input_vars[i]->Name();
+    }
+    auto* concat_op =
+        retrieve_node(name_scope + "/concat_op", subgraph, fused_pattern);
+    auto* concat_out_var =
+        retrieve_node(name_scope + "/concat_out_var", subgraph, fused_pattern);
+    auto* seqpool_op0 = retrieve_node(name_scope + "/sequence_pool_op_0",
+                                      subgraph, fused_pattern);
+
+    // Create New OpDesc
+    OpDesc op_desc;
+    op_desc.SetType("fusion_seqpool_concat");
+    op_desc.SetInput("X", input_names);
+    op_desc.SetAttr("pooltype", seqpool_op0->Op()->GetAttr("pooltype"));
+    op_desc.SetAttr("axis", concat_op->Op()->GetAttr("axis"));
+    op_desc.SetOutput("Out", {concat_out_var->Name()});
+    auto* op = graph->CreateOpNode(&op_desc);
+    for (size_t i = 0; i < input_vars.size(); ++i) {
+      IR_NODE_LINK_TO(input_vars[i], op);
+    }
+    IR_NODE_LINK_TO(op, concat_out_var);
+
+    std::unordered_set<const Node*> marked_nodes;
+    for (auto& item : subgraph) {
+      marked_nodes.insert(item.second);
+    }
+    for (size_t i = 0; i < input_vars.size(); ++i) {
+      marked_nodes.erase(input_vars[i]);
+    }
+    marked_nodes.erase(concat_out_var);
+    GraphSafeRemoveNodes(graph, marked_nodes);
+    ++fusion_count;
+  };
+
+  gpd(graph, handler);
+  return fusion_count;
+}
+
+std::unique_ptr<ir::Graph> SeqPoolConcatFusePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  FusePassBase::Init(name_scope_, graph.get());
+  int fusion_count = 0;
+  for (int i = MAX_CONCAT_INPUTS; i > 0; --i) {
+    fusion_count +=
+        BuildFusion(graph.get(), name_scope_ + "/" + std::to_string(i), i);
+  }
+  AddStatis(fusion_count);
+
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(seqpool_concat_fuse_pass,
+              paddle::framework::ir::SeqPoolConcatFusePass);

paddle/fluid/framework/ir/seqpool_concat_fuse_pass.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#pragma once
+
+#include <string>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+/**
+ * Fuse SequencePool(with sum pooltype yet) and Concat;
+ *
+ * Before fuse:
+ *    |         |             |
+ * seq_pool, seq_pool, ... seq_pool
+ *    \         |      ...   /
+ *            concat
+ *              |
+ * After fuse:
+ *    \      |       /
+ *   FusionSeqPoolConcat
+ *           |
+ */
+class SeqPoolConcatFusePass : public FusePassBase {
+ public:
+  virtual ~SeqPoolConcatFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+
+  const std::string name_scope_{"seqpool_concat_fuse"};
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/seqpool_concat_fuse_pass_tester.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/ir/seqpool_concat_fuse_pass.h"
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/op_proto_maker.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void SetOp(ProgramDesc* prog, const std::string& type,
+           const std::vector<std::string>& inputs,
+           const std::vector<std::string>& outputs) {
+  auto* op = prog->MutableBlock(0)->AppendOp();
+  op->SetType(type);
+  if (type == "sequence_pool") {
+    op->SetInput("X", {inputs[0]});
+    std::string pooltype = "SUM";
+    op->SetAttr("pooltype", pooltype);
+    op->SetOutput("MaxIndex", {outputs[0]});
+    op->SetOutput("Out", {outputs[1]});
+  } else if (type == "concat") {
+    op->SetInput("X", inputs);
+    op->SetAttr("axis", 1);
+    op->SetOutput("Out", {outputs[0]});
+  } else {
+    op->SetInput("X", inputs);
+    op->SetOutput("Out", outputs);
+  }
+  op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+              static_cast<int>(OpRole::kForward));
+}
+
+int CountOpType(const ir::Graph* graph,
+                const std::string& op_type = "fusion_seqpool_concat") {
+  int count = 0;
+  for (auto* node : graph->Nodes()) {
+    if (node->IsOp() && node->Op()->Type() == op_type) {
+      ++count;
+    }
+  }
+  return count;
+}
+
+std::unique_ptr<ir::Graph> GetNumNodesOfBeforeAfter(
+    std::unique_ptr<ir::Graph> graph, int* before, int* after,
+    const std::string& pass_type = "seqpool_concat_fuse_pass") {
+  auto pass = PassRegistry::Instance().Get(pass_type);
+  *before = graph->Nodes().size();
+  graph = pass->Apply(std::move(graph));
+  *after = graph->Nodes().size();
+  return graph;
+}
+
+/*
+ * Before fuse:
+ *    a         b         c
+ *    |         |         |
+ *   op1       op2       op3
+ *   / \       / \       / \
+ *  d  e      f   g     h   i
+ *      \         |        /
+ *            concat
+ *              |
+ *              j
+ * Type of op1, op2 and op3 are sequence_pool, with "SUM" pooltype attr
+ *
+ * After fuse:
+ *    a         b         c
+ *    \         |        /
+ *    fusion_seqpool_concat
+ *              |
+ *              j
+ */
+TEST(SeqPoolConcatFusePass, basic) {
+  ProgramDesc prog;
+  for (auto& v : std::vector<std::string>(
+           {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j"})) {
+    auto* var = prog.MutableBlock(0)->Var(v);
+    var->SetType(proto::VarType::LOD_TENSOR);
+  }
+
+  SetOp(&prog, "sequence_pool", std::vector<std::string>({"a"}),
+        std::vector<std::string>({"d", "e"}));
+  SetOp(&prog, "sequence_pool", std::vector<std::string>({"b"}),
+        std::vector<std::string>({"f", "g"}));
+  SetOp(&prog, "sequence_pool", std::vector<std::string>({"c"}),
+        std::vector<std::string>({"h", "i"}));
+  SetOp(&prog, "concat", std::vector<std::string>({"e", "g", "i"}),
+        std::vector<std::string>({"j"}));
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+  int before, after;
+  graph = GetNumNodesOfBeforeAfter(std::move(graph), &before, &after);
+  // Remove 10 Nodes: op1, op2, op3, d, e, f, g, h, i, concat_op
+  // Add 1 Node: fusion_seqpool_concat
+  EXPECT_EQ(after, before - 9);
+  EXPECT_EQ(CountOpType(graph.get()), 1);
+}
+
+/*
+ * Before fuse:
+ *    a            b
+ *    |           /  \
+ *   op1        op2  op3
+ *   / \        / \    \
+ *  c  d       e   f    g
+ *      \         /
+ *        concat
+ *          |
+ *          h
+ * Type of op1 and op2 are sequence_pool, with "SUM" pooltype attr
+ *
+ * After fuse:
+ *   a                         b
+ *    \                     /     \
+ *    fusion_seqpool_concat       op3
+ *              |                  |
+ *              h                  g
+ */
+TEST(SeqPoolConcatFusePass, advanced) {
+  ProgramDesc prog;
+  for (auto& v :
+       std::vector<std::string>({"a", "b", "c", "d", "e", "f", "g", "h"})) {
+    auto* var = prog.MutableBlock(0)->Var(v);
+    var->SetType(proto::VarType::LOD_TENSOR);
+  }
+
+  SetOp(&prog, "sequence_pool", std::vector<std::string>({"a"}),
+        std::vector<std::string>({"c", "d"}));
+  SetOp(&prog, "sequence_pool", std::vector<std::string>({"b"}),
+        std::vector<std::string>({"e", "f"}));
+  SetOp(&prog, "op3", std::vector<std::string>({"b"}),
+        std::vector<std::string>({"g"}));
+  SetOp(&prog, "concat", std::vector<std::string>({"d", "f"}),
+        std::vector<std::string>({"h"}));
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+  int before, after;
+  graph = GetNumNodesOfBeforeAfter(std::move(graph), &before, &after);
+  // Remove 7 Nodes: op1, op2, c, d, e, f concat_op
+  // Add 1 Node: fusion_seqpool_concat
+  EXPECT_EQ(after, before - 6);
+  EXPECT_EQ(CountOpType(graph.get()), 1);
+}
+
+ProgramDesc BuildProgramDesc(int num_inputs_of_concat) {
+  ProgramDesc prog;
+  auto new_var = [&](const std::string& name) {
+    auto* var = prog.MutableBlock(0)->Var(name);
+    var->SetType(proto::VarType::LOD_TENSOR);
+  };
+  std::vector<std::string> concat_inputs;
+  for (int i = 0; i < num_inputs_of_concat; ++i) {
+    std::string prefix = "seqpool_op_" + i;
+    new_var(prefix + "in");
+    new_var(prefix + "out");
+    new_var(prefix + "out_unused");
+    SetOp(&prog, "sequence_pool", std::vector<std::string>({prefix + "in"}),
+          std::vector<std::string>({prefix + "out", prefix + "out_unused"}));
+    concat_inputs.push_back(prefix + "out");
+  }
+  SetOp(&prog, "concat", concat_inputs,
+        std::vector<std::string>({"concat_out"}));
+  return prog;
+}
+
+// test more inputs of concat
+TEST(SeqPoolConcatFusePass, more_inputs) {
+  for (int num : {1, 2, 10}) {
+    ProgramDesc prog = BuildProgramDesc(num);
+    std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+    int before, after;
+    graph = GetNumNodesOfBeforeAfter(std::move(graph), &before, &after);
+    // Remove Nodes: n * (seqpool_op, out, out_unused), and concat_op
+    // Add Node: fusion_seqpool_concat op
+    EXPECT_EQ(after, before - num * 3);
+    EXPECT_EQ(CountOpType(graph.get()), 1);
+  }
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(seqpool_concat_fuse_pass);

paddle/fluid/framework/naive_executor.cc

@@ -40,14 +40,14 @@ void NaiveExecutor::Prepare(Scope *scope, const ProgramDesc &program_desc,
 
 void NaiveExecutor::Run() {
 #ifndef PADDLE_ON_INFERENCE
-  LOG_FIRST_N(WARNING, 15) << "The NaiveExecutor can not work properly if the "
-                              "cmake flag ON_INFER is not set.";
-  LOG_FIRST_N(WARNING, 15) << "Unlike the training phase, all the scopes and "
-                              "variables will be reused to save the allocation "
-                              "overhead.";
-  LOG_FIRST_N(WARNING, 15) << "Please re-compile the inference library by "
-                              "setting the cmake flag ON_INFER=ON if you are "
-                              "running Paddle Inference";
+  LOG_FIRST_N(WARNING, 5) << "The NaiveExecutor can not work properly if the "
+                             "cmake flag ON_INFER is not set.";
+  LOG_FIRST_N(WARNING, 5) << "Unlike the training phase, all the scopes and "
+                             "variables will be reused to save the allocation "
+                             "overhead.";
+  LOG_FIRST_N(WARNING, 5) << "Please re-compile the inference library by "
+                             "setting the cmake flag ON_INFER=ON if you are "
+                             "running Paddle Inference";
 #endif  // PADDLE_ON_INFERENCE
   for (auto &op : ops_) {
     VLOG(3) << std::this_thread::get_id() << " run " << op->Type()

paddle/fluid/framework/ngraph_bridge.cc

@@ -32,8 +32,11 @@ std::map<std::string,
                                 std::string, std::shared_ptr<ngraph::Node>>>)>>
     NgraphBridge::NG_NODE_MAP = {
         {"fill_constant", paddle::operators::ngraphs::BuildFillConstantNode},
+        {"mean", paddle::operators::ngraphs::BuildMeanNode},
+        {"mean_grad", paddle::operators::ngraphs::BuildMeanGradNode},
         {"mul", paddle::operators::ngraphs::BuildMulNode},
         {"mul_grad", paddle::operators::ngraphs::BuildMulGradNode},
+        {"scale", paddle::operators::ngraphs::BuildScaleNode},
         {"relu", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Relu>},
         {"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>},
         {"top_k", paddle::operators::ngraphs::BuildTopKNode}};

paddle/fluid/framework/ngraph_operator.cc

@@ -539,7 +539,7 @@ void NgraphEngine::Run(const Scope& scope, const platform::Place& place) const {
     }
   }
 
-  backend_->call(ngraph_function_, t_out, t_in);
+  backend_->call(backend_->compile(ngraph_function_), t_out, t_in);
 }  // NgraphEngine::RunImpl
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/operator.h

@@ -391,7 +391,7 @@ class ExecutionContext {
     PADDLE_ENFORCE(
         dynamic_cast<platform::TemporaryAllocation*>(allocation_ptr) != nullptr,
         "The AllocationPtr must be TemporaryAllocation.");
-    PADDLE_ENFORCE_EQ(allocation_ptr->size(),
+    PADDLE_ENFORCE_GE(allocation_ptr->size(),
                       framework::product(dim) * sizeof(T));
 
     paddle::framework::Tensor temp_tensor(

paddle/fluid/framework/parallel_executor.cc

@@ -21,12 +21,9 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/ir/graph.h"
 
-#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-#include "paddle/fluid/platform/nccl_helper.h"
-#endif
-
 #include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/details/parallel_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/reference_count_pass_helper.h"
 #include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
@@ -38,6 +35,8 @@ limitations under the License. */
 DEFINE_string(pe_profile_fname, "",
               "Profiler filename for PE, which generated by gperftools."
               "Only valid when compiled `WITH_PRIFILER=ON`. Empty if disable.");
+DEFINE_bool(enable_parallel_graph, false,
+            "Force disable parallel graph execution mode if set false.");
 
 namespace paddle {
 namespace framework {
@@ -106,6 +105,7 @@ class ParallelExecutorPrivate {
   bool own_local_scope_;
   bool use_cuda_;
   bool use_all_reduce_;
+  size_t nranks_;
 
   // global_ref_cnts_ is only initialized when ParallelExecutor constructs, and
   // then keeps unchanged
@@ -193,14 +193,14 @@ ParallelExecutor::ParallelExecutor(
     const std::unordered_set<std::string> &bcast_vars,
     const ProgramDesc &main_program, const std::string &loss_var_name,
     Scope *scope, const std::vector<Scope *> &local_scopes,
-    const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy,
-    size_t num_trainers, size_t trainer_id)
+    const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy)
     : member_(new ParallelExecutorPrivate(places)) {
   member_->global_scope_ = scope;
   member_->use_cuda_ = exec_strategy.use_cuda_;
   member_->build_strategy_ = build_strategy;
   member_->use_all_reduce_ =
       build_strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce;
+  member_->nranks_ = build_strategy.num_trainers_ * places.size();
 
   if (!member_->use_all_reduce_) {
     PADDLE_ENFORCE(places.size() > 1,
@@ -224,62 +224,99 @@ ParallelExecutor::ParallelExecutor(
     }
   }
 
+  // FIXME(Yancey1989): parallel graph mode get better performance
+  // in GPU allreduce distributed training. Need an elegant way to
+  // choice the execution strategy.
+  build_strategy.enable_parallel_graph_ =
+      EnableParallelGraphExecution(main_program, exec_strategy, build_strategy);
+
+  VLOG(1) << "Enable ParallelGraph Execution: "
+          << build_strategy.enable_parallel_graph_;
+
   if (member_->use_cuda_) {
 // Bcast Parameters to all GPUs
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-    auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
     ncclUniqueId *nccl_id = nullptr;
+    // gen_nccl_id operator can broadcast the ncclUniqueId for nccl2 collective
+    // distributed training
+    auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
     if (nccl_id_var != nullptr) {
       nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
     }
+    if (build_strategy.enable_parallel_graph_ && member_->nranks_ > 1UL) {
+      if (nccl_id == nullptr) {
+        local_nccl_id_.reset(new ncclUniqueId());
+        platform::dynload::ncclGetUniqueId(local_nccl_id_.get());
+        nccl_id = local_nccl_id_.get();
+      }
+    }
+
     member_->nccl_ctxs_.reset(new platform::NCCLContextMap(
-        member_->places_, nccl_id, num_trainers, trainer_id));
+        member_->places_, nccl_id, build_strategy.num_trainers_,
+        build_strategy.trainer_id_));
 #else
     PADDLE_THROW("Not compiled with CUDA");
 #endif
   }
-
   if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
     BCastParamsToDevices(bcast_vars);
   }
-// Startup Program has been run. All local scopes has correct parameters.
+  // Startup Program has been run. All local scopes has correct parameters.
 
-// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
-// ncclOp
+  // Step 2. Convert main_program to SSA form and dependency graph. Also, insert
+  // ncclOp
+  std::vector<std::unique_ptr<ir::Graph>> graphs;
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+  if (build_strategy.enable_parallel_graph_) {
+    for (size_t i = 0; i < member_->places_.size(); ++i) {
+      std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
+          main_program, {member_->places_[i]}, loss_var_name,
+          {member_->local_scopes_[i]}, member_->nranks_, member_->use_cuda_,
+          member_->nccl_ctxs_.get());
+      graphs.push_back(std::move(graph));
+    }
+  } else {
+    std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
+        main_program, member_->places_, loss_var_name, member_->local_scopes_,
+        member_->nranks_, member_->use_cuda_, member_->nccl_ctxs_.get());
+    graphs.push_back(std::move(graph));
+  }
+#else
   std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
       main_program, member_->places_, loss_var_name, member_->local_scopes_,
-      member_->use_cuda_, member_->nccl_ctxs_.get());
-#else
-  std::unique_ptr<ir::Graph> graph =
-      build_strategy.Apply(main_program, member_->places_, loss_var_name,
-                           member_->local_scopes_, member_->use_cuda_);
+      member_->nranks_, member_->use_cuda_);
+  graphs.push_back(std::move(graph));
 #endif
   auto max_memory_size = GetEagerDeletionThreshold();
   if (max_memory_size >= 0) {
-    graph = member_->PrepareGCAndRefCnts(std::move(graph),
-                                         static_cast<size_t>(max_memory_size));
+    for (size_t i = 0; i < graphs.size(); ++i) {
+      graphs[i] = member_->PrepareGCAndRefCnts(
+          std::move(graphs[i]), static_cast<size_t>(max_memory_size));
+    }
   }
 
   // Step 3. Create vars in each scope. Passes may also create new vars.
   //         skip control vars and empty vars
   std::vector<details::VariableInfo> var_infos;
-  for (auto &node : graph->Nodes()) {
-    if (node->IsVar() && !node->IsCtrlVar() && node->Var()) {
-      var_infos.emplace_back();
-      var_infos.back().name_ = node->Var()->Name();
-      var_infos.back().type_ = node->Var()->GetType();
-      var_infos.back().persistable_ = node->Var()->Persistable();
+  for (auto &graph : graphs) {
+    for (auto &node : graph->Nodes()) {
+      if (node->IsVar() && !node->IsCtrlVar() && node->Var()) {
+        var_infos.emplace_back();
+        var_infos.back().name_ = node->Var()->Name();
+        var_infos.back().type_ = node->Var()->GetType();
+        var_infos.back().persistable_ = node->Var()->Persistable();
+      }
     }
   }
+
   // If the loss_var_name is given, the number of graph should be only one.
   if (loss_var_name.size()) {
-    size_t graph_num = ir::GraphNum(*graph);
+    size_t graph_num = ir::GraphNum(*graphs[0]);
     if (graph_num > 1) {
       LOG(WARNING)
           << "The number of graph should be only one, "
              "but the current graph has "
-          << ir::GraphNum(*graph)
+          << ir::GraphNum(*graphs[0])
           << " sub_graphs. If you want to see the nodes of the "
              "sub_graphs, you should use 'FLAGS_print_sub_graph_dir' "
              "to specify the output dir. NOTES: if you not do training, "
@@ -287,14 +324,20 @@ ParallelExecutor::ParallelExecutor(
     }
   }
 
-  if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
-    member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
+  if (build_strategy.enable_parallel_graph_) {
+    member_->executor_.reset(new details::ParallelSSAGraphExecutor(
         exec_strategy, member_->local_scopes_, member_->places_,
-        std::move(graph)));
+        std::move(graphs)));
   } else {
-    member_->executor_.reset(new details::FastThreadedSSAGraphExecutor(
-        exec_strategy, member_->local_scopes_, member_->places_,
-        std::move(graph)));
+    if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
+      member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
+          exec_strategy, member_->local_scopes_, member_->places_,
+          std::move(graphs[0])));
+    } else {
+      member_->executor_.reset(new details::FastThreadedSSAGraphExecutor(
+          exec_strategy, member_->local_scopes_, member_->places_,
+          std::move(graphs[0])));
+    }
   }
 
   member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
@@ -423,6 +466,36 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
   }
 }
 
+bool ParallelExecutor::EnableParallelGraphExecution(
+    const ProgramDesc &main_program, const ExecutionStrategy &exec_strategy,
+    const BuildStrategy &build_strategy) const {
+  if (!FLAGS_enable_parallel_graph) return false;
+
+  bool enable_parallel_graph = true;
+  // TODO(Yancey1989): support sparse update in ParallelGraph mode.
+  for (auto &var_desc : main_program.Block(0).AllVars()) {
+    if (var_desc->GetType() == proto::VarType::SELECTED_ROWS) {
+      enable_parallel_graph = false;
+    }
+  }
+
+  // TODO(Yancey1989): support pserver mode
+  for (auto &op_desc : main_program.Block(0).AllOps()) {
+    if (op_desc->Type() == "send" || op_desc->Type() == "recv") {
+      enable_parallel_graph = false;
+      break;
+    }
+  }
+
+  if (!member_->use_all_reduce_ || !member_->use_cuda_)
+    enable_parallel_graph = false;
+
+  if (build_strategy.enable_sequential_execution_ ||
+      exec_strategy.type_ == ExecutionStrategy::ExecutorType::kExperimental)
+    enable_parallel_graph = false;
+  return enable_parallel_graph;
+}
+
 ParallelExecutor::~ParallelExecutor() {
   for (auto &p : member_->places_) {
     platform::DeviceContextPool::Instance().Get(p)->Wait();

paddle/fluid/framework/parallel_executor.h

@@ -28,6 +28,10 @@ limitations under the License. */
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/platform/device_context.h"
 
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+#include "paddle/fluid/platform/nccl_helper.h"
+#endif
+
 namespace paddle {
 namespace framework {
 
@@ -46,8 +50,7 @@ class ParallelExecutor {
                             const std::string &loss_var_name, Scope *scope,
                             const std::vector<Scope *> &local_scopes,
                             const ExecutionStrategy &exec_strategy,
-                            const BuildStrategy &build_strategy,
-                            size_t num_trainers = 1, size_t trainer_id = 0);
+                            const BuildStrategy &build_strategy);
 
   ~ParallelExecutor();
 
@@ -68,8 +71,14 @@ class ParallelExecutor {
 
  private:
   void BCastParamsToDevices(const std::unordered_set<std::string> &vars) const;
+  bool EnableParallelGraphExecution(const ProgramDesc &main_program,
+                                    const ExecutionStrategy &exec_strategy,
+                                    const BuildStrategy &build_strategy) const;
 
   ParallelExecutorPrivate *member_;
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+  std::unique_ptr<ncclUniqueId> local_nccl_id_;
+#endif
 };
 
 }  // namespace framework

paddle/fluid/framework/scope.cc

@@ -87,11 +87,12 @@ Variable* Scope::Var(const std::string& name) {
 }
 
 Variable* Scope::Var(std::string* name) {
-  auto new_name = string::Sprintf("%p.%d", this, vars_.size());
+  SCOPE_VARS_WRITER_LOCK
+  auto new_name = std::to_string(reinterpret_cast<uintptr_t>(this)) + "." +
+                  std::to_string(vars_.size());
   if (name != nullptr) {
     *name = new_name;
   }
-  SCOPE_VARS_WRITER_LOCK
   return VarInternal(new_name);
 }
 

paddle/fluid/framework/threadpool.cc

@@ -89,7 +89,6 @@ void ThreadPool::TaskLoop() {
       task = std::move(tasks_.front());
       tasks_.pop();
     }
-
     // run the task
     task();
   }

paddle/fluid/framework/var_type_traits.cc

@@ -105,13 +105,15 @@ struct VarIdToTypeIndexMapHolder {
 
 }  // namespace detail
 
-const std::type_index &ToTypeIndex(int var_id) {
+const std::type_index &VarTraitIdToTypeIndex(int var_id) {
   return detail::VarIdToTypeIndexMapHolder::ToTypeIndex(var_id);
 }
 
-const char *ToTypeName(int var_id) { return ToTypeIndex(var_id).name(); }
+const char *ToTypeName(int var_id) {
+  return VarTraitIdToTypeIndex(var_id).name();
+}
 
-int ToTypeId(const std::type_index &type) {
+int TypeIndexToVarTraitId(const std::type_index &type) {
   return detail::VarIdToTypeIndexMapHolder::ToTypeId(type);
 }
 

paddle/fluid/framework/var_type_traits.h

@@ -66,8 +66,8 @@ namespace paddle {
 namespace framework {
 
 const char *ToTypeName(int var_id);
-const std::type_index &ToTypeIndex(int var_id);
-int ToTypeId(const std::type_index &type);
+const std::type_index &VarTraitIdToTypeIndex(int var_id);
+int TypeIndexToVarTraitId(const std::type_index &type);
 
 namespace detail {
 

paddle/fluid/framework/var_type_traits_test.cc

@@ -45,10 +45,11 @@ struct TypeIndexChecker {
     constexpr auto kId = VarTypeTrait<Type>::kId;
     std::type_index actual_type(typeid(Type));
     EXPECT_EQ(std::string(ToTypeName(kId)), std::string(actual_type.name()));
-    EXPECT_EQ(ToTypeIndex(kId), actual_type);
-    EXPECT_EQ(ToTypeId(actual_type), kId);
-    EXPECT_EQ(ToTypeIndex(ToTypeId(actual_type)), actual_type);
-    EXPECT_EQ(ToTypeId(ToTypeIndex(kId)), kId);
+    EXPECT_EQ(VarTraitIdToTypeIndex(kId), actual_type);
+    EXPECT_EQ(TypeIndexToVarTraitId(actual_type), kId);
+    EXPECT_EQ(VarTraitIdToTypeIndex(TypeIndexToVarTraitId(actual_type)),
+              actual_type);
+    EXPECT_EQ(TypeIndexToVarTraitId(VarTraitIdToTypeIndex(kId)), kId);
 
     EXPECT_TRUE(var_id_set->count(kId) == 0);              // NOLINT
     EXPECT_TRUE(type_index_set->count(actual_type) == 0);  // NOLINT

paddle/fluid/imperative/layer.h

@@ -80,6 +80,7 @@ class PreparedOp {
   framework::OperatorWithKernel::OpKernelFunc func;
   platform::DeviceContext* dev_ctx;
 };
+
 class OpBase;
 
 /* The wrapper for Variable which holds a Variable and a VarBase of its

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -80,8 +80,8 @@ void TestWord2vecPrediction(const std::string& model_path) {
        i++) {
     LOG(INFO) << "data: " << static_cast<float*>(outputs.front().data.data())[i]
               << " result: " << result[i];
-    PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
-                   result[i]);
+    EXPECT_NEAR(static_cast<float*>(outputs.front().data.data())[i], result[i],
+                1e-3);
   }
 }
 

paddle/fluid/inference/analysis/argument.h

@@ -123,8 +123,6 @@ struct Argument {
   DECL_ARGUMENT_FIELD(use_gpu, UseGPU, bool);
   DECL_ARGUMENT_FIELD(gpu_device_id, GPUDeviceId, int);
   DECL_ARGUMENT_FIELD(use_tensorrt, UseTensorRT, bool);
-  DECL_ARGUMENT_FIELD(tensorrt_node_teller, TensorRtNodeTeller,
-                      std::function<bool(const framework::ir::Node*)>);
   DECL_ARGUMENT_FIELD(tensorrt_max_batch_size, TensorRtMaxBatchSize, int);
   DECL_ARGUMENT_FIELD(tensorrt_workspace_size, TensorRtWorkspaceSize, int);
   DECL_ARGUMENT_FIELD(tensorrt_min_subgraph_size, TensorRtMinSubgraphSize, int);

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -49,13 +49,6 @@ void IRPassManager::CreatePasses(Argument *argument,
   for (const std::string &pass_name : passes) {
     auto pass = framework::ir::PassRegistry::Instance().Get(pass_name);
 
-    // Set some pass attributes.
-    if (pass_name == "ir_analysis_pass") {
-      pass->Set("tensorrt_node_teller",
-                new SubgraphDetector::NodeInsideSubgraphTeller(
-                    argument->tensorrt_node_teller()));
-    }
-
     if (pass_name == "graph_viz_pass") {
       std::string dot_file_path = std::to_string(pass_num) + "_ir_" +
                                   (pre_pass.empty() ? "origin" : pre_pass) +
@@ -70,9 +63,6 @@ void IRPassManager::CreatePasses(Argument *argument,
     }
 
     if (pass_name == "tensorrt_subgraph_pass") {
-      PADDLE_ENFORCE(argument->tensorrt_node_teller_valid());
-      pass->SetNotOwned("tensorrt_node_teller",
-                        argument->tensorrt_node_teller_ptr());
       pass->Set("workspace_size", new int(argument->tensorrt_workspace_size()));
       pass->Set("max_batch_size", new int(argument->tensorrt_max_batch_size()));
       pass->Set("min_subgraph_size",

paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt

 cc_library(subgraph_detector SRCS subgraph_detector.cc DEPS proto_desc)
-cc_library(tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass.cc DEPS subgraph_detector)
-set(analysis_deps ${analysis_deps}
-        subgraph_detector tensorrt_subgraph_pass
-        CACHE INTERNAL "")
 
-set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
-file(APPEND ${pass_file} "USE_PASS(tensorrt_subgraph_pass);\n")
-set(INFER_IR_PASSES ${INFER_IR_PASSES} tensorrt_subgraph_pass CACHE INTERNAL "")
+if (TENSORRT_FOUND)
+  cc_library(tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass.cc DEPS subgraph_detector tensorrt_op_teller)
+
+  set(analysis_deps ${analysis_deps}
+          subgraph_detector tensorrt_subgraph_pass
+          CACHE INTERNAL "")
+
+  set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
+  file(APPEND ${pass_file} "USE_PASS(tensorrt_subgraph_pass);\n")
+  set(INFER_IR_PASSES ${INFER_IR_PASSES} tensorrt_subgraph_pass CACHE INTERNAL "")
+endif()

paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc

@@ -20,6 +20,7 @@
 #include "paddle/fluid/inference/analysis/helper.h"
 #include "paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h"
 #include "paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.h"
+#include "paddle/fluid/inference/tensorrt/op_teller.h"
 
 namespace paddle {
 namespace inference {
@@ -35,8 +36,10 @@ std::unique_ptr<framework::ir::Graph> analysis::TensorRtSubgraphPass::ApplyImpl(
     std::unique_ptr<framework::ir::Graph> graph) const {
   framework::ir::FusePassBase::Init("tensorrt_subgraph_pass", graph.get());
 
-  auto teller =
-      Get<SubgraphDetector::NodeInsideSubgraphTeller>("tensorrt_node_teller");
+  auto teller = [](const framework::ir::Node *node) {
+    if (!node->IsOp() || !node->Op()) return false;
+    return tensorrt::OpTeller::Global().Tell(node->Op()->Type(), *node->Op());
+  };
 
   SubGraphFuser fuser(graph.get(), teller,
                       Get<int>("min_subgraph_size") /*min subgraph size*/);
@@ -232,7 +235,6 @@ std::vector<std::string> ExtractParameters(
 
 REGISTER_PASS(tensorrt_subgraph_pass,
               paddle::inference::analysis::TensorRtSubgraphPass)
-    .RequirePassAttr("tensorrt_node_teller")
     .RequirePassAttr("max_batch_size")
     .RequirePassAttr("workspace_size")
     .RequirePassAttr("min_subgraph_size");

paddle/fluid/inference/analysis/passes/CMakeLists.txt

@@ -7,4 +7,5 @@ set(analysis_deps ${analysis_deps}
         ir_graph_build_pass
         ir_analysis_pass
         analysis_passes
+        subgraph_detector
         CACHE INTERNAL "")

paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.cc

@@ -27,9 +27,6 @@ namespace analysis {
 
 void IrAnalysisComposePass::RunImpl(Argument *argument) {
   ARGUMENT_CHECK_FIELD(argument, ir_analysis_passes);
-  if (argument->use_tensorrt_valid() && argument->use_tensorrt()) {
-    InitTensorRTAttrs(argument);
-  }
   ApplyIrPasses(argument);
   CollectFusionStatis(argument);
 }
@@ -38,26 +35,6 @@ std::string IrAnalysisComposePass::repr() const {
   return "ir-analysis-compose-pass";
 }
 
-void IrAnalysisComposePass::InitTensorRTAttrs(Argument *argument) {
-  if (argument->use_tensorrt_valid() && argument->use_tensorrt()) {
-    LOG(INFO) << "Initing TensorRT pass";
-    argument->SetTensorRtNodeTeller([](const framework::ir::Node *node) {
-      std::unordered_set<std::string> teller_set(
-          {"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
-           "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
-           "elementwise_add", "elementwise_mul", "dropout", "split", "prelu",
-           "conv2d_transpose", "leaky_relu"});
-      if (!node->IsOp()) return false;
-
-      if (teller_set.count(node->Op()->Type())) {
-        return true;
-      } else {
-        return false;
-      }
-    });
-  }
-}
-
 void IrAnalysisComposePass::ApplyIrPasses(Argument *argument) {
   std::vector<std::string> passes({
       "ir_graph_build_pass", "ir_analysis_pass",

paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.h

@@ -33,8 +33,6 @@ class IrAnalysisComposePass : public AnalysisPass {
   std::string repr() const override;
 
  private:
-  void InitTensorRTAttrs(Argument* argument);
-
   void ApplyIrPasses(Argument* argument);
 
   void CollectFusionStatis(Argument* argument);

paddle/fluid/inference/api/analysis_config.cc

@@ -14,86 +14,101 @@
 
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/inference/api/paddle_analysis_config.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_pass_builder.h"
 #include "paddle/fluid/platform/enforce.h"
-#include "paddle_pass_builder.h"  // NOLINT
+#include "paddle/fluid/platform/gpu_info.h"
 
 namespace paddle {
 
 PassStrategy *contrib::AnalysisConfig::pass_builder() const {
-  PADDLE_ENFORCE(
-      pass_builder_.get(),
-      "Should call constructor first, that will init the pass_builder_.");
+  if (!pass_builder_.get()) {
+    if (use_gpu_) {
+      LOG(INFO) << "Create GPU IR passes";
+      pass_builder_.reset(new GpuPassStrategy);
+    } else {
+      LOG(INFO) << "Create CPU IR passes";
+      pass_builder_.reset(new CpuPassStrategy);
+    }
+  } else if (pass_builder_->use_gpu() ^ use_gpu()) {
+    LOG(WARNING) << "The use_gpu flag is not compatible between Config and "
+                    "PassBuilder, the flags are "
+                 << use_gpu() << " " << pass_builder_->use_gpu();
+    LOG(WARNING) << "Please make them compatible, still use the existing "
+                    "PassBuilder.";
+  }
+
   return pass_builder_.get();
 }
 
-contrib::AnalysisConfig::AnalysisConfig(bool use_gpu) {
-  this->use_gpu = use_gpu;
-  if (use_gpu) {
-    pass_builder_.reset(new GpuPassStrategy);
-  } else {
-    pass_builder_.reset(new CpuPassStrategy);
-  }
+contrib::AnalysisConfig::AnalysisConfig(const std::string &model_dir) {
+  model_dir_ = model_dir;
+}
+contrib::AnalysisConfig::AnalysisConfig(const std::string &prog_file,
+                                        const std::string &params_file) {
+  prog_file_ = prog_file;
+  params_file_ = params_file;
+}
+void contrib::AnalysisConfig::SetModel(const std::string &prog_file_path,
+                                       const std::string &params_file_path) {
+  prog_file_ = prog_file_path;
+  params_file_ = params_file_path;
+}
+void contrib::AnalysisConfig::EnableUseGpu(uint64_t memory_pool_init_size_mb,
+                                           int device_id) {
+#ifdef PADDLE_WITH_CUDA
+  use_gpu_ = true;
+  memory_pool_init_size_mb_ = memory_pool_init_size_mb;
+  device_id_ = device_id;
+#else
+  LOG(ERROR) << "Please compile with gpu to EnableGpu";
+  use_gpu_ = false;
+#endif
 }
+void contrib::AnalysisConfig::DisableGpu() { use_gpu_ = false; }
 
 contrib::AnalysisConfig::AnalysisConfig(const contrib::AnalysisConfig &other) {
-  // fields from Config
-  model_dir = other.model_dir;
-  // fields from NativeConfig
-  use_gpu = other.use_gpu;
-  device = other.device;
-  fraction_of_gpu_memory = other.fraction_of_gpu_memory;
-  prog_file = other.prog_file;
-  param_file = other.param_file;
-  specify_input_name = other.specify_input_name;
-  cpu_math_library_num_threads_ = other.cpu_math_library_num_threads_;
-  // fields from this.
-  enable_ir_optim = other.enable_ir_optim;
-  // For mkldnn
-  use_mkldnn_ = other.use_mkldnn_;
-  mkldnn_enabled_op_types_ = other.mkldnn_enabled_op_types_;
-
-  use_feed_fetch_ops = other.use_feed_fetch_ops;
-  use_tensorrt_ = other.use_tensorrt_;
-  tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_;
-  tensorrt_workspace_size_ = other.tensorrt_workspace_size_;
-  tensorrt_min_subgraph_size_ = other.tensorrt_min_subgraph_size_;
-  model_from_memory_ = other.model_from_memory_;
-
-  if (use_gpu) {
+#define CP_MEMBER(member__) member__ = other.member__;
+
+  // Model related.
+  CP_MEMBER(model_dir_);
+  CP_MEMBER(prog_file_);
+  CP_MEMBER(params_file_);
+  CP_MEMBER(model_from_memory_);  // the memory model reuses prog_file_ and
+                                  // params_file_ fields.
+  // Gpu releated.
+  CP_MEMBER(use_gpu_);
+  CP_MEMBER(device_id_);
+  CP_MEMBER(memory_pool_init_size_mb_);
+  // TensorRT releated.
+  CP_MEMBER(use_tensorrt_);
+  CP_MEMBER(tensorrt_workspace_size_);
+  CP_MEMBER(tensorrt_max_batchsize_);
+  CP_MEMBER(tensorrt_min_subgraph_size_);
+  // MKLDNN releated.
+  CP_MEMBER(use_mkldnn_);
+  CP_MEMBER(mkldnn_enabled_op_types_);
+
+  // Ir related.
+  CP_MEMBER(enable_ir_optim_);
+  CP_MEMBER(use_feed_fetch_ops_);
+  CP_MEMBER(ir_debug_);
+  CP_MEMBER(specify_input_name_);
+
+  CP_MEMBER(cpu_math_library_num_threads_);
+
+  CP_MEMBER(serialized_info_cache_);
+
+  if (use_gpu_) {
     pass_builder_.reset(new GpuPassStrategy(
         *static_cast<GpuPassStrategy *>(other.pass_builder())));
   } else {
     pass_builder_.reset(new CpuPassStrategy(
         *static_cast<CpuPassStrategy *>(other.pass_builder())));
   }
-}
 
-contrib::AnalysisConfig::AnalysisConfig(contrib::AnalysisConfig &&other) {
-  // fields from Config
-  model_dir = other.model_dir;
-  // fields from NativeConfig
-  use_gpu = other.use_gpu;
-  device = other.device;
-  fraction_of_gpu_memory = other.fraction_of_gpu_memory;
-  prog_file = other.prog_file;
-  param_file = other.param_file;
-  specify_input_name = other.specify_input_name;
-  cpu_math_library_num_threads_ = other.cpu_math_library_num_threads_;
-  // fields from this.
-  enable_ir_optim = other.enable_ir_optim;
-  // For mkldnn
-  use_mkldnn_ = other.use_mkldnn_;
-  mkldnn_enabled_op_types_ = other.mkldnn_enabled_op_types_;
-
-  use_feed_fetch_ops = other.use_feed_fetch_ops;
-  use_tensorrt_ = other.use_tensorrt_;
-  tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_;
-  tensorrt_workspace_size_ = other.tensorrt_workspace_size_;
-  tensorrt_min_subgraph_size_ = other.tensorrt_min_subgraph_size_;
-  model_from_memory_ = other.model_from_memory_;
-
-  pass_builder_ = std::move(other.pass_builder_);
+#undef CP_MEMBER
 }
 
 void contrib::AnalysisConfig::EnableMKLDNN() {
@@ -112,17 +127,90 @@ void contrib::AnalysisConfig::EnableTensorRtEngine(int workspace_size,
   use_tensorrt_ = true;
   tensorrt_workspace_size_ = workspace_size;
   tensorrt_max_batchsize_ = max_batch_size;
-  tensorrt_min_subgraph_size_ = min_subgraph_size;
-  // Append after the conv+affine_channel fuse pass.
-  pass_builder()->InsertPass(3, "tensorrt_subgraph_pass");
+}
+
+void contrib::AnalysisConfig::Update() {
+  auto info = SerializeInfoCache();
+  if (info == serialized_info_cache_) return;
+
+  if (use_gpu_) {
+    pass_builder_.reset(new GpuPassStrategy);
+  } else {
+    pass_builder_.reset(new CpuPassStrategy);
+  }
+
+  if (use_tensorrt_) {
+    if (!use_gpu_) {
+      LOG(ERROR)
+          << "TensorRT engine is not available when EnableGpu() not actived.";
+    } else {
+      // Append after the infer_clean pass.
+      pass_builder()->InsertPass(1, "tensorrt_subgraph_pass");
+    }
+  }
+
+  if (use_mkldnn_) {
+    if (!enable_ir_optim_) {
+      LOG(ERROR)
+          << "EnableMKLDNN() only works when IR optimization is enabled.";
+    }
+#ifdef PADDLE_WITH_MKLDNN
+    pass_builder()->EnableMKLDNN();
+    use_mkldnn_ = true;
+#else
+    LOG(ERROR) << "Please compile with MKLDNN first to use MKLDNN";
+    use_mkldnn_ = false;
+#endif
+  }
+
+  if (ir_debug_) {
+    pass_builder()->TurnOnDebug();
+  }
+}
+
+std::string contrib::AnalysisConfig::SerializeInfoCache() {
+  std::stringstream ss;
+  ss << use_gpu_;
+  ss << memory_pool_init_size_mb_;
+
+  ss << use_tensorrt_;
+  ss << tensorrt_workspace_size_;
+  ss << tensorrt_max_batchsize_;
+
+  ss << use_mkldnn_;
+  ss << enable_ir_optim_;
+  ss << use_feed_fetch_ops_;
+  ss << ir_debug_;
+
+  return ss.str();
+}
+
+void contrib::AnalysisConfig::SetCpuMathLibraryNumThreads(
+    int cpu_math_library_num_threads) {
+  cpu_math_library_num_threads_ = cpu_math_library_num_threads;
+}
+
+float contrib::AnalysisConfig::fraction_of_gpu_memory_for_pool() const {
+#ifdef PADDLE_WITH_CUDA
+  // Get the GPU memory details and calculate the fraction of memory for the
+  // GPU memory pool.
+  size_t gpu_used, gpu_available;
+  platform::GpuMemoryUsage(&gpu_used, &gpu_available);
+  double total_gpu_memory = (gpu_used + gpu_available) / 1024. / 1024.;
+  float fraction_of_gpu_memory =
+      static_cast<double>(memory_pool_init_size_mb()) / total_gpu_memory;
+  return fraction_of_gpu_memory;
+#else
+  return 0.;
+#endif
 }
 
 void contrib::AnalysisConfig::SetModelBuffer(const char *prog_buffer,
                                              size_t prog_buffer_size,
                                              const char *param_buffer,
                                              size_t param_buffer_size) {
-  prog_file = std::string(prog_buffer, prog_buffer + prog_buffer_size);
-  param_file = std::string(param_buffer, param_buffer + param_buffer_size);
+  prog_file_ = std::string(prog_buffer, prog_buffer + prog_buffer_size);
+  params_file_ = std::string(param_buffer, param_buffer + param_buffer_size);
   model_from_memory_ = true;
 }
 

paddle/fluid/inference/api/analysis_predictor.cc

@@ -33,6 +33,7 @@
 #include "paddle/fluid/inference/utils/singleton.h"
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/platform/cpu_helper.h"
+#include "paddle/fluid/platform/gpu_info.h"
 #include "paddle/fluid/platform/profiler.h"
 
 DECLARE_bool(profile);
@@ -59,8 +60,8 @@ bool AnalysisPredictor::Init(
   if (FLAGS_profile) {
     LOG(WARNING) << "Profiler is actived, might affect the performance";
     LOG(INFO) << "You can turn off by set gflags '-profile false'";
-    auto tracking_device = config_.use_gpu ? platform::ProfilerState::kAll
-                                           : platform::ProfilerState::kCPU;
+    auto tracking_device = config_.use_gpu() ? platform::ProfilerState::kAll
+                                             : platform::ProfilerState::kCPU;
     platform::EnableProfiler(tracking_device);
   }
 
@@ -112,7 +113,7 @@ bool AnalysisPredictor::PrepareProgram(
     // Optimize the program, and load parameters and modify them in the
     // scope_.
     // This will change the scope_ address.
-    if (config_.enable_ir_optim) {
+    if (config_.ir_optim()) {
       status_ir_optim_enabled_ = true;
       OptimizeInferenceProgram();
     } else {
@@ -140,9 +141,9 @@ bool AnalysisPredictor::PrepareProgram(
   return true;
 }
 bool AnalysisPredictor::CreateExecutor() {
-  if (config_.use_gpu) {
+  if (config_.use_gpu_) {
     status_use_gpu_ = true;
-    place_ = paddle::platform::CUDAPlace(config_.device);
+    place_ = paddle::platform::CUDAPlace(config_.device_id_);
   } else {
     place_ = paddle::platform::CPUPlace();
   }
@@ -151,7 +152,7 @@ bool AnalysisPredictor::CreateExecutor() {
 }
 bool AnalysisPredictor::PrepareExecutor() {
   executor_->Prepare(sub_scope_, *inference_program_, 0,
-                     config_.use_feed_fetch_ops);
+                     config_.use_feed_fetch_ops_);
 
   PADDLE_ENFORCE_NOT_NULL(sub_scope_);
 
@@ -250,7 +251,7 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
     }
     input.set_lod(lod);
     int idx = -1;
-    if (config_.specify_input_name) {
+    if (config_.specify_input_name_) {
       auto name = inputs[i].name;
       if (feed_names_.find(name) == feed_names_.end()) {
         LOG(ERROR) << "feed names from program do not have name: [" << name
@@ -314,22 +315,22 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
 void AnalysisPredictor::OptimizeInferenceProgram() {
   status_program_optimized_ = true;
 
-  argument_.SetUseGPU(config_.use_gpu);
-  argument_.SetGPUDeviceId(config_.device);
+  argument_.SetUseGPU(config_.use_gpu());
+  argument_.SetGPUDeviceId(config_.gpu_device_id());
   argument_.SetModelFromMemory(config_.model_from_memory_);
   // Analyze inference_program
-  if (!config_.model_dir.empty()) {
-    argument_.SetModelDir(config_.model_dir);
+  if (!config_.model_dir().empty()) {
+    argument_.SetModelDir(config_.model_dir());
   } else {
     PADDLE_ENFORCE(
-        !config_.param_file.empty(),
+        !config_.params_file().empty(),
         "Either model_dir or (param_file, prog_file) should be set.");
-    PADDLE_ENFORCE(!config_.prog_file.empty());
-    argument_.SetModelProgramPath(config_.prog_file);
-    argument_.SetModelParamsPath(config_.param_file);
+    PADDLE_ENFORCE(!config_.prog_file().empty());
+    argument_.SetModelProgramPath(config_.prog_file());
+    argument_.SetModelParamsPath(config_.params_file());
   }
 
-  if (config_.use_gpu && config_.use_tensorrt_) {
+  if (config_.use_gpu() && config_.tensorrt_engine_enabled()) {
     argument_.SetUseTensorRT(true);
     argument_.SetTensorRtWorkspaceSize(config_.tensorrt_workspace_size_);
     argument_.SetTensorRtMaxBatchSize(config_.tensorrt_max_batchsize_);
@@ -341,7 +342,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
   }
 
   auto passes = config_.pass_builder()->AllPasses();
-  if (!config_.enable_ir_optim) passes.clear();
+  if (!config_.ir_optim()) passes.clear();
   argument_.SetIrAnalysisPasses(passes);
   argument_.SetScopeNotOwned(const_cast<framework::Scope *>(scope_.get()));
   Analyzer().Run(&argument_);
@@ -358,18 +359,26 @@ template <>
 std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
     AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) {
   VLOG(3) << "create AnalysisConfig";
-  if (config.use_gpu) {
+  if (config.use_gpu()) {
     // 1. GPU memeroy
-    PADDLE_ENFORCE_GT(
-        config.fraction_of_gpu_memory, 0.f,
-        "fraction_of_gpu_memory in the config should be set to range (0., 1.]");
-    PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
+    PADDLE_ENFORCE_GT(config.memory_pool_init_size_mb(), 0.f);
+    PADDLE_ENFORCE_GE(config.gpu_device_id(), 0, "Invalid device id %d",
+                      config.gpu_device_id());
     std::vector<std::string> flags;
-    if (config.fraction_of_gpu_memory >= 0.0f ||
-        config.fraction_of_gpu_memory <= 0.95f) {
+
+    float fraction_of_gpu_memory = config.fraction_of_gpu_memory_for_pool();
+    if (fraction_of_gpu_memory > 0.95f) {
+      LOG(ERROR)
+          << "Allocate too much memory for the GPU memory pool, assigned "
+          << config.memory_pool_init_size_mb() << " MB";
+      LOG(ERROR)
+          << "Try to shink the value by setting AnalysisConfig::EnableGpu(...)";
+    }
+
+    if (fraction_of_gpu_memory >= 0.0f || fraction_of_gpu_memory <= 0.95f) {
       flags.push_back("dummpy");
       std::string flag = "--fraction_of_gpu_memory_to_use=" +
-                         std::to_string(config.fraction_of_gpu_memory);
+                         std::to_string(fraction_of_gpu_memory);
       flags.push_back(flag);
       VLOG(3) << "set flag: " << flag;
       framework::InitGflags(flags);
@@ -443,22 +452,22 @@ bool AnalysisPredictor::ZeroCopyRun() {
 bool AnalysisPredictor::LoadProgramDesc() {
   // Initialize the inference program
   std::string filename;
-  if (!config_.model_dir.empty()) {
-    filename = config_.model_dir + "/__model__";
-  } else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
+  if (!config_.model_dir().empty()) {
+    filename = config_.model_dir() + "/__model__";
+  } else if (!config_.prog_file().empty() && !config_.params_file().empty()) {
     // All parameters are saved in a single file.
     // The file names should be consistent with that used
     // in Python API `fluid.io.save_inference_model`.
-    filename = config_.prog_file;
+    filename = config_.prog_file();
   } else {
-    if (config_.model_dir.empty() && config_.prog_file.empty()) {
+    if (config_.model_dir().empty() && config_.prog_file().empty()) {
       LOG(ERROR)
           << "Either model_dir or (prog_file, param_file) should be set.";
       return false;
     }
     LOG(ERROR) << string::Sprintf(
-        "not valid model path '%s' or program path '%s'.", config_.model_dir,
-        config_.param_file);
+        "not valid model path '%s' or program path '%s'.", config_.model_dir(),
+        config_.params_file());
     return false;
   }
 
@@ -478,7 +487,7 @@ bool AnalysisPredictor::LoadProgramDesc() {
 
     proto.ParseFromString(pb_content);
   } else {
-    proto.ParseFromString(config_.prog_file);
+    proto.ParseFromString(config_.prog_file());
   }
   inference_program_.reset(new framework::ProgramDesc(proto));
   return true;
@@ -508,27 +517,27 @@ bool AnalysisPredictor::LoadParameters() {
       new_var->SetLoDLevel(var->GetLoDLevel());
       new_var->SetPersistable(true);
 
-      if (!config_.param_file.empty()) {
+      if (!config_.params_file().empty()) {
         params.push_back(new_var->Name());
       } else {
         // append_op
         framework::OpDesc *op = load_block->AppendOp();
         op->SetType("load");
         op->SetOutput("Out", {new_var->Name()});
-        op->SetAttr("file_path", {config_.model_dir + "/" + new_var->Name()});
+        op->SetAttr("file_path", {config_.model_dir() + "/" + new_var->Name()});
         op->CheckAttrs();
       }
     }
   }
 
-  if (!config_.param_file.empty()) {
+  if (!config_.params_file().empty()) {
     // sort paramlist to have consistent ordering
     std::sort(params.begin(), params.end());
     // append just the load_combine op
     framework::OpDesc *op = load_block->AppendOp();
     op->SetType("load_combine");
     op->SetOutput("Out", params);
-    op->SetAttr("file_path", {config_.param_file});
+    op->SetAttr("file_path", {config_.params_file()});
     op->CheckAttrs();
   }
 

paddle/fluid/inference/api/analysis_predictor.h

@@ -35,8 +35,11 @@ using framework::proto::ProgramDesc;
 using framework::NaiveExecutor;
 using contrib::AnalysisConfig;
 
-/* This predictor is based on the original native predictor with IR and Analysis
- * support. It will optimize IR and Parameters in the runtime.
+/** \brief This predictor is based on the original native predictor with IR and
+ * Analysis support.
+ *
+ * It will optimize IR and Parameters in the runtime.
+ *
  * TODO(Superjomn) Replace the Navive predictor?
  */
 class AnalysisPredictor : public PaddlePredictor {

paddle/fluid/inference/api/analysis_predictor_tester.cc

@@ -25,9 +25,9 @@ namespace paddle {
 using contrib::AnalysisConfig;
 
 TEST(AnalysisPredictor, analysis_off) {
-  AnalysisConfig config(false);
-  config.model_dir = FLAGS_dirname;
-  config.enable_ir_optim = false;
+  AnalysisConfig config;
+  config.SetModel(FLAGS_dirname);
+  config.SwitchIrOptim(false);
 
   auto _predictor = CreatePaddlePredictor<AnalysisConfig>(config);
   auto* predictor = static_cast<AnalysisPredictor*>(_predictor.get());
@@ -55,14 +55,14 @@ TEST(AnalysisPredictor, analysis_off) {
 }
 
 TEST(AnalysisPredictor, analysis_on) {
+  AnalysisConfig config;
+  config.SetModel(FLAGS_dirname);
+  config.SwitchIrOptim(true);
 #ifdef PADDLE_WITH_CUDA
-  AnalysisConfig config(true);
-  config.fraction_of_gpu_memory = 0.15;
+  config.EnableUseGpu(100, 0);
 #else
-  AnalysisConfig config;
+  config.DisableGpu();
 #endif
-  config.model_dir = FLAGS_dirname;
-  config.enable_ir_optim = true;
 
   auto _predictor = CreatePaddlePredictor<AnalysisConfig>(config);
   auto* predictor = static_cast<AnalysisPredictor*>(_predictor.get());
@@ -89,7 +89,8 @@ TEST(AnalysisPredictor, analysis_on) {
   }
 
   // compare with NativePredictor
-  auto naive_predictor = CreatePaddlePredictor<NativeConfig>(config);
+  auto naive_predictor =
+      CreatePaddlePredictor<NativeConfig>(config.ToNativeConfig());
   std::vector<PaddleTensor> naive_outputs;
   ASSERT_TRUE(naive_predictor->Run(inputs, &naive_outputs));
   ASSERT_EQ(naive_outputs.size(), 1UL);
@@ -98,9 +99,8 @@ TEST(AnalysisPredictor, analysis_on) {
 
 TEST(AnalysisPredictor, ZeroCopy) {
   AnalysisConfig config;
-  config.model_dir = FLAGS_dirname;
-  config.use_feed_fetch_ops = false;
-
+  config.SetModel(FLAGS_dirname);
+  config.SwitchUseFeedFetchOps(false);
   auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
 
   auto w0 = predictor->GetInputTensor("firstw");
@@ -137,9 +137,9 @@ TEST(AnalysisPredictor, ZeroCopy) {
 
 TEST(AnalysisPredictor, Clone) {
   AnalysisConfig config;
-  config.model_dir = FLAGS_dirname;
-  config.use_feed_fetch_ops = true;
-  config.enable_ir_optim = true;
+  config.SetModel(FLAGS_dirname);
+  config.SwitchUseFeedFetchOps(true);
+  config.SwitchIrOptim(true);
 
   std::vector<std::unique_ptr<PaddlePredictor>> predictors;
   predictors.emplace_back(CreatePaddlePredictor(config));

paddle/fluid/inference/api/api_anakin_engine.h

@@ -19,8 +19,6 @@ limitations under the License. */
 
 #pragma once
 
-#define WITH_ANAKIN
-
 #include <vector>
 
 #include "framework/core/net/net.h"

paddle/fluid/inference/api/api_impl.cc

@@ -288,7 +288,7 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
   VLOG(3) << "create NativePaddlePredictor";
   if (config.use_gpu) {
     // 1. GPU memeroy
-    PADDLE_ENFORCE_GT(
+    PADDLE_ENFORCE_GE(
         config.fraction_of_gpu_memory, 0.f,
         "fraction_of_gpu_memory in the config should be set to range (0., 1.]");
     PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);

paddle/fluid/inference/api/api_impl.h

@@ -19,7 +19,6 @@ limitations under the License. */
 #include <memory>
 #include <string>
 #include <vector>
-
 #include "paddle/fluid/framework/ddim.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/lod_tensor_array.h"

paddle/fluid/inference/api/api_impl_tester.cc

@@ -295,7 +295,8 @@ TEST(inference_api_native, image_classification_gpu) {
 #endif
 
 TEST(PassBuilder, Delete) {
-  contrib::AnalysisConfig config(false);
+  contrib::AnalysisConfig config;
+  config.DisableGpu();
   config.pass_builder()->DeletePass("attention_lstm_fuse_pass");
   const auto& passes = config.pass_builder()->AllPasses();
   auto it = std::find(passes.begin(), passes.end(), "attention_lstm_fuse_pass");

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

@@ -92,10 +92,10 @@ if(WITH_MKL)
   if(NOT WIN32)
     set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
                  ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
-  else(WIN32)
+  else()
     set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml${CMAKE_SHARED_LIBRARY_SUFFIX}
             ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md${CMAKE_SHARED_LIBRARY_SUFFIX})
-  endif(WIN32)
+  endif()
   set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
   if(EXISTS ${MKLDNN_PATH})
     include_directories("${MKLDNN_PATH}/include")

paddle/fluid/inference/api/demo_ci/run.sh

@@ -116,6 +116,10 @@ D
       --modeldir=$DATA_DIR/mobilenet/model \
       --data=$DATA_DIR/mobilenet/data.txt \
       --refer=$DATA_DIR/mobilenet/result.txt 
+    if [ $? -ne 0 ]; then
+      echo "trt demo trt_mobilenet_demo runs fail."
+      exit 1
+    fi
   fi
 done
 set +x

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

@@ -36,12 +36,11 @@ namespace demo {
  */
 void Main() {
   std::unique_ptr<PaddlePredictor> predictor;
-  paddle::contrib::AnalysisConfig config(true);
-  config.param_file = FLAGS_modeldir + "/__params__";
-  config.prog_file = FLAGS_modeldir + "/__model__";
-  config.device = 0;
+  paddle::contrib::AnalysisConfig config;
+  config.EnableUseGpu(100, 0);
+  config.SetModel(FLAGS_modeldir + "/__model__",
+                  FLAGS_modeldir + "/__params__");
   config.EnableTensorRtEngine();
-  config.fraction_of_gpu_memory = 0.1;  // set by yourself
   predictor = CreatePaddlePredictor(config);
 
   VLOG(3) << "begin to process data";

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

@@ -40,15 +40,14 @@ using contrib::AnalysisConfig;
  */
 void Main(bool use_gpu) {
   std::unique_ptr<PaddlePredictor> predictor, analysis_predictor;
-  AnalysisConfig config(use_gpu);
-  config.param_file = FLAGS_modeldir + "/__params__";
-  config.prog_file = FLAGS_modeldir + "/__model__";
-  config.device = 0;
-  if (FLAGS_use_gpu) {
-    config.fraction_of_gpu_memory = 0.1;  // set by yourself
+  AnalysisConfig config;
+  if (use_gpu) {
+    config.EnableUseGpu(100, 0);
   }
+  config.SetModel(FLAGS_modeldir + "/__model__",
+                  FLAGS_modeldir + "/__params__");
 
-  predictor = CreatePaddlePredictor<NativeConfig>(config);
+  predictor = CreatePaddlePredictor<NativeConfig>(config.ToNativeConfig());
   analysis_predictor = CreatePaddlePredictor(config);
 
   // Just a single batch of data.

paddle/fluid/inference/api/helper.h

@@ -204,11 +204,14 @@ static std::string DescribeTensor(const PaddleTensor &tensor) {
     os << to_string(l) << "; ";
   }
   os << "\n";
-  os << " - data: ";
+  os << " - memory length: " << tensor.data.length();
+  os << "\n";
 
+  os << " - data: ";
   int dim = VecReduceToInt(tensor.shape);
+  float *pdata = static_cast<float *>(tensor.data.data());
   for (int i = 0; i < dim; i++) {
-    os << static_cast<float *>(tensor.data.data())[i] << " ";
+    os << pdata[i] << " ";
   }
   os << '\n';
   return os.str();
@@ -224,10 +227,12 @@ static std::string DescribeZeroCopyTensor(const ZeroCopyTensor &tensor) {
     os << to_string(l) << "; ";
   }
   os << "\n";
-  os << " - data: ";
   PaddlePlace place;
   int size;
   const auto *data = tensor.data<float>(&place, &size);
+  os << " - numel: " << size;
+  os << "\n";
+  os << " - data: ";
   for (int i = 0; i < size; i++) {
     os << data[i] << " ";
   }

paddle/fluid/inference/api/paddle_analysis_config.h

@@ -19,6 +19,8 @@
 #include <unordered_set>
 #include <vector>
 
+/*! \file */
+
 // Here we include some header files with relative paths, for that in deploy,
 // the abstract path of this header file will be changed.
 #include "paddle_api.h"           // NOLINT
@@ -34,41 +36,188 @@ class AnalysisPredictor;
 namespace contrib {
 
 // NOTE WIP, not stable yet.
-struct AnalysisConfig : public NativeConfig {
-  explicit AnalysisConfig(bool use_gpu = false);
+struct AnalysisConfig {
+  AnalysisConfig() = default;
   explicit AnalysisConfig(const AnalysisConfig& other);
-  explicit AnalysisConfig(AnalysisConfig&& other);
+  explicit AnalysisConfig(const std::string& model_dir);
+  explicit AnalysisConfig(const std::string& prog_file,
+                          const std::string& params_file);
 
-  // Determine whether to perform graph optimization.
-  bool enable_ir_optim = true;
+  /** Set model with a directory.
+   */
+  void SetModel(const std::string& model_dir) { model_dir_ = model_dir; }
+  /** Set model with two specific pathes for program and parameters.
+   */
+  void SetModel(const std::string& prog_file_path,
+                const std::string& params_file_path);
+  /** Set program file path.
+   */
+  void SetProgFile(const std::string& x) { prog_file_ = x; }
+  /** Set parameter composed file path.
+   */
+  void SetParamsFile(const std::string& x) { params_file_ = x; }
+  /** Get the model directory path.
+   */
+  const std::string& model_dir() const { return model_dir_; }
+  /** Get the program file path.
+   */
+  const std::string& prog_file() const { return prog_file_; }
+  /** Get the composed parameters file.
+   */
+  const std::string& params_file() const { return params_file_; }
 
-  // Get a pass builder for customize the passes in IR analysis phase.
-  PassStrategy* pass_builder() const;
+  // GPU related.
+
+  /**
+   * \brief Turn on GPU.
+   * @param memory_pool_init_size_mb initial size of the GPU memory pool in MB.
+   * @param device_id the GPU card to use (default is 0).
+   */
+  void EnableUseGpu(uint64_t memory_pool_init_size_mb, int device_id = 0);
+  /** Turn off the GPU.
+   */
+  void DisableGpu();
+  /** A bool state telling whether the GPU is turned on.
+   */
+  bool use_gpu() const { return use_gpu_; }
+  /** Get the GPU device id.
+   */
+  int gpu_device_id() const { return device_id_; }
+  /** Get the initial size in MB of the GPU memory pool.
+   */
+  int memory_pool_init_size_mb() const { return memory_pool_init_size_mb_; }
+  /** Get the proportion of the initial memory pool size compared to the device.
+   */
+  float fraction_of_gpu_memory_for_pool() const;
+
+  /** \brief Control whether to perform IR graph optimization.
+   *
+   * If turned off, the AnalysisConfig will act just like a NativeConfig.
+   */
+  void SwitchIrOptim(int x = true) { enable_ir_optim_ = x; }
+  /** A boolean state tell whether the ir graph optimization is actived.
+   */
+  bool ir_optim() const { return enable_ir_optim_; }
 
-  // NOT stable yet.
-  bool use_feed_fetch_ops{true};
+  /** \brief INTERNAL Determine whether to use the feed and fetch operators.
+   * Just for internal development, not stable yet.
+   * When ZeroCopyTensor is used, this should turned off.
+   */
+  void SwitchUseFeedFetchOps(int x = true) { use_feed_fetch_ops_ = x; }
+  /** A boolean state telling whether to use the feed and fetch operators.
+   */
+  bool use_feed_fetch_ops_enabled() const { return use_feed_fetch_ops_; }
 
+  /** \brief Control whether to specify the inputs' names.
+   *
+   * The PaddleTensor type has a `name` member, assign it with the corresponding
+   * variable name. This is used only when the input PaddleTensors passed to the
+   * `PaddlePredictor.Run(...)` cannot follow the order in the training phase.
+   */
+  void SwitchSpecifyInputNames(bool x = true) { specify_input_name_ = x; }
+
+  /** A boolean state tell whether the input PaddleTensor names specified should
+   * be used to reorder the inputs in `PaddlePredictor.Run(...)`.
+   */
+  bool specify_input_name() const { return specify_input_name_; }
+
+  /**
+   * \brief Turn on the TensorRT engine.
+   *
+   * The TensorRT engine will accelerate some subgraphes in the original Fluid
+   * computation graph. In some models such as TensorRT50, GoogleNet and so on,
+   * it gains significant performance acceleration.
+   *
+   * @param workspace_size the memory size(in byte) used for TensorRT workspace.
+   * @param max_batch_size the maximum batch size of this prediction task,
+   * better set as small as possible, or performance loss.
+   * @param min_subgrpah_size the minimum TensorRT subgraph size needed, if a
+   * subgraph is less than this, it will not transfer to TensorRT engine.
+   */
   void EnableTensorRtEngine(int workspace_size = 1 << 20,
                             int max_batch_size = 1, int min_subgraph_size = 3);
-  bool use_tensorrt() const { return use_tensorrt_; }
+  /** A boolean state telling whether the TensorRT engine is used.
+   */
+  bool tensorrt_engine_enabled() const { return use_tensorrt_; }
 
+  /** Control whther to debug IR graph analysis phase.
+   */
+  void SwitchIrDebug(int x = true) { ir_debug_ = x; }
+
+  /** Turn on MKLDNN.
+   */
   void EnableMKLDNN();
-  bool use_mkldnn() const { return use_mkldnn_; }
+  /** A boolean state telling whether to use the MKLDNN.
+   */
+  bool mkldnn_enabled() const { return use_mkldnn_; }
+
+  /** Set and get the number of cpu math library threads.
+   */
+  void SetCpuMathLibraryNumThreads(int cpu_math_library_num_threads);
+  /** An int state telling how many threads are used in the CPU math library.
+   */
+  int cpu_math_library_num_threads() const {
+    return cpu_math_library_num_threads_;
+  }
+
+  /** Transform the AnalysisConfig to NativeConfig.
+   */
+  NativeConfig ToNativeConfig() const {
+    NativeConfig config;
+    config.model_dir = model_dir_;
+    config.prog_file = prog_file_;
+    config.param_file = params_file_;
+    config.use_gpu = use_gpu_;
+    config.device = device_id_;
+    config.fraction_of_gpu_memory = fraction_of_gpu_memory_for_pool();
+    config.specify_input_name = specify_input_name_;
+    return config;
+  }
+  /** Specify the operator type list to use MKLDNN acceleration.
+   * @param op_list the operator type list.
+   */
   void SetMKLDNNOp(std::unordered_set<std::string> op_list) {
     mkldnn_enabled_op_types_ = op_list;
   }
 
-  // Specify the memory buffer of program and parameter
+  /** Specify the memory buffer of program and parameter
+   * @param prog_buffer the memory buffer of program.
+   * @param prog_buffer_size the size of the data.
+   * @param params_buffer the memory buffer of the composed parameters file.
+   * @param params_buffer_size the size of the commposed parameters data.
+   */
   void SetModelBuffer(const char* prog_buffer, size_t prog_buffer_size,
-                      const char* program_buffer, size_t program_buffer_size);
+                      const char* params_buffer, size_t params_buffer_size);
+  /** A boolean state telling whether the model is set from the CPU memory.
+   */
   bool model_from_memory() const { return model_from_memory_; }
 
   friend class ::paddle::AnalysisPredictor;
 
+  /** NOTE just for developer, not an official API, easily to be broken.
+   * Get a pass builder for customize the passes in IR analysis phase.
+   */
+  PassStrategy* pass_builder() const;
+
  protected:
+  // Update the config.
+  void Update();
+
+  std::string SerializeInfoCache();
+
+ protected:
+  // Model pathes.
+  std::string model_dir_;
+  std::string prog_file_;
+  std::string params_file_;
+
+  // GPU releated.
+  bool use_gpu_{false};
+  int device_id_{0};
+  uint64_t memory_pool_init_size_mb_{100};  // initial size is 100MB.
+
+  // TensorRT releated.
   bool use_tensorrt_{false};
-  bool use_mkldnn_{false};
-  std::unordered_set<std::string> mkldnn_enabled_op_types_;
   // For workspace_size, refer it from here:
   // https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
   int tensorrt_workspace_size_;
@@ -82,17 +231,24 @@ struct AnalysisConfig : public NativeConfig {
   //  We set this variable to control the minimum number of nodes in the
   //  subgraph, 3 as default value.
   int tensorrt_min_subgraph_size_{3};
-  std::unique_ptr<PassStrategy> pass_builder_;
+
+  bool use_mkldnn_{false};
+  std::unordered_set<std::string> mkldnn_enabled_op_types_;
+
   bool model_from_memory_{false};
-};
 
-// Configurations for Anakin engine.
-struct AnakinConfig : public PaddlePredictor::Config {
-  enum TargetType { NVGPU = 0, X86 };
-  int device;
-  std::string model_file;
-  int max_batch_size{-1};
-  TargetType target_type;
+  bool enable_ir_optim_{true};
+  bool use_feed_fetch_ops_{true};
+  bool ir_debug_{false};
+
+  bool specify_input_name_{false};
+
+  int cpu_math_library_num_threads_{1};
+
+  // A runtime cache, shouldn't be transferred to others.
+  std::string serialized_info_cache_;
+
+  mutable std::unique_ptr<PassStrategy> pass_builder_;
 };
 
 }  // namespace contrib

paddle/fluid/inference/api/paddle_api.h

@@ -13,61 +13,76 @@
 // limitations under the License.
 #pragma once
 
+/*! \file paddle_api.h
+ */
+
 #include <cassert>
 #include <memory>
 #include <string>
 #include <vector>
 
+/*! \namespace paddle
+ */
 namespace paddle {
 
-// Data type.
+/** paddle data type.
+ */
 enum PaddleDType {
   FLOAT32,
   INT64,
   // TODO(Superjomn) support more data types if needed.
 };
 
-/*
- * Memory menage for PaddleTensor.
- * The PaddleBuf holds a buffer for data input or output. The memory can be
- * allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
- * should be reused for better performance.
+/**
+ *\brief Memory menager for PaddleTensor.
  *
- * For user allocated memory, the following API can be used:
- * - PaddleBuf(void* data, size_t length) to set an external memory by
- * specifying
- *   the memory address and length.
- * - Reset(void* data, size_t length) to reset the PaddleBuf with an external
- * memory.
- * ATTENTION, for user allocated memory, deallocation should be done by users
- * externally after the program finished. The PaddleBuf won't do any allocation
- * or deallocation.
+ *The PaddleBuf holds a buffer for data input or output. The memory can be
+ *allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
+ *should be reused for better performance.
  *
- * To have the PaddleBuf allocate and manage the memory:
- * - PaddleBuf(size_t length) will allocate a memory of size `length`.
- * - Resize(size_t length) resize the memory to no less than `length`, ATTENTION
- *   if the allocated memory is larger than `length`, nothing will done.
+ *For user allocated memory, the following API can be used:
+ *- PaddleBuf(void* data, size_t length) to set an external memory by
+ *specifying
+ *  the memory address and length.
+ *- Reset(void* data, size_t length) to reset the PaddleBuf with an external
+ *memory.
+ *ATTENTION, for user allocated memory, deallocation should be done by users
+ *externally after the program finished. The PaddleBuf won't do any allocation
+ *or deallocation.
+ *
+ *To have the PaddleBuf allocate and manage the memory:
+ *- PaddleBuf(size_t length) will allocate a memory of size `length`.
+ *- Resize(size_t length) resize the memory to no less than `length`, ATTENTION
+ *  if the allocated memory is larger than `length`, nothing will done.
  */
 class PaddleBuf {
  public:
-  // PaddleBuf allocate memory internally, and manage it.
+  /** PaddleBuf allocate memory internally, and manage it.
+   */
   explicit PaddleBuf(size_t length)
       : data_(new char[length]), length_(length), memory_owned_(true) {}
-  // Set external memory, the PaddleBuf won't manage it.
+  /** Set external memory, the PaddleBuf won't manage it.
+   */
   PaddleBuf(void* data, size_t length)
       : data_(data), length_(length), memory_owned_{false} {}
-  // Copy only available when memory is managed externally.
+  /** Copy only available when memory is managed externally.
+   */
   explicit PaddleBuf(const PaddleBuf&);
 
-  // Resize the memory.
+  /** Resize the memory.
+   */
   void Resize(size_t length);
-  // Reset to external memory, with address and length set.
+  /** Reset to external memory, with address and length set.
+   */
   void Reset(void* data, size_t length);
-  // Tell whether the buffer is empty.
+  /** Tell whether the buffer is empty.
+   */
   bool empty() const { return length_ == 0; }
-  // Get the memory address.
+  /** Get the memory address.
+   */
   void* data() const { return data_; }
-  // Get the memory length.
+  /** Get the memory length.
+   */
   size_t length() const { return length_; }
 
   ~PaddleBuf() { Free(); }
@@ -83,7 +98,8 @@ class PaddleBuf {
   bool memory_owned_{true};
 };
 
-// Basic input and output data structure for PaddlePredictor.
+/** Basic input and output data structure for PaddlePredictor.
+ */
 struct PaddleTensor {
   PaddleTensor() = default;
   std::string name;  // variable name.
@@ -94,19 +110,23 @@ struct PaddleTensor {
 };
 
 enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
-// Tensor without copy, currently only supports AnalysisPredictor.
+/** Tensor without copy, currently only supports AnalysisPredictor.
+ */
 class ZeroCopyTensor {
  public:
   void Reshape(const std::vector<int>& shape);
 
-  // Get the memory in CPU or GPU with specific data type, should Reshape first
-  // to tell the data size.
-  // Once can directly call this data to feed the data.
-  // This is for write the input tensor.
+  /** Get the memory in CPU or GPU with specific data type, should Reshape first
+   * to tell the data size.
+   * Once can directly call this data to feed the data.
+   * This is for write the input tensor.
+   */
   template <typename T>
   T* mutable_data(PaddlePlace place);
-  // Get the memory directly, will return the place and memory size by pointer.
-  // This is for reading the output tensor.
+  /** Get the memory directly, will return the place and element size by
+   * pointer.
+   * This is for reading the output tensor.
+   */
   template <typename T>
   T* data(PaddlePlace* place, int* size) const;
 
@@ -128,8 +148,7 @@ class ZeroCopyTensor {
   void* scope_{nullptr};
 };
 
-/*
- * A simple Inference API for Paddle.
+/** A simple Inference API for Paddle.
  */
 class PaddlePredictor {
  public:
@@ -138,18 +157,20 @@ class PaddlePredictor {
   PaddlePredictor(const PaddlePredictor&) = delete;
   PaddlePredictor& operator=(const PaddlePredictor&) = delete;
 
-  // Predict an record.
-  // The caller should be responsible for allocating and releasing the memory of
-  // `inputs`. `inputs` should be available until Run returns. Caller should be
-  // responsible for the output tensor's buffer, either allocated or passed from
-  // outside.
+  /** Predict an record.
+   * The caller should be responsible for allocating and releasing the memory of
+   * `inputs`. `inputs` should be available until Run returns. Caller should be
+   * responsible for the output tensor's buffer, either allocated or passed from
+   * outside.
+   */
   virtual bool Run(const std::vector<PaddleTensor>& inputs,
                    std::vector<PaddleTensor>* output_data,
                    int batch_size = -1) = 0;
 
-  // Zero copy input and output optimization.
-  // Get the input or output tensors, and operate on their memory directly,
-  // without copy.
+  /** Zero copy input and output optimization.
+   * Get the input or output tensors, and operate on their memory directly,
+   * without copy.
+   */
   virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
       const std::string& name) {
     return nullptr;
@@ -160,16 +181,19 @@ class PaddlePredictor {
   }
   virtual bool ZeroCopyRun() { return false; }
 
-  // Clone a predictor that share the model weights, the Cloned predictor should
-  // be thread-safe.
+  /** Clone a predictor that share the model weights, the Cloned predictor
+   * should be thread-safe.
+   */
   virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
 
-  // Destroy the Predictor.
+  /** Destroy the Predictor.
+   */
   virtual ~PaddlePredictor() = default;
 
-  // The common configs for all the predictors.
+  /** The common configs for all the predictors.
+   */
   struct Config {
-    std::string model_dir;  // path to the model directory.
+    std::string model_dir; /*!< path to the model directory. */
   };
 };
 
@@ -177,17 +201,21 @@ struct NativeConfig : public PaddlePredictor::Config {
   // GPU related fields.
   bool use_gpu{false};
   int device{0};
-  float fraction_of_gpu_memory{-1.f};  // Change to a float in (0,1] if needed.
+  float fraction_of_gpu_memory{
+      -1.f}; /*!< Change to a float in (0,1] if needed. */
 
   // Specify the exact path of program and parameter files.
   std::string prog_file;
   std::string param_file;
 
-  // Specify the variable's name of each input if input tensors don't follow the
-  // `feeds` and `fetches` of the phase `save_inference_model`.
+  /** Specify the variable's name of each input if input tensors don't follow
+   * the
+   * `feeds` and `fetches` of the phase `save_inference_model`.
+   */
   bool specify_input_name{false};
 
-  // Set and get the number of cpu math library threads.
+  /** Set and get the number of cpu math library threads.
+   */
   void SetCpuMathLibraryNumThreads(int cpu_math_library_num_threads) {
     cpu_math_library_num_threads_ = cpu_math_library_num_threads;
   }
@@ -201,28 +229,33 @@ struct NativeConfig : public PaddlePredictor::Config {
   int cpu_math_library_num_threads_{1};
 };
 
-// A factory to help create different predictors.
-//
-// Usage:
-//
-// NativeConfig config;
-// ... // change the configs.
-// auto native_predictor = CreatePaddlePredictor(config);
-//
-// FOR EXTENSION DEVELOPER:
-// Different predictors are designated by config type. Similar configs can be
-// merged, but there shouldn't be a huge config containing different fields for
-// more than one kind of predictors.
+/*! \fn std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT&
+ * config);
+ *
+ * \brief A factory to help create different predictors.
+ *
+ * Usage:
+ *
+ * NativeConfig config;
+ * ... // change the configs.
+ * auto native_predictor = CreatePaddlePredictor(config);
+ *
+ * FOR EXTENSION DEVELOPER:
+ * Different predictors are designated by config type. Similar configs can be
+ * merged, but there shouldn't be a huge config containing different fields for
+ * more than one kind of predictors.
+ */
 template <typename ConfigT>
 std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
 
-// NOTE The following APIs are too trivial, we will discard it in the following
-// versions.
+/** NOTE The following APIs are too trivial, we will discard it in the following
+ * versions.
+ */
 enum class PaddleEngineKind {
-  kNative = 0,         // Use the native Fluid facility.
-  kAutoMixedTensorRT,  // Automatically mix Fluid with TensorRT.
-  kAnalysis,           // More optimization.
-  kAnakin              // Use Anakin for inference, not mature yet.
+  kNative = 0,        /*!< Use the native Fluid facility. */
+  kAutoMixedTensorRT, /*!< Automatically mix Fluid with TensorRT. */
+  kAnalysis,          /*!< More optimization. */
+  kAnakin             /*!< Use Anakin for inference, not mature yet. */
 };
 
 template <typename ConfigT, PaddleEngineKind engine>

paddle/fluid/inference/api/paddle_inference_api.h

@@ -26,9 +26,8 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
-#include "paddle_api.h"  // NOLINT
-#ifndef WITH_ANAKIN
 #include "paddle_analysis_config.h"  // NOLINT
-#else
+#include "paddle_api.h"              // NOLINT
+#ifdef WITH_ANAKIN
 #include "paddle_anakin_config.h"  // NOLINT
 #endif

paddle/fluid/inference/api/paddle_pass_builder.h

@@ -18,30 +18,39 @@
 #include <string>
 #include <vector>
 
+/*! \file */
+
+/*! \namespace paddle */
 namespace paddle {
-/*
- * This is a pass builder based on string. It is part of inference API.
+
+/** This is a pass builder based on string. It is part of inference API.
  */
 class PaddlePassBuilder {
  public:
   explicit PaddlePassBuilder(const std::vector<std::string> &passes)
       : passes_(passes) {}
 
+  /** Append a pass to the end of the passes. */
   void AppendPass(const std::string &pass_type);
 
+  /** Insert a pass to a specific position.
+   * @param idx the position to insert.
+   * @param pass_type the pass key.
+   */
   void InsertPass(size_t idx, const std::string &pass_type);
 
-  // Delete the `idx`-th pass.
+  /** Delete the `idx`-th pass. */
   void DeletePass(size_t idx);
 
-  // Delete all the passes that has type `pass_type`.
+  /** Delete all the passes that has type `pass_type`. */
   void DeletePass(const std::string &pass_type);
 
-  // Visualize the computation graph after each pass by generating a DOT
-  // language file, one can draw them with the Graphviz toolkit.
+  /** Visualize the computation graph after each pass by generating a DOT
+   * language file, one can draw them with the Graphviz toolkit.
+   */
   void TurnOnDebug();
 
-  // Human-readible information.
+  /** Human-readible information. */
   std::string DebugString();
 
   const std::vector<std::string> &AllPasses() const { return passes_; }
@@ -50,23 +59,27 @@ class PaddlePassBuilder {
   std::vector<std::string> passes_;
 };
 
-/*
- * Pass strategy to help control the IR passes.
+/**Pass strategy to help control the IR passes.
  */
 class PassStrategy : public PaddlePassBuilder {
  public:
   explicit PassStrategy(const std::vector<std::string> &passes)
       : PaddlePassBuilder(passes) {}
 
-  // The MKLDNN control exists in both CPU and GPU mode, because there can be
-  // still some CPU kernels running in CPU mode.
+  /** The MKLDNN control exists in both CPU and GPU mode, because there can be
+   * still some CPU kernels running in CPU mode.
+   */
   virtual void EnableMKLDNN() = 0;
 
+  bool use_gpu() const { return use_gpu_; }
+
   virtual ~PassStrategy() = default;
+
+ protected:
+  bool use_gpu_{false};
 };
 
-/*
- * The CPU passes controller, it is used in AnalysisPredictor with CPU mode.
+/** The CPU passes controller, it is used in AnalysisPredictor with CPU mode.
  */
 class CpuPassStrategy : public PassStrategy {
  public:
@@ -76,6 +89,7 @@ class CpuPassStrategy : public PassStrategy {
     passes_.assign({
         "infer_clean_graph_pass",         //
         "attention_lstm_fuse_pass",       //
+        "seqpool_concat_fuse_pass",       //
         "seqconv_eltadd_relu_fuse_pass",  //
         // "embedding_fc_lstm_fuse_pass", //
         "fc_lstm_fuse_pass",             //
@@ -88,6 +102,7 @@ class CpuPassStrategy : public PassStrategy {
         "conv_eltwiseadd_bn_fuse_pass",  //
         "is_test_pass",                  //
     });
+    use_gpu_ = false;
   }
 
   virtual ~CpuPassStrategy() = default;
@@ -111,8 +126,7 @@ class CpuPassStrategy : public PassStrategy {
   CpuPassStrategy(const CpuPassStrategy &other) : PassStrategy(other.passes_) {}
 };
 
-/*
- * The GPU passes strategy, it is used in
+/** The GPU passes strategy, it is used in AnalysisPredictor with GPU mode.
  */
 class GpuPassStrategy : public PassStrategy {
  public:
@@ -126,10 +140,14 @@ class GpuPassStrategy : public PassStrategy {
         "conv_elementwise_add2_act_fuse_pass",       //
         "conv_elementwise_add_fuse_pass",            //
     });
+
+    use_gpu_ = true;
   }
 
   GpuPassStrategy(const GpuPassStrategy &other)
-      : PassStrategy(other.AllPasses()) {}
+      : PassStrategy(other.AllPasses()) {
+    use_gpu_ = true;
+  }
 
   void EnableMKLDNN() override;
 

paddle/fluid/inference/tensorrt/CMakeLists.txt

 nv_library(tensorrt_engine SRCS engine.cc DEPS ${GLOB_OPERATOR_DEPS} framework_proto device_context)
+nv_library(tensorrt_op_teller SRCS op_teller.cc DEPS framework_proto)
 nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
 nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)
 add_subdirectory(plugin)

paddle/fluid/operators/conv_op.h

@@ -29,6 +29,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 constexpr int kConvMKLDNNFP32 = 1;
 constexpr int kConvMKLDNNINT8 = 2;
+constexpr int MaxKeyLength = 256;
 
 // Base convolution operator definations for other conv
 // like operators to reuse the implementation.