Merge develop

test=develop

CMakeLists.txt

@@ -126,16 +126,12 @@ if(ANDROID OR IOS)
     add_definitions(-DPADDLE_MOBILE_INFERENCE)
 endif()
 
-if (APPLE OR WIN32)
+if (APPLE)
     set(WITH_MKL OFF CACHE STRING
-        "Disable MKL for building on mac and windows" FORCE)
+        "Disable MKL for building on mac" FORCE)
 endif()
 
 if (WIN32)
-    set(WITH_DSO OFF CACHE STRING
-            "Disable DSO when compiling for Windows" FORCE)
-    set(WITH_MKL OFF CACHE STRING
-            "Disable MKL when compiling for Windows" FORCE)
     set(WITH_DISTRIBUTE OFF CACHE STRING
             "Disable DISTRIBUTE when compiling for Windows" FORCE)
     set(WITH_C_API OFF CACHE STRING

cmake/cudnn.cmake

@@ -44,9 +44,9 @@ if(WIN32)
 set(CUDNN_LIB_NAME "cudnn.lib" "cudnn64_7.dll")
 endif(WIN32)
 
-if(Apple)
+if(APPLE)
 set(CUDNN_LIB_NAME "libcudnn.dylib" "libcudnn.so")
-endif(Apple)
+endif(APPLE)
 
 find_library(CUDNN_LIBRARY NAMES ${CUDNN_LIB_NAME} # libcudnn_static.a
     PATHS ${CUDNN_CHECK_LIBRARY_DIRS} ${CUDNN_INCLUDE_DIR} ${__libpath_hist}

cmake/external/mkldnn.cmake

@@ -23,15 +23,14 @@ SET(MKLDNN_SOURCES_DIR    ${THIRD_PARTY_PATH}/mkldnn)
 SET(MKLDNN_INSTALL_DIR    ${THIRD_PARTY_PATH}/install/mkldnn)
 SET(MKLDNN_INC_DIR        "${MKLDNN_INSTALL_DIR}/include" CACHE PATH "mkldnn include directory." FORCE)
 
-IF(WIN32 OR APPLE)
+IF(APPLE)
     MESSAGE(WARNING
-        "Windows or Mac is not supported with MKLDNN in Paddle yet."
+        "Mac is not supported with MKLDNN in Paddle yet."
         "Force WITH_MKLDNN=OFF")
-    SET(WITH_MKLDNN OFF CACHE STRING "Disable MKLDNN in Windows and MacOS" FORCE)
+    SET(WITH_MKLDNN OFF CACHE STRING "Disable MKLDNN in MacOS" FORCE)
     return()
 ENDIF()
 
-SET(MKLDNN_LIB "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
 MESSAGE(STATUS "Set ${MKLDNN_INSTALL_DIR}/lib to runtime path")
 SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
 SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLDNN_INSTALL_DIR}/lib")
@@ -44,10 +43,14 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
 ELSE()
     MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
 ENDIF()
-SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result -Wno-error=array-bounds")
-SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
-SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
-SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
+
+IF(NOT WIN32)
+    SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result -Wno-error=array-bounds")
+    SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
+    SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
+    SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
+ENDIF(NOT WIN32)
+
 ExternalProject_Add(
     ${MKLDNN_PROJECT}
     ${EXTERNAL_PROJECT_LOG_ARGS}
@@ -58,8 +61,15 @@ ExternalProject_Add(
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
     CMAKE_ARGS          -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
+    CMAKE_ARGS          -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+    CMAKE_ARGS          -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+    CMAKE_ARGS          -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
+    CMAKE_ARGS          -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
+    CMAKE_ARGS          -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
+    CMAKE_ARGS          -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
     CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
     CMAKE_ARGS          -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+    CMAKE_ARGS          -DCMAKE_POSITION_INDEPENDENT_CODE=ON
     CMAKE_ARGS          -DMKLROOT=${MKLML_ROOT}
     CMAKE_ARGS          -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
     CMAKE_ARGS          -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
@@ -67,6 +77,11 @@ ExternalProject_Add(
     CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
                         -DMKLROOT:PATH=${MKLML_ROOT}
 )
+if(WIN32)
+    SET(MKLDNN_LIB "${MKLDNN_INSTALL_DIR}/lib/mkldnn.lib" CACHE FILEPATH "mkldnn library." FORCE)
+else(WIN32)
+    SET(MKLDNN_LIB "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
+endif(WIN32)
 
 ADD_LIBRARY(shared_mkldnn SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET shared_mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
@@ -85,10 +100,14 @@ ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
 
 # copy the real so.0 lib to install dir
 # it can be directly contained in wheel or capi
-SET(MKLDNN_SHARED_LIB ${MKLDNN_INSTALL_DIR}/libmkldnn.so.0)
-ADD_CUSTOM_COMMAND(OUTPUT ${MKLDNN_SHARED_LIB}
-    COMMAND cp ${MKLDNN_LIB} ${MKLDNN_SHARED_LIB}
-    DEPENDS mkldnn)
+if(WIN32)
+    SET(MKLDNN_SHARED_LIB ${MKLDNN_INSTALL_DIR}/lib/mkldnn.dll)
+else(WIN32)
+    SET(MKLDNN_SHARED_LIB ${MKLDNN_INSTALL_DIR}/libmkldnn.so.0)
+    ADD_CUSTOM_COMMAND(OUTPUT ${MKLDNN_SHARED_LIB}
+            COMMAND ${CMAKE_COMMAND} -E copy ${MKLDNN_LIB} ${MKLDNN_SHARED_LIB}
+            DEPENDS mkldnn)
+endif(WIN32)
 ADD_CUSTOM_TARGET(mkldnn_shared_lib ALL DEPENDS ${MKLDNN_SHARED_LIB})
 
 IF(WITH_C_API)

cmake/external/mklml.cmake

@@ -16,56 +16,67 @@ IF(NOT ${WITH_MKLML})
   return()
 ENDIF(NOT ${WITH_MKLML})
 
-IF(WIN32 OR APPLE)
+IF(APPLE)
     MESSAGE(WARNING
-        "Windows or Mac is not supported with MKLML in Paddle yet."
+        "Mac is not supported with MKLML in Paddle yet."
         "Force WITH_MKLML=OFF")
     SET(WITH_MKLML OFF CACHE STRING "Disable MKLML package in Windows and MacOS" FORCE)
     return()
 ENDIF()
 
 INCLUDE(ExternalProject)
-
-SET(MKLML_PROJECT       "extern_mklml")
-IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
-  MESSAGE(STATUS "use pre defined download url")
-  SET(MKLML_VER "mklml_lnx_2019.0.20180710" CACHE STRING "" FORCE)
-  SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
-ENDIF()
-MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
-SET(MKLML_SOURCE_DIR    "${THIRD_PARTY_PATH}/mklml")
-SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
 SET(MKLML_DST_DIR       "mklml")
 SET(MKLML_INSTALL_ROOT  "${THIRD_PARTY_PATH}/install")
 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)
-SET(MKLML_LIB           ${MKLML_LIB_DIR}/libmklml_intel.so)
-SET(MKLML_IOMP_LIB      ${MKLML_LIB_DIR}/libiomp5.so)
+if(WIN32)
+    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()
+    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")
 
-INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
+IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
+    MESSAGE(STATUS "use pre defined download url")
+    if(WIN32)
+        SET(MKLML_VER "mklml_win_2019.0.20180710" CACHE STRING "" FORCE)
+        SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
+    else()
+        SET(MKLML_VER "mklml_lnx_2019.0.20180710" CACHE STRING "" FORCE)
+        SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
+    ENDIF()
+endif()
 
-FILE(WRITE ${MKLML_DOWNLOAD_DIR}/CMakeLists.txt
-  "PROJECT(MKLML)\n"
-  "cmake_minimum_required(VERSION 3.0)\n"
-  "install(DIRECTORY ${MKLML_VER}/include ${MKLML_VER}/lib \n"
-  "        DESTINATION ${MKLML_DST_DIR})\n")
+SET(MKLML_PROJECT       "extern_mklml")
+MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
+SET(MKLML_SOURCE_DIR    "${THIRD_PARTY_PATH}/mklml")
+SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
 
 ExternalProject_Add(
     ${MKLML_PROJECT}
     ${EXTERNAL_PROJECT_LOG_ARGS}
-    PREFIX                ${MKLML_SOURCE_DIR}
+    PREFIX                 ${MKLML_SOURCE_DIR}
+    URL                    ${MKLML_URL}
     DOWNLOAD_DIR          ${MKLML_DOWNLOAD_DIR}
-    DOWNLOAD_COMMAND      wget --no-check-certificate ${MKLML_URL} -c -q -O ${MKLML_VER}.tgz 
-                          && tar zxf ${MKLML_VER}.tgz
     DOWNLOAD_NO_PROGRESS  1
-    UPDATE_COMMAND        ""
-    CMAKE_ARGS            -DCMAKE_INSTALL_PREFIX=${MKLML_INSTALL_ROOT}
-    CMAKE_CACHE_ARGS      -DCMAKE_INSTALL_PREFIX:PATH=${MKLML_INSTALL_ROOT}
+    CONFIGURE_COMMAND     ""
+    BUILD_COMMAND         ""
+    UPDATE_COMMAND ""
+    INSTALL_COMMAND
+        ${CMAKE_COMMAND} -E copy_directory ${MKLML_DOWNLOAD_DIR}/include ${MKLML_INC_DIR} &&
+        ${CMAKE_COMMAND} -E copy_directory ${MKLML_DOWNLOAD_DIR}/lib ${MKLML_LIB_DIR}
 )
 
+INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
+
 ADD_LIBRARY(mklml SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET mklml PROPERTY IMPORTED_LOCATION ${MKLML_LIB})
 ADD_DEPENDENCIES(mklml ${MKLML_PROJECT})

cmake/generic.cmake

@@ -267,7 +267,11 @@ function(cc_library TARGET_NAME)
           list(APPEND cc_library_DEPS dynload_mklml)
         endif()
         add_dependencies(${TARGET_NAME} mklml)
-        target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
+        if(WIN32)
+          target_link_libraries(${TARGET_NAME} ${MKLML_IOMP_LIB})
+        else(WIN32)
+          target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
+        endif(WIN32)
       endif()
       # remove link to python, see notes at:
       # https://github.com/pybind/pybind11/blob/master/docs/compiling.rst#building-manually

cmake/inference_lib.cmake

@@ -115,20 +115,20 @@ if (NOT PROTOBUF_FOUND OR WIN32)
             )
 endif ()
 
-if (NOT CBLAS_FOUND)
-    set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/openblas")
-    copy(openblas_lib
-            SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
-            DSTS ${dst_dir} ${dst_dir}
-            DEPS extern_openblas
-            )
-elseif (WITH_MKLML)
+if (WITH_MKLML)
     set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mklml")
     copy(mklml_lib
             SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
             DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
             DEPS mklml
             )
+elseif (NOT CBLAS_FOUND OR WIN32)
+    set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/openblas")
+    copy(openblas_lib
+            SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
+            DSTS ${dst_dir} ${dst_dir}
+            DEPS extern_openblas
+            )
 endif ()
 
 if (WITH_MKLDNN)

paddle/fluid/API.spec

@@ -208,6 +208,7 @@ paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act
 paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
+paddle.fluid.layers.py_func ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
@@ -350,6 +351,23 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
 paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
 paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.contrib.build_compressor ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
+paddle.fluid.contrib.CompressPass.__init__ ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
+paddle.fluid.contrib.CompressPass.add_strategy ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.CompressPass.apply ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.ImitationGraph.__init__ ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.contrib.ImitationGraph.all_parameters ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.SensitivePruneStrategy.__init__ ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None))
+paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.MagnitudePruner.__init__ ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.contrib.MagnitudePruner.prune ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.contrib.RatioPruner.__init__ ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.contrib.RatioPruner.prune ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.contrib.load_persistables_for_increment ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.load_persistables_for_inference ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.convert_dist_to_sparse_program ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)

paddle/fluid/framework/CMakeLists.txt

@@ -48,10 +48,10 @@ if(WITH_GPU)
     nv_library(tensor SRCS tensor.cc .tensor_util.cu DEPS place memory data_type device_context)
     add_dependencies(tensor tensor_util)
   else()
-    nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type device_context)
+    nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type device_context )
   endif(WIN32)
 else()
-  cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type device_context)
+  cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type device_context )
 endif()
 
 cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)

paddle/fluid/framework/lod_tensor.cc

@@ -157,13 +157,8 @@ bool CheckLoD(const LoD &in, int tensor_height) {
     if (level.size() < 2) return false;
     // check: the first offset(the begin offset) of each level should be 0.
     if (level.front() != 0) return false;
-    // check: all the offsets in a level should be ascending(no same items
-    // allows).
-    if (!std::is_sorted(level.begin(), level.begin(), [](size_t a, size_t b) {
-          if (a < b) return true;
-          return false;
-        })) {
-      LOG(INFO) << "ascending error";
+    // check: all the offsets in a level should be ascending(allow same items)
+    if (!std::is_sorted(level.begin(), level.end())) {
       return false;
     }
   }

paddle/fluid/framework/lod_tensor_test.cc

@@ -217,6 +217,11 @@ TEST(LoD, CheckLoD) {
   // check with underlying tensor storage.
   ASSERT_TRUE(CheckLoD(relative_lod, 5));
   ASSERT_FALSE(CheckLoD(relative_lod, 9));
+
+  // check whether lod is ascending-sorted (allow same items)
+  ASSERT_TRUE(CheckLoD({{0, 1, 2, 3, 4, 5}}, 5));
+  ASSERT_TRUE(CheckLoD({{0, 1, 3, 3, 4, 5}}, 5));
+  ASSERT_FALSE(CheckLoD({{0, 1, 3, 2, 5}}, 5));
 }
 
 TEST(LoD, CheckAbsLoD) {

paddle/fluid/framework/ngraph_bridge.cc

@@ -31,10 +31,12 @@ std::map<std::string,
                             std::shared_ptr<std::unordered_map<
                                 std::string, std::shared_ptr<ngraph::Node>>>)>>
     NgraphBridge::NG_NODE_MAP = {
+        {"fill_constant", paddle::operators::ngraphs::BuildFillConstantNode},
         {"mul", paddle::operators::ngraphs::BuildMulNode},
         {"mul_grad", paddle::operators::ngraphs::BuildMulGradNode},
         {"relu", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Relu>},
-        {"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>}};
+        {"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>},
+        {"top_k", paddle::operators::ngraphs::BuildTopKNode}};
 
 void NgraphBridge::BuildNgNode(const std::shared_ptr<OperatorBase>& op) {
   auto& op_type = op->Type();

paddle/fluid/framework/op_desc.h

@@ -123,6 +123,8 @@ class OpDesc {
 
   BlockDesc *Block() { return this->block_; }
 
+  const BlockDesc *Block() const { return this->block_; }
+
  private:
   template <typename MapType>
   static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {

paddle/fluid/framework/operator.cc

@@ -476,6 +476,28 @@ const Tensor* ExecutionContext::LegacyInput<Tensor>(
 template <>
 const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
     const std::string& name) const {
+  auto it = ctx_.inputs.find(name);
+  if (it == ctx_.inputs.end()) {
+    return {};
+  }
+  const std::vector<Variable*>& vars = it->second;
+  std::vector<const Tensor*> res;
+  res.reserve(vars.size());
+  std::transform(vars.begin(), vars.end(), std::back_inserter(res),
+                 [&](Variable* var) -> const Tensor* {
+                   if (var == nullptr) return nullptr;
+                   PADDLE_ENFORCE(
+                       var->IsType<LoDTensor>(),
+                       "should be LoDTensor, but the received type is %s",
+                       ToTypeName(var->Type()));
+                   return &(var->Get<LoDTensor>());
+                 });
+  return res;
+}
+
+template <>
+const std::vector<const Tensor*> ExecutionContext::LegacyMultiInput<Tensor>(
+    const std::string& name) const {
   auto names = op().Inputs(name);
   std::vector<const Tensor*> res;
   res.reserve(names.size());

paddle/fluid/framework/operator.h

@@ -197,8 +197,31 @@ class ExecutionContext {
 
   const std::vector<const Variable*> MultiInputVar(
       const std::string& name) const {
-    auto names = op_.Inputs(name);
+    auto it = ctx_.inputs.find(name);
+    if (it == ctx_.inputs.end()) {
+      return {};
+    }
     std::vector<const Variable*> res;
+    res.reserve(it->second.size());
+    std::transform(it->second.begin(), it->second.end(),
+                   std::back_inserter(res),
+                   [this](Variable* var) { return var; });
+    return res;
+  }
+
+  std::vector<Variable*> MultiOutputVar(const std::string& name) const {
+    auto names = op_.Outputs(name);
+    auto it = ctx_.outputs.find(name);
+    if (it == ctx_.outputs.end()) {
+      return {};
+    }
+    return it->second;
+  }
+
+  const std::vector<Variable*> LegacyMultiInputVar(
+      const std::string& name) const {
+    auto names = op_.Inputs(name);
+    std::vector<Variable*> res;
     res.reserve(names.size());
     std::transform(names.begin(), names.end(), std::back_inserter(res),
                    [this](const std::string& name) {
@@ -208,7 +231,7 @@ class ExecutionContext {
     return res;
   }
 
-  std::vector<Variable*> MultiOutputVar(const std::string& name) const {
+  std::vector<Variable*> LegacyMultiOutputVar(const std::string& name) const {
     auto names = op_.Outputs(name);
     std::vector<Variable*> res;
     res.reserve(names.size());
@@ -250,6 +273,38 @@ class ExecutionContext {
 
   template <typename T>
   const std::vector<const T*> MultiInput(const std::string& name) const {
+    auto it = ctx_.inputs.find(name);
+    if (it == ctx_.inputs.end()) {
+      return {};
+    }
+    const std::vector<Variable*>& vars = it->second;
+    std::vector<const T*> res;
+    res.reserve(vars.size());
+    std::transform(vars.begin(), vars.end(), std::back_inserter(res),
+                   [&](Variable* var) -> const T* {
+                     return var == nullptr ? nullptr : &var->Get<T>();
+                   });
+    return res;
+  }
+
+  template <typename T>
+  std::vector<T*> MultiOutput(const std::string& name) const {
+    auto it = ctx_.outputs.find(name);
+    if (it == ctx_.outputs.end()) {
+      return {};
+    }
+    const std::vector<Variable*>& vars = it->second;
+    std::vector<T*> res;
+    res.reserve(vars.size());
+    std::transform(vars.begin(), vars.end(), std::back_inserter(res),
+                   [&](Variable* var) -> T* {
+                     return var == nullptr ? nullptr : var->GetMutable<T>();
+                   });
+    return res;
+  }
+
+  template <typename T>
+  const std::vector<const T*> LegacyMultiInput(const std::string& name) const {
     auto names = op_.Inputs(name);
     std::vector<const T*> res;
     res.reserve(names.size());
@@ -262,7 +317,7 @@ class ExecutionContext {
   }
 
   template <typename T>
-  std::vector<T*> MultiOutput(const std::string& name) const {
+  std::vector<T*> LegacyMultiOutput(const std::string& name) const {
     auto names = op_.Outputs(name);
     std::vector<T*> res;
     res.reserve(names.size());
@@ -321,6 +376,10 @@ template <>
 const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
     const std::string& name) const;
 
+template <>
+const std::vector<const Tensor*> ExecutionContext::LegacyMultiInput<Tensor>(
+    const std::string& name) const;
+
 template <>
 Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const;
 

paddle/fluid/framework/shape_inference.h

@@ -25,6 +25,8 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+class OperatorBase;
+
 using InferShapeVarPtr = boost::variant<VarDesc *, Variable *>;
 
 class InferShapeContext {

paddle/fluid/framework/tensor.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/framework/var_type.h"
 
 namespace paddle {
 namespace framework {
@@ -27,6 +28,8 @@ void Tensor::check_memory_size() const {
       "or maybe the required data-type mismatches the data already stored.");
 }
 
+Tensor::Tensor(const proto::VarType::Type& dtype) : type_(dtype), offset_(0) {}
+
 size_t Tensor::memory_size() const {
   return holder_ == nullptr ? 0UL : holder_->size() - offset_;
 }
@@ -101,5 +104,12 @@ const DDim& Tensor::dims() const { return dims_; }
 
 int64_t Tensor::numel() const { return product(dims_); }
 
+void Tensor::ResetHolder(std::shared_ptr<memory::Allocation> holder) {
+  if (holder_) {
+    PADDLE_ENFORCE_EQ(numel() * SizeOfType(type()), holder->size());
+  }
+  holder_ = holder;
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/tensor.h

@@ -69,6 +69,8 @@ class Tensor {
  public:
   Tensor() : type_(proto::VarType::FP32), offset_(0) {}
 
+  explicit Tensor(const proto::VarType::Type&);
+
   /*! Return a pointer to mutable memory block. */
   template <typename T>
   T* data();
@@ -162,6 +164,8 @@ class Tensor {
     return std::move(holder_);
   }
 
+  void ResetHolder(std::shared_ptr<memory::Allocation> holder);
+
  private:
   /*! holds the memory block if allocated. */
   std::shared_ptr<memory::Allocation> holder_;

paddle/fluid/framework/tensor_util.h

@@ -19,6 +19,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/framework.pb.h"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/temporary_allocator.h"
 
 namespace paddle {
 namespace framework {
@@ -151,5 +152,26 @@ void TensorToVector(const Tensor& src, std::vector<T>* dst) {
                src_ptr, size);
 }
 
+template <typename T>
+paddle::framework::Tensor GetTensor(
+    memory::allocation::AllocationPtr temp_allocation_ptr,
+    const framework::DDim& dim) {
+  auto& deleter = temp_allocation_ptr.get_deleter();
+  auto* allocation_ptr = temp_allocation_ptr.release();
+  auto shared_allocation =
+      std::shared_ptr<memory::allocation::Allocation>(allocation_ptr, deleter);
+
+  PADDLE_ENFORCE(
+      dynamic_cast<platform::TemporaryAllocation*>(allocation_ptr) != nullptr,
+      "The AllocationPtr must be TemporaryAllocation.");
+  PADDLE_ENFORCE_EQ(allocation_ptr->size(),
+                    framework::product(dim) * sizeof(T));
+
+  paddle::framework::Tensor temp_tensor(
+      framework::ToDataType(std::type_index(typeid(T))));
+  temp_tensor.Resize(dim);
+  temp_tensor.ResetHolder(std::move(shared_allocation));
+  return temp_tensor;
+}
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/inference/api/analysis_predictor.cc

@@ -231,11 +231,14 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
                   inputs[i].data.length());
     } else {
 #ifdef PADDLE_WITH_CUDA
+      platform::DeviceContextPool &pool =
+          platform::DeviceContextPool::Instance();
+      auto *dev_ctx =
+          static_cast<const platform::CUDADeviceContext *>(pool.Get(place_));
       auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
       memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
                    platform::CPUPlace(), inputs[i].data.data(),
-                   inputs[i].data.length(),
-                   0);  // stream 0 for sync copy
+                   inputs[i].data.length(), dev_ctx->stream());
 #else
       PADDLE_THROW("Not compile with CUDA, should not reach here.");
 #endif

paddle/fluid/inference/api/api_impl.cc

@@ -208,11 +208,14 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
                   inputs[i].data.length());
     } else {
 #ifdef PADDLE_WITH_CUDA
+      platform::DeviceContextPool &pool =
+          platform::DeviceContextPool::Instance();
+      auto *dev_ctx =
+          static_cast<const platform::CUDADeviceContext *>(pool.Get(place_));
       auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
       memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
                    platform::CPUPlace(), inputs[i].data.data(),
-                   inputs[i].data.length(),
-                   0);  // stream 0 for sync copy
+                   inputs[i].data.length(), dev_ctx->stream());
 #else
       PADDLE_THROW("Not compile with CUDA, should not reach here.");
 #endif

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

@@ -89,12 +89,21 @@ endif()
 
 if(WITH_MKL)
   include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
-  set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
-               ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+  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)
+    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)
   set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
   if(EXISTS ${MKLDNN_PATH})
     include_directories("${MKLDNN_PATH}/include")
-    set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
+    if(WIN32)
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib)
+    else(WIN32)
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
+    endif(WIN32)
   endif()
 else()
   set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})

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

@@ -75,6 +75,11 @@ set(LAC_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/lac")
 download_model_and_data(${LAC_INSTALL_DIR} "lac_model.tar.gz" "lac_data.txt.tar.gz")
 inference_analysis_api_test(test_analyzer_lac ${LAC_INSTALL_DIR} analyzer_lac_tester.cc)
 
+# MM DNN
+set(MM_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mm_dnn")
+download_model_and_data(${MM_DNN_INSTALL_DIR} "MM_DNN_model.tar.gz" "MM_DNN_data.txt.tar.gz")
+inference_analysis_api_test(test_analyzer_mm_dnn ${MM_DNN_INSTALL_DIR} analyzer_mm_dnn_tester.cc)
+
 # text_classification
 set(TEXT_CLASSIFICATION_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/text_classification")
 download_model_and_data(${TEXT_CLASSIFICATION_INSTALL_DIR} "text-classification-Senta.tar.gz" "text_classification_data.txt.tar.gz")

paddle/fluid/inference/tests/api/analyzer_dam_tester.cc

@@ -254,5 +254,16 @@ TEST(Analyzer_dam, compare) { compare(); }
 TEST(Analyzer_dam, compare_mkldnn) { compare(true /* use_mkldnn */); }
 #endif
 
+// Compare Deterministic result
+TEST(Analyzer_dam, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_lac_tester.cc

@@ -180,6 +180,17 @@ TEST(Analyzer_LAC, compare) {
       reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
 }
 
+// Compare Deterministic result
+TEST(Analyzer_LAC, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_mm_dnn_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/inference/tests/api/tester_helper.h"
+
+namespace paddle {
+namespace inference {
+using contrib::AnalysisConfig;
+
+struct DataRecord {
+  std::vector<std::vector<int64_t>> query_data_all, title_data_all;
+  std::vector<size_t> lod1, lod2;
+  size_t batch_iter{0};
+  size_t batch_size{1};
+  size_t num_samples;  // total number of samples
+  DataRecord() = default;
+  explicit DataRecord(const std::string &path, int batch_size = 1)
+      : batch_size(batch_size) {
+    Load(path);
+  }
+  DataRecord NextBatch() {
+    DataRecord data;
+    size_t batch_end = batch_iter + batch_size;
+    // NOTE skip the final batch, if no enough data is provided.
+    if (batch_end <= query_data_all.size()) {
+      data.query_data_all.assign(query_data_all.begin() + batch_iter,
+                                 query_data_all.begin() + batch_end);
+      data.title_data_all.assign(title_data_all.begin() + batch_iter,
+                                 title_data_all.begin() + batch_end);
+      // Prepare LoDs
+      data.lod1.push_back(0);
+      data.lod2.push_back(0);
+      CHECK(!data.query_data_all.empty());
+      CHECK(!data.title_data_all.empty());
+      CHECK_EQ(data.query_data_all.size(), data.title_data_all.size());
+      for (size_t j = 0; j < data.query_data_all.size(); j++) {
+        // calculate lod
+        data.lod1.push_back(data.lod1.back() + data.query_data_all[j].size());
+        data.lod2.push_back(data.lod2.back() + data.title_data_all[j].size());
+      }
+    }
+    batch_iter += batch_size;
+    return data;
+  }
+  void Load(const std::string &path) {
+    std::ifstream file(path);
+    std::string line;
+    int num_lines = 0;
+    while (std::getline(file, line)) {
+      num_lines++;
+      std::vector<std::string> data;
+      split(line, '\t', &data);
+      // load query data
+      std::vector<int64_t> query_data;
+      split_to_int64(data[0], ' ', &query_data);
+      // load title data
+      std::vector<int64_t> title_data;
+      split_to_int64(data[1], ' ', &title_data);
+      query_data_all.push_back(std::move(query_data));
+      title_data_all.push_back(std::move(title_data));
+    }
+    num_samples = num_lines;
+  }
+};
+
+void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
+                   int batch_size) {
+  PaddleTensor lod_query_tensor, lod_title_tensor;
+  lod_query_tensor.name = "left";
+  lod_title_tensor.name = "right";
+  auto one_batch = data->NextBatch();
+  int size1 = one_batch.lod1[one_batch.lod1.size() - 1];  // token batch size
+  int size2 = one_batch.lod2[one_batch.lod2.size() - 1];  // token batch size
+  lod_query_tensor.shape.assign({size1, 1});
+  lod_query_tensor.lod.assign({one_batch.lod1});
+  lod_title_tensor.shape.assign({size2, 1});
+  lod_title_tensor.lod.assign({one_batch.lod2});
+  // assign data
+  TensorAssignData<int64_t>(&lod_query_tensor, one_batch.query_data_all);
+  TensorAssignData<int64_t>(&lod_title_tensor, one_batch.title_data_all);
+  // Set inputs.
+  input_slots->assign({lod_query_tensor, lod_title_tensor});
+  for (auto &tensor : *input_slots) {
+    tensor.dtype = PaddleDType::INT64;
+  }
+}
+
+void SetConfig(contrib::AnalysisConfig *cfg) {
+  cfg->model_dir = FLAGS_infer_model;
+  cfg->use_gpu = false;
+  cfg->device = 0;
+  cfg->specify_input_name = true;
+  cfg->enable_ir_optim = true;
+}
+
+void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
+  DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
+  std::vector<PaddleTensor> input_slots;
+  int epoch = FLAGS_test_all_data ? data.num_samples / FLAGS_batch_size : 1;
+  LOG(INFO) << "number of samples: " << epoch * FLAGS_batch_size;
+  for (int bid = 0; bid < epoch; ++bid) {
+    PrepareInputs(&input_slots, &data, FLAGS_batch_size);
+    (*inputs).emplace_back(input_slots);
+  }
+}
+
+// Easy for profiling independently.
+TEST(Analyzer_MM_DNN, profile) {
+  contrib::AnalysisConfig cfg;
+  SetConfig(&cfg);
+  std::vector<PaddleTensor> outputs;
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                 input_slots_all, &outputs, FLAGS_num_threads);
+
+  if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
+    PADDLE_ENFORCE_EQ(outputs.size(), 2UL);
+    for (auto &output : outputs) {
+      size_t size = GetSize(output);
+      PADDLE_ENFORCE_GT(size, 0);
+      float *result = static_cast<float *>(output.data.data());
+      // output is probability, which is in (-1, 1).
+      for (size_t i = 0; i < size; i++) {
+        EXPECT_GT(result[i], -1);
+        EXPECT_LT(result[i], 1);
+      }
+    }
+  }
+}
+
+// Check the fuse status
+TEST(Analyzer_MM_DNN, fuse_statis) {
+  contrib::AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  int num_ops;
+  auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
+  auto fuse_statis = GetFuseStatis(
+      static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
+}
+
+// Compare result of NativeConfig and AnalysisConfig
+TEST(Analyzer_MM_DNN, compare) {
+  contrib::AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareNativeAndAnalysis(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
+}
+
+// Compare Deterministic result
+TEST(Analyzer_MM_DNN, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_ner_tester.cc

@@ -179,5 +179,16 @@ TEST(Analyzer_Chinese_ner, compare) {
       reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
 }
 
+// Compare Deterministic result
+TEST(Analyzer_Chinese_ner, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -85,6 +85,17 @@ TEST(Analyzer_resnet50, compare) { compare(); }
 TEST(Analyzer_resnet50, compare_mkldnn) { compare(true /* use_mkldnn */); }
 #endif
 
+// Compare Deterministic result
+TEST(Analyzer_resnet50, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc

@@ -265,6 +265,17 @@ TEST(Analyzer_rnn1, compare) {
       reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
 }
 
+// Compare Deterministic result
+TEST(Analyzer_rnn1, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 // Test Multi-Thread.
 TEST(Analyzer_rnn1, multi_thread) {
   contrib::AnalysisConfig cfg;

paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc

@@ -158,5 +158,16 @@ TEST(Analyzer_rnn2, compare) {
       reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
 }
 
+// Compare Deterministic result
+TEST(Analyzer_rnn2, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc

@@ -204,5 +204,16 @@ TEST(Analyzer_seq_conv1, compare) {
       reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
 }
 
+// Compare Deterministic result
+TEST(Analyzer_seq_conv1, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc

@@ -106,6 +106,17 @@ TEST(Analyzer_Text_Classification, compare) {
       reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
 }
 
+// Compare Deterministic result
+TEST(Analyzer_Text_Classification, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 TEST(Analyzer_Text_Classification, compare_against_embedding_fc_lstm_fused) {
   AnalysisConfig cfg;
   SetConfig(&cfg);

paddle/fluid/inference/tests/api/analyzer_vis_tester.cc

@@ -145,6 +145,17 @@ TEST(Analyzer_vis, compare) { compare(); }
 TEST(Analyzer_vis, compare_mkldnn) { compare(true /* use_mkldnn */); }
 #endif
 
+// Compare Deterministic result
+TEST(Analyzer_vis, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/tester_helper.h

@@ -45,6 +45,7 @@ DEFINE_bool(use_analysis, true,
             "Running the inference program in analysis mode.");
 DEFINE_bool(record_benchmark, false,
             "Record benchmark after profiling the model");
+DEFINE_double(accuracy, 1e-3, "Result Accuracy.");
 
 DECLARE_bool(profile);
 DECLARE_int32(paddle_num_threads);
@@ -85,7 +86,7 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
         float *pdata = static_cast<float *>(out.data.data());
         float *pdata_ref = static_cast<float *>(ref_out.data.data());
         for (size_t j = 0; j < size; ++j) {
-          EXPECT_NEAR(pdata_ref[j], pdata[j], 1e-3);
+          EXPECT_NEAR(pdata_ref[j], pdata[j], FLAGS_accuracy);
         }
         break;
       }
@@ -283,6 +284,26 @@ void TestPrediction(const PaddlePredictor::Config *config,
   }
 }
 
+void CompareDeterministic(
+    const PaddlePredictor::Config *config,
+    const std::vector<std::vector<PaddleTensor>> &inputs) {
+  int batch_size = FLAGS_batch_size;
+  int num_times = FLAGS_repeat;
+  auto predictor = CreateTestPredictor(config, FLAGS_use_analysis);
+
+  // warmup run
+  std::vector<PaddleTensor> warmup_outputs, outputs;
+  predictor->Run(inputs[0], &warmup_outputs, batch_size);
+
+  // run num_times to Compare Deterministic Result.
+  for (int i = 0; i < num_times; i++) {
+    for (size_t j = 0; j < inputs.size(); j++) {
+      predictor->Run(inputs[j], &outputs, batch_size);
+      CompareResult(outputs, warmup_outputs);
+    }
+  }
+}
+
 void CompareNativeAndAnalysis(
     const PaddlePredictor::Config *config,
     const std::vector<std::vector<PaddleTensor>> &inputs) {

paddle/fluid/operators/CMakeLists.txt

@@ -16,6 +16,7 @@ add_subdirectory(metrics)
 add_subdirectory(optimizers)
 add_subdirectory(reduce_ops)
 add_subdirectory(sequence_ops)
+add_subdirectory(jit)
 
 if(WITH_DISTRIBUTE)
     add_subdirectory(distributed)
@@ -42,8 +43,7 @@ if (WITH_DISTRIBUTE)
     SET(OP_PREFETCH_DEPS ${OP_PREFETCH_DEPS} parameter_prefetch)
 endif()
 
-register_operators(EXCLUDES warpctc_op conv_fusion_op DEPS ${OP_HEADER_DEPS} ${OP_PREFETCH_DEPS})
-
+register_operators(EXCLUDES py_func_op warpctc_op conv_fusion_op DEPS ${OP_HEADER_DEPS} ${OP_PREFETCH_DEPS})
 
 # warpctc_op needs cudnn 7 above
 if (WITH_GPU AND NOT WIN32)
@@ -65,7 +65,7 @@ set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
 
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
-set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
 if (WITH_GPU)
   set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv prelu)
@@ -92,4 +92,8 @@ cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
 cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
 nv_test(dropout_op_test SRCS dropout_op_test.cc DEPS dropout_op tensor)
 
+if (WITH_PYTHON)
+  cc_library(py_func_op SRCS py_func_op.cc DEPS op_registry python pybind)
+endif()
+
 set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")

paddle/fluid/operators/conv_op.h

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/tensor_util.h"
 #include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/operators/math/depthwise_conv.h"
 #include "paddle/fluid/operators/math/im2col.h"
@@ -123,6 +124,8 @@ class GemmConvKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
 
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+
     const int batch_size = static_cast<int>(input->dims()[0]);
 
     // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, k_w}
@@ -155,13 +158,16 @@ class GemmConvKernel : public framework::OpKernel<T> {
     // to call the matrix multiplication interface.
     Tensor col_matrix;
     if (is_expand) {
-      col.mutable_data<T>(col_shape, context.GetPlace());
+      auto tmp_allocation_ptr =
+          platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx).Allocate(
+              framework::product(col_shape) * sizeof(T));
+      col = framework::GetTensor<T>(std::move(tmp_allocation_ptr), col_shape);
       col_matrix.ShareDataWith(col);
       col_matrix.Resize(col_matrix_shape);
     }
 
-    framework::DDim input_shape = framework::slice_ddim(
-        input->dims(), 1, static_cast<int>(input->dims().size()));
+    framework::DDim input_shape =
+        framework::slice_ddim(input->dims(), 1, input->dims().size());
 
     framework::DDim filter_matrix_shape = {filter.dims()[0],
                                            filter.numel() / filter.dims()[0]};
@@ -178,7 +184,6 @@ class GemmConvKernel : public framework::OpKernel<T> {
     math::Vol2ColFunctor<DeviceContext, T> vol2col;
     math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;
 
-    auto& dev_ctx = context.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     for (int i = 0; i < batch_size; i++) {
       Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
@@ -237,6 +242,8 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
 
     const int batch_size = static_cast<int>(input->dims()[0]);
 
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+
     // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, k_w}
     std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
     // output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h, o_w}
@@ -262,8 +269,8 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
     framework::DDim col_matrix_shape =
         framework::flatten_to_2d(col_shape, data_dim + 1);
 
-    framework::DDim input_shape = framework::slice_ddim(
-        input->dims(), 1, static_cast<int>(input->dims().size()));
+    framework::DDim input_shape =
+        framework::slice_ddim(input->dims(), 1, input->dims().size());
 
     framework::DDim filter_matrix_shape = {filter.dims()[0],
                                            filter.numel() / filter.dims()[0]};
@@ -286,13 +293,15 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
     // to call the matrix multiplication interface.
     Tensor col_matrix;
     if (is_expand) {
-      col.mutable_data<T>(col_shape, context.GetPlace());
+      auto tmp_allocation_ptr =
+          platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx).Allocate(
+              framework::product(col_shape) * sizeof(T));
+      col = framework::GetTensor<T>(std::move(tmp_allocation_ptr), col_shape);
       col_matrix.ShareDataWith(col);
       col_matrix.Resize(col_matrix_shape);
     }
 
     math::SetConstant<DeviceContext, T> set_zero;
-    auto& dev_ctx = context.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
 
     if (input_grad) {

paddle/fluid/operators/crf_decoding_op.h

@@ -16,7 +16,7 @@ limitations under the License. */
 #include <limits>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/math/jit_kernel.h"
+#include "paddle/fluid/operators/jit/kernels.h"
 #include "paddle/fluid/operators/math/math_function.h"
 
 namespace paddle {
@@ -82,10 +82,9 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
     Tensor track;
     int* track_value =
         track.mutable_data<int>(emission_dims, platform::CPUPlace());
-    const auto& ker = math::jitkernel::KernelPool::Instance()
-                          .template Get<math::jitkernel::CRFDecodeKernel<T>>(
-                              static_cast<int>(tag_num));
-    ker->Compute(static_cast<int>(seq_len), x, w, alpha_value, track_value);
+    auto ker = jit::Get<jit::kCRFDecoding, jit::CRFDecodingTuples<T>,
+                        platform::CPUPlace>(tag_num);
+    ker(static_cast<int>(seq_len), x, w, alpha_value, track_value, tag_num);
     T max_score = -std::numeric_limits<T>::max();
     int max_i = 0;
     for (size_t i = 0; i < tag_num; ++i) {

paddle/fluid/operators/dequantize_mkldnn_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "mkldnn.hpp"
+#include "paddle/fluid/framework/data_layout_transform.h"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/dequantize_op.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using mkldnn::memory;
+using mkldnn::primitive;
+using mkldnn::reorder;
+using platform::to_void_cast;
+using Tensor = framework::Tensor;
+using framework::DataLayout;
+using mkldnn::stream;
+using platform::GetMKLDNNFormat;
+
+template <typename T>
+class DeQuantOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("Input");
+    auto scale_data = ctx.Attr<float>("Scale");
+    auto* output = ctx.Output<Tensor>("Output");
+    auto& dev_ctx =
+        ctx.template device_context<platform::MKLDNNDeviceContext>();
+    const auto& engine = dev_ctx.GetEngine();
+
+    const T* input_data = input->data<T>();
+    float* output_data = output->mutable_data<float>(ctx.GetPlace());
+    std::vector<float> reorder_scale = {1.0f / scale_data};
+
+    std::vector<primitive> pipeline;
+    std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
+    std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
+    mkldnn::memory::data_type src_dt =
+        paddle::framework::ToMKLDNNDataType(input->type());
+    mkldnn::memory::format src_fmt = input->format();
+
+    mkldnn::primitive_attr attri;
+    int mask = 0;
+    attri.set_output_scales(mask, reorder_scale);
+
+    auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt);
+    auto src_pd = mkldnn::memory::primitive_desc(src_md, engine);
+    auto src_memory =
+        std::make_shared<mkldnn::memory>(src_pd, to_void_cast<T>(input_data));
+    std::shared_ptr<primitive::at> src_memory_p =
+        std::shared_ptr<primitive::at>(new primitive::at(*src_memory));
+
+    auto dst_md = platform::MKLDNNMemDesc({dst_tz}, memory::data_type::f32,
+                                          memory::format::nchw);
+    auto dst_pd = mkldnn::memory::primitive_desc(dst_md, engine);
+    auto dst_memory = mkldnn::memory(dst_pd, to_void_cast<float>(output_data));
+
+    auto reorder_pd = std::shared_ptr<reorder::primitive_desc>(
+        new reorder::primitive_desc(src_pd, dst_pd, attri));
+    auto reorder_p = std::shared_ptr<reorder>(
+        new reorder(*reorder_pd, *src_memory_p, dst_memory));
+    pipeline.push_back(*reorder_p);
+    stream(stream::kind::eager).submit(pipeline).wait();
+
+    output->set_format(GetMKLDNNFormat(dst_memory));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_KERNEL(dequantize, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::DeQuantOpKernel<uint8_t>, ops::DeQuantOpKernel<int8_t>);

paddle/fluid/operators/dequantize_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/dequantize_op.h"
+#ifdef PADDLE_WITH_MKLDNN
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#endif
+
+namespace paddle {
+namespace operators {
+
+framework::OpKernelType DeQuantOp::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  framework::LibraryType library_ = framework::LibraryType::kMKLDNN;
+  framework::DataLayout layout_ = framework::DataLayout::kMKLDNN;
+
+  return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
+                                 ctx.GetPlace(), layout_, library_);
+}
+
+void DeQuantOpMaker::Make() {
+  AddInput("Input", "input data");
+  AddOutput("Output", "output data");
+  AddAttr<float>("Scale", "scale data").SetDefault({1.0f});
+  AddComment(R"DOC(This op will dequantize data from INT8 to FP32)DOC");
+}
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(dequantize, ops::DeQuantOp, ops::DeQuantOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);

paddle/fluid/operators/dequantize_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::OpKernelType;
+using framework::Tensor;
+
+class DeQuantOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    ctx->SetOutputDim("Output", ctx->GetInputDim("Input"));
+    ctx->ShareLoD("Input", /*->*/ "Output");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
+};
+
+class DeQuantOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override;
+};
+
+class DeQuantGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {}
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/detection/density_prior_box_op.cu

@@ -142,12 +142,13 @@ class DensityPriorBoxOpCUDAKernel : public framework::OpKernel<T> {
     vars->mutable_data<T>(ctx.GetPlace());
 
     framework::Tensor d_temp;
-    framework::TensorCopySync(h_temp, ctx.GetPlace(), &d_temp);
+    framework::TensorCopy(h_temp, ctx.GetPlace(), &d_temp);
 
     // At least use 32 threads, at most 512 threads.
     // blockx is multiple of 32.
     int blockx = std::min(
-        static_cast<long>(((feature_width * num_priors + 31) >> 5) << 5), 512L);
+        static_cast<int64_t>(((feature_width * num_priors + 31) >> 5) << 5),
+        512L);
     int gridx = (feature_width * num_priors + blockx - 1) / blockx;
     dim3 threads(blockx, 1);
     dim3 grids(gridx, feature_height);

paddle/fluid/operators/elementwise/elementwise_mul_mkldnn_op.cc

@@ -16,11 +16,14 @@ limitations under the License. */
 #include "paddle/fluid/operators/elementwise/elementwise_op.h"
 #include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
 
+#include "paddle/fluid/operators/jit/kernels.h"
+#include "paddle/fluid/platform/cpu_info.h"
 #include "paddle/fluid/platform/mkldnn_helper.h"
 
-#include "paddle/fluid/operators/math/jit_kernel.h"
+#ifdef PADDLE_WITH_XBYAK
 #include "xbyak/xbyak.h"
 #include "xbyak/xbyak_util.h"
+#endif
 
 namespace paddle {
 namespace operators {
@@ -81,8 +84,7 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
     UpdateDataFormat(ctx, const_cast<Tensor*>(x), "x_data_format");
     UpdateDataFormat(ctx, const_cast<Tensor*>(y), "y_data_format");
 
-    Xbyak::util::Cpu cpu;
-    const bool is_avx512_enabled = cpu.has(Xbyak::util::Cpu::tAVX512F);
+    const bool is_avx512_enabled = platform::MayIUse(platform::avx512f);
     const bool are_dims_divisable = !(x_int_dims[1] % 16);
     const bool is_x_format_correct = x->format() == memory::format::nChw16c;
     const bool is_y_format_correct = y->format() == memory::format::nc;
@@ -108,10 +110,8 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
         constexpr int simd_width = 16;
         int C = c / simd_width;
 
-        const auto& multiply =
-            math::jitkernel::KernelPool::Instance()
-                .template Get<math::jitkernel::EltwiseMulnChw16cNCKernel<T>>(n);
-
+        auto multiply = jit::Get<jit::kNCHW16CMulNC, jit::NCHW16CMulNCTuples<T>,
+                                 platform::CPUPlace>(0);
 #pragma omp parallel for collapse(2)
         for (int ni = 0; ni < n; ni++) {
           for (int ci = 0; ci < C; ci++) {
@@ -122,7 +122,7 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
             auto ptr_z =
                 z_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
 
-            multiply->Compute(ptr_x, ptr_y, ptr_z, h, w);
+            multiply(ptr_x, ptr_y, ptr_z, h, w);
           }
         }
       }

paddle/fluid/operators/fused/fusion_gru_op.cc

@@ -15,9 +15,9 @@ limitations under the License. */
 #include "paddle/fluid/operators/fused/fusion_gru_op.h"
 #include <cstring>  // for memcpy
 #include <string>
+#include "paddle/fluid/operators/jit/kernels.h"
 #include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/operators/math/fc_compute.h"
-#include "paddle/fluid/operators/math/jit_kernel.h"
 #include "paddle/fluid/operators/math/sequence2batch.h"
 
 namespace paddle {
@@ -182,27 +182,29 @@ class FusionGRUKernel : public framework::OpKernel<T> {
   const int total_T = x_dims[0];           \
   const int D3 = wh_dims[1]
 
-#define INIT_OTHER_DEFINES                                         \
-  auto* h0 = ctx.Input<Tensor>("H0");                              \
-  auto* wx = ctx.Input<Tensor>("WeightX");                         \
-  auto* bias = ctx.Input<Tensor>("Bias");                          \
-  auto* hidden_out = ctx.Output<LoDTensor>("Hidden");              \
-  bool is_reverse = ctx.Attr<bool>("is_reverse");                  \
-  const int M = x_dims[1];                                         \
-  const int D = wh_dims[0];                                        \
-  const int D2 = D * 2;                                            \
-  const math::jitkernel::gru_attr_t attr(                          \
-      D, ctx.Attr<std::string>("gate_activation"),                 \
-      ctx.Attr<std::string>("activation"));                        \
-  math::jitkernel::gru_t one_step;                                 \
-  const auto& ker =                                                \
-      math::jitkernel::KernelPool::Instance()                      \
-          .template Get<math::jitkernel::GRUKernel<T>,             \
-                        const math::jitkernel::gru_attr_t&>(attr); \
-  const T* x_data = x->data<T>();                                  \
-  const T* wx_data = wx->data<T>();                                \
-  const T* wh_data = wh->data<T>();                                \
-  auto place = ctx.GetPlace();                                     \
+#define INIT_OTHER_DEFINES                                                     \
+  auto* h0 = ctx.Input<Tensor>("H0");                                          \
+  auto* wx = ctx.Input<Tensor>("WeightX");                                     \
+  auto* bias = ctx.Input<Tensor>("Bias");                                      \
+  auto* hidden_out = ctx.Output<LoDTensor>("Hidden");                          \
+  bool is_reverse = ctx.Attr<bool>("is_reverse");                              \
+  const int M = x_dims[1];                                                     \
+  const int D = wh_dims[0];                                                    \
+  const int D2 = D * 2;                                                        \
+  const jit::gru_attr_t attr(                                                  \
+      D, jit::to_kerneltype(ctx.Attr<std::string>("gate_activation")),         \
+      jit::to_kerneltype(ctx.Attr<std::string>("activation")));                \
+  jit::gru_t one_step;                                                         \
+  auto ComputeH1 =                                                             \
+      jit::Get<jit::kGRUH1, jit::GRUTuples<T>, platform::CPUPlace>(attr);      \
+  auto ComputeHtPart1 =                                                        \
+      jit::Get<jit::kGRUHtPart1, jit::GRUTuples<T>, platform::CPUPlace>(attr); \
+  auto ComputeHtPart2 =                                                        \
+      jit::Get<jit::kGRUHtPart2, jit::GRUTuples<T>, platform::CPUPlace>(attr); \
+  const T* x_data = x->data<T>();                                              \
+  const T* wx_data = wx->data<T>();                                            \
+  const T* wh_data = wh->data<T>();                                            \
+  auto place = ctx.GetPlace();                                                 \
   T* xx_data = xx->mutable_data<T>(place)
 
   void SeqCompute(const framework::ExecutionContext& ctx) const {
@@ -241,7 +243,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
       } else {
         one_step.gates = xx_data;
         one_step.ht = hidden_out_data;
-        ker->ComputeH1(&one_step, &attr);
+        ComputeH1(&one_step, &attr);
         prev_hidden_data = hidden_out_data;
         tstart = 1;
         move_step();
@@ -254,12 +256,12 @@ class FusionGRUKernel : public framework::OpKernel<T> {
         one_step.gates = xx_data;
         one_step.ht_1 = prev_hidden_data;
         one_step.ht = hidden_out_data;
-        ker->ComputeHtPart1(&one_step, &attr);
+        ComputeHtPart1(&one_step, &attr);
         // gemm rt * Ws
         blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D, D, static_cast<T>(1),
                   hidden_out_data, D, wh_state_data, D, static_cast<T>(1),
                   xx_data + D2, D3);
-        ker->ComputeHtPart2(&one_step, &attr);
+        ComputeHtPart2(&one_step, &attr);
         // save prev
         prev_hidden_data = hidden_out_data;
         move_step();
@@ -323,7 +325,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
       for (int i = 0; i < max_bs; ++i) {
         one_step.gates = cur_in_data;
         one_step.ht = cur_out_data;
-        ker->ComputeH1(&one_step, &attr);
+        ComputeH1(&one_step, &attr);
         // add offset
         cur_in_data += D3;
         cur_out_data += D;
@@ -351,7 +353,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
         one_step.gates = cur_batched_data;
         one_step.ht_1 = cur_prev_hidden_data;
         one_step.ht = cur_out_data;
-        ker->ComputeHtPart1(&one_step, &attr);
+        ComputeHtPart1(&one_step, &attr);
 
         cur_batched_data += D3;
         cur_prev_hidden_data += D;
@@ -369,7 +371,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
         one_step.gates = cur_batched_data;
         one_step.ht_1 = cur_prev_hidden_data;
         one_step.ht = cur_out_data;
-        ker->ComputeHtPart2(&one_step, &attr);
+        ComputeHtPart2(&one_step, &attr);
         cur_batched_data += D3;
         cur_prev_hidden_data += D;
         cur_out_data += D;

paddle/fluid/operators/fused/fusion_lstm_op.cc

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/fused/fusion_lstm_op.h"
 #include <string>
+#include "paddle/fluid/operators/jit/kernels.h"
 #include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/operators/math/fc_compute.h"
-#include "paddle/fluid/operators/math/jit_kernel.h"
 #include "paddle/fluid/operators/math/sequence2batch.h"
 
 namespace paddle {
@@ -235,31 +235,32 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
   const int D = wh_dims[0];                                 \
   const int D4 = wh_dims[1]
 
-#define INIT_OTHER_DEFINES                                      \
-  const T* x_data = x->data<T>();                               \
-  const T* wx_data = wx->data<T>();                             \
-  const T* wh_data = wh->data<T>();                             \
-  /* diagonal weight*/                                          \
-  const T* wp_data = bias->data<T>() + D4;                      \
-  /* for peephole only*/                                        \
-  T* checked_cell_data = nullptr;                               \
-  auto place = ctx.GetPlace();                                  \
-  if (use_peepholes) {                                          \
-    /* w_ic * Ct-1, w_fc * Ct-1  ; w_oc * Ct => ih*/            \
-    auto* checked_cell = ctx.Output<Tensor>("CheckedCell");     \
-    checked_cell_data = checked_cell->mutable_data<T>(place);   \
-  }                                                             \
-  const math::jitkernel::lstm_attr_t attr(                      \
-      D, ctx.Attr<std::string>("gate_activation"),              \
-      ctx.Attr<std::string>("candidate_activation"),            \
-      ctx.Attr<std::string>("cell_activation"), use_peepholes); \
-  math::jitkernel::lstm_t one_step;                             \
-  one_step.wp = wp_data;                                        \
-  one_step.checked = checked_cell_data;                         \
-  const auto& ker =                                             \
-      math::jitkernel::KernelPool::Instance()                   \
-          .template Get<math::jitkernel::LSTMKernel<T>,         \
-                        const math::jitkernel::lstm_attr_t&>(attr)
+#define INIT_OTHER_DEFINES                                                    \
+  const T* x_data = x->data<T>();                                             \
+  const T* wx_data = wx->data<T>();                                           \
+  const T* wh_data = wh->data<T>();                                           \
+  /* diagonal weight*/                                                        \
+  const T* wp_data = bias->data<T>() + D4;                                    \
+  /* for peephole only*/                                                      \
+  T* checked_cell_data = nullptr;                                             \
+  auto place = ctx.GetPlace();                                                \
+  if (use_peepholes) {                                                        \
+    /* w_ic * Ct-1, w_fc * Ct-1  ; w_oc * Ct => ih*/                          \
+    auto* checked_cell = ctx.Output<Tensor>("CheckedCell");                   \
+    checked_cell_data = checked_cell->mutable_data<T>(place);                 \
+  }                                                                           \
+  const jit::lstm_attr_t attr(                                                \
+      D, jit::to_kerneltype(ctx.Attr<std::string>("gate_activation")),        \
+      jit::to_kerneltype(ctx.Attr<std::string>("candidate_activation")),      \
+      jit::to_kerneltype(ctx.Attr<std::string>("cell_activation")),           \
+      use_peepholes);                                                         \
+  jit::lstm_t one_step;                                                       \
+  one_step.wp = wp_data;                                                      \
+  one_step.checked = checked_cell_data;                                       \
+  auto ComputeC1H1 =                                                          \
+      jit::Get<jit::kLSTMC1H1, jit::LSTMTuples<T>, platform::CPUPlace>(attr); \
+  auto ComputeCtHt =                                                          \
+      jit::Get<jit::kLSTMCtHt, jit::LSTMTuples<T>, platform::CPUPlace>(attr)
 
 // Wh GEMM
 #define GEMM_WH_ADDON(bs, prev, out)                                           \
@@ -305,7 +306,7 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
         one_step.gates = xx_data;
         one_step.ct = c_out_data;
         one_step.ht = h_out_data;
-        ker->ComputeC1H1(&one_step, &attr);
+        ComputeC1H1(&one_step, &attr);
         tstart = 1;
         // move one step
         prev_h_data = h_out_data;
@@ -321,7 +322,7 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
         one_step.ct_1 = prev_c_data;
         one_step.ct = c_out_data;
         one_step.ht = h_out_data;
-        ker->ComputeCtHt(&one_step, &attr);
+        ComputeCtHt(&one_step, &attr);
         // move one step
         prev_h_data = h_out_data;
         prev_c_data = c_out_data;
@@ -401,7 +402,7 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
         one_step.gates = cur_in_data;
         one_step.ct = cur_c_out_data;
         one_step.ht = cur_h_out_data;
-        ker->ComputeC1H1(&one_step, &attr);
+        ComputeC1H1(&one_step, &attr);
 
         cur_in_data += D4;
         cur_c_out_data += D;
@@ -431,7 +432,7 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
         one_step.ct_1 = cur_prev_c_data;
         one_step.ct = cur_c_out_data;
         one_step.ht = cur_h_out_data;
-        ker->ComputeCtHt(&one_step, &attr);
+        ComputeCtHt(&one_step, &attr);
 
         // move one batch
         cur_in_data += D4;

paddle/fluid/operators/jit/CMakeLists.txt

+
+set(jit_file ${PADDLE_BINARY_DIR}/paddle/fluid/operators/jit/kernels.h)
+file(WRITE ${jit_file} "// Generated by the paddle/fluid/operators/jit/CMakeLists.txt.  DO NOT EDIT!\n\n")
+file(APPEND ${jit_file} "\#pragma once\n")
+file(APPEND ${jit_file} "\#include \"paddle/fluid/operators/jit/helper.h\"\n")
+file(APPEND ${jit_file} "\#include \"paddle/fluid/operators/jit/registry.h\"\n\n")
+
+set(JIT_KERNEL_DEPS cpu_info cblas gflags enforce place)
+
+file(GLOB jit_kernel_cc_srcs RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cc")
+list(REMOVE_ITEM jit_kernel_cc_srcs test.cc benchmark.cc)
+cc_library(jit_kernel_base SRCS ${jit_kernel_cc_srcs} DEPS ${JIT_KERNEL_DEPS})
+
+# refer must go first
+add_subdirectory(refer)
+add_subdirectory(more)
+if(WITH_XBYAK)
+    add_subdirectory(gen)
+endif()
+
+cc_library(jit_kernel_helper SRCS ${jit_kernel_cc_srcs} DEPS ${JIT_KERNEL_DEPS})
+cc_test(jit_kernel_test SRCS test.cc DEPS jit_kernel_helper)
+if(NOT WIN32)
+    cc_binary(jit_kernel_benchmark SRCS benchmark.cc DEPS jit_kernel_helper device_tracer)
+endif()

paddle/fluid/operators/jit/README.en.md

+# JIT Kernel
+
+JIT(Just In Time) Kernel contains actually generated code and some other implemenations with the same logic.
+Each implementations has its own condition to use, defined in `UseMe`.
+They are combined together to get the best performance of one single independent function.
+They could be some very simple functions like vector multiply, or some complicated functions like LSTM.
+And they can be composed with some other exited jit kernels to build up a complex function. 
+Currently it's only supported on CPU yet.
+
+## Contents
+
+```txt
+PaddlePaddle/Paddle/paddle/fluid/
+├── ...
+└── operators/
+    ├── .../
+    └── jit/
+        ├── ...
+        ├── gen/
+        │   └── ...
+        |── more/
+        │   ├── ...
+        │   ├── mkl/
+        │   │   └── ...
+        │   ├── mkldnn/
+        │   │   └── ...
+        │   ├── mix/
+        │   │   └── ...
+        │   ├── intrinsic/
+        │   │   └── ...
+        │   └── openblas/
+        │       └── ...
+        └── refer/
+            └── ...
+```
+
+All basical definations of jit kernels are addressed in `paddle/fluid/operators/jit` including these three key folders `refer`, `gen`, `more`. There is only one unique name for each kernel while may have seraval implementations with same functionality.
+
+- `refer`: Each kernel must have one reference implementation on CPU, and it should only focus on the correctness and should not depends on any third-party libraries.
+- `gen`: The code generated should be kept here. They should be designed focusing on the best performance, which depends on Xbyak.
+- `more`: All other implementations should be kept in this folder with one directory corresponding to one library kind or method kind, such as mkl, mkldnn, openblas or intrinsic code. Each implementation should have it advantage. 
+
+## How to use
+
+One simple function `jit::Get`, which is very easy to use, is supported to get the kernel.
+It can automatically return the expected function with best performance under the given attributes. 
+All kernels are inlcuded in `paddle/fluid/operators/jit/kernels.h`, you can only include this one header to get all the registered kernels.
+
+## Solid Test
+
+- Unit Test
+    All functions should be compared with the corresponding reference functions, including data tyep `float` and `double`.
+- Benchmark
+    All functions should be tested, and make sure the `jit::Get` function obtain the best performance with all attributes.
+
+# How to add new kernel
+
+## Required
+
+1. Add `your_key` at `KernelType`.
+2. Add reference function of `your_key`. 
+Note:
+    - this should be run on CPU and do not depend on any third-party.
+    - Add `USE_JITKERNEL_REFER(your_key)` in `refer/CmakeLists.txt` to make sure this code can be used.
+3. Add unit test in `test.cc`, and verfiy at least `float` and `double`.
+Test more data type for some special functions if necessary, for example `int8`.
+4. Add functions in `benchmark.cc` to test all function of same `KernelType`. Make sure `jit::Get` always get the best one.
+
+## Optional
+
+Add more implementations of `your_kery` for performance enhancement.
+
+1. Add functions based on generated code in `gen`. It should be derived from `JitCode` and should have corepsonding creator from `JitCodeCreator` which will be registered on the `your_key`.
+Note: Add new `KernelTuples` if necessary，your can refer to `XYZNTuples`.
+Specialie method `JitCodeKey` when add new attribute type。
+2. Add more functions in `more`，you can use any third party you wish, like mkl, mkldnn or intrinsic code to reach the best performance.

paddle/fluid/operators/jit/README.md

+# JIT Kernel
+
+结合函数模板和JIT生成需要的kernel函数。
+这里的kernel是比Operator中kernel更小级别的算子单元，更侧重的是在不同硬件上的性能。可以有多重第三方库的实现，每种实现有自己的`UseMe`函数负责什么条件下可以被调用。
+这里实现的函数可以非常细粒度的函数方法，比如Vector MUL， 也可以是一个复杂的逻辑比如LSTM等。复杂的逻辑也可以由自己的底层函数拼接而成。
+目前仅支持CPU上的高性能计算。
+
+## 目录结构
+
+```txt
+PaddlePaddle/Paddle/paddle/fluid/
+├── ...
+└── operators/
+    ├── .../
+    └── jit/
+        ├── ...
+        ├── gen/
+        │   └── ...
+        |── more/
+        │   ├── ...
+        │   ├── mkl/
+        │   │   └── ...
+        │   ├── mkldnn/
+        │   │   └── ...
+        │   ├── mix/
+        │   │   └── ...
+        │   ├── intrinsic/
+        │   │   └── ...
+        │   └── openblas/
+        │       └── ...
+        └── refer/
+            └── ...
+```
+
+基本类的定义都放在根目录下，根目录下包括gen,more和refer三个目录。每个目录下都是一种或者多种实现，每种kernel算子都需要有reference的实现，用作单元测试的基准，其他的实现都是可选的。
+- gen: 代表使用jit生成的code，需要依赖xbyak库。该实现最关心的就是性能。
+- refer: 代表reference的实现，每种kernel算子都需要有在CPU上的reference的实现，他主要关心的算法逻辑的正确性。
+- more: 下面可以放入跟多实现，可以包括mkl，mkldnn，intrinsic，openblas等，也可以是自身已有的kernel组合。
+
+## 动态获取
+
+提供一个`jit::Get`方法，根据kernel类别获取，每种实现都有自己的使用范围，根据范围动态和当前条件选择需要的kernel函数。
+
+## 测试
+
+- 逻辑测试
+    所有实现都要与refer的code对比，需要满足精度要求， 包括float和double的数据类型
+- 性能测试
+    所有实现的性能对比，并且与最终的`jit::Get`方法对比，该方法拿到的性能需要在各种条件下都是最好的。
+
+# 如何添加新的算子
+
+- 在`KernelType` 中添加 `your_key` .
+- 实现Reference 的逻辑，这个是必须是在CPU上的实现，并且不能依赖任何第三方库。实现后在`refer/CmakeLists.txt`中添加`USE_JITKERNEL_REFER(your_key)`来使用该kernel.
+- (optional) 实现更多的算法在`more`目录下，可以依赖mkl，intrinsic或者mkldnn等第三方库。
+- (optional) 实现基于Xbyak的生成code，在`gen`目下。 jitcode需要实现自己的`JitCodeCreator`，并注册在与refer相同的`KernelType`上。
+- 必要时可以添加新的`KernelTuples`，可以参考`XYZNTuples`，新加的Attr类型需要特例化`JitCodeKey`方法。
+- 在`test.cc`中添加unit test，至少需要测试`float`和`double`两种数据类型，如有必要需要支持额外的数据类型，比如`int8`的相关函数。
+- 在`benchmark.cc`中添加相应的性能对比，同一种kernel需要对比所有实现，并且确保`jit::Get`得到的实现一直是速度最快的。
+
+# 优点
+- 统一的Get方法，接口简单。
+- 同一套逻辑可以有多套实现，可以依赖多套第三方库，互不影响。
+- 目录结构清晰，不会在某个文件中有多个宏定义，导致的可读性差问题。
+- 优化方便，可以直接针对某种属性针对性优化，并不影响其他属性下的性能。
+- 可以支持多种平台，包括Linux，Mac 和 Windows，至少可以保证每种平台都可以正常work。后期也可以针对不同平台有针对的优化。框架层面可以使用统一接口，不必关心底层实现。

paddle/fluid/operators/jit/benchmark.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 <iostream>
+#include <random>
+#include <string>
+#include <vector>
+#include "gflags/gflags.h"
+#include "glog/logging.h"
+#include "paddle/fluid/operators/jit/kernels.h"
+#include "paddle/fluid/platform/device_tracer.h"
+#include "paddle/fluid/platform/place.h"
+#include "paddle/fluid/platform/port.h"
+
+DEFINE_int32(burning, 10, "Burning times.");
+DEFINE_int32(repeat, 3000, "Repeat times.");
+DEFINE_int32(max_size, 1000, "The Max size would be tested.");
+
+template <typename T>
+void RandomVec(const int n, T* a, const T lower = static_cast<T>(-20.f),
+               const T upper = static_cast<T>(20.f), unsigned int seed = 100) {
+  std::mt19937 rng(seed);
+  std::uniform_real_distribution<double> uniform_dist(0, 1);
+  for (int i = 0; i < n; ++i) {
+    a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
+  }
+}
+
+std::vector<int> TestSizes() {
+  std::vector<int> s;
+  for (int i = 1; i <= FLAGS_max_size; ++i) {
+    s.push_back(i);
+  }
+  return s;
+}
+
+template <typename KernelTuples, typename... Args>
+struct BenchFunc {
+  // return this function avg time
+  double operator()(const typename KernelTuples::func_type tgt, Args... args) {
+    for (int i = 0; i < FLAGS_burning; ++i) {
+      tgt(args...);
+    }
+    auto start = paddle::platform::PosixInNsec() / 1e-3;
+    for (int i = 0; i < FLAGS_repeat; ++i) {
+      tgt(args...);
+    }
+    auto end = paddle::platform::PosixInNsec() / 1e-3;
+    return static_cast<double>(end - start) / FLAGS_repeat;
+  }
+};
+
+namespace jit = paddle::operators::jit;
+
+template <jit::KernelType KT, typename KernelTuples, typename PlaceType,
+          typename... Args>
+void BenchAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
+  BenchFunc<KernelTuples, Args...> benchmark;
+  std::vector<std::pair<std::string, double>> infos;
+  // test refer
+  auto refer = jit::GetRefer<KT, KernelTuples>();
+  if (!refer) {
+    LOG(FATAL) << "Refer can not be empty!";
+  }
+  infos.push_back(std::make_pair("Refer", benchmark(refer, args...)));
+
+  // test jitcode
+  auto jitcode = jit::GetJitCode<KT, KernelTuples, PlaceType>(attr);
+  if (jitcode) {
+    infos.push_back(std::make_pair("JitCode", benchmark(jitcode, args...)));
+  }
+  // test all impls in more
+  jit::KernelKey kkey(KT, PlaceType());
+  auto& pool = jit::KernelPool().Instance().AllKernels();
+  auto iter = pool.find(kkey);
+  if (iter != pool.end()) {
+    auto& impls = iter->second;
+    for (auto& impl : impls) {
+      auto i = dynamic_cast<const jit::KernelMore<KernelTuples>*>(impl.get());
+      if (i && i->UseMe(attr)) {
+        auto more = i->GetFunc();
+        infos.push_back(
+            std::make_pair(i->ImplType(), benchmark(more, args...)));
+      }
+    }
+  }
+  // Test result from Get function
+  auto tgt = jit::Get<KT, KernelTuples, PlaceType>(attr);
+  if (!tgt) {
+    LOG(FATAL) << "Target can not be empty!";
+  }
+  infos.push_back(std::make_pair("Target", benchmark(tgt, args...)));
+
+  // print
+  std::ostringstream loginfos;
+  loginfos << "Kernel Type " << jit::to_string(KT) << ": " << attr << ": ";
+  for (auto pair : infos) {
+    loginfos << pair.first << " takes " << pair.second << " us; ";
+  }
+  LOG(INFO) << loginfos.str();
+}
+
+template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
+void BenchXYZNKernel() {
+  for (int d : TestSizes()) {
+    std::vector<T> x(d), y(d), z(d);
+    RandomVec<T>(d, x.data());
+    RandomVec<T>(d, y.data());
+    BenchAllImpls<KT, jit::XYZNTuples<T>, PlaceType>(d, x.data(), y.data(),
+                                                     z.data(), d);
+  }
+}
+
+template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
+void BenchAXYNKernel() {
+  for (int d : TestSizes()) {
+    const T a = static_cast<T>(3);
+    std::vector<T> x(d), y(d);
+    RandomVec<T>(d, x.data());
+    BenchAllImpls<KT, jit::AXYNTuples<T>, PlaceType>(d, &a, x.data(), y.data(),
+                                                     d);
+  }
+}
+
+template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
+void BenchXYNKernel() {
+  for (int d : TestSizes()) {
+    std::vector<T> x(d), y(d);
+    RandomVec<T>(d, x.data());
+    BenchAllImpls<KT, jit::XYNTuples<T>, PlaceType>(d, x.data(), y.data(), d);
+  }
+}
+
+template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
+void BenchLSTMKernel() {
+  for (bool use_peephole : {true, false}) {
+    for (int d : TestSizes()) {
+      const jit::lstm_attr_t attr(d, jit::kVSigmoid, jit::kVTanh, jit::kVTanh,
+                                  use_peephole);
+      std::vector<T> x(4 * d), ct_1(d), ct(d), ht(d), wp(3 * d), checked(2 * d);
+      RandomVec<T>(4 * d, x.data(), -2.f, 2.f);
+      RandomVec<T>(3 * d, wp.data(), -2.f, 2.f);
+      RandomVec<T>(d, ct_1.data(), -2.f, 2.f);
+      const T* ct_1_data = ct_1.data();
+      const T* wp_data = wp.data();
+      T* x_data = x.data();
+      T* checked_data = checked.data();
+      T* ct_data = ct.data();
+      T* ht_data = ht.data();
+      jit::lstm_t step;
+      step.gates = x_data;
+      step.ct_1 = ct_1_data;
+      step.ct = ct_data;
+      step.ht = ht_data;
+      if (use_peephole) {
+        step.wp = wp_data;
+        step.checked = checked_data;
+      }
+      BenchAllImpls<KT, jit::LSTMTuples<T>, PlaceType>(attr, &step, &attr);
+    }
+  }
+}
+
+template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
+void BenchGRUKernel() {
+  for (int d : TestSizes()) {
+    const jit::gru_attr_t attr(d, jit::kVSigmoid, jit::kVTanh);
+    std::vector<T> x(3 * d), ht_1(d), ht(d);
+    RandomVec<T>(3 * d, x.data(), -2.f, 2.f);
+    RandomVec<T>(d, ht_1.data(), -2.f, 2.f);
+    const T* ht_1_data = ht_1.data();
+    T* x_data = x.data();
+    T* ht_data = ht.data();
+    jit::gru_t step;
+    step.gates = x_data;
+    step.ht_1 = ht_1_data;
+    step.ht = ht_data;
+    BenchAllImpls<KT, jit::GRUTuples<T>, PlaceType>(attr, &step, &attr);
+  }
+}
+
+// Benchmark all jit kernels including jitcode, mkl and refer.
+// To use this tool, run command: ./benchmark [options...]
+// Options:
+//     --burning: the burning time before count
+//     --repeat: the repeat times
+//     --max_size: the max size would be tested
+int main(int argc, char* argv[]) {
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+  google::InitGoogleLogging(argv[0]);
+  LOG(INFO) << "Burning " << FLAGS_burning << " times, Repeat " << FLAGS_repeat
+            << " times.";
+  using T = float;
+  using PlaceType = paddle::platform::CPUPlace;
+  // xyzn
+  BenchXYZNKernel<jit::kVMul, T, PlaceType>();
+  BenchXYZNKernel<jit::kVAdd, T, PlaceType>();
+  BenchXYZNKernel<jit::kVAddRelu, T, PlaceType>();
+  BenchXYZNKernel<jit::kVSub, T, PlaceType>();
+
+  // axyn
+  BenchAXYNKernel<jit::kVScal, T, PlaceType>();
+  BenchAXYNKernel<jit::kVAddBias, T, PlaceType>();
+
+  // xyn
+  BenchXYNKernel<jit::kVRelu, T, PlaceType>();
+  BenchXYNKernel<jit::kVIdentity, T, PlaceType>();
+  BenchXYNKernel<jit::kVExp, T, PlaceType>();
+  BenchXYNKernel<jit::kVSigmoid, T, PlaceType>();
+  BenchXYNKernel<jit::kVTanh, T, PlaceType>();
+
+  // lstm and peephole
+  BenchLSTMKernel<jit::kLSTMCtHt, T, PlaceType>();
+  BenchLSTMKernel<jit::kLSTMC1H1, T, PlaceType>();
+
+  // gru functions
+  BenchGRUKernel<jit::kGRUH1, T, PlaceType>();
+  BenchGRUKernel<jit::kGRUHtPart1, T, PlaceType>();
+  BenchGRUKernel<jit::kGRUHtPart2, T, PlaceType>();
+}

paddle/fluid/operators/jit/gen/CMakeLists.txt

+
+file(GLOB jitcode_cc_srcs RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cc")
+
+cc_library(jit_kernel_jitcode SRCS ${jitcode_cc_srcs} DEPS jit_kernel_base xbyak)
+set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} xbyak jit_kernel_jitcode PARENT_SCOPE)
+
+function(USE_JITKERNEL_GEN TARGET)
+    file(APPEND ${jit_file} "USE_JITKERNEL_GEN(${TARGET});\n")
+endfunction()
+
+# use gen jitcode kernel by name
+USE_JITKERNEL_GEN(kVMul)
+USE_JITKERNEL_GEN(kVAdd)
+#USE_JITKERNEL_GEN(kVSub) # TODO(TJ): enable me
+USE_JITKERNEL_GEN(kVAddRelu)
+USE_JITKERNEL_GEN(kVScal)
+USE_JITKERNEL_GEN(kVAddBias)
+USE_JITKERNEL_GEN(kVRelu)
+USE_JITKERNEL_GEN(kVIdentity)
+USE_JITKERNEL_GEN(kVExp)
+USE_JITKERNEL_GEN(kVSigmoid)
+USE_JITKERNEL_GEN(kVTanh)
+USE_JITKERNEL_GEN(kLSTMCtHt)
+USE_JITKERNEL_GEN(kLSTMC1H1)
+USE_JITKERNEL_GEN(kGRUH1)
+USE_JITKERNEL_GEN(kGRUHtPart1)
+USE_JITKERNEL_GEN(kGRUHtPart2)
+USE_JITKERNEL_GEN(kNCHW16CMulNC)

paddle/fluid/operators/jit/gen/act.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/gen/act.h"
+#include "paddle/fluid/operators/jit/registry.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+const float ALIGN32_BEG exp_float_consts[] ALIGN32_END = {
+    REPEAT_8TIMES(1.f),
+    REPEAT_8TIMES(2.f),
+    REPEAT_8TIMES(0.5f),
+    REPEAT_8TIMES(EXP_HIG),
+    REPEAT_8TIMES(EXP_LOW),
+    REPEAT_8TIMES(CEPHES_LOG2EF),
+    REPEAT_8TIMES(CEPHES_EXP_C1),
+    REPEAT_8TIMES(CEPHES_EXP_C2),
+    REPEAT_8TIMES(CEPHES_EXP_P0),
+    REPEAT_8TIMES(CEPHES_EXP_P1),
+    REPEAT_8TIMES(CEPHES_EXP_P2),
+    REPEAT_8TIMES(CEPHES_EXP_P3),
+    REPEAT_8TIMES(CEPHES_EXP_P4),
+    REPEAT_8TIMES(CEPHES_EXP_P5),
+    REPEAT_8TIMES(EXP_MAX_INPUT),
+    REPEAT_8TIMES(SIGMOID_THRESHOLD_MAX),
+    REPEAT_8TIMES(SIGMOID_THRESHOLD_MIN)};
+
+const int ALIGN32_BEG exp_int_0x7f[] ALIGN32_END = {REPEAT_8TIMES(0x7f)};
+int ALIGN32_BEG g_tmp_mem[16] ALIGN32_END = {0};
+
+void VActJitCode::genCode() {
+  int offset = 0;
+  for (int i = 0; i < num_ / YMM_FLOAT_BLOCK; ++i) {
+    vmovups(ymm_src, ptr[param1 + offset]);
+    act<ymm_t>(ymm_dst, ymm_src, type_);
+    vmovups(ptr[param2 + offset], ymm_dst);
+    offset += sizeof(float) * YMM_FLOAT_BLOCK;
+  }
+  int rest = num_ % YMM_FLOAT_BLOCK;
+  while (rest > 0) {
+    int block = XMM_FLOAT_BLOCK;
+    if (rest >= 4) {
+      block = 4;
+      vmovups(xmm_src, ptr[param1 + offset]);
+    } else if (rest >= 2) {
+      block = 2;
+      vmovq(xmm_src, ptr[param1 + offset]);
+    } else {
+      block = 1;
+      vmovss(xmm_src, ptr[param1 + offset]);
+    }
+    act<xmm_t>(xmm_dst, xmm_src, type_);
+    if (rest >= 4) {
+      vmovups(ptr[param2 + offset], xmm_dst);
+    } else if (rest >= 2) {
+      vmovq(ptr[param2 + offset], xmm_dst);
+    } else {
+      vmovss(ptr[param2 + offset], xmm_dst);
+    }
+    offset += sizeof(float) * block;
+    rest -= block;
+  }
+  ret();
+}
+
+#define DECLARE_ACT_CREATOR(name)                                            \
+  class name##Creator : public JitCodeCreator<int> {                         \
+   public:                                                                   \
+    bool UseMe(const int& attr) const override {                             \
+      return platform::MayIUse(platform::avx);                               \
+    }                                                                        \
+    size_t CodeSize(const int& d) const override;                            \
+    std::unique_ptr<GenBase> CreateJitCode(const int& attr) const override { \
+      return make_unique<name##JitCode>(attr, CodeSize(attr));               \
+    }                                                                        \
+  }
+
+DECLARE_ACT_CREATOR(VRelu);
+DECLARE_ACT_CREATOR(VIdentity);
+DECLARE_ACT_CREATOR(VExp);
+DECLARE_ACT_CREATOR(VSigmoid);
+DECLARE_ACT_CREATOR(VTanh);
+
+// TODO(TJ): tuning use me
+size_t VReluCreator::CodeSize(const int& d) const {
+  return 96 /* init size */ +
+         (d / YMM_FLOAT_BLOCK + 3) * 4 /* instructions */ *
+             8 /* average bytes for each instruction */;
+}
+
+size_t VIdentityCreator::CodeSize(const int& d) const {
+  return 96 + (d / YMM_FLOAT_BLOCK + 3) * 4 * 8;
+}
+
+size_t VExpCreator::CodeSize(const int& d) const {
+  return 96 + (d / YMM_FLOAT_BLOCK + 3) * 70 * 8;
+}
+
+size_t VSigmoidCreator::CodeSize(const int& d) const {
+  return 96 + (d / YMM_FLOAT_BLOCK + 3) * 82 * 8;
+}
+
+size_t VTanhCreator::CodeSize(const int& d) const {
+  return 96 + (d / YMM_FLOAT_BLOCK + 3) * 84 * 8;
+}
+
+#undef DECLARE_ACT_CREATOR
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle
+
+namespace gen = paddle::operators::jit::gen;
+
+REGISTER_JITKERNEL_GEN(kVRelu, gen::VReluCreator);
+REGISTER_JITKERNEL_GEN(kVIdentity, gen::VIdentityCreator);
+REGISTER_JITKERNEL_GEN(kVExp, gen::VExpCreator);
+REGISTER_JITKERNEL_GEN(kVSigmoid, gen::VSigmoidCreator);
+REGISTER_JITKERNEL_GEN(kVTanh, gen::VTanhCreator);

paddle/fluid/operators/math/jit_code.h → paddle/fluid/operators/jit/gen/act.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. */
+ *
+ * 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/operators/math/jit_gen.h"
-#include "paddle/fluid/operators/math/jit_kernel_impl.h"
-#include "paddle/fluid/platform/cpu_info.h"
+#include "glog/logging.h"
+#include "paddle/fluid/operators/jit/gen/jitcode.h"
 
 namespace paddle {
 namespace operators {
-namespace math {
-namespace jitkernel {
+namespace jit {
 namespace gen {
 
-using reg64_t = const Xbyak::Reg64;
-using reg32_t = const Xbyak::Reg32;
-using xmm_t = const Xbyak::Xmm;
-using ymm_t = const Xbyak::Ymm;
-using zmm_t = const Xbyak::Zmm;
-using Label = Xbyak::Label;
-
-typedef enum {
-  mul = 0,
-  add,
-  sub,
-  relu,
-  exp,
-  sigmoid,
-  tanh,
-  identity
-} operand_type;
-
 extern const float exp_float_consts[];
 extern const int exp_int_0x7f[];
 extern int g_tmp_mem[];
@@ -79,94 +59,15 @@ extern int g_tmp_mem[];
 #define OFFSET_SIGMOID_MAX 15 * YMM_FLOAT_BLOCK * sizeof(float)
 #define OFFSET_SIGMOID_MIN 16 * YMM_FLOAT_BLOCK * sizeof(float)
 
-// function: vec = Operand(vec(or scalar), vec(or scalar)) (maybe with relu)
-class VXXJitCode : public JitCode {
- public:
-  const char* name() const override {
-    std::string base = "VXXJitCode";
-    if (scalar_index_ == 1) {
-      base += "_Scalar";
-    } else {
-      base += "_Vec";
-    }
-    if (type_ == operand_type::mul) {
-      base += "_Mul";
-    } else if (type_ == operand_type::add) {
-      base += "_Add";
-    }
-    if (scalar_index_ == 2) {
-      base += "_Scalar";
-    } else {
-      base += "_Vec";
-    }
-    base += (with_relu_ ? "_Relu" : "");
-    return base.c_str();
-  }
-  explicit VXXJitCode(int d, operand_type type, int scalar_index,
-                      bool with_relu, size_t code_size = 256 * 1024,
-                      void* code_ptr = nullptr)
-      : JitCode(code_size, code_ptr),
-        num_(d),
-        type_(type),
-        scalar_index_(scalar_index),
-        with_relu_(with_relu) {}
-  static bool init(int d, int scalar_index = 0);
-  void generate() override;
-
- private:
-  int num_;
-  operand_type type_;
-  int scalar_index_;
-  bool with_relu_;
-  reg64_t param1{abi_param1};
-  reg64_t param2{abi_param2};
-  reg64_t param3{abi_param3};
-
-  xmm_t xmm_src1 = xmm_t(0);
-  xmm_t xmm_src2 = xmm_t(1);
-  xmm_t xmm_dst = xmm_t(2);
-  xmm_t xmm_zero = xmm_t(3);
-
-  ymm_t ymm_src1 = ymm_t(0);
-  ymm_t ymm_src2 = ymm_t(1);
-  ymm_t ymm_dst = ymm_t(2);
-  ymm_t ymm_zero = ymm_t(3);
-};
-
-class VActJitCode : public JitCode {
+class VActFunc : public JitCode {
  public:
-  const char* name() const override {
-    std::string base = "VActJitCode";
-    switch (type_) {
-      case operand_type::relu:
-        base += "_Relu";
-        break;
-      case operand_type::exp:
-        base += "_Exp";
-        break;
-      case operand_type::sigmoid:
-        base += "_Sigmoid";
-        break;
-      case operand_type::tanh:
-        base += "_Tanh";
-        break;
-      case operand_type::identity:
-        base += "_Identity";
-        break;
-      default:
-        break;
-    }
-    return base.c_str();
-  }
-
-  explicit VActJitCode(int d, operand_type type, size_t code_size = 256 * 1024,
-                       void* code_ptr = nullptr)
-      : JitCode(code_size, code_ptr), num_(d), type_(type) {}
-  static bool init(int d, operand_type type);
-  void generate() override;
+  explicit VActFunc(size_t code_size, void* code_ptr)
+      : JitCode(code_size, code_ptr) {}
+  virtual const char* name() const = 0;
+  virtual void genCode() = 0;
 
  protected:
-  // compute relu with ymm, xmm
+  // compute RELU with ymm, xmm
   template <typename JMM>
   void relu_jmm(JMM& dst, JMM& src, int zero_idx = 15) {  // NOLINT
     JMM zero = JMM(zero_idx);
@@ -174,7 +75,7 @@ class VActJitCode : public JitCode {
     vmaxps(dst, src, zero);
   }
 
-  // compute exp with ymm, xmm
+  // compute EXP with ymm, xmm
   template <typename JMM>
   void exp_jmm(JMM& dst, JMM& src, int src_idx = 11, int fx_idx = 12,  // NOLINT
                int fy_idx = 13, int mask_idx = 14, int tmp_idx = 15) {
@@ -258,7 +159,7 @@ class VActJitCode : public JitCode {
     pop(reg_ptr_global);
   }
 
-  // compute sigmoid with ymm, xmm
+  // compute SIGMOID with ymm, xmm
   template <typename JMM>
   void sigmoid_jmm(JMM& dst, JMM& src, int src_idx = 11,  // NOLINT
                    int fx_idx = 12, int fy_idx = 13, int mask_idx = 14,
@@ -283,7 +184,7 @@ class VActJitCode : public JitCode {
     pop(reg_ptr_global);
   }
 
-  // compute tanh with ymm, xmm
+  // compute TANH with ymm, xmm
   template <typename JMM>
   void tanh_jmm(JMM& dst, JMM& src, int src_idx = 11,  // NOLINT
                 int fx_idx = 12, int fy_idx = 13, int mask_idx = 14,
@@ -310,223 +211,109 @@ class VActJitCode : public JitCode {
     pop(reg_ptr_global);
   }
 
+  // compute IDENTITY with ymm, xmm
+  template <typename JMM>
+  void identity_jmm(JMM& dst, JMM& src, int zero_idx) {  // NOLINT
+    JMM zero = JMM(zero_idx);
+    vxorps(zero, zero, zero);
+    vaddps(dst, src, zero);
+    // TODO(TJ): use below
+    // dst.setIdx(src.getIdx());
+  }
+
   template <typename JMM>
   void act(JMM& dst, JMM& src, operand_type type) {  // NOLINT
     // use 11~15
     switch (type) {
-      case operand_type::relu:
+      case operand_type::RELU:
         relu_jmm<JMM>(dst, src, 15);
         break;
-      case operand_type::exp:
+      case operand_type::EXP:
         exp_jmm<JMM>(dst, src, 11, 12, 13, 14, 15);
         break;
-      case operand_type::sigmoid:
+      case operand_type::SIGMOID:
         sigmoid_jmm<JMM>(dst, src, 11, 12, 13, 14, 15);
         break;
-      case operand_type::tanh:
+      case operand_type::TANH:
         tanh_jmm<JMM>(dst, src, 11, 12, 13, 14, 15);
         break;
-      case operand_type::identity:
+      case operand_type::IDENTITY:
+        identity_jmm<JMM>(dst, src, 15);
         break;
       default:
-        // throw error
+        LOG(FATAL) << "Do not support this operand type: " << type;
         break;
     }
   }
-
- protected:
-  int num_;
-  operand_type type_;
-  reg64_t param1{abi_param1};
-  reg64_t param2{abi_param2};
-
-  xmm_t xmm_src = xmm_t(0);
-  ymm_t ymm_src = ymm_t(0);
-
-  xmm_t xmm_dst = xmm_t(1);
-  ymm_t ymm_dst = ymm_t(1);
 };
 
-class LSTMJitCode : public VActJitCode {
+class VActJitCode : public VActFunc {
  public:
-  const char* name() const override {
-    std::string base = "LSTMJitCode";
-    if (use_peephole_) {
-      base += "_Peephole";
-    }
-    if (compute_c1h1_) {
-      base += "_C1H1";
+  explicit VActJitCode(int d, operand_type type, size_t code_size,
+                       void* code_ptr = nullptr)
+      : VActFunc(code_size, code_ptr), num_(d), type_(type) {
+    if (!(type_ == operand_type::RELU || type_ == operand_type::EXP ||
+          type_ == operand_type::SIGMOID || type_ == operand_type::TANH ||
+          type_ == operand_type::IDENTITY)) {
+      LOG(FATAL) << "Do not support this operand type: " << type_;
     }
-    auto AddTypeStr = [&](operand_type type) {
-      switch (type) {
-        case operand_type::relu:
-          base += "_Relu";
-          break;
-        case operand_type::exp:
-          base += "_Exp";
-          break;
-        case operand_type::sigmoid:
-          base += "_Sigmoid";
-          break;
-        case operand_type::tanh:
-          base += "_Tanh";
-          break;
-        case operand_type::identity:
-          base += "_Identity";
-          break;
-        default:
-          break;
-      }
-    };
-    AddTypeStr(act_gate_);
-    AddTypeStr(act_cand_);
-    AddTypeStr(act_cell_);
-    return base.c_str();
-  }
-
-  explicit LSTMJitCode(bool compute_c1h1, const lstm_attr_t& attr,
-                       size_t code_size = 256 * 1024, void* code_ptr = nullptr)
-      : VActJitCode(attr.d, operand_type::sigmoid /* this is bugy*/, code_size,
-                    code_ptr),
-        compute_c1h1_(compute_c1h1) {
-    auto typeExchange = [](const std::string& type) -> gen::operand_type {
-      if (type == "sigmoid") {
-        return operand_type::sigmoid;
-      } else if (type == "relu") {
-        return operand_type::relu;
-      } else if (type == "tanh") {
-        return operand_type::tanh;
-      } else if (type == "identity" || type == "") {
-        return operand_type::identity;
-      }  // else throw error
-      return operand_type::identity;
-    };
-    num_ = attr.d;
-    use_peephole_ = attr.use_peephole;
-    act_gate_ = typeExchange(attr.act_gate);
-    act_cand_ = typeExchange(attr.act_cand);
-    act_cell_ = typeExchange(attr.act_cell);
+    this->genCode();
   }
-  static bool init(int d);
-  void generate() override;
-
- protected:
-  int num_;
-  bool compute_c1h1_;
-  bool use_peephole_;
-  operand_type act_gate_;
-  operand_type act_cand_;
-  operand_type act_cell_;
-  reg64_t param1{abi_param1};
-};
 
-class GRUJitCode : public VActJitCode {
- public:
   const char* name() const override {
-    std::string base = "GRUJitCode";
-    if (id_ == 0) {
-      base += "_H1";
-    } else if (id_ == 1) {
-      base += "_HtPart1";
-    } else if (id_ == 2) {
-      base += "_HtPart2";
+    std::string base = "VActJitCode";
+    switch (type_) {
+      case operand_type::RELU:
+        base += "_Relu";
+        break;
+      case operand_type::EXP:
+        base += "_Exp";
+        break;
+      case operand_type::SIGMOID:
+        base += "_Sigmoid";
+        break;
+      case operand_type::TANH:
+        base += "_Tanh";
+        break;
+      case operand_type::IDENTITY:
+        base += "_Identity";
+        break;
+      default:
+        break;
     }
-    auto AddTypeStr = [&](operand_type type) {
-      switch (type) {
-        case operand_type::relu:
-          base += "_Relu";
-          break;
-        case operand_type::exp:
-          base += "_Exp";
-          break;
-        case operand_type::sigmoid:
-          base += "_Sigmoid";
-          break;
-        case operand_type::tanh:
-          base += "_Tanh";
-          break;
-        case operand_type::identity:
-          base += "_Identity";
-          break;
-        default:
-          break;
-      }
-    };
-    AddTypeStr(act_gate_);
-    AddTypeStr(act_cand_);
     return base.c_str();
   }
-
-  explicit GRUJitCode(int id, const gru_attr_t& attr,
-                      size_t code_size = 256 * 1024, void* code_ptr = nullptr)
-      : VActJitCode(attr.d, operand_type::sigmoid /* this is bugy*/, code_size,
-                    code_ptr),
-        id_(id) {
-    auto typeExchange = [](const std::string& type) -> gen::operand_type {
-      if (type == "sigmoid") {
-        return operand_type::sigmoid;
-      } else if (type == "relu") {
-        return operand_type::relu;
-      } else if (type == "tanh") {
-        return operand_type::tanh;
-      } else if (type == "identity" || type == "") {
-        return operand_type::identity;
-      }  // else throw error
-      return operand_type::identity;
-    };
-    num_ = attr.d;
-    act_gate_ = typeExchange(attr.act_gate);
-    act_cand_ = typeExchange(attr.act_cand);
-  }
-  static bool init(int d);
-  void generate() override;
+  void genCode() override;
 
  protected:
-  int id_;
   int num_;
-  operand_type act_gate_;
-  operand_type act_cand_;
+  operand_type type_;
   reg64_t param1{abi_param1};
-};
+  reg64_t param2{abi_param2};
 
-#ifdef PADDLE_WITH_MKLDNN
-struct EltwiseMulnChw16cNC : public Xbyak::CodeGenerator {
-  explicit EltwiseMulnChw16cNC(size_t code_size = 256 * 1024)
-      : Xbyak::CodeGenerator(code_size) {
-    // RDI is ptr x_input
-    // RSI is ptr y_input
-    // RDX is ptr output
-    // RCX is height
-    // r8 is width
+  xmm_t xmm_src = xmm_t(0);
+  ymm_t ymm_src = ymm_t(0);
 
-    push(rbx);
+  xmm_t xmm_dst = xmm_t(1);
+  ymm_t ymm_dst = ymm_t(1);
+};
 
-    xor_(rax, rax);
-    xor_(r10, r10);
-    vmovups(zmm3, ptr[rsi]);
+#define DECLARE_ACT_JITCODE(name, op_type)                                    \
+  class name##JitCode : public VActJitCode {                                  \
+   public:                                                                    \
+    explicit name##JitCode(int d, size_t code_size, void* code_ptr = nullptr) \
+        : VActJitCode(d, op_type, code_size, code_ptr) {}                     \
+  };
 
-    L("h_loop");
-    xor_(rbx, rbx);
-    L("w_loop");
-    vmovups(zmm2, ptr[rdi + rax]);
-    vmulps(zmm1, zmm2, zmm3);
-    vmovups(ptr[rdx + rax], zmm1);
-    add(rax, 64);
-    inc(rbx);
-    cmp(r8, rbx);
-    jnz("w_loop");
-    inc(r10);
-    cmp(r10, rcx);
-    jnz("h_loop");
+DECLARE_ACT_JITCODE(VRelu, operand_type::RELU);
+DECLARE_ACT_JITCODE(VIdentity, operand_type::IDENTITY);
+DECLARE_ACT_JITCODE(VExp, operand_type::EXP);
+DECLARE_ACT_JITCODE(VSigmoid, operand_type::SIGMOID);
+DECLARE_ACT_JITCODE(VTanh, operand_type::TANH);
 
-    pop(rbx);
-    ret();
-  }
-};
-#endif
+#undef DECLARE_ACT_JITCODE
 
 }  // namespace gen
-}  // namespace jitkernel
-}  // namespace math
+}  // namespace jit
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/jit/gen/blas.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/gen/blas.h"
+#include "paddle/fluid/operators/jit/registry.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+void VXXJitCode::genCode() {
+  // do not need push stack, and do not need save avx512reg if do not use avx512
+  int offset = 0;
+  if (with_relu_) {
+    vxorps(ymm_zero, ymm_zero, ymm_zero);
+  }
+  if (scalar_index_ == 1) {
+    vbroadcastss(ymm_src1, ptr[param1]);
+  } else if (scalar_index_ == 2) {
+    vbroadcastss(ymm_src2, ptr[param2]);
+  }
+  for (int i = 0; i < num_ / YMM_FLOAT_BLOCK; ++i) {
+    if (scalar_index_ != 1) {
+      vmovups(ymm_src1, ptr[param1 + offset]);
+    }
+    if (scalar_index_ != 2) {
+      vmovups(ymm_src2, ptr[param2 + offset]);
+    }
+    if (type_ == operand_type::MUL) {
+      vmulps(ymm_dst, ymm_src1, ymm_src2);
+    } else if (type_ == operand_type::ADD) {
+      vaddps(ymm_dst, ymm_src1, ymm_src2);
+    }
+    if (with_relu_) {
+      vmaxps(ymm_dst, ymm_zero, ymm_dst);
+    }
+    vmovups(ptr[param3 + offset], ymm_dst);
+    offset += sizeof(float) * YMM_FLOAT_BLOCK;
+  }
+  int rest = num_ % YMM_FLOAT_BLOCK;
+  while (rest > 0) {
+    int block = XMM_FLOAT_BLOCK;
+    if (rest >= 4) {
+      block = 4;
+      if (scalar_index_ != 1) {
+        vmovups(xmm_src1, ptr[param1 + offset]);
+      }
+      if (scalar_index_ != 2) {
+        vmovups(xmm_src2, ptr[param2 + offset]);
+      }
+    } else if (rest >= 2) {
+      block = 2;
+      if (scalar_index_ != 1) {
+        vmovq(xmm_src1, ptr[param1 + offset]);
+      }
+      if (scalar_index_ != 2) {
+        vmovq(xmm_src2, ptr[param2 + offset]);
+      }
+    } else {
+      block = 1;
+      if (scalar_index_ != 1) {
+        vmovss(xmm_src1, ptr[param1 + offset]);
+      }
+      if (scalar_index_ != 2) {
+        vmovss(xmm_src2, ptr[param2 + offset]);
+      }
+    }
+    switch (type_) {
+      case operand_type::MUL:
+        vmulps(xmm_dst, xmm_src1, xmm_src2);
+        break;
+      case operand_type::ADD:
+        vaddps(xmm_dst, xmm_src1, xmm_src2);
+        break;
+      default:
+        break;
+    }
+    if (with_relu_) {
+      vmaxps(xmm_dst, xmm_zero, xmm_dst);
+    }
+    if (rest >= 4) {
+      vmovups(ptr[param3 + offset], xmm_dst);
+    } else if (rest >= 2) {
+      vmovq(ptr[param3 + offset], xmm_dst);
+    } else {
+      vmovss(ptr[param3 + offset], xmm_dst);
+    }
+    offset += sizeof(float) * block;
+    rest -= block;
+  }
+  ret();
+}
+
+void NCHW16CMulNCJitCode::genCode() {
+  // RDI is ptr x_input
+  // RSI is ptr y_input
+  // RDX is ptr output
+  // RCX is height
+  // r8 is width
+
+  push(rbx);
+
+  xor_(rax, rax);
+  xor_(r10, r10);
+  vmovups(zmm3, ptr[rsi]);
+
+  L("h_loop");
+  xor_(rbx, rbx);
+  L("w_loop");
+  vmovups(zmm2, ptr[rdi + rax]);
+  vmulps(zmm1, zmm2, zmm3);
+  vmovups(ptr[rdx + rax], zmm1);
+  add(rax, 64);
+  inc(rbx);
+  cmp(r8, rbx);
+  jnz("w_loop");
+  inc(r10);
+  cmp(r10, rcx);
+  jnz("h_loop");
+
+  pop(rbx);
+  ret();
+}
+
+class NCHW16CMulNCCreator : public JitCodeCreator<int> {
+ public:
+  bool UseMe(const int& attr) const override {
+    return platform::MayIUse(platform::avx512f);
+  }
+  size_t CodeSize(const int& d) const override { return 256 * 1024; }
+  std::unique_ptr<GenBase> CreateJitCode(const int& attr) const override {
+    return make_unique<NCHW16CMulNCJitCode>(attr, CodeSize(attr));
+  }
+};
+
+#define DECLARE_BLAS_CREATOR(name)                                           \
+  class name##Creator : public JitCodeCreator<int> {                         \
+   public:                                                                   \
+    bool UseMe(const int& attr) const override {                             \
+      return platform::MayIUse(platform::avx);                               \
+    }                                                                        \
+    size_t CodeSize(const int& d) const override {                           \
+      return 96 + d / YMM_FLOAT_BLOCK * 4 * 8;                               \
+    }                                                                        \
+    std::unique_ptr<GenBase> CreateJitCode(const int& attr) const override { \
+      return make_unique<name##JitCode>(attr, CodeSize(attr));               \
+    }                                                                        \
+  }
+
+DECLARE_BLAS_CREATOR(VMul);
+DECLARE_BLAS_CREATOR(VAdd);
+DECLARE_BLAS_CREATOR(VSub);
+DECLARE_BLAS_CREATOR(VAddRelu);
+DECLARE_BLAS_CREATOR(VScal);
+DECLARE_BLAS_CREATOR(VAddBias);
+
+#undef DECLARE_BLAS_CREATOR
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle
+
+namespace gen = paddle::operators::jit::gen;
+
+REGISTER_JITKERNEL_GEN(kVMul, gen::VMulCreator);
+REGISTER_JITKERNEL_GEN(kVAdd, gen::VAddCreator);
+// TODO(TJ): enable sub
+// REGISTER_JITKERNEL_GEN(kVSub, gen::VSubCreator);
+REGISTER_JITKERNEL_GEN(kVAddRelu, gen::VAddReluCreator);
+REGISTER_JITKERNEL_GEN(kVScal, gen::VScalCreator);
+REGISTER_JITKERNEL_GEN(kVAddBias, gen::VAddBiasCreator);
+REGISTER_JITKERNEL_GEN(kNCHW16CMulNC, gen::NCHW16CMulNCCreator);

paddle/fluid/operators/jit/gen/blas.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 "glog/logging.h"
+#include "paddle/fluid/operators/jit/gen/jitcode.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+// function: vec = Operand(vec(or scalar), vec(or scalar)) (maybe with relu)
+class VXXJitCode : public JitCode {
+ public:
+  explicit VXXJitCode(int d, operand_type type, int scalar_index,
+                      bool with_relu, size_t code_size = 256 * 1024,
+                      void* code_ptr = nullptr)
+      : JitCode(code_size, code_ptr),
+        num_(d),
+        type_(type),
+        scalar_index_(scalar_index),
+        with_relu_(with_relu) {
+    if (!(type_ == operand_type::MUL || type_ == operand_type::ADD)) {
+      LOG(FATAL) << "Do not support this operand type: " << type_;
+    }
+    this->genCode();
+  }
+
+  virtual const char* name() const {
+    std::string base = "VXXJitCode";
+    if (scalar_index_ == 1) {
+      base += "_Scalar";
+    } else {
+      base += "_Vec";
+    }
+    if (type_ == operand_type::MUL) {
+      base += "_Mul";
+    } else if (type_ == operand_type::ADD) {
+      base += "_Add";
+    }
+    if (scalar_index_ == 2) {
+      base += "_Scalar";
+    } else {
+      base += "_Vec";
+    }
+    base += (with_relu_ ? "_Relu" : "");
+    return base.c_str();
+  }
+  void genCode() override;
+
+ private:
+  int num_;
+  operand_type type_;
+  int scalar_index_;
+  bool with_relu_;
+  reg64_t param1{abi_param1};
+  reg64_t param2{abi_param2};
+  reg64_t param3{abi_param3};
+
+  xmm_t xmm_src1 = xmm_t(0);
+  xmm_t xmm_src2 = xmm_t(1);
+  xmm_t xmm_dst = xmm_t(2);
+  xmm_t xmm_zero = xmm_t(3);
+
+  ymm_t ymm_src1 = ymm_t(0);
+  ymm_t ymm_src2 = ymm_t(1);
+  ymm_t ymm_dst = ymm_t(2);
+  ymm_t ymm_zero = ymm_t(3);
+};
+
+#define DECLARE_BLAS_JITCODE(name, op_type, scalar_idx, with_relu)             \
+  class name##JitCode : public VXXJitCode {                                    \
+   public:                                                                     \
+    explicit name##JitCode(int d, size_t code_size, void* code_ptr = nullptr)  \
+        : VXXJitCode(d, op_type, scalar_idx, with_relu, code_size, code_ptr) { \
+    }                                                                          \
+  };
+
+DECLARE_BLAS_JITCODE(VMul, operand_type::MUL, 0, false);
+DECLARE_BLAS_JITCODE(VAdd, operand_type::ADD, 0, false);
+DECLARE_BLAS_JITCODE(VSub, operand_type::SUB, 0, false);
+DECLARE_BLAS_JITCODE(VAddRelu, operand_type::ADD, 0, true);
+DECLARE_BLAS_JITCODE(VScal, operand_type::MUL, 1, false);
+DECLARE_BLAS_JITCODE(VAddBias, operand_type::ADD, 1, false);
+
+#undef DECLARE_BLAS_JITCODE
+
+// nChw16c = nChw16c .* NC
+class NCHW16CMulNCJitCode : public JitCode {
+ public:
+  DECLARE_JIT_CODE(NCHW16CMulNCJitCode);
+  explicit NCHW16CMulNCJitCode(int d /*unused*/, size_t code_size,
+                               void* code_ptr = nullptr)
+      : JitCode(code_size, code_ptr) {
+    this->genCode();
+  }
+  void genCode() override;
+};
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/gen/gru.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/gen/gru.h"
+#include <stddef.h>  // offsetof
+#include "paddle/fluid/operators/jit/registry.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+void GRUJitCode::genCode() {
+  reg64_t reg_ptr_gates = rax;
+  reg64_t reg_ptr_ht_1 = r9;
+  reg64_t reg_ptr_ht = r10;
+  mov(reg_ptr_gates, ptr[param1 + offsetof(gru_t, gates)]);
+  mov(reg_ptr_ht_1, ptr[param1 + offsetof(gru_t, ht_1)]);
+  mov(reg_ptr_ht, ptr[param1 + offsetof(gru_t, ht)]);
+  ymm_t ymm_one = ymm_t(0);
+
+  if (id_ == 2) {
+    reg64_t reg_ptr_tmp = r11;
+    mov(reg_ptr_tmp, reinterpret_cast<size_t>(exp_float_consts));
+    vmovaps(ymm_one, ptr[reg_ptr_tmp + OFFSET_EXP_ONE]);
+  }
+  int offset = 0;
+  int d = num_ * sizeof(float);
+  for (int i = 0; i < num_ / YMM_FLOAT_BLOCK; ++i) {
+    ymm_t ymm_u = ymm_t(1);
+    ymm_t ymm_r = ymm_t(2);
+    ymm_t ymm_s = ymm_t(3);
+    ymm_t ymm_ht_1 = ymm_t(4);
+    // W: {W_update, W_reset; W_state}
+    if (id_ == 0 || id_ == 2) {
+      vmovups(ymm_u, ptr[reg_ptr_gates + offset]);
+      vmovups(ymm_s, ptr[reg_ptr_gates + offset + 2 * d]);
+    }
+    if (id_ == 1) {
+      vmovups(ymm_r, ptr[reg_ptr_gates + offset + d]);
+    }
+    if (id_ == 1 || id_ == 2) {
+      vmovups(ymm_ht_1, ptr[reg_ptr_ht_1 + offset]);
+    }
+
+    if (id_ == 0) {
+      // ht = act_gate(u) * act_cand(s)
+      act<ymm_t>(ymm_u, ymm_u, act_gate_);
+      act<ymm_t>(ymm_s, ymm_s, act_cand_);
+      vmulps(ymm_s, ymm_s, ymm_u);
+      vmovups(ptr[reg_ptr_ht + offset], ymm_s);
+    } else if (id_ == 1) {
+      // ht = act_gate(r) * ht_1
+      act<ymm_t>(ymm_r, ymm_r, act_gate_);
+      vmulps(ymm_r, ymm_r, ymm_ht_1);
+      vmovups(ptr[reg_ptr_ht + offset], ymm_r);
+    } else if (id_ == 2) {
+      // ht = act_gate(u) * act_cand(s) + (1-act_gate(u)) * ht_1
+      ymm_t ymm_one_inner = ymm_t(ymm_one.getIdx());
+      act<ymm_t>(ymm_u, ymm_u, act_gate_);
+      act<ymm_t>(ymm_s, ymm_s, act_cand_);
+      vmulps(ymm_s, ymm_s, ymm_u);
+      vsubps(ymm_u, ymm_one_inner, ymm_u);
+      vmulps(ymm_u, ymm_ht_1, ymm_u);
+      vaddps(ymm_u, ymm_s, ymm_u);
+      vmovups(ptr[reg_ptr_ht + offset], ymm_u);
+    }
+    offset += sizeof(float) * YMM_FLOAT_BLOCK;
+  }
+  ret();
+}
+
+#define DECLARE_GRU_CREATOR(name)                                 \
+  class name##Creator : public JitCodeCreator<gru_attr_t> {       \
+   public:                                                        \
+    /* TODO(TJ): enable more */                                   \
+    bool UseMe(const gru_attr_t& attr) const override {           \
+      return platform::MayIUse(platform::avx) && attr.d % 8 == 0; \
+    }                                                             \
+    size_t CodeSize(const gru_attr_t& attr) const override {      \
+      return 96 + attr.d / YMM_FLOAT_BLOCK * 96 * 2 * 8;          \
+    }                                                             \
+    std::unique_ptr<GenBase> CreateJitCode(                       \
+        const gru_attr_t& attr) const override {                  \
+      return make_unique<name##JitCode>(attr, CodeSize(attr));    \
+    }                                                             \
+  }
+
+DECLARE_GRU_CREATOR(GRUH1);
+DECLARE_GRU_CREATOR(GRUHtPart1);
+DECLARE_GRU_CREATOR(GRUHtPart2);
+
+#undef DECLARE_GRU_CREATOR
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle
+
+namespace gen = paddle::operators::jit::gen;
+
+REGISTER_JITKERNEL_GEN(kGRUH1, gen::GRUH1Creator);
+REGISTER_JITKERNEL_GEN(kGRUHtPart1, gen::GRUHtPart1Creator);
+REGISTER_JITKERNEL_GEN(kGRUHtPart2, gen::GRUHtPart2Creator);

paddle/fluid/operators/jit/gen/gru.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 "glog/logging.h"
+#include "paddle/fluid/operators/jit/gen/act.h"
+#include "paddle/fluid/operators/jit/gen/jitcode.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+class GRUJitCode : public VActFunc {
+ public:
+  explicit GRUJitCode(int id, const gru_attr_t& attr, size_t code_size,
+                      void* code_ptr = nullptr)
+      : VActFunc(code_size, code_ptr), id_(id), num_(attr.d) {
+    auto typeExchange = [](KernelType type) -> gen::operand_type {
+      if (type == KernelType::kVSigmoid) {
+        return operand_type::SIGMOID;
+      } else if (type == KernelType::kVRelu) {
+        return operand_type::RELU;
+      } else if (type == KernelType::kVTanh) {
+        return operand_type::TANH;
+      } else if (type == KernelType::kVIdentity) {
+        return operand_type::IDENTITY;
+      } else {
+        LOG(FATAL) << "Do not support this jit::KernelType: " << type;
+      }
+      return operand_type::IDENTITY;
+    };
+    act_gate_ = typeExchange(attr.act_gate);
+    act_cand_ = typeExchange(attr.act_cand);
+
+    this->genCode();
+  }
+
+  const char* name() const override {
+    std::string base = "GRUJitCode";
+    if (id_ == 0) {
+      base += "_H1";
+    } else if (id_ == 1) {
+      base += "_HtPart1";
+    } else if (id_ == 2) {
+      base += "_HtPart2";
+    }
+    auto AddTypeStr = [&](operand_type type) {
+      switch (type) {
+        case operand_type::RELU:
+          base += "_Relu";
+          break;
+        case operand_type::EXP:
+          base += "_Exp";
+          break;
+        case operand_type::SIGMOID:
+          base += "_Sigmoid";
+          break;
+        case operand_type::TANH:
+          base += "_Tanh";
+          break;
+        case operand_type::IDENTITY:
+          base += "_Identity";
+          break;
+        default:
+          break;
+      }
+    };
+    AddTypeStr(act_gate_);
+    AddTypeStr(act_cand_);
+    return base.c_str();
+  }
+  void genCode() override;
+
+ protected:
+  int id_;
+  int num_;
+  operand_type act_gate_;
+  operand_type act_cand_;
+  reg64_t param1{abi_param1};
+};
+
+#define DECLARE_GRU_JITCODE(name, id)                                \
+  class name##JitCode : public GRUJitCode {                          \
+   public:                                                           \
+    explicit name##JitCode(const gru_attr_t& attr, size_t code_size, \
+                           void* code_ptr = nullptr)                 \
+        : GRUJitCode(id, attr, code_size, code_ptr) {}               \
+  };
+
+DECLARE_GRU_JITCODE(GRUH1, 0);
+DECLARE_GRU_JITCODE(GRUHtPart1, 1);
+DECLARE_GRU_JITCODE(GRUHtPart2, 2);
+
+#undef DECLARE_GRU_JITCODE
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/gen/jitcode.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 <type_traits>
+#include "paddle/fluid/operators/jit/gen_base.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+#define XBYAK_USE_MMAP_ALLOCATOR
+#include "xbyak/xbyak.h"
+#include "xbyak/xbyak_util.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+// Application Binary Interface
+constexpr Xbyak::Operand::Code abi_param1(Xbyak::Operand::RDI),
+    abi_param2(Xbyak::Operand::RSI), abi_param3(Xbyak::Operand::RDX),
+    abi_param4(Xbyak::Operand::RCX);
+
+constexpr Xbyak::Operand::Code g_abi_regs[] = {
+    Xbyak::Operand::RBX, Xbyak::Operand::RBP, Xbyak::Operand::R12,
+    Xbyak::Operand::R13, Xbyak::Operand::R14, Xbyak::Operand::R15};
+
+constexpr int num_g_abi_regs = sizeof(g_abi_regs) / sizeof(g_abi_regs[0]);
+
+using reg64_t = const Xbyak::Reg64;
+using reg32_t = const Xbyak::Reg32;
+using xmm_t = const Xbyak::Xmm;
+using ymm_t = const Xbyak::Ymm;
+using zmm_t = const Xbyak::Zmm;
+using Label = Xbyak::Label;
+
+typedef enum {
+  MUL = 0,
+  ADD,
+  SUB,
+  RELU,
+  EXP,
+  SIGMOID,
+  TANH,
+  IDENTITY
+} operand_type;
+
+#define DECLARE_JIT_CODE(codename) \
+  const char* name() const override { return #codename; }
+
+class JitCode : public GenBase, public Xbyak::CodeGenerator {
+ public:
+  explicit JitCode(size_t code_size, void* code_ptr = nullptr)
+      : Xbyak::CodeGenerator(
+            (code_size % 4096 != 0 ? (code_size / 4096 + 1) * 4096 : code_size),
+            code_ptr) {}
+
+  virtual const char* name() const = 0;
+  virtual void genCode() = 0;
+
+  size_t getSize() const override { return CodeGenerator::getSize(); }
+  const unsigned char* getCodeInternal() override {
+    const Xbyak::uint8* code = CodeGenerator::getCode();
+    return code;
+  }
+
+ protected:
+  Xbyak::Reg64 param1{abi_param1};
+  const int EVEX_max_8b_offt = 0x200;
+  const Xbyak::Reg64 reg_EVEX_max_8b_offt = rbp;
+
+  virtual void preCode() {
+    for (int i = 0; i < num_g_abi_regs; ++i) {
+      push(Xbyak::Reg64(g_abi_regs[i]));
+    }
+    if (platform::MayIUse(platform::avx512f)) {
+      mov(reg_EVEX_max_8b_offt, 2 * EVEX_max_8b_offt);
+    }
+  }
+  virtual void postCode() {
+    for (int i = 0; i < num_g_abi_regs; ++i) {
+      pop(Xbyak::Reg64(g_abi_regs[num_g_abi_regs - 1 - i]));
+    }
+    ret();
+  }
+  void L(const char* label) { Xbyak::CodeGenerator::L(label); }
+  void L(const Xbyak::Label& label) { Xbyak::CodeGenerator::L(label); }
+  // Enhanced vector extension
+  Xbyak::Address EVEX_compress_addr(Xbyak::Reg64 base, int offt,
+                                    bool bcast = false) {
+    int scale = 0;
+    // Learn from https://github.com/intel/mkl-dnn
+    if (EVEX_max_8b_offt <= offt && offt < 3 * EVEX_max_8b_offt) {
+      offt = offt - 2 * EVEX_max_8b_offt;
+      scale = 1;
+    } else if (3 * EVEX_max_8b_offt <= offt && offt < 5 * EVEX_max_8b_offt) {
+      offt = offt - 4 * EVEX_max_8b_offt;
+      scale = 2;
+    }
+    auto re = Xbyak::RegExp() + base + offt;
+    if (scale) {
+      re = re + reg_EVEX_max_8b_offt * scale;
+    }
+    if (bcast) {
+      return zword_b[re];
+    } else {
+      return zword[re];
+    }
+  }
+};
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/gen/lstm.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/gen/lstm.h"
+#include <stddef.h>  // offsetof
+#include "paddle/fluid/operators/jit/registry.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+void LSTMJitCode::genCode() {
+  if (use_peephole_) {
+    preCode();
+  }
+  reg64_t reg_ptr_gates = rax;
+  reg64_t reg_ptr_ct_1 = r9;
+  reg64_t reg_ptr_ct = r10;
+  reg64_t reg_ptr_ht = r11;
+  reg64_t reg_ptr_wp = r12;
+  mov(reg_ptr_gates, ptr[param1 + offsetof(lstm_t, gates)]);
+  mov(reg_ptr_ct_1, ptr[param1 + offsetof(lstm_t, ct_1)]);
+  mov(reg_ptr_ct, ptr[param1 + offsetof(lstm_t, ct)]);
+  mov(reg_ptr_ht, ptr[param1 + offsetof(lstm_t, ht)]);
+  if (use_peephole_) {
+    mov(reg_ptr_wp, ptr[param1 + offsetof(lstm_t, wp)]);
+  }
+
+  int offset = 0;
+  int d = num_ * sizeof(float);
+  for (int i = 0; i < num_ / YMM_FLOAT_BLOCK; ++i) {
+    /* gates: W_ch, W_ih, W_fh, W_oh */
+    ymm_t ymm_c = ymm_t(0);
+    ymm_t ymm_i = ymm_t(1);
+    ymm_t ymm_f = ymm_t(2);
+    ymm_t ymm_o = ymm_t(3);
+    ymm_t ymm_ct_1 = ymm_t(4);
+    ymm_t ymm_wp0 = ymm_t(5);
+    ymm_t ymm_wp1 = ymm_t(6);
+    ymm_t ymm_wp2 = ymm_t(7);
+    vmovups(ymm_c, ptr[reg_ptr_gates + offset]);
+    vmovups(ymm_i, ptr[reg_ptr_gates + offset + d]);
+    vmovups(ymm_f, ptr[reg_ptr_gates + offset + 2 * d]);
+    vmovups(ymm_o, ptr[reg_ptr_gates + offset + 3 * d]);
+    if (!compute_c1h1_) {
+      vmovups(ymm_ct_1, ptr[reg_ptr_ct_1 + offset]);
+    }
+    if (use_peephole_) {
+      vmovups(ymm_wp0, ptr[reg_ptr_wp + offset]);
+      vmovups(ymm_wp1, ptr[reg_ptr_wp + offset + d]);
+      vmovups(ymm_wp2, ptr[reg_ptr_wp + offset + 2 * d]);
+    }
+    /* C_t = act_cand(c) * act_gate(i) + C_t-1 * act_gate(f) */
+    // act_cand(c)
+    act<ymm_t>(ymm_c, ymm_c, act_cand_);
+    // act_gate(i) or act_gate(ct_1 * wp0 + i)
+    if (!compute_c1h1_ && use_peephole_) {
+      vmulps(ymm_wp0, ymm_ct_1, ymm_wp0);
+      vaddps(ymm_i, ymm_i, ymm_wp0);
+    }
+    act<ymm_t>(ymm_i, ymm_i, act_gate_);
+    vmulps(ymm_c, ymm_c, ymm_i);
+    if (!compute_c1h1_) {
+      // act_gate(f) or act_gate(ct_1 * wp1 + f)
+      if (use_peephole_) {
+        vmulps(ymm_wp1, ymm_ct_1, ymm_wp1);
+        vaddps(ymm_f, ymm_f, ymm_wp1);
+      }
+      act<ymm_t>(ymm_f, ymm_f, act_gate_);
+      // ct
+      vmulps(ymm_f, ymm_f, ymm_ct_1);
+      vaddps(ymm_f, ymm_f, ymm_c);
+    }
+    /* H_t = act_cell(C_t) * act_gate(o) */
+    // act_cell(C_t)
+    ymm_t ymm_ct = compute_c1h1_ ? ymm_c : ymm_f;
+    ymm_t ymm_tmp = ymm_i;
+    act<ymm_t>(ymm_tmp, ymm_ct, act_cell_);
+    // act_gate(o) or act_gate(ct * wp2 + o)
+    if (use_peephole_) {
+      vmulps(ymm_wp2, ymm_ct, ymm_wp2);
+      vaddps(ymm_o, ymm_o, ymm_wp2);
+    }
+    act<ymm_t>(ymm_o, ymm_o, act_gate_);
+    // ht
+    vmulps(ymm_o, ymm_o, ymm_tmp);
+    // save ct and ht
+    vmovups(ptr[reg_ptr_ct + offset], ymm_ct);
+    vmovups(ptr[reg_ptr_ht + offset], ymm_o);
+    offset += sizeof(float) * YMM_FLOAT_BLOCK;
+  }
+
+  if (use_peephole_) {
+    postCode();
+  } else {
+    ret();
+  }
+}
+
+#define DECLARE_LSTM_CREATOR(name)                                \
+  class name##Creator : public JitCodeCreator<lstm_attr_t> {      \
+   public:                                                        \
+    /* TODO(TJ): enable more */                                   \
+    bool UseMe(const lstm_attr_t& attr) const override {          \
+      return platform::MayIUse(platform::avx) && attr.d % 8 == 0; \
+    }                                                             \
+    size_t CodeSize(const lstm_attr_t& attr) const override {     \
+      return 96 + attr.d / YMM_FLOAT_BLOCK * 90 * 4 * 8;          \
+    }                                                             \
+    std::unique_ptr<GenBase> CreateJitCode(                       \
+        const lstm_attr_t& attr) const override {                 \
+      return make_unique<name##JitCode>(attr, CodeSize(attr));    \
+    }                                                             \
+  }
+
+DECLARE_LSTM_CREATOR(LSTMCtHt);
+DECLARE_LSTM_CREATOR(LSTMC1H1);
+
+#undef DECLARE_LSTM_CREATOR
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle
+
+namespace gen = paddle::operators::jit::gen;
+
+REGISTER_JITKERNEL_GEN(kLSTMCtHt, gen::LSTMCtHtCreator);
+REGISTER_JITKERNEL_GEN(kLSTMC1H1, gen::LSTMC1H1Creator);

paddle/fluid/operators/jit/gen/lstm.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 "glog/logging.h"
+#include "paddle/fluid/operators/jit/gen/act.h"
+#include "paddle/fluid/operators/jit/gen/jitcode.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+class LSTMJitCode : public VActFunc {
+ public:
+  explicit LSTMJitCode(bool compute_c1h1, const lstm_attr_t& attr,
+                       size_t code_size, void* code_ptr = nullptr)
+      : VActFunc(code_size, code_ptr),
+        num_(attr.d),
+        compute_c1h1_(compute_c1h1),
+        use_peephole_(attr.use_peephole) {
+    auto typeExchange = [](KernelType type) -> gen::operand_type {
+      if (type == KernelType::kVSigmoid) {
+        return operand_type::SIGMOID;
+      } else if (type == KernelType::kVRelu) {
+        return operand_type::RELU;
+      } else if (type == KernelType::kVTanh) {
+        return operand_type::TANH;
+      } else if (type == KernelType::kVIdentity) {
+        return operand_type::IDENTITY;
+      } else {
+        LOG(FATAL) << "Do not support this jit::KernelType: " << type;
+      }
+      return operand_type::IDENTITY;
+    };
+    act_gate_ = typeExchange(attr.act_gate);
+    act_cand_ = typeExchange(attr.act_cand);
+    act_cell_ = typeExchange(attr.act_cell);
+
+    this->genCode();
+  }
+
+  const char* name() const override {
+    std::string base = "LSTMJitCode";
+    if (use_peephole_) {
+      base += "_Peephole";
+    }
+    if (compute_c1h1_) {
+      base += "_C1H1";
+    }
+    auto AddTypeStr = [&](operand_type type) {
+      switch (type) {
+        case operand_type::RELU:
+          base += "_Relu";
+          break;
+        case operand_type::EXP:
+          base += "_Exp";
+          break;
+        case operand_type::SIGMOID:
+          base += "_Sigmoid";
+          break;
+        case operand_type::TANH:
+          base += "_Tanh";
+          break;
+        case operand_type::IDENTITY:
+          base += "_Identity";
+          break;
+        default:
+          break;
+      }
+    };
+    AddTypeStr(act_gate_);
+    AddTypeStr(act_cand_);
+    AddTypeStr(act_cell_);
+    return base.c_str();
+  }
+  void genCode() override;
+
+ protected:
+  int num_;
+  bool compute_c1h1_;
+  bool use_peephole_;
+  operand_type act_gate_;
+  operand_type act_cand_;
+  operand_type act_cell_;
+  reg64_t param1{abi_param1};
+};
+
+#define DECLARE_LSTM_JITCODE(name, compute_c1h1)                      \
+  class name##JitCode : public LSTMJitCode {                          \
+   public:                                                            \
+    explicit name##JitCode(const lstm_attr_t& attr, size_t code_size, \
+                           void* code_ptr = nullptr)                  \
+        : LSTMJitCode(compute_c1h1, attr, code_size, code_ptr) {}     \
+  };
+
+DECLARE_LSTM_JITCODE(LSTMCtHt, false);
+DECLARE_LSTM_JITCODE(LSTMC1H1, true);
+
+#undef DECLARE_LSTM_JITCODE
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/gen_base.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/gen_base.h"
+#include <fstream>
+#include <iostream>
+#include <sstream>
+
+DEFINE_bool(dump_jitcode, false, "Whether to dump the jitcode to file");
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+// refer do not need useme, it would be the last one.
+void GenBase::dumpCode(const unsigned char* code) const {
+  if (code) {
+    static int counter = 0;
+    std::ostringstream filename;
+    filename << "paddle_jitcode_" << name() << "." << counter << ".bin";
+    counter++;
+    std::ofstream fout(filename.str(), std::ios::out);
+    if (fout.is_open()) {
+      fout.write(reinterpret_cast<const char*>(code), this->getSize());
+      fout.close();
+    }
+  }
+}
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/gen_base.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 <gflags/gflags.h>
+#include <memory>  // for unique_ptr
+#include "paddle/fluid/operators/jit/kernel_base.h"
+
+DECLARE_bool(dump_jitcode);
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+class GenBase : public Kernel {
+ public:
+  virtual ~GenBase() = default;
+  virtual const char* name() const = 0;
+  virtual size_t getSize() const = 0;
+  virtual const unsigned char* getCodeInternal() = 0;
+  template <typename Func>
+  Func getCode() {
+    const unsigned char* code = this->getCodeInternal();
+    if (FLAGS_dump_jitcode) {
+      this->dumpCode(code);
+    }
+    // Note: failed to cast with reinterpret_cast<const Func> on Mac clang,
+    // then workaround with const_cast. Any better idea is appreciated.
+    return reinterpret_cast<Func>(const_cast<unsigned char*>(code));
+  }
+
+ protected:
+  void dumpCode(const unsigned char* code) const;
+};
+
+// Creator is used to creat the jitcode and save in pool.
+// Every JitCode should have one creator.
+class GenCreator {
+ public:
+  virtual ~GenCreator() = default;
+};
+
+template <typename Attr>
+class JitCodeCreator : public GenCreator {
+ public:
+  virtual ~JitCodeCreator() = default;
+
+  // condition when this jit code can be used.
+  virtual bool UseMe(const Attr& attr) const = 0;
+
+  // estimate this code size
+  virtual size_t CodeSize(const Attr& attr) const = 0;
+
+  // create this code
+  virtual std::unique_ptr<GenBase> CreateJitCode(const Attr& attr) const = 0;
+};
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/helper.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/helper.h"
+#include <algorithm>  // tolower
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+#define ONE_CASE(key) \
+  case key:           \
+    return #key
+
+const char* to_string(KernelType kt) {
+  switch (kt) {
+    ONE_CASE(kVMul);
+    ONE_CASE(kVAdd);
+    ONE_CASE(kVAddRelu);
+    ONE_CASE(kVSub);
+    ONE_CASE(kVScal);
+    ONE_CASE(kVAddBias);
+    ONE_CASE(kVRelu);
+    ONE_CASE(kVIdentity);
+    ONE_CASE(kVExp);
+    ONE_CASE(kVSigmoid);
+    ONE_CASE(kVTanh);
+    ONE_CASE(kLSTMCtHt);
+    ONE_CASE(kLSTMC1H1);
+    ONE_CASE(kGRUH1);
+    ONE_CASE(kGRUHtPart1);
+    ONE_CASE(kGRUHtPart2);
+    ONE_CASE(kCRFDecoding);
+    ONE_CASE(kLayerNorm);
+    ONE_CASE(kNCHW16CMulNC);
+    default:
+      PADDLE_THROW("Not support type: %d, or forget to add it.", kt);
+      return "NOT JITKernel";
+  }
+  return nullptr;
+}
+#undef ONE_CASE
+
+KernelType to_kerneltype(const std::string& act) {
+  std::string lower = act;
+  std::transform(lower.begin(), lower.end(), lower.begin(), ::tolower);
+  if (lower == "relu" || lower == "vrelu") {
+    return kVRelu;
+  } else if (lower == "identity" || lower == "videntity" || lower == "") {
+    return kVIdentity;
+  } else if (lower == "exp" || lower == "vexp") {
+    return kVExp;
+  } else if (lower == "sigmoid" || lower == "vsigmoid") {
+    return kVSigmoid;
+  } else if (lower == "tanh" || lower == "vtanh") {
+    return kVTanh;
+  }
+  PADDLE_THROW("Not support type: %s, or forget to add this case", act);
+  return kNone;
+}
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/helper.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 <iostream>
+#include <string>
+#include <vector>
+#include "paddle/fluid/operators/jit/gen_base.h"
+#include "paddle/fluid/operators/jit/kernel_base.h"
+#include "paddle/fluid/operators/jit/kernel_key.h"
+#include "paddle/fluid/operators/jit/kernel_pool.h"
+#include "paddle/fluid/platform/place.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+template <KernelType KT, typename KernelTuples, typename PlaceType>
+inline typename std::enable_if<
+    std::is_same<typename KernelTuples::data_type, float>::value &&
+        std::is_same<PlaceType, platform::CPUPlace>::value,
+    typename KernelTuples::func_type>::type
+GetJitCode(const typename KernelTuples::attr_type& attr) {
+  using Func = typename KernelTuples::func_type;
+  using Attr = typename KernelTuples::attr_type;
+  size_t key = JitCodeKey<Attr>(attr);
+  auto& codes = JitCodePool<KT>().Instance();
+  if (codes.Has(key)) {
+    return codes.AllKernels().at(key)->template getCode<Func>();
+  }
+
+  // creator is not related with attr, so can use KernelKey as key
+  KernelKey kkey(KT, PlaceType());
+  // pool: (KernelKey(type, place), vector<GenCreatorPtr>)
+  auto& creator_map = JitCodeCreatorPool().Instance().AllCreators();
+  auto iter = creator_map.find(kkey);
+  if (iter != creator_map.end()) {
+    auto& creators = iter->second;
+    for (auto& cur : creators) {
+      auto i = dynamic_cast<const JitCodeCreator<Attr>*>(cur.get());
+      if (i && i->UseMe(attr)) {
+        auto p = i->CreateJitCode(attr);
+        if (p) {
+          auto f = p->template getCode<Func>();
+          codes.Insert(key, std::move(p));
+          return f;
+        }
+      }
+    }
+  }
+  return nullptr;
+}
+
+template <KernelType KT, typename KernelTuples, typename PlaceType>
+inline typename std::enable_if<
+    !std::is_same<typename KernelTuples::data_type, float>::value ||
+        !std::is_same<PlaceType, platform::CPUPlace>::value,
+    typename KernelTuples::func_type>::type
+GetJitCode(const typename KernelTuples::attr_type& attr) {
+  return nullptr;
+}
+
+// Refer code do not related with attr, which is just for cast
+// Refer is always on CPUPlace
+template <KernelType KT, typename KernelTuples>
+inline typename KernelTuples::func_type GetRefer() {
+  auto& ref_pool = ReferKernelPool().Instance().AllKernels();
+  KernelKey kkey(KT, platform::CPUPlace());
+  auto ref_iter = ref_pool.find(kkey);
+  PADDLE_ENFORCE(ref_iter != ref_pool.end(),
+                 "Every Kernel should have reference function.");
+  auto& ref_impls = ref_iter->second;
+  for (auto& impl : ref_impls) {
+    auto i = dynamic_cast<const ReferKernel<KernelTuples>*>(impl.get());
+    if (i) {
+      return i->GetFunc();
+    }
+  }
+  return nullptr;
+}
+
+template <KernelType KT, typename KernelTuples,
+          typename PlaceType = platform::CPUPlace>
+typename KernelTuples::func_type Get(
+    const typename KernelTuples::attr_type& attr) {
+  auto jitfunc = GetJitCode<KT, KernelTuples, PlaceType>(attr);
+  if (jitfunc) {
+    return jitfunc;
+  }
+
+  // pool: (KernelKey(type, place), vector<KernelPtr>)
+  KernelKey kkey(KT, PlaceType());
+  auto& pool = KernelPool().Instance().AllKernels();
+  auto iter = pool.find(kkey);
+  if (iter != pool.end()) {
+    auto& impls = iter->second;
+    for (auto& impl : impls) {
+      auto i = dynamic_cast<const KernelMore<KernelTuples>*>(impl.get());
+      if (i && i->UseMe(attr)) {
+        return i->GetFunc();
+      }
+    }
+  }
+
+  // The last implementation should be reference function on CPUPlace.
+  return GetRefer<KT, KernelTuples>();
+}
+
+const char* to_string(KernelType kt);
+
+KernelType to_kerneltype(const std::string& act);
+
+inline std::ostream& operator<<(std::ostream& os, const lstm_attr_t& attr) {
+  os << "dim_size[" << attr.d << "],act_gate[" << to_string(attr.act_gate)
+     << "],act_cand[" << to_string(attr.act_cand) << "],act_cell["
+     << to_string(attr.act_cell) << "],use_peephole["
+     << (attr.use_peephole ? "True" : "False") << "]";
+  return os;
+}
+inline std::ostream& operator<<(std::ostream& os, const gru_attr_t& attr) {
+  os << "dim_size[" << attr.d << "],act_gate[" << to_string(attr.act_gate)
+     << "],act_cand[" << to_string(attr.act_cand) << "]";
+  return os;
+}
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/kernel_base.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 "paddle/fluid/operators/jit/macro.h"
+#include "paddle/fluid/platform/macros.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+typedef enum {
+  kNone = 0,
+  kVMul = 1,
+  kVAdd = 2,
+  kVAddRelu,
+  kVSub,
+  kVScal,
+  kVAddBias,
+  kVRelu,
+  kVIdentity,
+  kVExp,
+  kVSigmoid,
+  kVTanh,
+  kLSTMCtHt,
+  kLSTMC1H1,
+  kGRUH1,
+  kGRUHtPart1,
+  kGRUHtPart2,
+  kCRFDecoding,
+  kLayerNorm,
+  kNCHW16CMulNC,
+} KernelType;
+
+template <typename T>
+struct XYZNTuples {
+  typedef T data_type;
+  typedef int attr_type;
+  typedef void (*func_type)(const T*, const T*, T*, int);
+};
+
+template <typename T>
+struct AXYNTuples : public XYZNTuples<T> {};
+
+template <typename T>
+struct XYNTuples {
+  typedef T data_type;
+  typedef int attr_type;
+  typedef void (*func_type)(const T*, T*, int);
+};
+
+typedef struct {
+  void* gates;  // gates: x_ch, x_ih, x_fh, x_oh
+  const void* ct_1;
+  void* ct;
+  void* ht;
+  /* weight_peephole and checked data are only used in peephole*/
+  const void* wp{nullptr};  //  W_ic, W_fc, W_oc
+  void* checked{nullptr};   // size: 2 * d
+} lstm_t;
+
+typedef struct {
+  void* gates;  // gates: {x_update, x_reset; x_state}
+  const void* ht_1;
+  void* ht;
+} gru_t;
+
+struct rnn_attr_s {
+  int d;
+  KernelType act_gate, act_cand;
+  rnn_attr_s() = default;
+  explicit rnn_attr_s(int _d, KernelType _act_gate, KernelType _act_cand)
+      : d(_d), act_gate(_act_gate), act_cand(_act_cand) {}
+};
+
+struct lstm_attr_s : public rnn_attr_s {
+  bool use_peephole;
+  KernelType act_cell;
+  lstm_attr_s() = default;
+  explicit lstm_attr_s(int _d, KernelType _act_gate, KernelType _act_cand,
+                       KernelType _act_cell, bool _use_peephole = false)
+      : rnn_attr_s(_d, _act_gate, _act_cand),
+        use_peephole(_use_peephole),
+        act_cell(_act_cell) {}
+};
+
+typedef struct rnn_attr_s gru_attr_t;
+typedef struct lstm_attr_s lstm_attr_t;
+
+template <typename T>
+struct LSTMTuples {
+  typedef T data_type;
+  typedef lstm_attr_t attr_type;
+  typedef void (*func_type)(lstm_t*, const lstm_attr_t*);
+};
+
+template <typename T>
+struct GRUTuples {
+  typedef T data_type;
+  typedef gru_attr_t attr_type;
+  typedef void (*func_type)(gru_t*, const gru_attr_t*);
+};
+
+template <typename T>
+struct CRFDecodingTuples {
+  typedef T data_type;
+  typedef int attr_type;
+  typedef void (*func_type)(const int, const T*, const T*, T*, int*, int);
+};
+
+template <typename T>
+struct LayerNormTuples {
+  typedef T data_type;
+  typedef int attr_type;
+  typedef void (*func_type)(T*, T*, T*, T*, const T*, const T*, int,
+                            const float, int);
+};
+
+// nChw16c = nChw16c .* NC
+template <typename T>
+struct NCHW16CMulNCTuples {
+  typedef T data_type;
+  typedef int attr_type;
+  typedef void (*func_type)(const T*, const T*, T*, int, int);
+};
+
+// Just for adding to kernel pool without template
+class Kernel {
+ public:
+  Kernel() = default;
+  virtual ~Kernel() = default;
+  DISABLE_COPY_AND_ASSIGN(Kernel);
+};
+
+template <typename KernelTuples>
+class KernelMore : public Kernel {
+ public:
+  using T = typename KernelTuples::data_type;
+  using Func = typename KernelTuples::func_type;
+  using Attr = typename KernelTuples::attr_type;
+  virtual Func GetFunc() const { return func; }
+  virtual bool UseMe(const Attr& attr) const = 0;
+  virtual const char* ImplType() const = 0;
+
+ protected:
+  Func func{nullptr};
+};
+
+template <typename KernelTuples>
+class ReferKernel : public KernelMore<KernelTuples> {
+ public:
+  // Refer code can always be used
+  bool UseMe(const typename KernelTuples::attr_type& attr) const override {
+    return true;
+  }
+  const char* ImplType() const override { return "Refer"; }
+};
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/kernel_key.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/kernel_key.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+template <>
+size_t JitCodeKey<int>(const int& d) {
+  return d;
+}
+
+constexpr int act_type_shift = 3;  // suppot 2^3 act types
+
+template <>
+size_t JitCodeKey<lstm_attr_t>(const lstm_attr_t& attr) {
+  size_t key = attr.d;
+  int gate_key = static_cast<int>(attr.act_gate) << 1;
+  int cand_key = static_cast<int>(attr.act_cand) << (1 + act_type_shift);
+  int cell_key = static_cast<int>(attr.act_cell) << (1 + act_type_shift * 2);
+  return (key << (1 + act_type_shift * 3)) + gate_key + cand_key + cell_key +
+         attr.use_peephole;
+}
+
+template <>
+size_t JitCodeKey<gru_attr_t>(const gru_attr_t& attr) {
+  size_t key = attr.d;
+  return (key << (act_type_shift * 2)) + static_cast<int>(attr.act_gate) +
+         (static_cast<int>(attr.act_cand) << act_type_shift);
+}
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/kernel_key.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 "paddle/fluid/operators/jit/kernel_base.h"
+#include "paddle/fluid/platform/place.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+struct KernelKey {
+  struct Hash {
+    size_t operator()(const KernelKey& key) const {
+      int place = key.place_.which();               // less than 2^8
+      int type = static_cast<int>(key.type_) << 8;  // less than 2^(32-8)
+      std::hash<int> hasher;
+      return hasher(place + type);
+    }
+  };
+
+  KernelType type_;
+  platform::Place place_;
+
+  KernelKey(KernelType type, platform::Place place)
+      : type_(type), place_(place) {}
+  size_t hash_key() const { return Hash()(*this); }
+
+  bool operator==(const KernelKey& o) const {
+    return platform::places_are_same_class(place_, o.place_) &&
+           type_ == o.type_;
+  }
+  bool operator!=(const KernelKey& o) const { return !(*this == o); }
+};
+
+// Every JitCode should have a method to get the key from attribution
+template <typename Attr>
+size_t JitCodeKey(const Attr& attr);
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/kernel_pool.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/kernel_pool.h"
+#include <memory>  // for shared_ptr
+#include <string>
+#include <unordered_map>
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+JitCodeCreatorPool& JitCodeCreatorPool::Instance() {
+  static JitCodeCreatorPool g_creator_pool;
+  return g_creator_pool;
+}
+
+KernelPool& KernelPool::Instance() {
+  static KernelPool g_kernel_pool;
+  return g_kernel_pool;
+}
+
+ReferKernelPool& ReferKernelPool::Instance() {
+  static ReferKernelPool g_refer_kernel_pool;
+  return g_refer_kernel_pool;
+}
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/kernel_pool.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 <memory>  // for unique_ptr
+#include <string>
+#include <unordered_map>
+#include <vector>
+#include "paddle/fluid/operators/jit/gen_base.h"
+#include "paddle/fluid/operators/jit/kernel_base.h"
+#include "paddle/fluid/operators/jit/kernel_key.h"
+#include "paddle/fluid/platform/place.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+template <KernelType KT>
+class JitCodePool {
+  typedef std::unique_ptr<GenBase> GenBasePtr;
+  typedef std::unordered_map<size_t, GenBasePtr> JitCodeMap;
+
+ public:
+  JitCodePool() = default;
+  static JitCodePool& Instance() {
+    static thread_local JitCodePool<KT> g_jit_codes;
+    return g_jit_codes;
+  }
+
+  const JitCodeMap& AllKernels() { return codes_; }
+
+  bool Has(size_t key) const { return codes_.find(key) != codes_.end(); }
+
+  void Insert(size_t key, GenBasePtr value) {
+    codes_.emplace(key, std::move(value));
+  }
+
+ private:
+  JitCodeMap codes_;
+  DISABLE_COPY_AND_ASSIGN(JitCodePool);
+};
+
+class JitCodeCreatorPool {
+  typedef std::unique_ptr<const GenCreator> GenCreatorPtr;
+  typedef std::unordered_map<KernelKey, std::vector<GenCreatorPtr>,
+                             KernelKey::Hash>
+      GenCreatorPtrMap;
+
+ public:
+  JitCodeCreatorPool() = default;
+  static JitCodeCreatorPool& Instance();
+  GenCreatorPtrMap& AllCreators() { return creators_; }
+  void Insert(const KernelKey& key, GenCreatorPtr value) {
+    if (creators_.find(key) == creators_.end()) {
+      creators_.emplace(key, std::vector<GenCreatorPtr>());
+    }
+    creators_.at(key).emplace_back(std::move(value));
+  }
+
+ private:
+  GenCreatorPtrMap creators_;
+  DISABLE_COPY_AND_ASSIGN(JitCodeCreatorPool);
+};
+
+typedef std::unique_ptr<const Kernel> KernelPtr;
+typedef std::unordered_map<KernelKey, std::vector<KernelPtr>, KernelKey::Hash>
+    KernelMap;
+
+class KernelPool {
+ public:
+  static KernelPool& Instance();
+  KernelPool() = default;
+  KernelMap& AllKernels() { return pool_; }
+  void Insert(const KernelKey& key, KernelPtr value) {
+    if (pool_.find(key) == pool_.end()) {
+      pool_.emplace(key, std::vector<KernelPtr>());
+    }
+    pool_.at(key).emplace_back(std::move(value));
+  }
+
+ private:
+  KernelMap pool_;
+  DISABLE_COPY_AND_ASSIGN(KernelPool);
+};
+
+// Every kernel should have refer code and it should be used in unit tests,
+// so refer kernels should have it's independent kernel pool
+class ReferKernelPool {
+ public:
+  static ReferKernelPool& Instance();
+  ReferKernelPool() = default;
+  KernelMap& AllKernels() { return pool_; }
+  void Insert(const KernelKey& key, KernelPtr value) {
+    if (pool_.find(key) == pool_.end()) {
+      pool_.emplace(key, std::vector<KernelPtr>());
+    }
+    pool_.at(key).emplace_back(std::move(value));
+  }
+
+ private:
+  KernelMap pool_;
+  DISABLE_COPY_AND_ASSIGN(ReferKernelPool);
+};
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/macro.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 <type_traits>
+
+namespace paddle {
+namespace operators {
+namespace jit {
+
+#define SIGMOID_THRESHOLD_MIN -40.0
+#define SIGMOID_THRESHOLD_MAX 13.0
+#define EXP_MAX_INPUT 40.0
+
+#define XMM_FLOAT_BLOCK 4
+#define YMM_FLOAT_BLOCK 8
+#define ZMM_FLOAT_BLOCK 16
+
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/more/CMakeLists.txt

+
+function(USE_JITKERNEL_MORE TARGET TYPE)
+    file(APPEND ${jit_file} "USE_JITKERNEL_MORE(${TARGET} ${TYPE});\n")
+endfunction()
+
+if(WITH_MKLML)
+    add_subdirectory(mkl)
+endif()
+
+if(WITH_AVX)
+    add_subdirectory(intrinsic)
+endif()
+
+# mix should be last
+add_subdirectory(mix)
+
+set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} PARENT_SCOPE)

paddle/fluid/operators/jit/more/intrinsic/CMakeLists.txt

+
+file(GLOB jit_kernel_cc_intrinsic RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cc")
+cc_library(jit_kernel_intrinsic SRCS ${jit_kernel_cc_intrinsic} DEPS jit_kernel_base)
+
+set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} jit_kernel_intrinsic PARENT_SCOPE)
+
+# use mkl kernels by name and type
+USE_JITKERNEL_MORE(kCRFDecoding, intrinsic)
+USE_JITKERNEL_MORE(kLayerNorm, intrinsic)

paddle/fluid/operators/jit/more/intrinsic/crf_decoding.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/more/intrinsic/crf_decoding.h"
+#include <limits>
+#include "paddle/fluid/operators/jit/registry.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace more {
+namespace intrinsic {
+// Note: intrinsic code is not runtime build.
+// For example, if you build code on AVX, and run on AVX512 it can only use AVX
+
+void CRFDecoding(const int seq_len, const float* x, const float* w,
+                 float* alpha, int* track, int tag_num) {
+#ifdef __AVX512F__
+  const int step_size = ZMM_FLOAT_BLOCK;
+#else
+  const int step_size = YMM_FLOAT_BLOCK;
+#endif
+  const int end = tag_num / step_size;
+  const int rest = tag_num % step_size;
+  /* Setup the alpha initial value.*/
+  int i_offset = 0;
+  int last_offset = rest - step_size;
+  for (int i = 0; i <= end; ++i) {
+#ifdef __AVX512F__
+    // Declare the variable for the content of weights, input and alpha values.
+    __m512 w_content, x_content, alpha_content;
+    // Load the relevant data into the variables from un-aligned address.
+    w_content = _mm512_loadu_ps(w + i_offset);
+    x_content = _mm512_loadu_ps(x + i_offset);
+    alpha_content = _mm512_add_ps(w_content, x_content);
+    // Save the alpha value.
+    _mm512_storeu_ps(alpha_value + i_offset, alpha_content);
+#else
+    // AVX or AVX2
+    // weights, input and alpha values.
+    __m256 w_content, x_content, alpha_content;
+    // Load the relevant data into the variables from un-aligned address.
+    w_content = _mm256_loadu_ps(w + i_offset);
+    x_content = _mm256_loadu_ps(x + i_offset);
+    alpha_content = _mm256_add_ps(w_content, x_content);
+    _mm256_storeu_ps(alpha + i_offset, alpha_content);
+#endif
+    i_offset += step_size;
+    if (i == end - 1) {
+      if (rest > 0) {
+        i_offset += last_offset;
+      } else {
+        break;
+      }
+    }
+  }
+  // Use the column-major strategy to get the location of maximum score.
+  int seq_offset = 0;
+  constexpr int state_trans_base_idx = 2;
+  for (int k = 1; k < seq_len; ++k) {
+    int j_offset = 0;
+    for (int j = 0; j <= end; ++j) {
+/* Initialize the variables of maximum score and location.*/
+#ifdef __AVX512F__
+      __m512 max_score = _mm512_set1_ps(-std::numeric_limits<float>::max());
+      __m512i max_j = _mm512_setzero_si512();
+#else
+      __m256 max_score = _mm256_set1_ps(-std::numeric_limits<float>::max());
+      __m256i max_j = _mm256_set1_epi32(0);
+#endif
+      /* Calculate the offset of transition_weights.*/
+      int trans_offset = state_trans_base_idx * tag_num + j_offset;
+      for (int i = 0; i < tag_num; ++i) {
+/* Initalize the content of alpha variable with related offset.*/
+#ifdef __AVX512F__
+        __m512 alpha_content = _mm512_set1_ps(*(alpha + seq_offset + i));
+        /* Obtain the content of weights from un-aligned address.*/
+        __m512 w_content = _mm512_loadu_ps(w + trans_offset);
+        __m512 score_v = _mm512_add_ps(alpha_content, w_content);
+        __mmask16 mask = _mm512_cmp_ps_mask(score_v, max_score, _CMP_GT_OS);
+        /* AVX512 instructions.*/
+        max_j = _mm512_mask_set1_epi32(max_j, mask, i);
+        /* Update the max_score value.*/
+        max_score = _mm512_max_ps(max_score, score_v);
+
+#else
+        __m256 alpha_content = _mm256_broadcast_ss(alpha + seq_offset + i);
+        /* Obtain the content of weights from un-aligned address.*/
+        __m256 w_content = _mm256_loadu_ps(w + trans_offset);
+        __m256 score_v = _mm256_add_ps(alpha_content, w_content);
+        __m256 mask = _mm256_cmp_ps(score_v, max_score, _CMP_GT_OS);
+/* According to the mask value, update the index of the max_score.*/
+#ifdef __AVX2__
+        max_j = _mm256_or_si256(
+            _mm256_andnot_si256((__m256i)mask, max_j),
+            _mm256_and_si256((__m256i)mask, _mm256_set1_epi32(i)));
+#else
+        __m128i lo_max_j = _mm256_extractf128_si256(max_j, 0);
+        __m128i hi_max_j = _mm256_extractf128_si256(max_j, 1);
+        __m128i lo_mask =
+            _mm256_extractf128_si256(*(__m256i*)&mask, 0);  // NOLINT
+        __m128i hi_mask =
+            _mm256_extractf128_si256(*(__m256i*)&mask, 1);  // NOLINT
+        lo_max_j = _mm_andnot_si128(lo_mask, lo_max_j);
+        hi_max_j = _mm_andnot_si128(hi_mask, hi_max_j);
+        lo_mask = _mm_and_si128(lo_mask, _mm_set1_epi32(i));
+        hi_mask = _mm_and_si128(hi_mask, _mm_set1_epi32(i));
+        lo_max_j = _mm_or_si128(lo_mask, lo_max_j);
+        hi_max_j = _mm_or_si128(hi_mask, hi_max_j);
+        max_j = _mm256_insertf128_si256(max_j, lo_max_j, 0);
+        max_j = _mm256_insertf128_si256(max_j, hi_max_j, 1);
+#endif
+        /* Update the max_score value.*/
+        max_score = _mm256_max_ps(max_score, score_v);
+
+#endif
+
+        trans_offset += tag_num;
+      }
+/* Update the alpha and track values. */
+#ifdef __AVX512F__
+      __m512 x_content =
+          _mm512_loadu_ps(x + seq_offset + this->num_ + j_offset);
+      max_score = _mm512_add_ps(max_score, x_content);
+      _mm512_storeu_ps(alpha + seq_offset + this->num_ + j_offset, max_score);
+      _mm512_storeu_si512(reinterpret_cast<__m512i*>(track + seq_offset +
+                                                     this->num_ + j_offset),
+                          max_j);
+#else
+      __m256 x_content = _mm256_loadu_ps(x + seq_offset + tag_num + j_offset);
+      max_score = _mm256_add_ps(max_score, x_content);
+      _mm256_storeu_ps(alpha + seq_offset + tag_num + j_offset, max_score);
+      _mm256_storeu_si256(
+          reinterpret_cast<__m256i*>(track + seq_offset + tag_num + j_offset),
+          max_j);
+#endif
+
+      /* Calculate the offset of next step*/
+      j_offset += step_size;
+      if (j == end - 1) {
+        if (rest > 0) {
+          j_offset += last_offset;
+        } else {
+          break;
+        }
+      }
+    }
+    seq_offset += tag_num;
+  }
+}
+
+bool CRFDecodingKernel::UseMe(const int& d) const {
+#ifdef __AVX512F__
+  constexpr int block = ZMM_FLOAT_BLOCK;
+#else
+  constexpr int block = YMM_FLOAT_BLOCK;
+#endif
+  return platform::MayIUse(platform::avx) && d >= block;
+}
+
+}  // namespace intrinsic
+}  // namespace more
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle
+
+namespace intrinsic = paddle::operators::jit::more::intrinsic;
+
+REGISTER_JITKERNEL_MORE(kCRFDecoding, intrinsic, intrinsic::CRFDecodingKernel);

paddle/fluid/operators/jit/more/intrinsic/crf_decoding.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 <type_traits>
+#include "paddle/fluid/operators/jit/kernel_base.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace more {
+namespace intrinsic {
+
+void CRFDecoding(const int seq_len, const float* x, const float* w,
+                 float* alpha, int* track, int tag_num);
+
+class CRFDecodingKernel : public KernelMore<CRFDecodingTuples<float>> {
+ public:
+  CRFDecodingKernel() { this->func = CRFDecoding; }
+  bool UseMe(
+      const typename CRFDecodingTuples<float>::attr_type&) const override;
+  const char* ImplType() const override { return "Intrinsic"; }
+};
+
+}  // namespace intrinsic
+}  // namespace more
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/more/intrinsic/layer_norm.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/more/intrinsic/layer_norm.h"
+#include <limits>
+#include "paddle/fluid/operators/jit/registry.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace more {
+namespace intrinsic {
+
+void LayerNorm(float* x, float* out, float* mean, float* var,
+               const float* scale, const float* bias, int height,
+               const float epsilon, int right) {
+  __m256 sum;
+  __m256 mean_vec, var_vec;
+  __m128 hi, lo;
+  __m256 tmp;
+  size_t offset;
+  size_t j;
+  int block = YMM_FLOAT_BLOCK;
+  const int rest = right % block;
+  const int end = right - rest;
+
+  __m256 reverse_num_vec =
+      _mm256_div_ps(_mm256_set1_ps(1.0), _mm256_set1_ps(right));
+  __m256 epsilon_vec = _mm256_set1_ps(epsilon);
+  int rest_mask =
+      ((-1) & (~((~0U) >> (sizeof(int) * 8 - (block - rest))))) & 0x0ff;
+  __m256i mask_vec = _mm256_set_epi32(
+      rest_mask & 0x80 ? 0xffffffff : 0, rest_mask & 0x40 ? 0xffffffff : 0,
+      rest_mask & 0x20 ? 0xffffffff : 0, rest_mask & 0x10 ? 0xffffffff : 0,
+      rest_mask & 0x8 ? 0xffffffff : 0, rest_mask & 0x4 ? 0xffffffff : 0,
+      rest_mask & 0x2 ? 0xffffffff : 0, rest_mask & 0x1 ? 0xffffffff : 0);
+
+  for (int i = 0; i < height; ++i) {
+    offset = i * right;
+
+    /* get mean */
+    sum = _mm256_setzero_ps();
+    for (j = offset; j < end + offset; j += block) {
+      sum = _mm256_add_ps(sum, _mm256_loadu_ps((const float*)x + j));
+    }
+    if (rest != 0) {
+      j = offset + right - block;
+      tmp = _mm256_loadu_ps((const float*)x + j);
+      tmp = _mm256_blendv_ps(_mm256_setzero_ps(), tmp,
+                             *(__m256*)&mask_vec);  // NOLINT
+      sum = _mm256_add_ps(sum, tmp);
+    }
+    hi = _mm256_extractf128_ps(sum, 1);
+    lo = _mm256_extractf128_ps(sum, 0);
+    sum = _mm256_add_ps(
+        sum, _mm256_insertf128_ps(
+                 _mm256_insertf128_ps(_mm256_setzero_ps(), hi, 0), lo, 1));
+    sum = _mm256_hadd_ps(sum, sum);
+    sum = _mm256_hadd_ps(sum, sum);
+    mean_vec = _mm256_mul_ps(sum, reverse_num_vec);
+    mean[i] = *reinterpret_cast<float*>(&mean_vec);
+
+    /* get variance */
+    sum = _mm256_setzero_ps();
+    for (j = offset; j < end + offset; j += block) {
+      tmp = _mm256_sub_ps(_mm256_loadu_ps((const float*)x + j), mean_vec);
+      tmp = _mm256_mul_ps(tmp, tmp);
+      sum = _mm256_add_ps(sum, tmp);
+    }
+    if (rest != 0) {
+      j = offset + right - block;
+      tmp = _mm256_sub_ps(_mm256_loadu_ps((const float*)x + j), mean_vec);
+      tmp = _mm256_mul_ps(tmp, tmp);
+      tmp = _mm256_blendv_ps(_mm256_setzero_ps(), tmp,
+                             *(__m256*)&mask_vec);  // NOLINT
+      sum = _mm256_add_ps(sum, tmp);
+    }
+    hi = _mm256_extractf128_ps(sum, 1);
+    lo = _mm256_extractf128_ps(sum, 0);
+    sum = _mm256_add_ps(
+        sum, _mm256_insertf128_ps(
+                 _mm256_insertf128_ps(_mm256_setzero_ps(), hi, 0), lo, 1));
+    sum = _mm256_hadd_ps(sum, sum);
+    sum = _mm256_hadd_ps(sum, sum);
+    var_vec = _mm256_mul_ps(sum, reverse_num_vec);
+    var[i] = *reinterpret_cast<float*>(&var_vec);
+
+    /* get x_norm and calculate output*/
+    for (j = offset; j < end + offset; j += block) {
+      tmp = _mm256_sub_ps(_mm256_loadu_ps((const float*)x + j), mean_vec);
+      tmp = _mm256_div_ps(tmp,
+                          _mm256_sqrt_ps(_mm256_add_ps(var_vec, epsilon_vec)));
+      _mm256_storeu_ps(reinterpret_cast<float*>(out) + j, tmp);
+    }
+    if (rest != 0) {
+      j = offset + right - block;
+      tmp = _mm256_sub_ps(_mm256_loadu_ps((const float*)x + j), mean_vec);
+      tmp = _mm256_div_ps(tmp,
+                          _mm256_sqrt_ps(_mm256_add_ps(var_vec, epsilon_vec)));
+      _mm256_storeu_ps(reinterpret_cast<float*>(out) + j, tmp);
+    }
+
+    if (scale) {
+      if (rest != 0) {
+        j = offset + right - block;
+        tmp = _mm256_loadu_ps((const float*)out + j);
+      }
+      for (j = offset; j < end + offset; j += block) {
+        _mm256_storeu_ps(
+            reinterpret_cast<float*>(out) + j,
+            _mm256_mul_ps(_mm256_loadu_ps((const float*)out + j),
+                          _mm256_loadu_ps((const float*)scale + j - offset)));
+      }
+      if (rest != 0) {
+        j = offset + right - block;
+        _mm256_storeu_ps(
+            reinterpret_cast<float*>(out) + j,
+            _mm256_mul_ps(tmp,
+                          _mm256_loadu_ps((const float*)scale + j - offset)));
+      }
+    }
+
+    if (bias) {
+      if (rest != 0) {
+        j = offset + right - block;
+        tmp = _mm256_loadu_ps((const float*)out + j);
+      }
+      for (j = offset; j < end + offset; j += block) {
+        _mm256_storeu_ps(
+            reinterpret_cast<float*>(out) + j,
+            _mm256_add_ps(_mm256_loadu_ps((const float*)out + j),
+                          _mm256_loadu_ps((const float*)bias + j - offset)));
+      }
+      if (rest != 0) {
+        j = offset + right - block;
+        _mm256_storeu_ps(reinterpret_cast<float*>(out) + j,
+                         _mm256_add_ps(tmp, _mm256_loadu_ps((const float*)bias +
+                                                            j - offset)));
+      }
+    }
+  }
+}
+
+bool LayerNormKernel::UseMe(const int& d) const {
+  return platform::MayIUse(platform::avx) && d >= YMM_FLOAT_BLOCK;
+}
+
+}  // namespace intrinsic
+}  // namespace more
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle
+
+namespace intrinsic = paddle::operators::jit::more::intrinsic;
+
+REGISTER_JITKERNEL_MORE(kLayerNorm, intrinsic, intrinsic::LayerNormKernel);

paddle/fluid/operators/jit/more/intrinsic/layer_norm.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 <type_traits>
+#include "paddle/fluid/operators/jit/kernel_base.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace more {
+namespace intrinsic {
+
+void LayerNorm(float* x, float* out, float* mean, float* var,
+               const float* scale, const float* bias, int height,
+               const float epsilon, int right);
+
+class LayerNormKernel : public KernelMore<LayerNormTuples<float>> {
+ public:
+  LayerNormKernel() { this->func = LayerNorm; }
+  bool UseMe(const typename LayerNormTuples<float>::attr_type&) const override;
+  const char* ImplType() const override { return "Intrinsic"; }
+};
+
+}  // namespace intrinsic
+}  // namespace more
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/more/mix/CMakeLists.txt

+
+
+file(GLOB jit_kernel_mix_cc RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cc")
+cc_library(jit_kernel_mix SRCS ${jit_kernel_mix_cc} DEPS jit_kernel_base)
+
+set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} jit_kernel_mix PARENT_SCOPE)
+
+USE_JITKERNEL_MORE(kVSigmoid, mix)
+USE_JITKERNEL_MORE(kVTanh, mix)
+USE_JITKERNEL_MORE(kLSTMCtHt, mix)
+USE_JITKERNEL_MORE(kLSTMC1H1, mix)
+USE_JITKERNEL_MORE(kGRUH1, mix)
+USE_JITKERNEL_MORE(kGRUHtPart1, mix)
+USE_JITKERNEL_MORE(kGRUHtPart2, mix)

paddle/fluid/operators/jit/more/mix/mix.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License. */
+
+#include "paddle/fluid/operators/jit/more/mix/mix.h"
+#include "paddle/fluid/operators/jit/kernels.h"
+#include "paddle/fluid/operators/jit/registry.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace more {
+namespace mix {
+
+void VSigmoid(const T* x, T* y, int n) {
+  const float min = SIGMOID_THRESHOLD_MIN;
+  const float max = SIGMOID_THRESHOLD_MAX;
+  for (int i = 0; i < n; ++i) {
+    y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
+    y[i] = static_cast<T>(0) - y[i];
+  }
+  auto compute = Get<KernelType::kVExp, XYNTuples<T>, platform::CPUPlace>(n);
+  compute(y, y, n);
+  for (int i = 0; i < n; ++i) {
+    y[i] = static_cast<T>(1) / (static_cast<T>(1) + y[i]);
+  }
+}
+
+void VTanh(const T* x, T* y, int n) {
+  const T a = 2, b = -1;
+  auto compute_scal = Get<kVScal, AXYNTuples<T>, platform::CPUPlace>(n);
+  auto compute_addbias = Get<kVAddBias, AXYNTuples<T>, platform::CPUPlace>(n);
+  auto compute_sigmoid = Get<kVSigmoid, XYNTuples<T>, platform::CPUPlace>(n);
+  compute_scal(&a, x, y, n);
+  compute_sigmoid(y, y, n);
+  compute_scal(&a, y, y, n);
+  compute_addbias(&b, y, y, n);
+}
+
+void (*getActFunc(KernelType type, int d))(const T*, T*, int) {  // NOLINT
+  if (type == kVSigmoid) {
+    return Get<kVSigmoid, XYNTuples<T>, platform::CPUPlace>(d);
+  } else if (type == kVRelu) {
+    return Get<kVRelu, XYNTuples<T>, platform::CPUPlace>(d);
+  } else if (type == kVTanh) {
+    return Get<kVTanh, XYNTuples<T>, platform::CPUPlace>(d);
+  } else if (type == kVIdentity) {
+    return Get<kVIdentity, XYNTuples<T>, platform::CPUPlace>(d);
+  }
+  PADDLE_THROW("Not support type: %s", type);
+  return nullptr;
+}
+
+void LSTMCtHt(lstm_t* step, const lstm_attr_t* attr) {
+  T* gates = reinterpret_cast<T*>(step->gates);
+  const T* ct_1 = reinterpret_cast<const T*>(step->ct_1);
+  T* ct = reinterpret_cast<T*>(step->ct);
+  T* ht = reinterpret_cast<T*>(step->ht);
+  const T* wp = reinterpret_cast<const T*>(step->wp);
+  T* checked = reinterpret_cast<T*>(step->checked);
+  const int d = attr->d;
+  const int d2 = d * 2;
+  const int d3 = d * 3;
+  auto vmul_d = Get<kVMul, XYZNTuples<T>, platform::CPUPlace>(d);
+  auto vadd_d = Get<kVAdd, XYZNTuples<T>, platform::CPUPlace>(d);
+  auto vadd_d2 = Get<kVAdd, XYZNTuples<T>, platform::CPUPlace>(d2);
+  auto act_gate_d = getActFunc(attr->act_gate, d);
+  auto act_gate_d2 = getActFunc(attr->act_gate, d2);
+  auto act_gate_d3 = getActFunc(attr->act_gate, d3);
+  auto act_cand_d = getActFunc(attr->act_cand, d);
+  auto act_cell_d = getActFunc(attr->act_cell, d);
+
+  if (attr->use_peephole) {
+    vmul_d(wp, ct_1, checked, d);
+    vmul_d(wp + d, ct_1, checked + d, d);
+    vadd_d2(checked, gates + d, gates + d, d2);
+    act_gate_d2(gates + d, gates + d, d2);
+  } else {
+    act_gate_d3(gates + d, gates + d, d3);
+  }
+
+  // C_t = C_t-1 * fgated + cand_gated * igated
+  act_cand_d(gates, gates, d);
+  vmul_d(gates, gates + d, gates + d, d);
+  vmul_d(ct_1, gates + d2, gates + d2, d);
+  vadd_d(gates + d, gates + d2, ct, d);
+
+  if (attr->use_peephole) {
+    // get ogated
+    vmul_d(wp + d2, ct, gates + d, d);
+    vadd_d(gates + d, gates + d3, gates + d3, d);
+    act_gate_d(gates + d3, gates + d3, d);
+  }
+  // H_t = act_cell(C_t) * ogated
+  act_cell_d(ct, gates + d2, d);
+  vmul_d(gates + d2, gates + d3, ht, d);
+}
+
+void LSTMC1H1(lstm_t* step, const lstm_attr_t* attr) {
+  T* gates = reinterpret_cast<T*>(step->gates);
+  T* ct = reinterpret_cast<T*>(step->ct);
+  T* ht = reinterpret_cast<T*>(step->ht);
+  int d = attr->d;
+  int d2 = d * 2;
+  int d3 = d * 3;
+  auto vmul_d = Get<kVMul, XYZNTuples<T>, platform::CPUPlace>(d);
+  auto vadd_d = Get<kVAdd, XYZNTuples<T>, platform::CPUPlace>(d);
+  auto act_gate_d = getActFunc(attr->act_gate, d);
+  auto act_cand_d = getActFunc(attr->act_cand, d);
+  auto act_cell_d = getActFunc(attr->act_cell, d);
+  /* C_t = igated * cgated*/
+  act_gate_d(gates + d, gates + d, d);
+  act_cand_d(gates, gates, d);
+  vmul_d(gates, gates + d, ct, d);
+  if (attr->use_peephole) {
+    // get outgated, put W_oc * C_t on igated
+    const T* wp = reinterpret_cast<const T*>(step->wp);
+    vmul_d(wp + d2, ct, gates + d, d);
+    vadd_d(gates + d, gates + d3, gates + d3, d);
+  }
+  /* H_t = act_cell(C_t) * ogated */
+  act_gate_d(gates + d3, gates + d3, d);
+  act_cell_d(ct, gates + d2, d);
+  vmul_d(gates + d2, gates + d3, ht, d);
+}
+
+// compute h1 without h0
+void GRUH1(gru_t* step, const gru_attr_t* attr) {
+  T* gates = reinterpret_cast<T*>(step->gates);
+  T* ht = reinterpret_cast<T*>(step->ht);
+  int d = attr->d;
+  int d2 = d * 2;
+  auto act_gate = getActFunc(attr->act_gate, d);
+  auto act_cand = getActFunc(attr->act_cand, d);
+  auto vmul_d = Get<kVMul, XYZNTuples<T>, platform::CPUPlace>(d);
+  act_gate(gates, gates, d);
+  act_cand(gates + d2, gates + d2, d);
+  vmul_d(gates, gates + d2, ht, d);
+}
+
+// compute the first part of GRU: ht = act_gate(r) * ht_1
+void GRUHtPart1(gru_t* step, const gru_attr_t* attr) {
+  // W: {W_update, W_reset; W_state}
+  T* gates = reinterpret_cast<T*>(step->gates);
+  T* ht = reinterpret_cast<T*>(step->ht);
+  const T* ht_1 = reinterpret_cast<const T*>(step->ht_1);
+  auto act_gate = getActFunc(attr->act_gate, attr->d);
+  auto vmul_d = Get<kVMul, XYZNTuples<T>, platform::CPUPlace>(attr->d);
+  act_gate(gates + attr->d, gates + attr->d, attr->d);
+  vmul_d(ht_1, gates + attr->d, ht, attr->d);
+}
+
+// compute the second part of GRU:
+// ht = act_gate(u) * act_cand(s) + (1-act_gate(u)) * ht_1
+void GRUHtPart2(gru_t* step, const gru_attr_t* attr) {
+  T* gates = reinterpret_cast<T*>(step->gates);
+  T* ht = reinterpret_cast<T*>(step->ht);
+  const T* ht_1 = reinterpret_cast<const T*>(step->ht_1);
+  int d = attr->d;
+  auto act_gate = getActFunc(attr->act_gate, d);
+  auto act_cand = getActFunc(attr->act_cand, d);
+  T* y = gates + d * 2;
+  act_gate(gates, gates, d);
+  act_cand(y, y, d);
+  // out = zt*ht~ + (1-zt)*ht_1
+  for (int i = 0; i < d; ++i) {
+    ht[i] = gates[i] * y[i] + (static_cast<T>(1) - gates[i]) * ht_1[i];
+  }
+}
+
+// TODO(TJ): tuning me
+bool VSigmoidKernel::UseMe(const int& d) const { return true; }
+
+bool VTanhKernel::UseMe(const int& d) const { return true; }
+
+bool LSTMCtHtKernel::UseMe(const lstm_attr_t& attr) const { return true; }
+
+bool LSTMC1H1Kernel::UseMe(const lstm_attr_t& attr) const { return true; }
+
+bool GRUH1Kernel::UseMe(const gru_attr_t& attr) const { return true; }
+
+bool GRUHtPart1Kernel::UseMe(const gru_attr_t& attr) const { return true; }
+
+bool GRUHtPart2Kernel::UseMe(const gru_attr_t& attr) const { return true; }
+
+}  // namespace mix
+}  // namespace more
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle
+
+namespace mix = paddle::operators::jit::more::mix;
+
+#define REGISTER_MORE_KERNEL(key, func) \
+  REGISTER_JITKERNEL_MORE(key, mix, mix::func##Kernel)
+
+REGISTER_MORE_KERNEL(kVSigmoid, VSigmoid);
+REGISTER_MORE_KERNEL(kVTanh, VTanh);
+REGISTER_MORE_KERNEL(kLSTMCtHt, LSTMCtHt);
+REGISTER_MORE_KERNEL(kLSTMC1H1, LSTMC1H1);
+REGISTER_MORE_KERNEL(kGRUH1, GRUH1);
+REGISTER_MORE_KERNEL(kGRUHtPart1, GRUHtPart1);
+REGISTER_MORE_KERNEL(kGRUHtPart2, GRUHtPart2);
+
+#undef REGISTER_MORE_KERNEL