Merge branch 'incubate/lite' of http://10.87.145.36/inference/paddlelite into hongming/fix_pooling

.gitlab-ci.yml

@@ -76,8 +76,8 @@ build:mobile_android:
     cache:
         key: mobile_thirdparty
         paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
+            - build.lite.android.armv8.gcc/third_party
+            - build.lite.android.armv7.gcc/third_party
             - ~/.ccache
             - $CI_PROJECT_DIR/build_mobile_ccache
     script:
@@ -96,8 +96,9 @@ build:mobile_armlinux:
     cache:
         key: mobile_thirdparty
         paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
+            - build.lite.armlinux.armv8.gcc
+            - build.lite.armlinux.armv7.gcc
+            - build.lite.armlinux.armv7hf.gcc
             - ~/.ccache
             - $CI_PROJECT_DIR/build_mobile_ccache2
     script:
@@ -107,24 +108,13 @@ build:mobile_armlinux:
     dependencies:
         - build:server
 
-    cache:
-        key: mobile_thirdparty
-        paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
-            - ~/.ccache
 
 build:mobile_model_mobilenetv1:
     tags:
         - lite
     stage: build_mobile
     image: $MOBILE_LITE_DOCKER_IMAGE
-    cache:
-        key: mobile_thirdparty
-        paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
-            - ~/.ccache
+
     script:
         - export CCACHE_DIR=$CI_PROJECT_DIR/build_mobile_model_mobilenetv1
         - ./paddle/fluid/lite/tools/build.sh build_test_arm_model_mobilenetv1
@@ -135,8 +125,7 @@ build:mobile_model_mobilenetv1:
     cache:
         key: mobile_thirdparty
         paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
+            - build.lite.android.armv8.gcc
             - ~/.ccache
             - $CI_PROJECT_DIR/build_mobile_model_mobilenetv1
 
@@ -145,12 +134,7 @@ build:mobile_model_mobilenetv2:
         - lite
     stage: build_mobile
     image: $MOBILE_LITE_DOCKER_IMAGE
-    cache:
-        key: mobile_thirdparty
-        paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
-            - ~/.ccache
+
     script:
         - export CCACHE_DIR=$CI_PROJECT_DIR/build_mobile_model_mobilenetv2
         - ./paddle/fluid/lite/tools/build.sh build_test_arm_model_mobilenetv2
@@ -161,8 +145,7 @@ build:mobile_model_mobilenetv2:
     cache:
         key: mobile_thirdparty
         paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
+            - build.lite.android.armv8.gcc
             - ~/.ccache
             - $CI_PROJECT_DIR/build_mobile_model_mobilenetv2
 
@@ -171,12 +154,7 @@ build:mobile_model_resnet50:
         - lite
     stage: build_mobile
     image: $MOBILE_LITE_DOCKER_IMAGE
-    cache:
-        key: mobile_thirdparty
-        paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
-            - ~/.ccache
+
     script:
         - export CCACHE_DIR=$CI_PROJECT_DIR/build_mobile_model_resnet50
         - ./paddle/fluid/lite/tools/build.sh build_test_arm_model_resnet50
@@ -187,8 +165,7 @@ build:mobile_model_resnet50:
     cache:
         key: mobile_thirdparty
         paths:
-            - $MOBILE_LITE_CACHE0
-            - $MOBILE_LITE_CACHE1
+            - build.lite.android.armv8.gcc
             - ~/.ccache
             - $CI_PROJECT_DIR/build_mobile_model_resnet50
 

CMakeLists.txt

@@ -150,6 +150,7 @@ option(WITH_LITE "Enable lite framework" OFF)
 option(LITE_WITH_CUDA "Enable CUDA in lite mode" OFF)
 option(LITE_WITH_X86  "Enable X86 in lite mode"  ON)
 option(LITE_WITH_ARM  "Enable ARM in lite mode"  OFF)
+option(LITE_WITH_OPENCL   "Enable OpenCL support in lite" OFF)
 option(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK  "Enable light-weight framework" OFF)
 option(LITE_WITH_PROFILE  "Enable profile mode in lite framework"  OFF)
 
@@ -181,6 +182,12 @@ if (WITH_LITE AND LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
     include(external/eigen)     # download eigen3
     include(ccache)             # set ccache for compilation
 
+    # for opencl
+    if (LITE_WITH_OPENCL)
+        include(external/opencl-headers)
+        include(external/opencl-clhpp)
+    endif()
+
     include(generic)            # simplify cmake module
     include(configure)          # add paddle env configuration
 

cmake/configure.cmake

@@ -176,6 +176,10 @@ if (LITE_WITH_ARM)
     add_definitions("-DLITE_WITH_ARM")
 endif()
 
+if (LITE_WITH_OPENCL)
+    add_definitions("-DLITE_WITH_OPENCL")
+endif()
+
 if (LITE_WITH_PROFILE)
     add_definitions("-DLITE_WITH_PROFILE")
 endif()

cmake/external/opencl-clhpp.cmake

+# 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(ExternalProject)
+
+SET(OPENCL_CLHPP_SRCS_DIR    ${THIRD_PARTY_PATH}/opencl-clhpp)
+SET(OPENCL_CLHPP_INSTALL_DIR ${THIRD_PARTY_PATH}/install/opencl-clhpp)
+SET(OPENCL_CLHPP_INCLUDE_DIR "${OPENCL_CLHPP_INSTALL_DIR}" CACHE PATH "opencl-clhpp include directory." FORCE)
+
+INCLUDE_DIRECTORIES(${OPENCL_CLHPP_INCLUDE_DIR})
+
+ExternalProject_Add(
+  opencl_clhpp
+  GIT_REPOSITORY    "https://github.com/KhronosGroup/OpenCL-CLHPP.git"
+  GIT_TAG           "v2.0.10"
+  PREFIX            "${OPENCL_CLHPP_SRCS_DIR}"
+  CMAKE_ARGS        -DBUILD_DOCS=OFF
+                    -DBUILD_EXAMPLES=OFF
+                    -DBUILD_TESTS=OFF
+                    -DCMAKE_INSTALL_PREFIX=${OPENCL_CLHPP_INSTALL_DIR}
+  CMAKE_CACHE_ARGS  -DCMAKE_INSTALL_PREFIX:PATH=${OPENCL_CLHPP_INSTALL_DIR}
+                    -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
+)
+
+ADD_DEPENDENCIES(opencl_clhpp opencl_headers)

cmake/external/opencl-headers.cmake

+# 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(ExternalProject)
+
+SET(OPENCL_HEADERS_SRCS_DIR    ${THIRD_PARTY_PATH}/opencl-headers)
+SET(OPENCL_HEADERS_INCLUDE_DIR "${OPENCL_HEADERS_SRCS_DIR}/src/opencl_headers" CACHE PATH "opencl-headers include directory." FORCE)
+
+INCLUDE_DIRECTORIES(${OPENCL_HEADERS_INCLUDE_DIR})
+
+ExternalProject_Add(
+  opencl_headers
+  ${EXTERNAL_PROJECT_LOG_ARGS}
+  GIT_REPOSITORY    "https://github.com/KhronosGroup/OpenCL-Headers.git"
+  GIT_TAG           "c5a4bbeabb10d8ed3d1c651b93aa31737bc473dd"
+  PREFIX            ${OPENCL_HEADERS_SRCS_DIR}
+  DOWNLOAD_NAME     "OpenCL-Headers"
+  CONFIGURE_COMMAND ""
+  BUILD_COMMAND     ""
+  INSTALL_COMMAND   ""
+  TEST_COMMAND      ""
+)

paddle/fluid/lite/CMakeLists.txt

@@ -24,8 +24,7 @@ function(lite_download_and_uncompress INSTALL_DIR URL FILENAME)
             ${EXTERNAL_PROJECT_NAME}
             ${EXTERNAL_PROJECT_LOG_ARGS}
             PREFIX                ${INSTALL_DIR}
-            DOWNLOAD_COMMAND      wget --no-check-certificate -q -O ${INSTALL_DIR}/${FILENAME} ${URL}/${FILENAME} &&
-            ${CMAKE_COMMAND} -E tar xzf ${INSTALL_DIR}/${FILENAME}
+            DOWNLOAD_COMMAND      wget --no-check-certificate -q -O ${INSTALL_DIR}/${FILENAME} ${URL}/${FILENAME} && ${CMAKE_COMMAND} -E tar xzf ${INSTALL_DIR}/${FILENAME}
             DOWNLOAD_DIR          ${INSTALL_DIR}
             DOWNLOAD_NO_PROGRESS  1
             CONFIGURE_COMMAND     ""
@@ -143,6 +142,8 @@ function(lite_cc_binary TARGET)
             HVY_DEPS ${args_HVY_DEPS}
             )
     cc_binary(${TARGET} SRCS ${args_SRCS} DEPS ${deps} ${args_DEPS})
+    # collect targets need to compile for lite
+    add_dependencies(lite_compile_deps ${TARGET})
 endfunction()
 
 # Add a unit-test name to file for latter offline manual test.
@@ -181,6 +182,7 @@ add_subdirectory(x86)
 add_subdirectory(arm)
 add_subdirectory(host)
 add_subdirectory(cuda)
+add_subdirectory(opencl)
 add_subdirectory(model_parser)
 add_subdirectory(utils)
 add_subdirectory(api)

paddle/fluid/lite/api/CMakeLists.txt

@@ -12,7 +12,6 @@ lite_cc_library(lite_api_test_helper SRCS lite_api_test_helper.cc
   CUDA_DEPS kernels_cuda
   X86_DEPS ${x86_kernels}
   )
-lite_cc_library(cxx_api_lite SRCS cxx_api.cc DEPS lite_api_test_helper)
 
 set(light_api_deps
     scope_lite target_wrapper_host model_parser_lite program_lite)
@@ -21,27 +20,34 @@ if(LITE_WITH_CUDA)
     set(light_api_deps ${light_api_deps} target_wrapper_cuda)
 endif()
 
-lite_cc_library(light_api_lite SRCS light_api.cc
-  DEPS ${light_api_deps} ${ops_lite} ${host_kernels}
-  )
-
 message(STATUS "get ops ${ops_lite}")
 message(STATUS "get Host kernels ${host_kernels}")
 message(STATUS "get ARM kernels ${arm_kernels}")
 
+lite_cc_library(cxx_api_lite SRCS cxx_api.cc DEPS ${cxx_api_lite_deps} ${ops_lite} ${host_kernels} program_lite)
+
+lite_cc_library(light_api_lite SRCS light_api.cc
+        DEPS scope_lite target_wrapper_host model_parser_lite
+            ${light_api_deps} ${ops_lite} ${host_kernels} program_lite
+        CUDA_DEPS target_wrapper_cuda
+        X86_DEPS ${x86_kernels} operator
+        ARM_DEPS ${arm_kernels}
+        )
+
 include(ExternalProject)
 set(LITE_DEMO_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo" CACHE STRING
         "A path setting inference demo download directories.")
 
 if(NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND WITH_TESTING)
     lite_cc_test(test_cxx_api_lite SRCS cxx_api_test.cc
-       DEPS cxx_api_lite mir_passes 
+       DEPS cxx_api_lite mir_passes lite_api_test_helper
        ${ops_lite} ${host_kernels} ${x86_kernels}
        ARGS --model_dir=${LITE_MODEL_DIR}/lite_naive_model
             --optimized_model=${LITE_MODEL_DIR}/lite_naive_model_opt SERIAL)
     add_dependencies(test_cxx_api_lite extern_lite_download_lite_naive_model_tar_gz)
 endif()
 
+
 if(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND WITH_TESTING)
     set(lite_model_test_DEPS cxx_api_lite mir_passes ${ops_lite} ${host_kernels} ${arm_kernels})
 
@@ -68,25 +74,18 @@ endif()
 
 # These tests needs CLI arguments, and is not supported in ARM CI.
 # TODO(Superjomn) support latter.
-if(NOT LITE_ON_MOBILE)
-    lite_cc_test(test_light_api SRCS light_api_test.cc
-      DEPS light_api_lite mir_passes
-      X86_DEPS ${x86_kernels}
-      ARGS --optimized_model=${LITE_MODEL_DIR}/lite_naive_model_opt
-      SERIAL)
-
-    lite_cc_test(test_apis_lite SRCS apis_test.cc
-      DEPS cxx_api_lite light_api_lite ${ops_lite} mir_passes
-      X86_DEPS ${x86_kernels}
-      ARGS --model_dir=${LITE_MODEL_DIR}/lite_naive_model
-          --optimized_model=${LITE_MODEL_DIR}/lite_naive_model_opt SERIAL)
-endif()
+lite_cc_test(test_light_api_lite SRCS light_api_test.cc
+    DEPS light_api_lite program_lite mir_passes
+    ARGS --optimized_model=${LITE_MODEL_DIR}/lite_naive_model_opt
+    SERIAL)
+
+lite_cc_test(test_apis_lite SRCS apis_test.cc
+  DEPS cxx_api_lite light_api_lite ${ops_lite}
+  X86_DEPS ${x86_kernels} operator
+  ARGS --model_dir=${LITE_MODEL_DIR}/lite_naive_model
+      --optimized_model=${LITE_MODEL_DIR}/lite_naive_model_opt SERIAL)
 
-lite_cc_binary(cxx_api_lite_bin SRCS cxx_api_bin.cc
-    DEPS
-    cxx_api_lite
-    model_parser_lite
-    target_wrapper_host
-    mir_passes
-    ${ops_lite} ${host_kernels}
-    ARM_DEPS ${arm_kernels})
+#lite_cc_binary(cxx_api_lite_bin SRCS cxx_api_bin.cc
+    #X86_DEPS operator
+    #DEPS light_api_lite model_parser_lite target_wrapper_host mir_passes
+    #ARM_DEPS ${arm_kernels})

paddle/fluid/lite/api/apis_test.cc

@@ -39,23 +39,41 @@ void SetConstInput(lite::Tensor* x) {
   }
 }
 
-bool CompareTensors(const std::string& name, const ExecutorLite& cxx_api,
+bool CompareTensors(const std::string& name, const Predictor& cxx_api,
                     const LightPredictor& light_api) {
   const auto* a = cxx_api.GetTensor(name);
   const auto* b = light_api.GetTensor(name);
   return TensorCompareWith(*a, *b);
 }
 
-#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
+TEST(CXXApi_LightApi, optim_model) {
+  lite::Predictor cxx_api;
+  std::vector<Place> valid_places({
+      Place{TARGET(kHost), PRECISION(kFloat)},
+      Place{TARGET(kX86), PRECISION(kFloat)},
+      Place{TARGET(kARM), PRECISION(kFloat)},  // Both works on X86 and ARM
+  });
+  // On ARM devices, the preferred X86 target not works, but it can still
+  // select ARM kernels.
+  cxx_api.Build(FLAGS_model_dir, Place{TARGET(kX86), PRECISION(kFloat)},
+                valid_places);
+  cxx_api.SaveModel(FLAGS_optimized_model);
+}
+
 TEST(CXXApi_LightApi, save_and_load_model) {
-  lite::ExecutorLite cxx_api;
-  lite::LightPredictor light_api;
+  lite::Predictor cxx_api;
+  lite::LightPredictor light_api(FLAGS_optimized_model);
 
   // CXXAPi
   {
-    std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
-                                     Place{TARGET(kX86), PRECISION(kFloat)}});
-    cxx_api.Build(FLAGS_model_dir, Place{TARGET(kCUDA), PRECISION(kFloat)},
+    std::vector<Place> valid_places({
+        Place{TARGET(kHost), PRECISION(kFloat)},
+        Place{TARGET(kX86), PRECISION(kFloat)},
+        Place{TARGET(kARM), PRECISION(kFloat)},  // Both works on X86 and ARM
+    });
+    // On ARM devices, the preferred X86 target not works, but it can still
+    // select ARM kernels.
+    cxx_api.Build(FLAGS_model_dir, Place{TARGET(kX86), PRECISION(kFloat)},
                   valid_places);
 
     auto* x = cxx_api.GetInput(0);
@@ -69,8 +87,6 @@ TEST(CXXApi_LightApi, save_and_load_model) {
 
   // LightApi
   {
-    light_api.Build(FLAGS_optimized_model);
-
     auto* x = light_api.GetInput(0);
     SetConstInput(x);
 
@@ -89,7 +105,6 @@ TEST(CXXApi_LightApi, save_and_load_model) {
     ASSERT_TRUE(CompareTensors(tensor_name, cxx_api, light_api));
   }
 }
-#endif  // LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 
 }  // namespace lite
 }  // namespace paddle

paddle/fluid/lite/api/cxx_api.cc

@@ -17,19 +17,66 @@
 #include <string>
 #include <utility>
 #include <vector>
-#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
-#include "paddle/fluid/platform/port.h"
-#endif
+#include "paddle/fluid/lite/utils/io.h"
 
 namespace paddle {
 namespace lite {
 
-#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
-void ExecutorLite::SaveModel(const std::string &dir) {
-  MkDirRecursively(dir.c_str());
+void Predictor::SaveModel(const std::string &dir) {
+#ifndef LITE_WITH_ARM
+  MkDirRecur(dir);
+#else
+#endif
   program_->PersistModel(dir, program_desc_);
+  LOG(INFO) << "Save model to " << dir;
+}
+
+lite::Tensor *Predictor::GetInput(size_t offset) {
+  auto *_feed_list = program_->exec_scope()->FindVar("feed");
+  CHECK(_feed_list) << "no feed variable in exec_scope";
+  auto *feed_list = _feed_list->GetMutable<std::vector<lite::Tensor>>();
+  if (offset >= feed_list->size()) {
+    feed_list->resize(offset + 1);
+  }
+  return &feed_list->at(offset);
+}
+
+const lite::Tensor *Predictor::GetOutput(size_t offset) {
+  auto *_fetch_list = program_->exec_scope()->FindVar("fetch");
+  CHECK(_fetch_list) << "no fatch variable in exec_scope";
+  auto &fetch_list = *_fetch_list->GetMutable<std::vector<lite::Tensor>>();
+  CHECK_LT(offset, fetch_list.size()) << "offset " << offset << " overflow";
+  return &fetch_list.at(offset);
+}
+
+void Predictor::Build(const std::string &model_path, const Place &prefer_place,
+                      const std::vector<Place> &valid_places) {
+  LoadModel(model_path, scope_.get(), &program_desc_);
+  Build(program_desc_, prefer_place, valid_places);
+}
+
+const framework::proto::ProgramDesc &Predictor::program_desc() const {
+  return program_desc_;
+}
+
+void Predictor::Build(const framework::proto::ProgramDesc &desc,
+                      const Place &prefer_place,
+                      const std::vector<Place> &valid_places) {
+  program_desc_ = desc;
+  Program program(desc, scope_, valid_places);
+
+  optimizer_.KernelPickPreferPlace(prefer_place);
+  core::KernelPickFactor factor;
+  factor.ConsiderTarget();
+  factor.ConsiderPrecision();
+  optimizer_.Run(std::move(program), valid_places, factor);
+  program_ = optimizer_.GenRuntimeProgram();
+}
+
+const lite::Tensor *Predictor::GetTensor(const std::string &name) const {
+  auto *var = program_->exec_scope()->FindVar(name);
+  return &var->Get<lite::Tensor>();
 }
-#endif
 
 }  // namespace lite
 }  // namespace paddle

paddle/fluid/lite/api/cxx_api.h

@@ -26,68 +26,39 @@
 namespace paddle {
 namespace lite {
 
-struct Config {};
-
-class ExecutorLite {
+/*
+ * Predictor for inference, input a model, it will optimize and execute it.
+ */
+class Predictor {
  public:
-  ExecutorLite() { scope_ = std::make_shared<Scope>(); }
-  explicit ExecutorLite(const std::shared_ptr<lite::Scope>& root_scope) {
-    scope_ = root_scope;
-  }
+  // Create an empty predictor.
+  Predictor() { scope_ = std::make_shared<Scope>(); }
+  // Create a predictor with the weight variable scope set.
+  explicit Predictor(const std::shared_ptr<lite::Scope>& root_scope)
+      : scope_(root_scope) {}
 
+  // Build from a model, with places set for hardware config.
   void Build(const std::string& model_path, const Place& prefer_place,
-             const std::vector<Place>& valid_places) {
-    LoadModel(model_path, scope_.get(), &program_desc_);
-    Build(program_desc_, prefer_place, valid_places);
-  }
+             const std::vector<Place>& valid_places);
 
   void Build(const framework::proto::ProgramDesc& desc,
-             const Place& prefer_place,
-             const std::vector<Place>& valid_places) {
-    program_desc_ = desc;
-    Program program(desc, scope_, valid_places);
-
-    optimizer_.KernelPickPreferPlace(prefer_place);
-    core::KernelPickFactor factor;
-    factor.ConsiderTarget();
-    optimizer_.Run(std::move(program), valid_places, factor);
-    program_ = optimizer_.GenRuntimeProgram();
-  }
-
-// This method is disabled in mobile, or unnecessary dependencies required.
-#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
-  void SaveModel(const std::string& dir);
-#endif
+             const Place& prefer_place, const std::vector<Place>& valid_places);
 
-  // Get offset-th col of feed.
-  lite::Tensor* GetInput(size_t offset) {
-    auto* _feed_list = program_->exec_scope()->FindVar("feed");
-    CHECK(_feed_list) << "no feed variable in exec_scope";
-    auto* feed_list = _feed_list->GetMutable<std::vector<lite::Tensor>>();
-    if (offset >= feed_list->size()) {
-      feed_list->resize(offset + 1);
-    }
-    return &feed_list->at(offset);
-  }
+  // Run the predictor for a single batch of data.
+  void Run() { program_->Run(); }
 
-  const lite::Tensor* GetOutput(size_t offset) {
-    auto* _fetch_list = program_->exec_scope()->FindVar("fetch");
-    CHECK(_fetch_list) << "no fatch variable in exec_scope";
-    auto& fetch_list = *_fetch_list->GetMutable<std::vector<lite::Tensor>>();
-    CHECK_LT(offset, fetch_list.size()) << "offset " << offset << " overflow";
-    return &fetch_list.at(offset);
-  }
+  // Get offset-th col of feed inputs.
+  lite::Tensor* GetInput(size_t offset);
 
-  const lite::Tensor* GetTensor(const std::string& name) const {
-    auto* var = program_->exec_scope()->FindVar(name);
-    return &var->Get<lite::Tensor>();
-  }
+  // Get offset-th col of fetch results.
+  const lite::Tensor* GetOutput(size_t offset);
 
-  void Run() { program_->Run(); }
+  // Return the program desc for debug.
+  const framework::proto::ProgramDesc& program_desc() const;
+  const lite::Tensor* GetTensor(const std::string& name) const;
 
-  const framework::proto::ProgramDesc& program_desc() const {
-    return program_desc_;
-  }
+  // This method is disabled in mobile, for unnecessary dependencies required.
+  void SaveModel(const std::string& dir);
 
  private:
   Optimizer optimizer_;
@@ -96,6 +67,7 @@ class ExecutorLite {
   std::unique_ptr<RuntimeProgram> program_;
 };
 
+#ifdef LITE_WITH_X86
 /*
  * An executor for training.
  *
@@ -119,13 +91,13 @@ class CXXTrainer {
       : scope_(root_scope),
         preferred_place_(preferred_place),
         valid_places_(valid_places),
-        main_program_executor_(ExecutorLite(scope_)) {}
+        main_program_executor_(Predictor(scope_)) {}
 
   // Build the RuntimeProgram cache for the main program. The cache will run
   // multiple times for the epoches.
   // NOTE Just support to execute the 0-th block currently.
-  ExecutorLite& BuildMainProgramExecutor(
-      const framework::proto::ProgramDesc& desc, int block_id = 0) {
+  Predictor& BuildMainProgramExecutor(const framework::proto::ProgramDesc& desc,
+                                      int block_id = 0) {
     main_program_executor_.Build(desc, preferred_place_, valid_places_);
     return main_program_executor_;
   }
@@ -133,7 +105,7 @@ class CXXTrainer {
   // Run the startup program. It just executes once, no cache needed.
   void RunStartupProgram(const framework::proto::ProgramDesc& desc,
                          int block_id = 0) {
-    ExecutorLite exe(scope_);
+    Predictor exe(scope_);
     exe.Build(desc, preferred_place_, valid_places_);
     exe.Run();
   }
@@ -145,8 +117,9 @@ class CXXTrainer {
   std::vector<Place> valid_places_;
 
   // The training program.
-  ExecutorLite main_program_executor_;
+  Predictor main_program_executor_;
 };
+#endif
 
 }  // namespace lite
 }  // namespace paddle

paddle/fluid/lite/api/cxx_api_bin.cc

@@ -34,7 +34,7 @@ void Run(const char* model_dir, int repeat, int thread_num) {
   DeviceInfo::Init();
   DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, thread_num);
 #endif
-  lite::ExecutorLite predictor;
+  lite::Predictor predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kARM), PRECISION(kFloat)}});
 

paddle/fluid/lite/api/cxx_api_test.cc

@@ -42,7 +42,7 @@ TEST(CXXApi, test) {
 }
 
 TEST(CXXApi, save_model) {
-  lite::ExecutorLite predictor;
+  lite::Predictor predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kX86), PRECISION(kFloat)}});
   predictor.Build(FLAGS_model_dir, Place{TARGET(kCUDA), PRECISION(kFloat)},

paddle/fluid/lite/api/inceptionv4_test.cc

@@ -16,21 +16,20 @@
 #include <gtest/gtest.h>
 #include <vector>
 #include "paddle/fluid/lite/api/cxx_api.h"
+#include "paddle/fluid/lite/api/test_helper.h"
 #include "paddle/fluid/lite/core/mir/use_passes.h"
 #include "paddle/fluid/lite/core/op_registry.h"
 #include "paddle/fluid/lite/kernels/use_kernels.h"
 #include "paddle/fluid/lite/operators/use_ops.h"
 
-// for eval
-DEFINE_string(model_dir, "", "");
-
 namespace paddle {
 namespace lite {
 
 #ifdef LITE_WITH_ARM
 TEST(InceptionV4, test) {
   DeviceInfo::Init();
-  lite::ExecutorLite predictor;
+  DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, FLAGS_threads);
+  lite::Predictor predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kARM), PRECISION(kFloat)}});
 
@@ -44,7 +43,20 @@ TEST(InceptionV4, test) {
     data[i] = 1;
   }
 
-  predictor.Run();
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    predictor.Run();
+  }
+
+  auto start = GetCurrentUS();
+  for (int i = 0; i < FLAGS_repeats; ++i) {
+    predictor.Run();
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", repeats: " << FLAGS_repeats
+            << ", spend " << (GetCurrentUS() - start) / FLAGS_repeats / 1000.0
+            << " ms in average.";
 
   auto* out = predictor.GetOutput(0);
   std::vector<float> results({0.00078033, 0.00083865, 0.00060029, 0.00057083,

paddle/fluid/lite/api/light_api.cc

@@ -13,3 +13,67 @@
 // limitations under the License.
 
 #include "paddle/fluid/lite/api/light_api.h"
+
+namespace paddle {
+namespace lite {
+
+void LightPredictor::Build(const std::string& model_dir) {
+  framework::proto::ProgramDesc desc;
+  LoadModel(model_dir, scope_.get(), &desc);
+  BuildRuntimeProgram(desc);
+}
+
+Tensor* LightPredictor::GetInput(size_t offset) {
+  auto* _feed_list = program_->exec_scope()->FindVar("feed");
+  CHECK(_feed_list) << "no feed variable in exec_scope";
+  auto* feed_list = _feed_list->GetMutable<std::vector<Tensor>>();
+  if (offset >= feed_list->size()) {
+    feed_list->resize(offset + 1);
+  }
+  return &feed_list->at(offset);
+}
+
+const Tensor* LightPredictor::GetOutput(size_t offset) {
+  auto* _fetch_list = program_->exec_scope()->FindVar("fetch");
+  CHECK(_fetch_list) << "no fatch variable in exec_scope";
+  auto& fetch_list = *_fetch_list->GetMutable<std::vector<lite::Tensor>>();
+  CHECK_LT(offset, fetch_list.size()) << "offset " << offset << " overflow";
+  return &fetch_list.at(offset);
+}
+
+void LightPredictor::BuildRuntimeProgram(
+    const framework::proto::ProgramDesc& prog) {
+  std::vector<Instruction> insts;
+  // 1. Create op first
+  Program program(prog, scope_, {});
+
+  // 2. Create Instructs
+
+  // Create the kernels of the target places, and filter out the specific
+  // kernel with the target alias.
+  for (auto& op : program.ops()) {
+    auto kernel_type = op->op_info()->GetAttr<std::string>(kKernelTypeAttr);
+    std::string op_type, alias;
+    Place place;
+    KernelBase::ParseKernelType(kernel_type, &op_type, &alias, &place);
+    auto kernels = op->CreateKernels({place});
+    // filter out a kernel
+    auto it = std::find_if(
+        kernels.begin(), kernels.end(),
+        [&](std::unique_ptr<KernelBase>& it) { return it->alias() == alias; });
+    CHECK(it != kernels.end());
+    (*it)->SetContext(ContextScheduler::Global().NewContext((*it)->target()));
+    insts.emplace_back(op, std::move(*it));
+  }
+  program_.reset(new RuntimeProgram(std::move(insts)));
+  CHECK(program.exec_scope());
+  program_->set_exec_scope(program.exec_scope());
+}
+
+LightPredictor::LightPredictor(const std::string& model_dir) {
+  scope_ = std::make_shared<Scope>();
+  Build(model_dir);
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/api/light_api.h

@@ -32,36 +32,21 @@
 namespace paddle {
 namespace lite {
 
+/*
+ * The light weight predictor, mainly for mobile. It loads an optimized model,
+ * and will not depend on the MIR or perform latter optimization.
+ */
 class LightPredictor {
  public:
-  LightPredictor() { scope_ = std::make_shared<Scope>(); }
-
-  void Build(const std::string& model_dir) {
-    framework::proto::ProgramDesc desc;
-    LoadModel(model_dir, scope_.get(), &desc);
-    BuildRuntimeProgram(desc);
-  }
+  explicit LightPredictor(const std::string& model_dir);
 
   void Run() { program_->Run(); }
 
-  // Get offset-th col of feed.
-  Tensor* GetInput(size_t offset) {
-    auto* _feed_list = program_->exec_scope()->FindVar("feed");
-    CHECK(_feed_list) << "no feed variable in exec_scope";
-    auto* feed_list = _feed_list->GetMutable<std::vector<Tensor>>();
-    if (offset >= feed_list->size()) {
-      feed_list->resize(offset + 1);
-    }
-    return &feed_list->at(offset);
-  }
+  // Get offset-th col of feed inputs.
+  Tensor* GetInput(size_t offset);
 
-  const Tensor* GetOutput(size_t offset) {
-    auto* _fetch_list = program_->exec_scope()->FindVar("fetch");
-    CHECK(_fetch_list) << "no fatch variable in exec_scope";
-    auto& fetch_list = *_fetch_list->GetMutable<std::vector<lite::Tensor>>();
-    CHECK_LT(offset, fetch_list.size()) << "offset " << offset << " overflow";
-    return &fetch_list.at(offset);
-  }
+  // Get offset-th col of fetch outputs.
+  const Tensor* GetOutput(size_t offset);
 
   const lite::Tensor* GetTensor(const std::string& name) const {
     auto* var = program_->exec_scope()->FindVar(name);
@@ -69,34 +54,8 @@ class LightPredictor {
   }
 
  private:
-  void BuildRuntimeProgram(const framework::proto::ProgramDesc& prog) {
-    std::vector<Instruction> insts;
-    // 1. Create op first
-    Program program(prog, scope_, {});
-
-    // 2. Create Instructs
-
-    // Create the kernels of the target places, and filter out the specific
-    // kernel with the target alias.
-    for (auto& op : program.ops()) {
-      auto kernel_type = op->op_info()->GetAttr<std::string>(kKernelTypeAttr);
-      std::string op_type, alias;
-      Place place;
-      KernelBase::ParseKernelType(kernel_type, &op_type, &alias, &place);
-      auto kernels = op->CreateKernels({place});
-      // filter out a kernel
-      auto it = std::find_if(kernels.begin(), kernels.end(),
-                             [&](std::unique_ptr<KernelBase>& it) {
-                               return it->alias() == alias;
-                             });
-      CHECK(it != kernels.end());
-      (*it)->SetContext(ContextScheduler::Global().NewContext((*it)->target()));
-      insts.emplace_back(op, std::move(*it));
-    }
-    program_.reset(new RuntimeProgram(std::move(insts)));
-    CHECK(program.exec_scope());
-    program_->set_exec_scope(program.exec_scope());
-  }
+  void Build(const std::string& model_dir);
+  void BuildRuntimeProgram(const framework::proto::ProgramDesc& prog);
 
  private:
   std::shared_ptr<Scope> scope_;

paddle/fluid/lite/api/light_api_test.cc

@@ -25,8 +25,10 @@ namespace paddle {
 namespace lite {
 
 TEST(LightAPI, load) {
-  LightPredictor predictor;
-  predictor.Build(FLAGS_optimized_model);
+  if (FLAGS_optimized_model.empty()) {
+    FLAGS_optimized_model = "lite_naive_model";
+  }
+  LightPredictor predictor(FLAGS_optimized_model);
 
   auto* input_tensor = predictor.GetInput(0);
   input_tensor->Resize(DDim(std::vector<int64_t>({100, 100})));

paddle/fluid/lite/api/lite_api_test_helper.cc

@@ -22,7 +22,7 @@ namespace paddle {
 namespace lite {
 
 const lite::Tensor* RunHvyModel() {
-  lite::ExecutorLite predictor;
+  lite::Predictor predictor;
 #ifndef LITE_WITH_CUDA
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kX86), PRECISION(kFloat)}});

paddle/fluid/lite/api/mobilenetv1_test.cc

@@ -16,21 +16,20 @@
 #include <gtest/gtest.h>
 #include <vector>
 #include "paddle/fluid/lite/api/cxx_api.h"
+#include "paddle/fluid/lite/api/test_helper.h"
 #include "paddle/fluid/lite/core/mir/use_passes.h"
 #include "paddle/fluid/lite/core/op_registry.h"
 #include "paddle/fluid/lite/kernels/use_kernels.h"
 #include "paddle/fluid/lite/operators/use_ops.h"
 
-// for eval
-DEFINE_string(model_dir, "", "");
-
 namespace paddle {
 namespace lite {
 
 #ifdef LITE_WITH_ARM
 TEST(MobileNetV1, test) {
   DeviceInfo::Init();
-  lite::ExecutorLite predictor;
+  DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, FLAGS_threads);
+  lite::Predictor predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kARM), PRECISION(kFloat)}});
 
@@ -44,7 +43,20 @@ TEST(MobileNetV1, test) {
     data[i] = 1;
   }
 
-  predictor.Run();
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    predictor.Run();
+  }
+
+  auto start = GetCurrentUS();
+  for (int i = 0; i < FLAGS_repeats; ++i) {
+    predictor.Run();
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", repeats: " << FLAGS_repeats
+            << ", spend " << (GetCurrentUS() - start) / FLAGS_repeats / 1000.0
+            << " ms in average.";
 
   auto* out = predictor.GetOutput(0);
   std::vector<float> results({1.91308980e-04, 5.92055148e-04, 1.12303176e-04,

paddle/fluid/lite/api/mobilenetv2_test.cc

@@ -16,21 +16,20 @@
 #include <gtest/gtest.h>
 #include <vector>
 #include "paddle/fluid/lite/api/cxx_api.h"
+#include "paddle/fluid/lite/api/test_helper.h"
 #include "paddle/fluid/lite/core/mir/use_passes.h"
 #include "paddle/fluid/lite/core/op_registry.h"
 #include "paddle/fluid/lite/kernels/use_kernels.h"
 #include "paddle/fluid/lite/operators/use_ops.h"
 
-// for eval
-DEFINE_string(model_dir, "", "");
-
 namespace paddle {
 namespace lite {
 
 #ifdef LITE_WITH_ARM
 TEST(MobileNetV2, test) {
   DeviceInfo::Init();
-  lite::ExecutorLite predictor;
+  DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, FLAGS_threads);
+  lite::Predictor predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kARM), PRECISION(kFloat)}});
 
@@ -44,7 +43,20 @@ TEST(MobileNetV2, test) {
     data[i] = 1;
   }
 
-  predictor.Run();
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    predictor.Run();
+  }
+
+  auto start = GetCurrentUS();
+  for (int i = 0; i < FLAGS_repeats; ++i) {
+    predictor.Run();
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", repeats: " << FLAGS_repeats
+            << ", spend " << (GetCurrentUS() - start) / FLAGS_repeats / 1000.0
+            << " ms in average.";
 
   auto* out = predictor.GetOutput(0);
   std::vector<float> results({0.00097802, 0.00099822, 0.00103093, 0.00100121,

paddle/fluid/lite/api/resnet50_test.cc

@@ -16,21 +16,20 @@
 #include <gtest/gtest.h>
 #include <vector>
 #include "paddle/fluid/lite/api/cxx_api.h"
+#include "paddle/fluid/lite/api/test_helper.h"
 #include "paddle/fluid/lite/core/mir/use_passes.h"
 #include "paddle/fluid/lite/core/op_registry.h"
 #include "paddle/fluid/lite/kernels/use_kernels.h"
 #include "paddle/fluid/lite/operators/use_ops.h"
 
-// for eval
-DEFINE_string(model_dir, "", "");
-
 namespace paddle {
 namespace lite {
 
 #ifdef LITE_WITH_ARM
 TEST(ResNet50, test) {
   DeviceInfo::Init();
-  lite::ExecutorLite predictor;
+  DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, FLAGS_threads);
+  lite::Predictor predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kARM), PRECISION(kFloat)}});
 
@@ -44,7 +43,20 @@ TEST(ResNet50, test) {
     data[i] = 1;
   }
 
-  predictor.Run();
+  for (int i = 0; i < FLAGS_warmup; ++i) {
+    predictor.Run();
+  }
+
+  auto start = GetCurrentUS();
+  for (int i = 0; i < FLAGS_repeats; ++i) {
+    predictor.Run();
+  }
+
+  LOG(INFO) << "================== Speed Report ===================";
+  LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
+            << ", warmup: " << FLAGS_warmup << ", repeats: " << FLAGS_repeats
+            << ", spend " << (GetCurrentUS() - start) / FLAGS_repeats / 1000.0
+            << " ms in average.";
 
   auto* out = predictor.GetOutput(0);
   std::vector<float> results({2.41399175e-04, 4.13724629e-04, 2.64324830e-04,

paddle/fluid/lite/kernels/arm/use_kernels.h → paddle/fluid/lite/api/test_helper.h

@@ -13,13 +13,24 @@
 // limitations under the License.
 
 #pragma once
-#include "paddle/fluid/lite/core/op_registry.h"
 
-USE_LITE_KERNEL(fc, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(mul, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(scale, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(softmax, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(concat, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(pool, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(feed, kARM, kAny, kAny, def);
-USE_LITE_KERNEL(fetch, kARM, kAny, kAny, def);
+#include <gflags/gflags.h>
+#include <time.h>
+
+// for eval
+DEFINE_string(model_dir, "", "model dir");
+DEFINE_int32(warmup, 0, "warmup times");
+DEFINE_int32(repeats, 1, "repeats times");
+DEFINE_int32(threads, 1, "threads num");
+
+namespace paddle {
+namespace lite {
+
+inline double GetCurrentUS() {
+  struct timeval time;
+  gettimeofday(&time, NULL);
+  return 1e+6 * time.tv_sec + time.tv_usec;
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/arm/math/CMakeLists.txt

@@ -35,6 +35,8 @@ cc_library(math_arm SRCS
     split.cc
     activation.cc
     dropout.cc
+    gemm_prepacked_int8.cc
+    gemv_arm_int8.cc
     DEPS ${lite_kernel_deps} eigen3 framework_proto_lite)
   # TODO(TJ): fix me do not deps proto
  

paddle/fluid/lite/core/CMakeLists.txt

@@ -25,7 +25,7 @@ cc_library(op_registry_lite SRCS op_registry.cc DEPS framework_proto_lite)
 cc_library(scope_lite SRCS scope.cc DEPS ${tensor_lite})
 cc_library(cpu_info_lite SRCS cpu_info.cc)
 lite_cc_library(context_lite SRCS context.cc DEPS ${tensor_lite} any_lite cpu_info_lite eigen3)
-cc_library(op_lite SRCS op_lite.cc DEPS scope_lite op_registry_lite target_wrapper_lite
+cc_library(op_lite SRCS op_lite.cc DEPS scope_lite op_registry_lite target_wrapper_lite kernel_lite
   cpp_op_desc_lite ${tensor_lite})
 cc_library(types_lite SRCS types.cc)
 cc_library(type_system SRCS type_system.cc DEPS ${tensor_lite} target_wrapper_lite)

paddle/fluid/lite/core/kernel.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/lite/core/kernel.h"
+#include <cstdlib>
 
 namespace paddle {
 namespace lite {
@@ -49,6 +50,36 @@ std::string KernelBase::GenParamTypeKey() const {
   return ss.str();
 }
 
+void KernelBase::ParseKernelType(const std::string &kernel_type,
+                                 std::string *op_type, std::string *alias,
+                                 Place *place) {
+  std::stringstream ss(kernel_type);
+  std::getline(ss, *op_type, '/');
+  std::getline(ss, *alias, '/');
+  std::string target, precision, layout;
+  std::getline(ss, target, '/');
+  std::getline(ss, precision, '/');
+  std::getline(ss, layout, '/');
+
+  place->target = static_cast<TargetType>(std::atoi(target.c_str()));
+  place->precision = static_cast<PrecisionType>(std::atoi(precision.c_str()));
+  place->layout = static_cast<DataLayoutType>(std::atoi(layout.c_str()));
+}
+
+std::string KernelBase::SerializeKernelType(const std::string &op_type,
+                                            const std::string &alias,
+                                            const Place &place) {
+  std::stringstream ss;
+  ss << op_type << "/";
+  ss << alias << "/";
+  // We serialize the place value not the string representation here for
+  // easier deserialization.
+  ss << static_cast<int>(place.target) << "/";
+  ss << static_cast<int>(place.precision) << "/";
+  ss << static_cast<int>(place.layout);
+  return ss.str();
+}
+
 bool ParamTypeRegistry::KeyCmp::operator()(
     const ParamTypeRegistry::key_t &a,
     const ParamTypeRegistry::key_t &b) const {

paddle/fluid/lite/core/kernel.h

@@ -118,33 +118,11 @@ class KernelBase {
 
   static std::string SerializeKernelType(const std::string& op_type,
                                          const std::string& alias,
-                                         const Place& place) {
-    std::stringstream ss;
-    ss << op_type << "/";
-    ss << alias << "/";
-    // We serialize the place value not the string representation here for
-    // easier deserialization.
-    ss << static_cast<int>(place.target) << "/";
-    ss << static_cast<int>(place.precision) << "/";
-    ss << static_cast<int>(place.layout);
-    return ss.str();
-  }
+                                         const Place& place);
 
   static void ParseKernelType(const std::string& kernel_type,
                               std::string* op_type, std::string* alias,
-                              Place* place) {
-    std::stringstream ss(kernel_type);
-    std::getline(ss, *op_type, '/');
-    std::getline(ss, *alias, '/');
-    std::string target, precision, layout;
-    std::getline(ss, target, '/');
-    std::getline(ss, precision, '/');
-    std::getline(ss, layout, '/');
-
-    place->target = static_cast<TargetType>(std::stoi(target));
-    place->precision = static_cast<PrecisionType>(std::stoi(precision));
-    place->layout = static_cast<DataLayoutType>(std::stoi(layout));
-  }
+                              Place* place);
 
   virtual ~KernelBase() = default;
   void Torch() {}

paddle/fluid/lite/core/mir/fusion/fc_fuse_pass_test.cc

@@ -28,7 +28,7 @@ namespace lite {
 namespace mir {
 
 TEST(fc_fuse_pass, fuse_test) {
-  lite::ExecutorLite predictor;
+  lite::Predictor predictor;
 #ifndef LITE_WITH_CUDA
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kX86), PRECISION(kFloat)}});
@@ -69,7 +69,7 @@ TEST(fc_fuse_pass, fuse_test) {
 
 #ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 TEST(fc_fuse_pass, save_model_test) {
-  lite::ExecutorLite predictor;
+  lite::Predictor predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kX86), PRECISION(kFloat)}});
   predictor.Build(FLAGS_model_dir, Place{TARGET(kX86), PRECISION(kFloat)},

paddle/fluid/lite/core/mir/fusion/quant_dequant_op_fuser.h

@@ -25,7 +25,7 @@ namespace fusion {
 
 /* The model trained by fluid quantization is a simulation of real int8.
  * The quantized Ops(conv2d, mul, depthwise conv2d etc) have fake_quantop
- * in front  and fake_dequantop behind.
+ * in front and fake_dequantop behind.
  *
  * When in int8 mode, the pattern like "fake_quant + quantized_op +
  * fake_dequant"

paddle/fluid/lite/core/mir/pattern_matcher_high_api.cc

@@ -41,7 +41,7 @@ void FuseBase::DeleteInterNodes(SSAGraph *graph) {
     }
   }
 
-  LOG(INFO) << "keys: " << key2nodes_.size();
+  VLOG(4) << "keys: " << key2nodes_.size();
   std::unordered_set<const Node *> nodes2rm;
   for (auto &matched : key2nodes_) {
     for (const auto &key : keys) {

paddle/fluid/lite/core/op_registry.h

@@ -80,6 +80,8 @@ class KernelRegistry final {
               KernelRegistryForTarget<TARGET(kARM), PRECISION(kAny),
                                       DATALAYOUT(kAny)> *,  //
               KernelRegistryForTarget<TARGET(kARM), PRECISION(kFloat),
+                                      DATALAYOUT(kNCHW)> *,  //
+              KernelRegistryForTarget<TARGET(kARM), PRECISION(kInt8),
                                       DATALAYOUT(kNCHW)> *  //
               >;
 

paddle/fluid/lite/core/optimizer.h

@@ -58,7 +58,6 @@ class Optimizer {
 #ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
           "lite_elementwise_add_activation_fuse_pass",  //
 #endif
-          "lite_fc_fuse_pass",              //
           "static_kernel_pick_pass",        //
           "variable_place_inference_pass",  //
           "argument_type_display_pass",     //

paddle/fluid/lite/core/target_wrapper.h

@@ -38,6 +38,7 @@ enum class PrecisionType : int {
   kUnk = 0,
   kFloat,
   kInt8,
+  kInt32,
   kAny,  // any precision
   NUM,   // number of fields.
 };
@@ -48,6 +49,19 @@ enum class DataLayoutType : int {
   NUM,   // number of fields.
 };
 
+static size_t PrecisionTypeLength(PrecisionType type) {
+  switch (type) {
+    case PrecisionType::kFloat:
+      return 4;
+    case PrecisionType::kInt8:
+      return 1;
+    case PrecisionType::kInt32:
+      return 4;
+    default:
+      return 4;
+  }
+}
+
 // Some helper macro to get a specific TargetType.
 #define TARGET(item__) paddle::lite::TargetType::item__
 // Some helper macro to get a specific PrecisionType.
@@ -87,7 +101,7 @@ static const std::string& TargetRepr(TargetType target) {
 
 static const std::string& PrecisionRepr(PrecisionType precision) {
   static const std::string precision2string[] = {"kUnk", "kFloat", "kInt8",
-                                                 "kAny"};
+                                                 "kInt32", "kAny"};
   auto x = static_cast<int>(precision);
   CHECK_LT(x, static_cast<int>(PRECISION(NUM)));
   return precision2string[x];

paddle/fluid/lite/kernels/arm/CMakeLists.txt

@@ -51,5 +51,3 @@ set(arm_kernels
     )
 
 set(arm_kernels "${arm_kernels}" CACHE INTERNAL "arm kernels")
- 
- 

paddle/fluid/lite/kernels/arm/conv_compute.cc

@@ -92,6 +92,9 @@ void ConvCompute::Run() {
   // }
 }
 
+void ConvComputeInt8::PrepareForRun() {}
+void ConvComputeInt8::Run() {}
+
 }  // namespace arm
 }  // namespace kernels
 }  // namespace lite
@@ -112,3 +115,23 @@ REGISTER_LITE_KERNEL(depthwise_conv2d, kARM, kFloat, kNCHW,
     .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
+
+REGISTER_LITE_KERNEL(conv2d, kARM, kInt8, kNCHW,
+                     paddle::lite::kernels::arm::ConvComputeInt8, def)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
+    .BindInput("Filter",
+               {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindOutput("Output",
+                {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .Finalize();
+
+REGISTER_LITE_KERNEL(depthwise_conv2d, kARM, kInt8, kNCHW,
+                     paddle::lite::kernels::arm::ConvComputeInt8, def)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
+    .BindInput("Filter",
+               {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindOutput("Output",
+                {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .Finalize();

paddle/fluid/lite/kernels/arm/conv_compute.h

@@ -41,6 +41,25 @@ class ConvCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
       nullptr};
 };
 
+class ConvComputeInt8 : public KernelLite<TARGET(kARM), PRECISION(kInt8)> {
+ public:
+  using param_t = operators::ConvParam;
+
+  void PrepareForRun() override;
+
+  void Run() override;
+
+  ~ConvComputeInt8() {
+    if (impl_ != nullptr) {
+      delete impl_;
+    }
+  }
+
+ private:
+  lite::arm::math::ImplBase<TARGET(kARM), PRECISION(kInt8), param_t>* impl_{
+      nullptr};
+};
+
 }  // namespace arm
 }  // namespace kernels
 }  // namespace lite

paddle/fluid/lite/kernels/use_kernels.h

@@ -12,14 +12,33 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#pragma once
 /*
  * ATTENTION this header file can only include in .cc file.
  */
 
+#pragma once
+#include "paddle/fluid/lite/core/op_registry.h"
+
 USE_LITE_KERNEL(feed, kHost, kAny, kAny, def);
 USE_LITE_KERNEL(fetch, kHost, kAny, kAny, def);
 
+#ifdef LITE_WITH_ARM
+USE_LITE_KERNEL(fc, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(mul, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(scale, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(softmax, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(conv2d, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(depthwise_conv2d, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(elementwise_add, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(split, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(dropout, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(concat, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(pool2d, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(relu, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(transpose, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(transpose2, kARM, kFloat, kNCHW, def);
+#endif
+
 #ifdef LITE_WITH_X86
 USE_LITE_KERNEL(relu, kX86, kFloat, kNCHW, def);
 USE_LITE_KERNEL(mul, kX86, kFloat, kNCHW, def);
@@ -36,21 +55,6 @@ USE_LITE_KERNEL(depthwise_conv2d, kX86, kFloat, kNCHW, def);
 USE_LITE_KERNEL(pool2d, kX86, kFloat, kNCHW, def);
 #endif
 
-#ifdef LITE_WITH_ARM
-USE_LITE_KERNEL(fc, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(mul, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(scale, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(conv2d, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(batch_norm, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(relu, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(depthwise_conv2d, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(pool2d, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(elementwise_add, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(softmax, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(concat, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(dropout, kARM, kFloat, kNCHW, def);
-#endif
-
 #ifdef LITE_WITH_CUDA
 USE_LITE_KERNEL(mul, kCUDA, kFloat, kNCHW, def);
 USE_LITE_KERNEL(io_copy, kCUDA, kAny, kAny, host_to_device);

paddle/fluid/lite/kernels/x86/CMakeLists.txt

@@ -44,10 +44,9 @@ set(x86_kernels
     softmax_compute_x86
     dropout_compute_x86
     concat_compute_x86
-    conv_compute_x86 
-    pool_compute_x86  
-    batch_norm_compute_x86 
+    conv_compute_x86
+    pool_compute_x86
+    batch_norm_compute_x86
     )
 
 set(x86_kernels "${x86_kernels}" CACHE INTERNAL "x86 kernels")
- 

paddle/fluid/lite/opencl/CMakeLists.txt

+if (NOT LITE_WITH_OPENCL)
+    return()
+endif()
+
+if (WITH_LITE AND LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
+    cc_library(cl_wrapper SRCS cl_wrapper.cc)
+    cc_library(cl_tool SRCS cl_tool.cc)
+    target_compile_options(cl_tool BEFORE PUBLIC -Wno-ignored-qualifiers)
+    cc_library(cl_half SRCS cl_half.cc)
+    target_compile_options(cl_half BEFORE PUBLIC -fno-strict-aliasing)
+    cc_library(cl_engine SRCS cl_engine.cc DEPS cl_tool)
+    cc_library(cl_context SRCS cl_context.cc DEPS cl_engine)
+    cc_library(cl_helper SRCS cl_helper.cc DEPS cl_context)
+    cc_library(cl_image_converter SRCS cl_image_converter.cc DEPS cl_half lite_tensor)
+    cc_library(cl_image SRCS cl_image.cc DEPS cl_half lite_tensor cl_image_converter cl_engine)
+    cc_library(cl_caller SRCS cl_caller.cc  DEPS cl_helper cl_image)
+    lite_cc_test(test_cl_runtime SRCS cl_test.cc DEPS cl_helper cl_image cl_caller cl_wrapper)
+    add_dependencies(cl_tool opencl_clhpp)
+endif()

paddle/fluid/lite/opencl/cl2_header.h

+// Copyright (c) 2019 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
+
+#define CL_TARGET_OPENCL_VERSION 200
+#define CL_HPP_TARGET_OPENCL_VERSION 200
+#define CL_HPP_MINIMUM_OPENCL_VERSION 110
+
+#include <CL/cl2.hpp>

paddle/fluid/lite/opencl/cl_caller.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/lite/opencl/cl_caller.h"
+#include <string>
+#include "paddle/fluid/lite/core/compatible_tensor.h"
+#include "paddle/fluid/lite/opencl/cl_context.h"
+#include "paddle/fluid/lite/opencl/cl_engine.h"
+#include "paddle/fluid/lite/opencl/cl_helper.h"
+#include "paddle/fluid/lite/opencl/cl_image.h"
+#include "paddle/fluid/lite/opencl/cl_tool.h"
+
+namespace paddle {
+namespace lite {
+static void CopyImageData(const CLImage& cl_image, float* out) {
+  int width = cl_image.image_dims()[0];
+  int height = cl_image.image_dims()[1];
+
+  half_t* image_data = new half_t[height * width * 4];
+  cl::Image* image = cl_image.cl_image();
+  const std::array<size_t, 3> origin{0, 0, 0};
+  const std::array<size_t, 3> region{static_cast<size_t>(width),
+                                     static_cast<size_t>(height), 1};
+  cl_int err = CLEngine::Global()->command_queue().enqueueReadImage(
+      *image, CL_TRUE, origin, region, 0, 0, image_data, nullptr, nullptr);
+  CL_CHECK_ERRORS(err);
+
+  auto* converter = cl_image.image_converter();
+  converter->ImageToNCHW(image_data, out, cl_image.image_dims(),
+                         cl_image.tensor_dims());
+
+  delete[] image_data;
+}
+
+bool InitOpenCLEngine(std::string cl_path) {
+  auto* engine = CLEngine::Global();
+  engine->set_cl_path(cl_path);
+  return engine->IsInitSuccess();
+}
+
+void elementwise_add(CLContext* context, float* in, const DDim& in_dim,
+                     float* bias, const DDim& bias_dim, float* out,
+                     const DDim& out_dim) {
+  CLHelper helper(context);
+  helper.AddKernel("elementwise_add", "elementwise_add_kernel.cl");
+  auto kernel = helper.KernelAt(0);
+  CLImage in_image;
+  in_image.set_tensor_data(in, in_dim);
+  in_image.InitNormalCLImage(helper.OpenCLContext());
+  VLOG(3) << " --- Inpu image: " << in_image << " --- ";
+  CLImage bias_image;
+  bias_image.set_tensor_data(bias, bias_dim);
+  bias_image.InitNormalCLImage(helper.OpenCLContext());
+  VLOG(3) << " --- Bias image: " << bias_image << " --- ";
+  CLImage out_image;
+  out_image.InitEmptyImage(helper.OpenCLContext(), out_dim);
+  cl_int status;
+  status = kernel.setArg(0, *in_image.cl_image());
+  CL_CHECK_ERRORS(status);
+  status = kernel.setArg(1, *bias_image.cl_image());
+  CL_CHECK_ERRORS(status);
+  status = kernel.setArg(2, *out_image.cl_image());
+  CL_CHECK_ERRORS(status);
+  size_t width = in_image.ImageWidth();
+  size_t height = in_image.ImageHeight();
+  auto global_work_size = cl::NDRange{width, height};
+  status = helper.OpenCLCommandQueue().enqueueNDRangeKernel(
+      kernel, cl::NullRange, global_work_size, cl::NullRange, nullptr, nullptr);
+  CL_CHECK_ERRORS(status);
+
+  VLOG(3) << " --- Out image: " << out_image << " --- ";
+
+  CopyImageData(out_image, out);
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_caller.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <string>
+#include "paddle/fluid/lite/core/compatible_tensor.h"
+#include "paddle/fluid/lite/opencl/cl_context.h"
+
+namespace paddle {
+namespace lite {
+
+bool InitOpenCLEngine(std::string cl_path);
+void elementwise_add(CLContext* context, float* in, const DDim& in_dim,
+                     float* bias, const DDim& bias_dim, float* out,
+                     const DDim& out_dim);
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_context.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 <glog/logging.h>
+#include <memory>
+#include <string>
+#include <utility>
+
+#include "paddle/fluid/lite/opencl/cl_context.h"
+#include "paddle/fluid/lite/opencl/cl_engine.h"
+#include "paddle/fluid/lite/opencl/cl_tool.h"
+
+namespace paddle {
+namespace lite {
+
+cl::CommandQueue &CLContext::GetCommandQueue() {
+  return CLEngine::Global()->command_queue();
+}
+
+cl::Context &CLContext::GetContext() { return CLEngine::Global()->context(); }
+
+cl::Program &CLContext::GetProgram(const std::string &file_name,
+                                   const std::string &options) {
+  std::string program_key = file_name;
+  if (!options.empty()) {
+    program_key += options;
+  }
+  auto it = programs_.find(program_key);
+  if (it != programs_.end()) {
+    VLOG(3) << " --- program -> " << program_key << " has been built --- ";
+    return *(it->second);
+  }
+
+  auto program = CLEngine::Global()->CreateProgram(
+      GetContext(), CLEngine::Global()->cl_path() + "/cl_kernel/" + file_name);
+
+  VLOG(3) << " --- begin build program -> " << program_key << " --- ";
+  CLEngine::Global()->BuildProgram(program.get(), options);
+  VLOG(3) << " --- end build program -> " << program_key << " --- ";
+
+  programs_[program_key] = std::move(program);
+
+  return *(programs_[program_key]);
+}
+
+std::unique_ptr<cl::Kernel> CLContext::GetKernel(const std::string &kernel_name,
+                                                 const std::string &file_name,
+                                                 const std::string &options) {
+  cl_int status{CL_SUCCESS};
+  VLOG(3) << " --- to get program " << file_name << " --- ";
+  auto program = GetProgram(file_name, options);
+  VLOG(3) << " --- end get program --- ";
+  VLOG(3) << " --- to create kernel: " << kernel_name << " --- ";
+  std::unique_ptr<cl::Kernel> kernel(
+      new cl::Kernel(program, kernel_name.c_str(), &status));
+  CL_CHECK_ERRORS(status);
+  VLOG(3) << " --- end create kernel --- ";
+  return std::move(kernel);
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_context.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>
+#include <string>
+#include <unordered_map>
+#include "paddle/fluid/lite/opencl/cl2_header.h"
+
+namespace paddle {
+namespace lite {
+
+class CLContext {
+ public:
+  cl::CommandQueue &GetCommandQueue();
+
+  cl::Context &GetContext();
+
+  cl::Program &GetProgram(const std::string &file_name,
+                          const std::string &options);
+
+  std::unique_ptr<cl::Kernel> GetKernel(const std::string &kernel_name,
+                                        const std::string &file_name,
+                                        const std::string &options);
+
+ private:
+  std::unordered_map<std::string, std::unique_ptr<cl::Program>> programs_;
+};
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_engine.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/lite/opencl/cl_engine.h"
+#include <glog/logging.h>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace paddle {
+namespace lite {
+
+CLEngine* CLEngine::Global() {
+  static CLEngine cl_engine_;
+  cl_engine_.Init();
+  return &cl_engine_;
+}
+
+CLEngine::~CLEngine() {
+  if (command_queue_ != nullptr) {
+    command_queue_->finish();
+  }
+  // For controlling the destruction order:
+  command_queue_.reset();
+  context_.reset();
+  device_.reset();
+  platform_.reset();
+}
+
+bool CLEngine::Init() {
+  if (initialized_) {
+    return true;
+  }
+  bool is_platform_init = InitializePlatform();
+  bool is_device_init = InitializeDevice();
+  is_init_success_ = is_platform_init && is_device_init;
+  initialized_ = true;
+  return initialized_;
+}
+
+cl::Platform& CLEngine::platform() {
+  CHECK(platform_ != nullptr) << "platform_ is not initialized!";
+  return *platform_;
+}
+
+cl::Context& CLEngine::context() {
+  if (context_ == nullptr) {
+    context_ = CreateContext();
+  }
+  return *context_;
+}
+
+cl::Device& CLEngine::device() {
+  CHECK(device_ != nullptr) << "device_ is not initialized!";
+  return *device_;
+}
+
+cl::CommandQueue& CLEngine::command_queue() {
+  if (command_queue_ == nullptr) {
+    command_queue_ = CreateCommandQueue(context());
+  }
+  return *command_queue_;
+}
+
+std::unique_ptr<cl::Program> CLEngine::CreateProgram(const cl::Context& context,
+                                                     std::string file_name) {
+  std::ifstream file{file_name, std::ios::binary | std::ios::ate};
+  CHECK(file.is_open()) << "Can't open file from " << file_name;
+  auto size = file.tellg();
+  CHECK(size > 0) << "size is too small.";
+  std::string content(size, '\0');
+  file.seekg(0);
+  file.read(&content[0], size);
+  cl::Program::Sources sources;
+  sources.push_back(content);
+  auto prog =
+      std::unique_ptr<cl::Program>(new cl::Program(context, sources, &status_));
+  LOG(INFO) << "OpenCL kernel file name: " << file_name;
+  LOG(INFO) << "Program source size: " << content.size();
+  CL_CHECK_ERRORS(status_);
+  return std::move(prog);
+}
+
+std::unique_ptr<cl::UserEvent> CLEngine::CreateEvent(
+    const cl::Context& context) {
+  auto event =
+      std::unique_ptr<cl::UserEvent>(new cl::UserEvent(context, &status_));
+  CL_CHECK_ERRORS(status_);
+  return std::move(event);
+}
+
+bool CLEngine::BuildProgram(cl::Program* program, const std::string& options) {
+  std::string build_option = options + " -cl-fast-relaxed-math -I " +
+                             CLEngine::Global()->cl_path() + "/cl_kernel";
+  status_ = program->build({*device_}, build_option.c_str());
+  CL_CHECK_ERRORS(status_);
+
+  if (status_ != CL_SUCCESS) {
+    if (program->getBuildInfo<CL_PROGRAM_BUILD_STATUS>(device()) ==
+        CL_BUILD_ERROR) {
+      std::string log = program->getBuildInfo<CL_PROGRAM_BUILD_LOG>(device());
+      LOG(INFO) << "Program build error: " << log;
+    }
+    return false;
+  }
+
+  return true;
+}
+
+bool CLEngine::InitializePlatform() {
+  std::vector<cl::Platform> all_platforms;
+  status_ = cl::Platform::get(&all_platforms);
+  CL_CHECK_ERRORS(status_);
+  if (all_platforms.empty()) {
+    LOG(ERROR) << "No OpenCL platform found!";
+    return false;
+  }
+  platform_ = std::make_shared<cl::Platform>();
+  *platform_ = all_platforms[0];
+  return true;
+}
+
+bool CLEngine::InitializeDevice() {
+  std::vector<cl::Device> all_devices;
+  status_ = platform_->getDevices(CL_DEVICE_TYPE_GPU, &all_devices);
+  CL_CHECK_ERRORS(status_);
+  if (all_devices.empty()) {
+    LOG(ERROR) << "No OpenCL GPU device found!";
+    return false;
+  }
+  device_ = std::make_shared<cl::Device>();
+  *device_ = all_devices[0];
+
+  auto device_name = device_->getInfo<CL_DEVICE_NAME>();
+  LOG(INFO) << "Using device: " << device_name;
+  auto image_support = device_->getInfo<CL_DEVICE_IMAGE_SUPPORT>();
+  if (image_support) {
+    LOG(INFO) << "The chosen device supports image processing.";
+  } else {
+    LOG(ERROR) << "The chosen device doesn't support image processing!";
+    return false;
+  }
+  auto ext_data = device_->getInfo<CL_DEVICE_EXTENSIONS>();
+  LOG(INFO) << "The extensions supported by this device: " << ext_data;
+  if (ext_data.find("cl_khr_fp16") != std::string::npos) {
+    LOG(INFO) << "The chosen device supports the half data type.";
+  } else {
+    LOG(ERROR) << "The chosen device doesn't support the half data type!";
+    return false;
+  }
+  auto max_units = device_->getInfo<CL_DEVICE_MAX_COMPUTE_UNITS>();
+  LOG(INFO) << "The chosen device has " << max_units << " compute units.";
+  auto local_mem = device_->getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
+  LOG(INFO) << "The local memory size of the chosen device is "
+            << static_cast<float>(local_mem) / 1024 << " KB.";
+  return true;
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_engine.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 <fstream>
+#include <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/lite/opencl/cl2_header.h"
+#include "paddle/fluid/lite/opencl/cl_tool.h"
+
+namespace paddle {
+namespace lite {
+
+class CLEngine {
+ public:
+  static CLEngine* Global();
+
+  bool Init();
+
+  cl::Platform& platform();
+
+  cl::Context& context();
+
+  cl::Device& device();
+
+  cl::CommandQueue& command_queue();
+
+  std::unique_ptr<cl::Program> CreateProgram(const cl::Context& context,
+                                             std::string file_name);
+
+  std::unique_ptr<cl::UserEvent> CreateEvent(const cl::Context& context);
+
+  bool BuildProgram(cl::Program* program, const std::string& options = "");
+
+  bool IsInitSuccess() { return is_init_success_; }
+
+  std::string cl_path() { return cl_path_; }
+
+  void set_cl_path(std::string cl_path) { cl_path_ = cl_path; }
+
+ private:
+  CLEngine() = default;
+
+  ~CLEngine();
+
+  bool InitializePlatform();
+
+  bool InitializeDevice();
+
+  std::shared_ptr<cl::Context> CreateContext() {
+    auto context = std::make_shared<cl::Context>(
+        std::vector<cl::Device>{device()}, nullptr, nullptr, nullptr, &status_);
+    CL_CHECK_ERRORS(status_);
+    return context;
+  }
+
+  std::shared_ptr<cl::CommandQueue> CreateCommandQueue(
+      const cl::Context& context) {
+    auto queue =
+        std::make_shared<cl::CommandQueue>(context, device(), 0, &status_);
+    CL_CHECK_ERRORS(status_);
+    return queue;
+  }
+
+  std::string cl_path_;
+
+  std::shared_ptr<cl::Platform> platform_{nullptr};
+
+  std::shared_ptr<cl::Context> context_{nullptr};
+
+  std::shared_ptr<cl::Device> device_{nullptr};
+
+  std::shared_ptr<cl::CommandQueue> command_queue_{nullptr};
+
+  cl_int status_{CL_SUCCESS};
+
+  bool initialized_{false};
+
+  bool is_init_success_{false};
+};
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_half.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 <cstdint>
+
+namespace paddle {
+namespace lite {
+
+typedef uint16_t half_t;
+
+half_t Float2Half(float f);
+
+float Half2Float(half_t h);
+
+void FloatArray2HalfArray(float *f_array, half_t *h_array, int count);
+
+void HalfArray2FloatArray(half_t *h_array, float *f_array, int count);
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_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/lite/opencl/cl_helper.h"
+#include <glog/logging.h>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace paddle {
+namespace lite {
+
+void CLHelper::set_context(CLContext *context) { context_ = context; }
+
+void CLHelper::AddKernel(const std::string &kernel_name,
+                         const std::string &file_name,
+                         const std::string &options) {
+  CHECK(context_ != nullptr) << "Please use set_context first!";
+  VLOG(3) << " --- begin to add kernel ---";
+  auto kernel = context_->GetKernel(kernel_name, file_name, options);
+  kernels.emplace_back(std::move(kernel));
+  VLOG(3) << " --- end to add kernel --- ";
+}
+
+cl::Kernel &CLHelper::KernelAt(const int index) {
+  VLOG(3) << " --- kernel count: " << kernels.size() << " --- ";
+  CHECK(static_cast<size_t>(index) < kernels.size())
+      << "The index must be less than the size of kernels.";
+  CHECK(kernels[index] != nullptr)
+      << "The target kernel pointer cannot be null.";
+  return *(kernels[index]);
+}
+
+cl::CommandQueue &CLHelper::OpenCLCommandQueue() {
+  CHECK(context_ != nullptr) << "Please use set_context first!";
+  return context_->GetCommandQueue();
+}
+
+cl::Context &CLHelper::OpenCLContext() {
+  CHECK(context_ != nullptr) << "Please use set_context first!";
+  return context_->GetContext();
+}
+
+cl::NDRange CLHelper::DefaultWorkSize(const CLImage &image) {
+  // n c h w
+  auto image_dim = image.tensor_dims();
+  if (image_dim.size() == 4) {
+    auto n = image_dim[0];
+    auto h = image_dim[2];
+    auto w = image_dim[3];
+    auto image_width = image.ImageWidth();
+    auto work_size_0 = image_width / w;
+    auto work_size_1 = w;
+    auto work_size_2 = n * h;
+    return cl::NDRange{static_cast<size_t>(work_size_0),
+                       static_cast<size_t>(work_size_1),
+                       static_cast<size_t>(work_size_2)};
+  } else if (image_dim.size() == 2) {
+    return cl::NDRange{static_cast<size_t>(1),
+                       static_cast<size_t>(image.ImageWidth()),
+                       static_cast<size_t>(image.ImageHeight())};
+  } else if (image_dim.size() == 1) {
+    return cl::NDRange{static_cast<size_t>(1),
+                       static_cast<size_t>(image.ImageWidth()),
+                       static_cast<size_t>(1)};
+  } else if (image_dim.size() == 3) {
+    auto c = image_dim[0];
+    auto h = image_dim[1];
+    auto w = image_dim[2];
+    return cl::NDRange{static_cast<size_t>((c + 3) / 4), static_cast<size_t>(w),
+                       static_cast<size_t>(h)};
+  } else {
+    LOG(FATAL) << "Not support this dimension, need to be implemented!";
+    return cl::NDRange{};
+  }
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_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 <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/lite/opencl/cl2_header.h"
+#include "paddle/fluid/lite/opencl/cl_context.h"
+#include "paddle/fluid/lite/opencl/cl_image.h"
+
+namespace paddle {
+namespace lite {
+
+class CLHelper {
+ public:
+  CLHelper() = default;
+
+  explicit CLHelper(CLContext *context) : context_(context) {}
+
+  void set_context(CLContext *context);
+
+  void AddKernel(const std::string &kernel_name, const std::string &file_name,
+                 const std::string &options = "");
+
+  cl::Kernel &KernelAt(const int index);
+
+  cl::CommandQueue &OpenCLCommandQueue();
+
+  cl::Context &OpenCLContext();
+
+  cl::NDRange DefaultWorkSize(const CLImage &image);
+
+ private:
+  CLContext *context_{nullptr};
+  std::vector<std::unique_ptr<cl::Kernel>> kernels;
+};
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_image.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/lite/opencl/cl_image.h"
+#include <glog/logging.h>
+#include <array>
+#include "paddle/fluid/lite/opencl/cl_engine.h"
+#include "paddle/fluid/lite/opencl/cl_half.h"
+#include "paddle/fluid/lite/opencl/cl_tool.h"
+
+namespace paddle {
+namespace lite {
+
+std::ostream& operator<<(std::ostream& os, const CLImage& cl_image) {
+  int width = cl_image.image_dims_[0];
+  int height = cl_image.image_dims_[1];
+
+  half_t* image_data = new half_t[height * width * 4];
+  cl::Image* image = cl_image.cl_image();
+  const std::array<size_t, 3> origin{0, 0, 0};
+  const std::array<size_t, 3> region{static_cast<size_t>(width),
+                                     static_cast<size_t>(height), 1};
+  cl_int err = CLEngine::Global()->command_queue().enqueueReadImage(
+      *image, CL_TRUE, origin, region, 0, 0, image_data, nullptr, nullptr);
+  CL_CHECK_ERRORS(err);
+
+  float* tensor_data = new float[cl_image.numel()];
+  auto* converter = cl_image.image_converter();
+  converter->ImageToNCHW(image_data, tensor_data, cl_image.image_dims_,
+                         cl_image.tensor_dims_);
+  int stride = cl_image.numel() / 20;
+  stride = stride > 0 ? stride : 1;
+
+  os << " dims: " << cl_image.tensor_dims_ << "\n";
+  for (int i = 0; i < cl_image.numel(); i += stride) {
+    os << tensor_data[i] << " ";
+  }
+
+  delete[] tensor_data;
+  delete[] image_data;
+
+  return os;
+}
+
+void CLImage::set_tensor_data(float* tensor_data, const DDim& dim) {
+#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
+  auto numel = dim.product();
+#else
+  auto numel = dim.production();
+#endif
+  tensor_data_.reset(new float[numel]);
+  memcpy(tensor_data_.get(), tensor_data, numel * sizeof(float));
+  tensor_dims_ = dim;
+}
+
+void CLImage::InitCLImage(const cl::Context& context) {
+  CHECK(tensor_data_ != nullptr) << " Please call "
+                                    "set_tensohelper->DefaultWorkSize(out_"
+                                    "image)r_data first!";
+  image_converter_.reset(new CLImageConverterFolder);
+  InitCLImage(context, image_converter_.get());
+}
+
+void CLImage::InitNormalCLImage(const cl::Context& context) {
+  CHECK(tensor_data_ != nullptr) << " Please call set_tensor_data first!";
+  image_converter_.reset(new CLImageConverterNormal);
+  InitCLImage(context, image_converter_.get());
+}
+
+void CLImage::InitNImage(const cl::Context& context) {
+  CHECK(tensor_data_ != nullptr) << " Please call set_tensor_data first!";
+  CHECK(tensor_dims_.size() == 4) << " Tensor dim is not 4.";
+  image_converter_.reset(new CLImageConverterNWBlock);
+  InitCLImage(context, image_converter_.get());
+}
+
+void CLImage::InitDWImage(const cl::Context& context) {
+  CHECK(tensor_data_ != nullptr) << " Please call set_tensor_data first!";
+  CHECK(tensor_dims_.size() == 4) << " Tensor dim is not 4.";
+  image_converter_.reset(new CLImageConverterDWBlock);
+  InitCLImage(context, image_converter_.get());
+}
+
+void CLImage::InitEmptyImage(const cl::Context& context, const DDim& dim) {
+  CHECK(tensor_data_ == nullptr)
+      << " Empty image tensor data shouldn't have value";
+
+  tensor_dims_ = dim;
+  image_converter_.reset(new CLImageConverterNormal);
+
+  VLOG(3) << " to get image dims ";
+  image_dims_ = image_converter_->InitImageDimInfoWith(tensor_dims_);
+  VLOG(3) << " end get image dims " << image_dims_;
+
+  InitCLImage(context, image_dims_[0], image_dims_[1], nullptr);
+
+  cl_event_ = CLEngine::Global()->CreateEvent(context);
+  initialized_ = true;
+  VLOG(3) << " end init cl image ";
+}
+
+void CLImage::InitEmptyWithImageDim(const cl::Context& context,
+                                    const DDim& image_dims) {
+  VLOG(3) << " to get image dims ";
+  image_dims_ = image_dims;
+  VLOG(3) << " end get image dims " << image_dims_;
+
+  InitCLImage(context, image_dims_[0], image_dims_[1], nullptr);
+
+  cl_event_ = CLEngine::Global()->CreateEvent(context);
+  initialized_ = true;
+  VLOG(3) << " end init cl image";
+}
+
+void CLImage::InitCLImage(const cl::Context& context,
+                          CLImageConverterBase* converter) {
+  CHECK(tensor_data_ != nullptr) << " Please call set_tensor_data first!";
+
+  VLOG(3) << " begin init cl image ";
+  image_dims_ = converter->InitImageDimInfoWith(tensor_dims_);
+
+#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
+  half_t* image_data = new half_t[image_dims_.product() * 4];
+#else
+  half_t* image_data = new half_t[image_dims_.production() * 4];
+#endif
+
+  VLOG(3) << " convert to image ";
+  converter->NCHWToImage(tensor_data_.get(), image_data, tensor_dims_);
+  VLOG(3) << " end convert to image ";
+
+  InitCLImage(context, image_dims_[0], image_dims_[1], image_data);
+
+  delete[] image_data;
+  tensor_data_ = nullptr;
+  cl_event_ = CLEngine::Global()->CreateEvent(context);
+  initialized_ = true;
+  VLOG(3) << " end init cl image ";
+}
+
+void CLImage::InitCLImage(const cl::Context& context, int width, int height,
+                          void* data) {
+  cl::ImageFormat img_format(CL_RGBA, CL_HALF_FLOAT);
+  cl_int err;
+  cl_image_.reset(new cl::Image2D(
+      context, CL_MEM_READ_WRITE | (data ? CL_MEM_COPY_HOST_PTR : 0),
+      img_format, width, height, 0, data, &err));
+  CL_CHECK_ERRORS(err);
+  CHECK(err == CL_SUCCESS) << " Create image 2d error.";
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_image.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 <memory>
+#include <vector>
+#include "paddle/fluid/lite/core/compatible_tensor.h"
+#include "paddle/fluid/lite/opencl/cl2_header.h"
+#include "paddle/fluid/lite/opencl/cl_image_converter.h"
+
+namespace paddle {
+namespace lite {
+
+class CLImage {
+  // For debug
+  friend std::ostream& operator<<(std::ostream& os, const CLImage& image);
+
+ public:
+  CLImage() = default;
+  /*
+   * Will not hold input tensor data, memcpy in this method.
+   * */
+  void set_tensor_data(float* tensor_data, const DDim& dim);
+
+  bool IsInit() { return initialized_; }
+  /*
+   * Need call set_tensor_data first.
+   * Folder when one dim or two dim.
+   * */
+  void InitCLImage(const cl::Context& context);
+
+  void InitNormalCLImage(const cl::Context& context);
+
+  void InitNImage(const cl::Context& context);
+
+  void InitDWImage(const cl::Context& context);
+
+  void InitEmptyImage(const cl::Context& context, const DDim& dim);
+
+  void InitEmptyWithImageDim(const cl::Context& context,
+                             const DDim& image_dims);
+
+  cl::Image* cl_image() const { return cl_image_.get(); }
+
+  const DDim& image_dims() const { return image_dims_; }
+
+  inline size_t ImageWidth() const { return image_dims_[0]; }
+
+  inline size_t ImageHeight() const { return image_dims_[1]; }
+
+  const DDim& tensor_dims() const { return tensor_dims_; }
+
+  /*with_da
+   * Resize original tensor dim.
+   * */
+  inline CLImage& Resize(const DDim& dims) {
+    tensor_dims_ = dims;
+    return *this;
+  }
+
+  template <typename T>
+  T* data() const {
+    CHECK(!initialized_) << "CL image has initialized, tensor data has been "
+                            "deleted, can't use tensor data!";
+    return reinterpret_cast<T*>(tensor_data_);
+  }
+
+  /*
+   *  Numel of tensor dim
+   * */
+  inline int64_t numel() const {
+#ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
+    return tensor_dims_.product();
+#else
+    return tensor_dims_.production();
+#endif
+  }
+
+  /*
+   *  Original tensor dim
+   * */
+
+  cl::UserEvent& cl_event() const { return *cl_event_; }
+
+  CLImageConverterBase* image_converter() const {
+    return image_converter_.get();
+  }
+
+ private:
+  void InitCLImage(const cl::Context& context, CLImageConverterBase* converter);
+
+  void InitCLImage(const cl::Context& context, int width, int height,
+                   void* data);
+
+  bool initialized_ = false;
+  std::unique_ptr<cl::Image2D> cl_image_{nullptr};
+  std::unique_ptr<cl::UserEvent> cl_event_{nullptr};
+  DDim tensor_dims_;
+  DDim image_dims_;
+  std::unique_ptr<float> tensor_data_{nullptr};
+  std::unique_ptr<CLImageConverterBase> image_converter_{nullptr};
+};
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_image_converter.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/lite/opencl/cl_image_converter.h"
+#include <glog/logging.h>
+#include <vector>
+
+namespace paddle {
+namespace lite {
+
+DDim CLImageConverterDefault::InitImageDimInfoWith(const DDim &tensor_dim) {
+  size_t new_dims[] = {1, 1, 1, 1};
+  for (size_t j = 0; j < tensor_dim.size(); ++j) {
+    new_dims[4 - tensor_dim.size() + j] = tensor_dim[j];
+  }
+  size_t N, C, H, W;
+  N = new_dims[0];
+  C = new_dims[1];
+  H = new_dims[2];
+  W = new_dims[3];
+  size_t width = W * ((C + 3) / 4);
+  size_t height = H * N;
+  return DDim(
+      std::vector<DDim::value_type>({static_cast<DDim::value_type>(width),
+                                     static_cast<DDim::value_type>(height)}));
+}
+
+void CLImageConverterDefault::NCHWToImage(float *nchw, half_t *image,
+                                          const DDim &tensor_dim) {
+  size_t new_dims[] = {1, 1, 1, 1};
+  for (size_t j = 0; j < tensor_dim.size(); ++j) {
+    new_dims[4 - tensor_dim.size() + j] = tensor_dim[j];
+  }
+
+  size_t N, C, H, W;
+  N = new_dims[0];
+  C = new_dims[1];
+  H = new_dims[2];
+  W = new_dims[3];
+
+  DDim in_image_dim = InitImageDimInfoWith(tensor_dim);
+
+  VLOG(3) << " tensor dim: " << tensor_dim;
+  VLOG(3) << " image dim: " << in_image_dim;
+
+  size_t width = in_image_dim[0];
+  size_t w_block = width / W;
+
+  float *p = nchw;
+  size_t i0 = 0;
+  for (size_t n = 0; n < N; n++) {
+    for (size_t c = 0; c < w_block * 4; c++) {
+      size_t i1 = i0 + (c / 4) * W;
+      for (size_t h = 0; h < H; h++) {
+        size_t i2 = (i1 << 2) + c % 4;
+        for (size_t w = 0; w < W; w++) {
+          if (c < C) {
+            // size_t x = (n * width * H + h * width + (c / 4) * W + w) * 4 +
+            // (c % 4);
+            image[i2] = Float2Half(*p);
+            i2 += 4;
+            p++;
+          } else {
+            image[i2] = 0.0;
+            i2 += 4;
+          }
+        }
+        i1 += width;
+      }
+    }
+    i0 += width * H;
+  }
+}
+
+void CLImageConverterDefault::ImageToNCHW(half_t *image, float *tensor,
+                                          const DDim &image_dim,
+                                          const DDim &tensor_dim) {
+  size_t new_dims[] = {1, 1, 1, 1};
+  for (size_t j = 0; j < tensor_dim.size(); ++j) {
+    new_dims[4 - tensor_dim.size() + j] = tensor_dim[j];
+  }
+
+  size_t N, C, H, W;
+  N = new_dims[0];
+  C = new_dims[1];
+  H = new_dims[2];
+  W = new_dims[3];
+
+  size_t width = image_dim[0];
+  float *p = tensor;
+
+  size_t i0 = 0;
+  for (size_t n = 0; n < N; n++) {
+    for (size_t c = 0; c < C; c++) {
+      size_t i1 = i0 + (c / 4) * W;
+      for (size_t h = 0; h < H; h++) {
+        size_t i2 = (i1 << 2) + c % 4;
+        for (size_t w = 0; w < W; w++) {
+          *p = Half2Float(image[i2]);
+          i2 += 4;
+          p++;
+        }
+        i1 += width;
+      }
+    }
+    i0 += width * H;
+  }
+}
+
+DDim CLImageConverterFolder::InitImageDimInfoWith(const DDim &tensor_dim) {
+  if (tensor_dim.size() <= 2) {
+    size_t tdim[2] = {1, 1};
+    if (tensor_dim.size() == 1) {
+      tdim[1] = tensor_dim[0];
+    } else {
+      tdim[0] = tensor_dim[0];
+      tdim[1] = tensor_dim[1];
+    }
+    size_t width = (tdim[1] + 3) / 4;
+    size_t height = tdim[0];
+
+    width_of_one_block_ = width;
+    height_of_one_block_ = height;
+    c_block_ = 1;
+
+    return DDim(
+        std::vector<DDim::value_type>({static_cast<DDim::value_type>(width),
+                                       static_cast<DDim::value_type>(height)}));
+
+  } else {
+    size_t new_dims[] = {1, 1, 1, 1};
+    for (size_t j = 0; j < tensor_dim.size(); ++j) {
+      new_dims[4 - tensor_dim.size() + j] = tensor_dim[j];
+    }
+    size_t N, C, H, W;
+    N = new_dims[0];
+    C = new_dims[1];
+    H = new_dims[2];
+    W = new_dims[3];
+    size_t width = W * ((C + 3) / 4);
+    size_t height = H * N;
+
+    width_of_one_block_ = W;
+    height_of_one_block_ = H;
+    c_block_ = width / W;
+
+    return DDim(
+        std::vector<DDim::value_type>({static_cast<DDim::value_type>(width),
+                                       static_cast<DDim::value_type>(height)}));
+  }
+}
+
+void CLImageConverterFolder::NCHWToImage(float *tensor, half_t *image,
+                                         const DDim &tensor_dim) {
+  CHECK(tensor_dim.size() <= 4 && tensor_dim.size() > 0)
+      << " Tensor dim is not support!";
+
+  if (tensor_dim.size() > 2) {
+    CLImageConverterDefault default_converter;
+    default_converter.NCHWToImage(tensor, image, tensor_dim);
+
+  } else {
+    size_t tdim[2] = {1, 1};
+    if (tensor_dim.size() == 1) {
+      tdim[1] = tensor_dim[0];
+    } else {
+      tdim[0] = tensor_dim[0];
+      tdim[1] = tensor_dim[1];
+    }
+
+    DDim image_dim = InitImageDimInfoWith(tensor_dim);
+    size_t width = image_dim[0];
+
+    for (size_t h = 0; h < tdim[0]; h++) {
+      for (size_t w = 0; w < tdim[1]; w++) {
+        image[(h * width + w / 4) * 4 + (w % 4)] =
+            Float2Half(tensor[h * tdim[1] + w]);
+      }
+    }
+  }
+}
+
+void CLImageConverterFolder::ImageToNCHW(half_t *image, float *tensor,
+                                         const DDim &image_dim,
+                                         const DDim &tensor_dim) {
+  if (tensor_dim.size() > 2) {
+    CLImageConverterDefault default_converter;
+    default_converter.ImageToNCHW(image, tensor, image_dim, tensor_dim);
+
+  } else {
+    size_t width = image_dim[0];
+    size_t H = 1, W = 1;
+
+    if (tensor_dim.size() == 2) {
+      H = tensor_dim[0];
+      W = tensor_dim[1];
+    } else if (tensor_dim.size() == 1) {
+      W = tensor_dim[0];
+    }
+
+    float *p = tensor;
+
+    for (size_t h = 0; h < H; h++) {
+      for (size_t w = 0; w < W; w++) {
+        p[h * W + w] = Half2Float(image[(h * width + w / 4) * 4 + (w % 4)]);
+      }
+    }
+  }
+}
+
+DDim CLImageConverterNWBlock::InitImageDimInfoWith(const DDim &tensor_dim) {
+  CHECK(tensor_dim.size() == 4) << " Tensor dim is not 4.";
+  size_t N, C, H, W;
+  N = tensor_dim[0];
+  C = tensor_dim[1];
+  H = tensor_dim[2];
+  W = tensor_dim[3];
+  size_t width = W * ((N + 3) / 4);
+  size_t height = C * H;
+  return DDim(
+      std::vector<DDim::value_type>({static_cast<DDim::value_type>(width),
+                                     static_cast<DDim::value_type>(height)}));
+}
+
+void CLImageConverterNWBlock::NCHWToImage(float *tensor, half_t *image,
+                                          const DDim &tensor_dim) {
+  CHECK(tensor_dim.size() == 4) << " Tensor dim is not 4.";
+  auto image_dim = InitImageDimInfoWith(tensor_dim);
+  float *p = tensor;
+  size_t N = tensor_dim[0];
+  size_t C = tensor_dim[1];
+  size_t H = tensor_dim[2];
+  size_t W = tensor_dim[3];
+  size_t width = image_dim[0];
+  size_t height = image_dim[1];
+  size_t block = image_dim[0] / tensor_dim[3];
+
+  for (size_t n = 0; n < block * 4; n++) {
+    for (size_t c = 0; c < C; c++) {
+      for (size_t h = 0; h < H; ++h) {
+        for (size_t w = 0; w < W; ++w) {
+          size_t index = 4 * c * (width * H) + 4 * h * width + 4 * W * (n / 4) +
+                         w * 4 + n % 4;
+          if (n < N) {
+            image[index] = Float2Half(*p);
+            p++;
+          } else {
+            image[index] = 0.0;
+          }
+          if (index >= (width * height * 4)) {
+            LOG(INFO) << " index out of range ";
+          }
+        }
+      }
+    }
+  }
+  VLOG(3) << " init done";
+}
+
+void CLImageConverterNWBlock::ImageToNCHW(half_t *image, float *tensor,
+                                          const DDim &image_dim,
+                                          const DDim &tensor_dim) {
+  CHECK(tensor_dim.size() == 4) << " Tensor dim is not 4.";
+  float *p = tensor;
+  size_t N = tensor_dim[0];
+  size_t C = tensor_dim[1];
+  size_t H = tensor_dim[2];
+  size_t W = tensor_dim[3];
+  size_t width = image_dim[0];
+  size_t height = image_dim[1];
+
+  for (size_t n = 0; n < N; n++) {
+    for (size_t c = 0; c < C; c++) {
+      for (size_t h = 0; h < H; ++h) {
+        for (size_t w = 0; w < W; ++w) {
+          size_t index = 4 * c * (width * H) + 4 * h * width + 4 * W * (n / 4) +
+                         w * 4 + n % 4;
+          *p = Half2Float(image[index]);
+          p++;
+          if (index >= (width * height * 4)) {
+            LOG(INFO) << " index out of range ";
+          }
+        }
+      }
+    }
+  }
+  VLOG(3) << " init done";
+}
+
+DDim CLImageConverterDWBlock::InitImageDimInfoWith(const DDim &tensor_dim) {
+  CHECK(tensor_dim.size() == 4) << " Tensor dim is not 4.";
+  size_t N, C, H, W;
+  N = tensor_dim[0];
+  C = tensor_dim[1];
+  H = tensor_dim[2];
+  W = tensor_dim[3];
+  size_t width = W * ((N + 3) / 4);
+  size_t height = C * H;
+  return DDim(
+      std::vector<DDim::value_type>({static_cast<DDim::value_type>(width),
+                                     static_cast<DDim::value_type>(height)}));
+}
+
+void CLImageConverterDWBlock::NCHWToImage(float *tensor, half_t *image,
+                                          const DDim &tensor_dim) {
+  size_t new_dims[] = {1, 1, 1, 1};
+  for (size_t j = 0; j < tensor_dim.size(); ++j) {
+    new_dims[4 - tensor_dim.size() + j] = tensor_dim[j];
+  }
+
+  size_t N, C, H, W;
+  N = new_dims[1];
+  C = new_dims[0];
+  H = new_dims[2];
+  W = new_dims[3];
+
+  DDim in_image_dim = InitImageDimInfoWith(tensor_dim);
+
+  VLOG(3) << " tensor dim: " << tensor_dim;
+  VLOG(3) << " image dim: " << in_image_dim;
+
+  size_t width = in_image_dim[0];
+  size_t w_block = width / W;
+
+  float *p = tensor;
+  size_t i0 = 0;
+  for (size_t n = 0; n < N; n++) {
+    for (size_t c = 0; c < w_block * 4; c++) {
+      size_t i1 = i0 + (c / 4) * W;
+      for (size_t h = 0; h < H; h++) {
+        size_t i2 = (i1 << 2) + c % 4;
+        for (size_t w = 0; w < W; w++) {
+          if (c < C) {
+            // size_t x = (n * width * H + h * width + (c / 4) * W + w) * 4 +
+            // (c % 4);
+            image[i2] = Float2Half(*p);
+            i2 += 4;
+            p++;
+          } else {
+            image[i2] = 0.0;
+            i2 += 4;
+          }
+        }
+        i1 += width;
+      }
+    }
+    i0 += width * H;
+  }
+}
+
+void CLImageConverterDWBlock::ImageToNCHW(half_t *image, float *tensor,
+                                          const DDim &image_dim,
+                                          const DDim &tensor_dim) {
+  CHECK(tensor_dim.size() == 4) << " Tensor dim is not 4.";
+  float *p = tensor;
+  size_t N = tensor_dim[1];
+  size_t C = tensor_dim[0];
+  size_t H = tensor_dim[2];
+  size_t W = tensor_dim[3];
+  size_t width = image_dim[0];
+
+  size_t i0 = 0;
+  for (size_t n = 0; n < N; n++) {
+    for (size_t c = 0; c < C; c++) {
+      size_t i1 = i0 + (c / 4) * W;
+      for (size_t h = 0; h < H; h++) {
+        size_t i2 = (i1 << 2) + c % 4;
+        for (size_t w = 0; w < W; w++) {
+          *p = Half2Float(image[i2]);
+          i2 += 4;
+          p++;
+        }
+        i1 += width;
+      }
+    }
+    i0 += width * H;
+  }
+}
+
+DDim CLImageConverterNormal::InitImageDimInfoWith(const DDim &tensor_dim) {
+  size_t new_dims[] = {1, 1, 1, 1};
+  for (size_t j = 0; j < tensor_dim.size(); ++j) {
+    new_dims[4 - tensor_dim.size() + j] = tensor_dim[j];
+  }
+  size_t N, C, H, W;
+  N = new_dims[0];
+  C = new_dims[1];
+  H = new_dims[2];
+  W = new_dims[3];
+  size_t width = W * ((C + 3) / 4);
+  size_t height = H * N;
+
+  width_of_one_block_ = W;
+  height_of_one_block_ = H;
+  c_block_ = width / W;
+
+  return DDim(
+      std::vector<DDim::value_type>({static_cast<DDim::value_type>(width),
+                                     static_cast<DDim::value_type>(height)}));
+}
+
+void CLImageConverterNormal::NCHWToImage(float *tensor, half_t *image,
+                                         const DDim &tensor_dim) {
+  CHECK(tensor_dim.size() <= 4 && tensor_dim.size() > 0)
+      << " Tensor dim is not support!";
+
+  CLImageConverterDefault default_converter;
+  default_converter.NCHWToImage(tensor, image, tensor_dim);
+}
+
+void CLImageConverterNormal::ImageToNCHW(half_t *image, float *tensor,
+                                         const DDim &image_dim,
+                                         const DDim &tensor_dim) {
+  CLImageConverterDefault default_converter;
+  default_converter.ImageToNCHW(image, tensor, image_dim, tensor_dim);
+}
+
+DDim CLImageConverterWinoTransWeight::InitImageDimInfoWith(
+    const DDim &tensor_dim) {
+  CHECK(tensor_dim.size() == 4) << " Tensor dim is not 4.";
+  size_t N, C;
+  N = tensor_dim[0];
+  C = tensor_dim[1];
+  size_t width = (C + 3) / 4;
+  size_t height = N * 16;  // N * (wino_blk_size + 2) * (wino_blk_size + 2)
+  return DDim(
+      std::vector<DDim::value_type>({static_cast<DDim::value_type>(width),
+                                     static_cast<DDim::value_type>(height)}));
+}
+
+void CLImageConverterWinoTransWeight::NCHWToImage(float *tensor, half_t *image,
+                                                  const DDim &tensor_dim) {}
+
+void CLImageConverterWinoTransWeight::ImageToNCHW(half_t *image, float *tensor,
+                                                  const DDim &image_dim,
+                                                  const DDim &tensor_dim) {}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_image_converter.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/lite/core/compatible_tensor.h"
+#include "paddle/fluid/lite/opencl/cl_half.h"
+
+namespace paddle {
+namespace lite {
+
+class CLImageConverterBase {
+ public:
+  virtual ~CLImageConverterBase() {}
+
+  virtual void NCHWToImage(float *nchw, half_t *image,
+                           const DDim &tensor_dim) = 0;
+
+  virtual void ImageToNCHW(half_t *image, float *nchw, const DDim &image_dim,
+                           const DDim &tensor_dim) = 0;
+  virtual DDim InitImageDimInfoWith(const DDim &tensor_dim) = 0;
+};
+
+class CLImageConverterDefault : public CLImageConverterBase {
+ public:
+  DDim InitImageDimInfoWith(const DDim &tensor_dim);
+  void NCHWToImage(float *nchw, half_t *image, const DDim &tensor_dim);
+  void ImageToNCHW(half_t *image, float *tensor, const DDim &image_dim,
+                   const DDim &tensor_dim);
+};
+
+class CLImageConverterFolder : public CLImageConverterBase {
+ public:
+  DDim InitImageDimInfoWith(const DDim &tensor_dim);
+  void NCHWToImage(float *tensor, half_t *image, const DDim &tensor_dim);
+  void ImageToNCHW(half_t *image, float *tensor, const DDim &image_dim,
+                   const DDim &tensor_dim);
+
+  /*
+   *  width of original tensor
+   * */
+  inline size_t WidthOfOneBlock() const { return width_of_one_block_; }
+
+  /*
+   *  height of original tensor
+   * */
+  inline size_t HeightOfOneBlock() const { return height_of_one_block_; }
+
+  int GetCBlock() const { return c_block_; }
+
+ private:
+  int c_block_;
+  int width_of_one_block_;
+  int height_of_one_block_;
+};
+
+class CLImageConverterNormal : public CLImageConverterBase {
+ public:
+  DDim InitImageDimInfoWith(const DDim &tensor_dim);
+  void NCHWToImage(float *tensor, half_t *image, const DDim &tensor_dim);
+  void ImageToNCHW(half_t *image, float *tensor, const DDim &image_dim,
+                   const DDim &tensor_dim);
+
+  /*
+   *  width of original tensor
+   * */
+  inline size_t WidthOfOneBlock() const { return width_of_one_block_; }
+
+  /*
+   *  height of original tensor
+   * */
+  inline size_t HeightOfOneBlock() const { return height_of_one_block_; }
+
+  int GetCBlock() const { return c_block_; }
+
+ private:
+  int c_block_;
+  int width_of_one_block_;
+  int height_of_one_block_;
+};
+
+class CLImageConverterNWBlock : public CLImageConverterBase {
+  DDim InitImageDimInfoWith(const DDim &tensor_dim);
+  void NCHWToImage(float *tensor, half_t *image, const DDim &tensor_dim);
+  void ImageToNCHW(half_t *image, float *tensor, const DDim &image_dim,
+                   const DDim &tensor_dim);
+};
+class CLImageConverterDWBlock : public CLImageConverterBase {
+  DDim InitImageDimInfoWith(const DDim &tensor_dim);
+  void NCHWToImage(float *tensor, half_t *image, const DDim &tensor_dim);
+  void ImageToNCHW(half_t *image, float *tensor, const DDim &image_dim,
+                   const DDim &tensor_dim);
+};
+
+class CLImageConverterWinoTransWeight : public CLImageConverterBase {
+ public:
+  DDim InitImageDimInfoWith(const DDim &tensor_dim);
+  void NCHWToImage(float *tensor, half_t *image, const DDim &tensor_dim);
+  void ImageToNCHW(half_t *image, float *tensor, const DDim &image_dim,
+                   const DDim &tensor_dim);
+};
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_kernel/cl_common.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
+
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+inline half4 activation(half4 in
+#ifdef PRELU
+                        ,
+                        half4 prelu_alpha
+#endif
+                        ) {
+  half4 output;
+#ifdef PRELU
+  output = select(prelu_alpha * in, in, in >= (half4)0.0);
+#endif
+
+#ifdef RELU
+  output = fmax(in, (half4)(0.0f));
+#endif
+  return output;
+}

paddle/fluid/lite/opencl/cl_kernel/elementwise_add_kernel.cl

+/* 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 OPENCL EXTENSION cl_khr_fp16 : enable
+__kernel void elementwise_add(__global image2d_t input, __global image2d_t bias,__write_only image2d_t outputImage) {
+     int x = get_global_id(0);
+     int y = get_global_id(1);
+     const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+     int2 coords;
+     coords.x = x;
+     coords.y = y;
+     half4 in = read_imageh(input, sampler, coords);
+     half4 biase = read_imageh(bias, sampler, coords);
+     half4 output = in + biase;
+     write_imageh(outputImage,coords,output);
+ }

paddle/fluid/lite/opencl/cl_kernel/pool_kernel.cl

+/* 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 OPENCL EXTENSION cl_khr_fp16 : enable
+#define MIN_VALUE -FLT_MAX
+
+__kernel void pool_max(
+    __private const int in_height, __private const int in_width,
+    __private const int out_height, __private const int out_width,
+    __private const int pad_top, __private const int pad_left,
+    __private const int stride_h, __private const int stride_w,
+    __private const int ksize_h, __private const int ksize_w,
+    __read_only image2d_t input, __write_only image2d_t output) {
+  const int out_c = get_global_id(0);
+  const int out_w = get_global_id(1);
+  const int out_nh = get_global_id(2);
+  const int out_n = out_nh / out_height;
+  const int out_h = out_nh % out_height;
+
+  const sampler_t sampler =
+      CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+
+  int start_h = out_h * stride_h - pad_top;
+  int end_h = min(start_h + ksize_h, in_height);
+  start_h = max(start_h,0);
+
+  int start_w = out_w * stride_w - pad_left;
+  int end_w = min(start_w + ksize_w, in_width);
+  start_w = max(start_w,0);
+
+  const int pos_in_x = out_c * in_width;
+  const int pos_in_y = out_n * in_height;
+  half4 max_value = (half4)(MIN_VALUE);
+  for (int y = start_h; y < end_h; ++y) {
+    for (int x = start_w; x < end_w; ++x) {
+      half4 tmp = read_imageh(input, sampler, (int2)(pos_in_x + x, pos_in_y + y));
+      max_value = max(max_value, tmp);
+    }
+  }
+
+  const int pos_out_x = mad24(out_c, out_width, out_w);
+  write_imageh(output, (int2)(pos_out_x, out_nh), max_value);
+}
+
+__kernel void pool_avg(
+    __private const int in_height, __private const int in_width,
+    __private const int out_height, __private const int out_width,
+    __private const int pad_top, __private const int pad_left,
+    __private const int stride_h, __private const int stride_w,
+    __private const int ksize_h, __private const int ksize_w,
+    __read_only image2d_t input, __write_only image2d_t output) {
+  const int out_c = get_global_id(0);
+  const int out_w = get_global_id(1);
+  const int out_nh = get_global_id(2);
+  const int out_n = out_nh / out_height;
+  const int out_h = out_nh % out_height;
+
+  const sampler_t sampler =
+      CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+
+  int start_h = max(out_h * stride_h - pad_top, 0);
+  int end_h = min(start_h + ksize_h, in_height);
+
+  int start_w = max(out_w * stride_w - pad_left, 0);
+  int end_w = min(start_w + ksize_w, in_width);
+
+  const int pos_in_x = out_c * in_width;
+  const int pos_in_y = out_n * in_height;
+  half4 sum = (half4)(0.0f);
+  int num = 0;
+  for (int y = start_h; y < end_h; ++y) {
+    for (int x = start_w; x < end_w; ++x) {
+      sum += read_imageh(input, sampler, (int2)(pos_in_x + x, pos_in_y + y));
+      num++;
+    }
+  }
+  half4 avg = sum / num;
+  const int pos_out_x = mad24(out_c, out_width, out_w);
+  write_imageh(output, (int2)(pos_out_x, out_nh), avg);
+}

paddle/fluid/lite/opencl/cl_test.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gflags/gflags.h>
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+#include <memory>
+#include <random>
+#include <vector>
+#include "paddle/fluid/lite/core/compatible_tensor.h"
+#include "paddle/fluid/lite/opencl/cl_caller.h"
+#include "paddle/fluid/lite/opencl/cl_context.h"
+#include "paddle/fluid/lite/opencl/cl_engine.h"
+#include "paddle/fluid/lite/opencl/cl_helper.h"
+#include "paddle/fluid/lite/opencl/cl_image.h"
+
+DEFINE_string(cl_path, "/data/local/tmp/opencl", "The OpenCL kernels path.");
+
+namespace paddle {
+namespace lite {
+
+TEST(cl_test, engine_test) {
+  auto* engine = CLEngine::Global();
+  CHECK(engine->IsInitSuccess());
+  engine->set_cl_path(FLAGS_cl_path);
+  engine->platform();
+  engine->device();
+  engine->command_queue();
+  auto& context = engine->context();
+  auto program = engine->CreateProgram(
+      context, engine->cl_path() + "/cl_kernel/" + "elementwise_add_kernel.cl");
+  auto event = engine->CreateEvent(context);
+  CHECK(engine->BuildProgram(program.get()));
+}
+
+TEST(cl_test, context_test) {
+  auto* engine = CLEngine::Global();
+  CHECK(engine->IsInitSuccess());
+  engine->set_cl_path(FLAGS_cl_path);
+  CLContext context;
+  context.GetKernel("pool_max", "pool_kernel.cl", "");
+  context.GetKernel("elementwise_add", "elementwise_add_kernel.cl", "");
+  context.GetKernel("elementwise_add", "elementwise_add_kernel.cl", "");
+}
+
+TEST(cl_test, kernel_test) {
+  auto* engine = CLEngine::Global();
+  CHECK(engine->IsInitSuccess());
+  engine->set_cl_path(FLAGS_cl_path);
+  std::unique_ptr<CLContext> context(new CLContext);
+  // std::unique_ptr<CLHelper> helper(new CLHelper(context.get()));
+  std::unique_ptr<CLHelper> helper(new CLHelper);
+  helper->set_context(context.get());
+  helper->AddKernel("elementwise_add", "elementwise_add_kernel.cl");
+  helper->AddKernel("pool_max", "pool_kernel.cl");
+  helper->AddKernel("elementwise_add", "elementwise_add_kernel.cl");
+  auto kernel = helper->KernelAt(2);
+
+  std::unique_ptr<float[]> in_data(new float[1024 * 512]);
+  for (int i = 0; i < 1024 * 512; i++) {
+    in_data[i] = 1.f;
+  }
+  const DDim in_dim = DDim(std::vector<DDim::value_type>{1024, 512});
+  CLImage in_image;
+  in_image.set_tensor_data(in_data.get(), in_dim);
+  in_image.InitNormalCLImage(helper->OpenCLContext());
+  LOG(INFO) << in_image;
+
+  std::unique_ptr<float[]> bias_data(new float[1024 * 512]);
+  for (int i = 0; i < 1024 * 512; i++) {
+    bias_data[i] = 2.f;
+  }
+  const DDim bias_dim = DDim(std::vector<DDim::value_type>{1024, 512});
+  CLImage bias_image;
+  bias_image.set_tensor_data(bias_data.get(), bias_dim);
+  bias_image.InitNormalCLImage(helper->OpenCLContext());
+  LOG(INFO) << bias_image;
+
+  CLImage out_image;
+  const DDim out_dim = DDim(std::vector<DDim::value_type>{1024, 512});
+  out_image.InitEmptyImage(helper->OpenCLContext(), out_dim);
+  LOG(INFO) << out_image;
+
+  cl_int status;
+  status = kernel.setArg(0, *in_image.cl_image());
+  CL_CHECK_ERRORS(status);
+  status = kernel.setArg(1, *bias_image.cl_image());
+  CL_CHECK_ERRORS(status);
+  status = kernel.setArg(2, *out_image.cl_image());
+  CL_CHECK_ERRORS(status);
+
+  // auto global_work_size = helper->DefaultWorkSize(out_image);
+  size_t width = in_image.ImageWidth();
+  size_t height = in_image.ImageHeight();
+  auto global_work_size = cl::NDRange{width, height};
+  cl::Event event;
+  status = helper->OpenCLCommandQueue().enqueueNDRangeKernel(
+      kernel, cl::NullRange, global_work_size, cl::NullRange, nullptr, &event);
+  CL_CHECK_ERRORS(status);
+
+  double start_nanos = event.getProfilingInfo<CL_PROFILING_COMMAND_START>();
+  double stop_nanos = event.getProfilingInfo<CL_PROFILING_COMMAND_END>();
+  double elapsed_micros = (stop_nanos - start_nanos) / 1000.0;
+  LOG(INFO) << "Kernel Run Cost Time: " << elapsed_micros << " us.";
+  LOG(INFO) << out_image;
+}
+
+TEST(cl_test, elementwise_add_test) {
+  std::default_random_engine engine;
+  std::uniform_real_distribution<float> dist(-5, 5);
+
+  const DDim in_dim = DDim(std::vector<DDim::value_type>{1024, 512});
+  std::unique_ptr<float[]> in_data(new float[1024 * 512]);
+  for (int i = 0; i < 1024 * 512; i++) {
+    in_data[i] = dist(engine);
+  }
+
+  const DDim bias_dim = DDim(std::vector<DDim::value_type>{1024, 512});
+  std::unique_ptr<float[]> bias_data(new float[1024 * 512]);
+  for (int i = 0; i < 1024 * 512; i++) {
+    bias_data[i] = dist(engine);
+  }
+
+  const DDim out_dim = DDim(std::vector<DDim::value_type>{1024, 512});
+  std::unique_ptr<float[]> out(new float[1024 * 512]);
+
+  bool status = InitOpenCLEngine(FLAGS_cl_path);
+  CHECK(status) << "Fail to initialize OpenCL engine.";
+  CLContext context;
+
+  elementwise_add(&context, in_data.get(), in_dim, bias_data.get(), bias_dim,
+                  out.get(), out_dim);
+
+  int stride = 1024 * 512 / 20;
+  for (int i = 0; i < 1024 * 512; i += stride) {
+    std::cout << out[i] << " ";
+  }
+
+  std::cout << std::endl;
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_tool.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/lite/opencl/cl_tool.h"
+
+namespace paddle {
+namespace lite {
+
+const char *opencl_error_to_str(cl_int error) {
+#define CASE_CL_CONSTANT(NAME) \
+  case NAME:                   \
+    return #NAME;
+  // Suppose that no combinations are possible.
+  switch (error) {
+    CASE_CL_CONSTANT(CL_SUCCESS)
+    CASE_CL_CONSTANT(CL_DEVICE_NOT_FOUND)
+    CASE_CL_CONSTANT(CL_DEVICE_NOT_AVAILABLE)
+    CASE_CL_CONSTANT(CL_COMPILER_NOT_AVAILABLE)
+    CASE_CL_CONSTANT(CL_MEM_OBJECT_ALLOCATION_FAILURE)
+    CASE_CL_CONSTANT(CL_OUT_OF_RESOURCES)
+    CASE_CL_CONSTANT(CL_OUT_OF_HOST_MEMORY)
+    CASE_CL_CONSTANT(CL_PROFILING_INFO_NOT_AVAILABLE)
+    CASE_CL_CONSTANT(CL_MEM_COPY_OVERLAP)
+    CASE_CL_CONSTANT(CL_IMAGE_FORMAT_MISMATCH)
+    CASE_CL_CONSTANT(CL_IMAGE_FORMAT_NOT_SUPPORTED)
+    CASE_CL_CONSTANT(CL_BUILD_PROGRAM_FAILURE)
+    CASE_CL_CONSTANT(CL_MAP_FAILURE)
+    CASE_CL_CONSTANT(CL_MISALIGNED_SUB_BUFFER_OFFSET)
+    CASE_CL_CONSTANT(CL_EXEC_STATUS_ERROR_FOR_EVENTS_IN_WAIT_LIST)
+    CASE_CL_CONSTANT(CL_INVALID_VALUE)
+    CASE_CL_CONSTANT(CL_INVALID_DEVICE_TYPE)
+    CASE_CL_CONSTANT(CL_INVALID_PLATFORM)
+    CASE_CL_CONSTANT(CL_INVALID_DEVICE)
+    CASE_CL_CONSTANT(CL_INVALID_CONTEXT)
+    CASE_CL_CONSTANT(CL_INVALID_QUEUE_PROPERTIES)
+    CASE_CL_CONSTANT(CL_INVALID_COMMAND_QUEUE)
+    CASE_CL_CONSTANT(CL_INVALID_HOST_PTR)
+    CASE_CL_CONSTANT(CL_INVALID_MEM_OBJECT)
+    CASE_CL_CONSTANT(CL_INVALID_IMAGE_FORMAT_DESCRIPTOR)
+    CASE_CL_CONSTANT(CL_INVALID_IMAGE_SIZE)
+    CASE_CL_CONSTANT(CL_INVALID_SAMPLER)
+    CASE_CL_CONSTANT(CL_INVALID_BINARY)
+    CASE_CL_CONSTANT(CL_INVALID_BUILD_OPTIONS)
+    CASE_CL_CONSTANT(CL_INVALID_PROGRAM)
+    CASE_CL_CONSTANT(CL_INVALID_PROGRAM_EXECUTABLE)
+    CASE_CL_CONSTANT(CL_INVALID_KERNEL_NAME)
+    CASE_CL_CONSTANT(CL_INVALID_KERNEL_DEFINITION)
+    CASE_CL_CONSTANT(CL_INVALID_KERNEL)
+    CASE_CL_CONSTANT(CL_INVALID_ARG_INDEX)
+    CASE_CL_CONSTANT(CL_INVALID_ARG_VALUE)
+    CASE_CL_CONSTANT(CL_INVALID_ARG_SIZE)
+    CASE_CL_CONSTANT(CL_INVALID_KERNEL_ARGS)
+    CASE_CL_CONSTANT(CL_INVALID_WORK_DIMENSION)
+    CASE_CL_CONSTANT(CL_INVALID_WORK_GROUP_SIZE)
+    CASE_CL_CONSTANT(CL_INVALID_WORK_ITEM_SIZE)
+    CASE_CL_CONSTANT(CL_INVALID_GLOBAL_OFFSET)
+    CASE_CL_CONSTANT(CL_INVALID_EVENT_WAIT_LIST)
+    CASE_CL_CONSTANT(CL_INVALID_EVENT)
+    CASE_CL_CONSTANT(CL_INVALID_OPERATION)
+    CASE_CL_CONSTANT(CL_INVALID_GL_OBJECT)
+    CASE_CL_CONSTANT(CL_INVALID_BUFFER_SIZE)
+    CASE_CL_CONSTANT(CL_INVALID_MIP_LEVEL)
+    CASE_CL_CONSTANT(CL_INVALID_GLOBAL_WORK_SIZE)
+    CASE_CL_CONSTANT(CL_INVALID_PROPERTY)
+
+    default:
+      return "UNKNOWN ERROR CODE";
+  }
+#undef CASE_CL_CONSTANT
+}
+
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/opencl/cl_tool.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/lite/opencl/cl2_header.h"
+
+namespace paddle {
+namespace lite {
+
+const char* opencl_error_to_str(cl_int error);
+
+#define CL_CHECK_ERRORS(ERR)                                         \
+  if (ERR != CL_SUCCESS) {                                           \
+    printf(                                                          \
+        "OpenCL error with code %s happened in file %s at line %d. " \
+        "Exiting.\n",                                                \
+        opencl_error_to_str(ERR), __FILE__, __LINE__);               \
+  }
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/operators/use_ops.h

@@ -13,9 +13,10 @@
 // limitations under the License.
 
 #pragma once
-/*
- * ATTENTION this header file can only include in .cc file.
- */
+
+// ATTENTION This can only include in a .cc file.
+
+#include "paddle/fluid/lite/core/op_registry.h"
 
 USE_LITE_OP(mul);
 USE_LITE_OP(fc);

paddle/fluid/lite/tools/build.sh

@@ -25,6 +25,23 @@ function cmake_x86 {
     cmake ..  -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DLITE_WITH_X86=ON ${common_flags}
 }
 
+function cmake_opencl {
+    # $1: ARM_TARGET_OS in "android" , "armlinux"
+    # $2: ARM_TARGET_ARCH_ABI in "arm64-v8a", "armeabi-v7a" ,"armeabi-v7a-hf"
+    cmake .. \
+        -DLITE_WITH_OPENCL=ON \
+        -DWITH_GPU=OFF \
+        -DWITH_MKL=OFF \
+        -DWITH_LITE=ON \
+        -DLITE_WITH_CUDA=OFF \
+        -DLITE_WITH_X86=OFF \
+        -DLITE_WITH_ARM=ON \
+        -DLITE_WITH_LIGHT_WEIGHT_FRAMEWORK=ON \
+        -DWITH_TESTING=ON \
+        -DARM_TARGET_OS=$1 -DARM_TARGET_ARCH_ABI=$2
+}
+
+
 # This method is only called in CI.
 function cmake_x86_for_CI {
     prepare_for_codegen # fake an empty __generated_code__.cc to pass cmake.
@@ -85,8 +102,8 @@ function build_test_server {
 
 # test_arm_android <some_test_name> <adb_port_number>
 function test_arm_android {
-    test_name=$1
-    port=$2
+    local test_name=$1
+    local port=$2
     if [[ "${test_name}x" == "x" ]]; then
         echo "test_name can not be empty"
         exit 1
@@ -99,12 +116,18 @@ function test_arm_android {
     echo "test name: ${test_name}"
     adb_work_dir="/data/local/tmp"
 
-    skip_list=("test_model_parser_lite" "test_mobilenetv1_lite" "test_mobilenetv2_lite" "test_resnet50_lite" "test_inceptionv4_lite")
+    skip_list=("test_model_parser_lite" "test_mobilenetv1_lite" "test_mobilenetv2_lite" "test_resnet50_lite" "test_inceptionv4_lite" "test_light_api_lite" "test_apis_lite")
     for skip_name in ${skip_list[@]} ; do
         [[ $skip_name =~ (^|[[:space:]])$test_name($|[[:space:]]) ]] && echo "skip $test_name" && return
     done
 
-    testpath=$(find ./paddle/fluid -name ${test_name})
+    local testpath=$(find ./paddle/fluid -name ${test_name})
+
+    # if [[ "$test_name" == "test_light_api" ]]; then
+    #     local model_path=$(find . -name "lite_naive_model")
+    #     arm_push_necessary_file $port $model_path $adb_work_dir
+    # fi
+
     adb -s emulator-${port} push ${testpath} ${adb_work_dir}
     adb -s emulator-${port} shell chmod +x "${adb_work_dir}/${test_name}"
     adb -s emulator-${port} shell "./${adb_work_dir}/${test_name}"
@@ -204,6 +227,7 @@ function test_arm {
     abi=$2
     lang=$3
     port=$4
+
     if [[ ${os} == "armlinux" ]]; then
         # TODO(hongming): enable test armlinux on armv8, armv7 and armv7hf
         echo "Skip test arm linux yet. armlinux must in another docker"
@@ -214,13 +238,14 @@ function test_arm {
         echo "android do not need armv7hf"
         return 0
     fi
-    
+
     # TODO(yuanshuai): enable armv7 on android
     if [[ ${abi} == "armv7" ]]; then
         echo "skip android v7 test yet"
         return 0
     fi
 
+
     echo "test file: ${TESTS_FILE}"
     for _test in $(cat $TESTS_FILE); do
         test_arm_android $_test $port
@@ -235,13 +260,21 @@ function prepare_emulator {
     adb devices | grep emulator | cut -f1 | while read line; do adb -s $line emu kill; done
     # start android armv8 and armv7 emulators first
     echo n | avdmanager create avd -f -n paddle-armv8 -k "system-images;android-24;google_apis;arm64-v8a"
-    echo -ne '\n' | ${ANDROID_HOME}/emulator/emulator -avd paddle-armv8 -noaudio -no-window -gpu off -verbose -port ${port_armv8} &
+    echo -ne '\n' | ${ANDROID_HOME}/emulator/emulator -avd paddle-armv8 -noaudio -no-window -gpu off -port ${port_armv8} &
     sleep 1m
     echo n | avdmanager create avd -f -n paddle-armv7 -k "system-images;android-24;google_apis;armeabi-v7a"
-    echo -ne '\n' | ${ANDROID_HOME}/emulator/emulator -avd paddle-armv7 -noaudio -no-window -gpu off -verbose -port ${port_armv7} &
+    echo -ne '\n' | ${ANDROID_HOME}/emulator/emulator -avd paddle-armv7 -noaudio -no-window -gpu off -port ${port_armv7} &
     sleep 1m
 }
 
+function arm_push_necessary_file {
+    local port=$1
+    local testpath=$2
+    local adb_work_dir=$3
+
+    adb -s emulator-${port} push ${testpath} ${adb_work_dir}
+}
+
 
 # We split the arm unittest into several sub-tasks to parallel and reduce the overall CI timetime.
 # sub-task1
@@ -286,20 +319,22 @@ function build_test_arm_subtask_armlinux {
 
     prepare_emulator $port_armv8 $port_armv7
 
+    cur=$PWD
+
     # job 5
-    build_arm "armlinux" "armv8"
-    test_arm "armlinux" "armv8"
-    cd -
+    build_arm "armlinux" "armv8" "gcc" $port_armv8
+    test_arm "armlinux" "armv8" "gcc" $port_armv8
+    cd $cur
 
     # job 6
-    build_arm "armlinux" "armv7"
-    test_arm "armlinux" "armv7"
-    cd -
+    build_arm "armlinux" "armv7" "gcc" $port_armv8
+    test_arm "armlinux" "armv7" "gcc" $port_armv8
+    cd $cur
 
     # job 7
-    build_arm "armlinux" "armv7hf"
-    test_arm "armlinux" "armv7hf"
-    cd -
+    build_arm "armlinux" "armv7hf" "gcc" $port_armv8
+    test_arm "armlinux" "armv7hf" "gcc" $port_armv8
+    cd $cur
 
     adb devices | grep emulator | cut -f1 | while read line; do adb -s $line emu kill; done
     echo "Done"
@@ -333,6 +368,22 @@ function build_test_arm_subtask_model {
     echo "Done"
 }
 
+
+# this test load a model, optimize it and check the prediction result of both cxx and light APIS.
+function test_arm_predict_apis {
+    local port=$1
+    local workspace=$2
+    local naive_model_path=$3
+    local api_test_path=$(find . -name "test_apis_lite")
+    # the model is pushed to ./lite_naive_model
+    adb -s emulator-${port} push ${naive_model_path} ${workspace}
+    adb -s emulator-${port} push $api_test_path ${workspace}
+
+    # test cxx_api first to store the optimized model.
+    adb -s emulator-${port} shell ./test_apis_lite --model_dir ./lite_naive_model --optimized_model ./lite_naive_model_opt
+}
+
+
 # Build the code and run lite arm tests. This is executed in the CI system.
 function build_test_arm {
     ########################################################################
@@ -404,6 +455,10 @@ function main {
                 cmake_x86
                 shift
                 ;;
+            cmake_opencl)
+                cmake_opencl $ARM_OS $ARM_ABI
+                shift
+                ;;
             cmake_cuda)
                 cmake_cuda
                 shift

paddle/fluid/lite/utils/io.h

@@ -18,11 +18,12 @@
 #include <fstream>
 #include <string>
 #include "paddle/fluid/lite/utils/cp_logging.h"
+#include "paddle/fluid/lite/utils/string.h"
 
 namespace paddle {
 namespace lite {
 
-static bool IsFileExists(const std::string &path) {
+static bool IsFileExists(const std::string& path) {
   std::ifstream file(path);
   bool res = file.is_open();
   if (res) {
@@ -31,5 +32,15 @@ static bool IsFileExists(const std::string &path) {
   return res;
 }
 
+// ARM mobile not support mkdir in C++
+static void MkDirRecur(const std::string& path) {
+#ifndef LITE_WITH_ARM
+  CHECK_EQ(system(string_format("mkdir -p %s", path.c_str()).c_str()), 0)
+      << "Cann't mkdir " << path;
+#else  // On ARM
+  CHECK_NE(mkdir(path.c_str(), S_IRWXU), -1) << "Cann't mkdir " << path;
+#endif
+}
+
 }  // namespace lite
 }  // namespace paddle

paddle/fluid/lite/utils/string.h

@@ -74,5 +74,15 @@ static std::string Repr(const std::vector<std::string>& v) {
   return "{" + Join(tmp, ",") + "}";
 }
 
+static std::vector<std::string> Split(const std::string& s, char delim) {
+  std::stringstream ss(s);
+  std::string line;
+  std::vector<std::string> res;
+  while (std::getline(ss, line, delim)) {
+    res.push_back(line);
+  }
+  return res;
+}
+
 }  // namespace lite
 }  // namespace paddle