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

README.md

@@ -44,7 +44,7 @@ Framework compatibility: In addition to models trained on PaddlePaddle, those tr
 
 Paddle Lite is designed to support a wide range of hardwares and devices, and it enables mixed execution of a single model on multiple devices, optimization on various phases, and leight-weighted applications on devices.
 
-![img](https://github.com/Superjomn/_tmp_images/raw/master/images/paddle-lite-architecture.png)
+![img](https://user-images.githubusercontent.com/45189361/70908123-6ce4fd00-2045-11ea-97e1-ad08446c5c86.png)
 
 As is shown in the figure above, analysis phase includes Machine IR module, and it enables optimizations like Op fusion and redundant computation pruning. Besides, excecution phase only involves Kernal exevution, so it can be deployed on its own to ensure maximized light-weighted deployment.
 

README_cn.md

@@ -34,7 +34,7 @@ Paddle Lite为Paddle-Mobile的升级版，定位支持包括手机移动端在
 
 PaddleLite 的架构设计着重考虑了对多硬件和平台的支持，并且强化了多个硬件在一个模型中混合执行的能力，多个层面的性能优化处理，以及对端侧应用的轻量化设计。
 
-![](https://github.com/Superjomn/_tmp_images/raw/master/images/paddle-lite-architecture.png)
+![](https://user-images.githubusercontent.com/45189361/70908123-6ce4fd00-2045-11ea-97e1-ad08446c5c86.png)
 
 其中，Analysis Phase 包括了 MIR(Machine IR) 相关模块，能够对原有的模型的计算图针对具体的硬件列表进行算子融合、计算裁剪 在内的多种优化。Execution Phase 只涉及到Kernel 的执行，且可以单独部署，以支持极致的轻量级部署。
 

cmake/cross_compiling/postproject.cmake

@@ -63,7 +63,7 @@ if (LITE_ON_TINY_PUBLISH)
 	set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-exceptions")
     endif()
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -ffast-math -Ofast -Os -fomit-frame-pointer -fno-asynchronous-unwind-tables -fno-unwind-tables")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -flto -fvisibility=hidden -fvisibility-inlines-hidden -fdata-sections -ffunction-sections")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden -ffunction-sections")
     check_linker_flag(-Wl,--gc-sections)
 endif()
 

cmake/cudnn.cmake

@@ -32,10 +32,9 @@ list(APPEND CUDNN_CHECK_LIBRARY_DIRS
     $ENV{CUDNN_ROOT}/lib64
     $ENV{CUDNN_ROOT}/lib
     /usr/lib
-	${CUDA_TOOLKIT_ROOT_DIR}
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
-	${CUDA_TOOLKIT_ROOT_DIR}/lib64
-	)
+    ${CUDA_TOOLKIT_ROOT_DIR}
+    ${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
+    ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
 
 if((${CUDA_VERSION} GREATER 10.0) OR (${CUDA_VERSION} EQUAL 10.0))
     find_library(CUBLAS_LIBRARY  NAMES libcublas.so PATHS ${CUDNN_CHECK_LIBRARY_DIRS} NO_DEFAULT_PATH)

cmake/lite.cmake

@@ -118,7 +118,7 @@ file(WRITE ${offline_lib_registry_file} "") # clean
 function(lite_cc_library TARGET)
     set(options SHARED shared STATIC static MODULE module)
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS NPU_DEPS XPU_DEPS ARM_DEPS FPGA_DEPS PROFILE_DEPS LIGHT_DEPS
+    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS NPU_DEPS XPU_DEPS PROFILE_DEPS LIGHT_DEPS
       HVY_DEPS EXCLUDE_COMPILE_DEPS ARGS)
     cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
@@ -128,10 +128,10 @@ function(lite_cc_library TARGET)
             X86_DEPS ${args_X86_DEPS}
             CUDA_DEPS ${args_CUDA_DEPS}
             CL_DEPS ${args_CL_DEPS}
-            NPU_DEPS ${args_NPU_DEPS}
-            XPU_DEPS ${args_XPU_DEPS}
             ARM_DEPS ${args_ARM_DEPS}
             FPGA_DEPS ${args_FPGA_DEPS}
+            NPU_DEPS ${args_NPU_DEPS}
+            XPU_DEPS ${args_XPU_DEPS}
             PROFILE_DEPS ${args_PROFILE_DEPS}
             LIGHT_DEPS ${args_LIGHT_DEPS}
             HVY_DEPS ${args_HVY_DEPS}
@@ -161,7 +161,7 @@ function(lite_cc_binary TARGET)
         set(options " -g ")
     endif()
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS PROFILE_DEPS
+    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS NPU_DEPS XPU_DEPS PROFILE_DEPS
       LIGHT_DEPS HVY_DEPS EXCLUDE_COMPILE_DEPS ARGS)
     cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
@@ -173,6 +173,8 @@ function(lite_cc_binary TARGET)
             CL_DEPS ${args_CL_DEPS}
             ARM_DEPS ${args_ARM_DEPS}
             FPGA_DEPS ${args_FPGA_DEPS}
+            NPU_DEPS ${args_NPU_DEPS}
+            XPU_DEPS ${args_XPU_DEPS}
             PROFILE_DEPS ${args_PROFILE_DEPS}
             LIGHT_DEPS ${args_LIGHT_DEPS}
             HVY_DEPS ${args_HVY_DEPS}
@@ -205,7 +207,7 @@ function(lite_cc_test TARGET)
     endif()
     set(options "")
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS PROFILE_DEPS
+    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS NPU_DEPS XPU_DEPS PROFILE_DEPS
         LIGHT_DEPS HVY_DEPS EXCLUDE_COMPILE_DEPS
         ARGS
         COMPILE_LEVEL # (basic|extra)
@@ -225,6 +227,8 @@ function(lite_cc_test TARGET)
               CL_DEPS ${args_CL_DEPS}
               ARM_DEPS ${args_ARM_DEPS}
               FPGA_DEPS ${args_FPGA_DEPS}
+              NPU_DEPS ${args_NPU_DEPS}
+              XPU_DEPS ${args_XPU_DEPS}
               PROFILE_DEPS ${args_PROFILE_DEPS}
               LIGHT_DEPS ${args_LIGHT_DEPS}
               HVY_DEPS ${args_HVY_DEPS}
@@ -267,7 +271,7 @@ endif()
 function(add_kernel TARGET device level)
     set(options "")
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS PROFILE_DEPS
+    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS NPU_DEPS XPU_DEPS PROFILE_DEPS
         LIGHT_DEPS HVY_DEPS EXCLUDE_COMPILE_DEPS
         ARGS)
     cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
@@ -360,11 +364,12 @@ function(add_kernel TARGET device level)
     lite_cc_library(${TARGET} SRCS ${args_SRCS}
               DEPS ${args_DEPS}
               X86_DEPS ${args_X86_DEPS}
-              XPU_DEPS ${args_XPU_DEPS}
               CUDA_DEPS ${args_CUDA_DEPS}
               CL_DEPS ${args_CL_DEPS}
               ARM_DEPS ${args_ARM_DEPS}
               FPGA_DEPS ${args_FPGA_DEPS}
+              NPU_DEPS ${args_NPU_DEPS}
+              XPU_DEPS ${args_XPU_DEPS}
               PROFILE_DEPS ${args_PROFILE_DEPS}
               LIGHT_DEPS ${args_LIGHT_DEPS}
               HVY_DEPS ${args_HVY_DEPS}
@@ -383,7 +388,7 @@ endif()
 function(add_operator TARGET level)
     set(options "")
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS PROFILE_DEPS
+    set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS CL_DEPS ARM_DEPS FPGA_DEPS NPU_DEPS XPU_DEPS PROFILE_DEPS
         LIGHT_DEPS HVY_DEPS EXCLUDE_COMPILE_DEPS
         ARGS)
     cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
@@ -409,11 +414,12 @@ function(add_operator TARGET level)
     lite_cc_library(${TARGET} SRCS ${args_SRCS}
               DEPS ${args_DEPS}
               X86_DEPS ${args_X86_DEPS}
-              XPU_DEPS ${args_XPU_DEPS}
               CUDA_DEPS ${args_CUDA_DEPS}
               CL_DEPS ${args_CL_DEPS}
               ARM_DEPS ${args_ARM_DEPS}
               FPGA_DEPS ${args_FPGA_DEPS}
+              NPU_DEPS ${args_NPU_DEPS}
+              XPU_DEPS ${args_XPU_DEPS}
               PROFILE_DEPS ${args_PROFILE_DEPS}
               LIGHT_DEPS ${args_LIGHT_DEPS}
               HVY_DEPS ${args_HVY_DEPS}

cmake/xpu.cmake

@@ -89,7 +89,7 @@ else()
 endif()
 
 find_library(XPU_SDK_LLVM_FILE NAMES LLVM-8
-  PATHS ${XPU_SDK_ROOT}/XTDK/shlib)
+  PATHS ${XPU_SDK_ROOT}/XTDK/shlib/gcc482)
 
 if(NOT XPU_SDK_LLVM_FILE)
   message(FATAL_ERROR "Can not find LLVM Library in ${XPU_SDK_ROOT}")

lite/CMakeLists.txt

@@ -172,13 +172,17 @@ if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND LITE_WITH_ARM)
                     COMMAND mkdir -p "${INFER_LITE_PUBLISH_ROOT}/cxx/include"
                     COMMAND mkdir -p "${INFER_LITE_PUBLISH_ROOT}/cxx/lib"
                     COMMAND cp "${CMAKE_SOURCE_DIR}/lite/api/paddle_*.h" "${INFER_LITE_PUBLISH_ROOT}/cxx/include"
+                    COMMAND cp "${CMAKE_BINARY_DIR}/libpaddle_api_light_bundled.a" "${INFER_LITE_PUBLISH_ROOT}/cxx/lib"
                     COMMAND cp "${CMAKE_BINARY_DIR}/lite/api/libpaddle_light_api_shared.so" "${INFER_LITE_PUBLISH_ROOT}/cxx/lib"
                     COMMAND cp "${CMAKE_SOURCE_DIR}/lite/utils/cv/paddle_*.h" "${INFER_LITE_PUBLISH_ROOT}/cxx/include"
                     )
                 add_dependencies(tiny_publish_cxx_lib paddle_light_api_shared)
+                add_dependencies(tiny_publish_cxx_lib bundle_light_api)
                 add_dependencies(publish_inference tiny_publish_cxx_lib)
-                add_custom_command(TARGET tiny_publish_cxx_lib POST_BUILD
-                            COMMAND ${CMAKE_STRIP} "-s" ${INFER_LITE_PUBLISH_ROOT}/cxx/lib/libpaddle_light_api_shared.so)
+                if(NOT "${CMAKE_BUILD_TYPE}" STREQUAL "Debug")
+                    add_custom_command(TARGET tiny_publish_cxx_lib POST_BUILD
+                                COMMAND ${CMAKE_STRIP} "-s" ${INFER_LITE_PUBLISH_ROOT}/cxx/lib/libpaddle_light_api_shared.so)
+                endif()
             endif()
         endif()
     endif()

lite/api/CMakeLists.txt

@@ -16,8 +16,11 @@ if ((NOT LITE_ON_TINY_PUBLISH) AND (LITE_WITH_CUDA OR LITE_WITH_X86 OR ARM_TARGE
     add_dependencies(paddle_full_api_shared op_list_h kernel_list_h framework_proto)
     target_link_libraries(paddle_full_api_shared framework_proto)
     if(LITE_WITH_X86)
-       add_dependencies(paddle_full_api_shared xxhash)
-       target_link_libraries(paddle_full_api_shared xxhash)
+        add_dependencies(paddle_full_api_shared xxhash)
+        target_link_libraries(paddle_full_api_shared xxhash)
+        if (NOT LITE_ON_MODEL_OPTIMIZE_TOOL) 
+            add_dependencies(paddle_full_api_shared dynload_mklml)
+        endif()
     endif()
     if(LITE_WITH_CUDA)
         target_link_libraries(paddle_full_api_shared ${math_cuda} "-Wl,--whole-archive" ${cuda_kernels} "-Wl,--no-whole-archive")
@@ -38,10 +41,11 @@ else()
     if ((ARM_TARGET_OS STREQUAL "android") OR (ARM_TARGET_OS STREQUAL "armlinux"))
         add_library(paddle_light_api_shared SHARED "")
         target_sources(paddle_light_api_shared PUBLIC ${__lite_cc_files} paddle_api.cc light_api.cc light_api_impl.cc)
-        add_dependencies(paddle_light_api_shared op_list_h kernel_list_h)
+       set_target_properties(paddle_light_api_shared PROPERTIES COMPILE_FLAGS "-flto -fdata-sections")
+       add_dependencies(paddle_light_api_shared op_list_h kernel_list_h)
         if (LITE_WITH_NPU)
             # Need to add HIAI runtime libs (libhiai.so) dependency
-            target_link_libraries(paddle_light_api_shared ${npu_runtime_libs})
+            target_link_libraries(paddle_light_api_shared ${npu_builder_libs} ${npu_runtime_libs})
         endif()
     endif()
 endif()
@@ -77,8 +81,8 @@ if (NOT LITE_ON_TINY_PUBLISH)
                     DEPS ${cxx_api_deps} ${ops} ${host_kernels} program
                     X86_DEPS ${x86_kernels}
                     ARM_DEPS ${arm_kernels}
-                    NPU_DEPS ${npu_kernels} ${npu_bridges} npu_pass
-                    XPU_DEPS ${xpu_kernels} ${xpu_bridges} xpu_pass
+                    NPU_DEPS ${npu_kernels}
+                    XPU_DEPS ${xpu_kernels}
                     CL_DEPS ${opencl_kernels}
                     FPGA_DEPS ${fpga_kernels})
 endif()

lite/api/_paddle_use_ops.h

@@ -108,7 +108,7 @@ USE_LITE_OP(while)
 USE_LITE_OP(lod_reset)
 USE_LITE_OP(lookup_table)
 USE_LITE_OP(multiclass_nms)
-USE_LITE_OP(graph_op)
+USE_LITE_OP(subgraph)
 USE_LITE_OP(sequence_expand)
 USE_LITE_OP(sequence_pool)
 USE_LITE_OP(reduce_max)

lite/api/android/jni/native/CMakeLists.txt

@@ -25,11 +25,12 @@ if (NOT LITE_ON_TINY_PUBLISH)
     endif()
 else()
     add_library(paddle_lite_jni SHARED "")
+    set_target_properties(paddle_lite_jni PROPERTIES COMPILE_FLAGS "-flto -fdata-sections")
     target_sources(paddle_lite_jni PUBLIC ${__lite_cc_files} paddle_lite_jni.cc tensor_jni.cc)
     add_dependencies(paddle_lite_jni op_list_h kernel_list_h)
     if (LITE_WITH_NPU)
         # Need to add HIAI runtime libs (libhiai.so) dependency
-        target_link_libraries(paddle_lite_jni ${npu_runtime_libs})
+        target_link_libraries(paddle_lite_jni ${npu_builder_libs} ${npu_runtime_libs})
     endif()
 endif()
 

lite/api/android/jni/native/tensor_jni.cc

@@ -120,6 +120,22 @@ JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_lite_Tensor_nativeSetData___3B(
   return JNI_TRUE;
 }
 
+JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_lite_Tensor_nativeSetData___3I(
+    JNIEnv *env, jobject jtensor, jintArray buf) {
+  std::unique_ptr<Tensor> *tensor = get_writable_tensor_pointer(env, jtensor);
+  if (tensor == nullptr || (*tensor == nullptr)) {
+    return JNI_FALSE;
+  }
+  int64_t buf_size = (int64_t)env->GetArrayLength(buf);
+  if (buf_size != product((*tensor)->shape())) {
+    return JNI_FALSE;
+  }
+
+  int32_t *input = (*tensor)->mutable_data<int32_t>();
+  env->GetIntArrayRegion(buf, 0, buf_size, input);
+  return JNI_TRUE;
+}
+
 JNIEXPORT jfloatArray JNICALL
 Java_com_baidu_paddle_lite_Tensor_getFloatData(JNIEnv *env, jobject jtensor) {
   if (is_const_tensor(env, jtensor)) {
@@ -148,6 +164,20 @@ Java_com_baidu_paddle_lite_Tensor_getByteData(JNIEnv *env, jobject jtensor) {
   }
 }
 
+JNIEXPORT jintArray JNICALL
+Java_com_baidu_paddle_lite_Tensor_getIntData(JNIEnv *env, jobject jtensor) {
+  if (is_const_tensor(env, jtensor)) {
+    std::unique_ptr<const Tensor> *tensor =
+        get_read_only_tensor_pointer(env, jtensor);
+    return cpp_array_to_jintarray(
+        env, (*tensor)->data<int32_t>(), product((*tensor)->shape()));
+  } else {
+    std::unique_ptr<Tensor> *tensor = get_writable_tensor_pointer(env, jtensor);
+    return cpp_array_to_jintarray(
+        env, (*tensor)->data<int32_t>(), product((*tensor)->shape()));
+  }
+}
+
 JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_lite_Tensor_deleteCppTensor(
     JNIEnv *env, jobject jtensor, jlong java_pointer) {
   if (java_pointer == 0) {

lite/api/android/jni/native/tensor_jni.h

@@ -16,8 +16,8 @@
 #include <jni.h>
 /* Header for class com_baidu_paddle_lite_Tensor */
 
-#ifndef PADDLE_FLUID_LITE_API_ANDROID_JNI_NATIVE_TENSOR_JNI_H_
-#define PADDLE_FLUID_LITE_API_ANDROID_JNI_NATIVE_TENSOR_JNI_H_
+#ifndef LITE_API_ANDROID_JNI_NATIVE_TENSOR_JNI_H_
+#define LITE_API_ANDROID_JNI_NATIVE_TENSOR_JNI_H_
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -49,6 +49,14 @@ Java_com_baidu_paddle_lite_Tensor_getFloatData(JNIEnv *, jobject);
 JNIEXPORT jbyteArray JNICALL
 Java_com_baidu_paddle_lite_Tensor_getByteData(JNIEnv *, jobject);
 
+/*
+ * Class:     com_baidu_paddle_lite_Tensor
+ * Method:    getIntData
+ * Signature: ()[I
+ */
+JNIEXPORT jintArray JNICALL
+Java_com_baidu_paddle_lite_Tensor_getIntData(JNIEnv *, jobject);
+
 /*
  * Class:     com_baidu_paddle_lite_Tensor
  * Method:    nativeResize
@@ -73,6 +81,14 @@ JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_lite_Tensor_nativeSetData___3F(
 JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_lite_Tensor_nativeSetData___3B(
     JNIEnv *, jobject, jbyteArray);
 
+/*
+ * Class:     com_baidu_paddle_lite_Tensor
+ * Method:    nativeSetData
+ * Signature: ([I)Z
+ */
+JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_lite_Tensor_nativeSetData___3I(
+    JNIEnv *, jobject, jintArray);
+
 /*
  * Class:     com_baidu_paddle_lite_Tensor
  * Method:    deleteCppTensor
@@ -87,4 +103,4 @@ Java_com_baidu_paddle_lite_Tensor_deleteCppTensor(JNIEnv *, jobject, jlong);
 #ifdef __cplusplus
 }
 #endif
-#endif  // PADDLE_FLUID_LITE_API_ANDROID_JNI_NATIVE_TENSOR_JNI_H_
+#endif  // LITE_API_ANDROID_JNI_NATIVE_TENSOR_JNI_H_

lite/api/android/jni/src/com/baidu/paddle/lite/Tensor.java

@@ -108,6 +108,19 @@ public class Tensor {
         return nativeSetData(buf);
     }
 
+    /**
+     * Set the tensor int data.
+     *
+     * @param buf the int array buffer which will be copied into tensor.
+     * @return true if set data successfully.
+     */
+    public boolean setData(int[] buf) {
+        if (readOnly) {
+            return false;
+        }
+        return nativeSetData(buf);
+    }
+
     /**
      * @return shape of the tensor as long array.
      */
@@ -123,12 +136,19 @@ public class Tensor {
      */
     public native byte[] getByteData();
 
+    /**
+     * @return the tensor data as int array.
+     */
+    public native int[] getIntData();
+
     private native boolean nativeResize(long[] dims);
 
     private native boolean nativeSetData(float[] buf);
 
     private native boolean nativeSetData(byte[] buf);
 
+    private native boolean nativeSetData(int[] buf);
+
     /**
      * Delete C++ Tenor object pointed by the input pointer, which is presented by a
      * long value.

lite/api/cxx_api.cc

@@ -139,22 +139,15 @@ std::vector<std::string> Predictor::GetOutputNames() { return output_names_; }
 
 // append the names of inputs and outputs into input_names_ and output_names_
 void Predictor::PrepareFeedFetch() {
-  std::vector<const cpp::OpDesc *> feeds;
-  std::vector<const cpp::OpDesc *> fetchs;
-#if defined(LITE_WITH_NPU) || defined(LITE_WITH_XPU)
-  // The shape of input tensors must be determined before generating NPU and XPU
-  // program.
-  auto current_block = program_desc_.GetBlock<cpp::BlockDesc>(0);
-  for (size_t i = 0; i < current_block->OpsSize(); i++) {
-    auto op = current_block->GetOp<cpp::OpDesc>(i);
-#else
   if (!program_) {
     GenRuntimeProgram();
   }
+
+  std::vector<const cpp::OpDesc *> feeds;
+  std::vector<const cpp::OpDesc *> fetchs;
   const auto &insts = program_->instructions();
   for (size_t i = 0; i < program_->num_instructions(); i++) {
     const auto &op = insts[i].op()->op_info();
-#endif
     if (op->Type() == "feed") {
       feeds.push_back(op);
     } else if (op->Type() == "fetch") {

lite/api/cxx_api_impl.cc

@@ -20,6 +20,12 @@
 #include "lite/core/device_info.h"
 #include "lite/core/version.h"
 
+#if (defined LITE_WITH_X86) && (defined PADDLE_WITH_MKLML) && \
+    !(defined LITE_ON_MODEL_OPTIMIZE_TOOL)
+#include <omp.h>
+#include "lite/backends/x86/mklml.h"
+#endif
+
 namespace paddle {
 namespace lite {
 
@@ -33,6 +39,17 @@ void CxxPaddleApiImpl::Init(const lite_api::CxxConfig &config) {
 
   mode_ = config.power_mode();
   threads_ = config.threads();
+
+#if (defined LITE_WITH_X86) && (defined PADDLE_WITH_MKLML) && \
+    !(defined LITE_ON_MODEL_OPTIMIZE_TOOL)
+  int num_threads = config.cpu_math_library_num_threads();
+  int real_num_threads = num_threads > 1 ? num_threads : 1;
+  paddle::lite::x86::MKL_Set_Num_Threads(real_num_threads);
+  omp_set_num_threads(real_num_threads);
+  VLOG(3) << "set_cpu_math_library_math_threads() is set successfully and the "
+             "number of threads is:"
+          << num_threads;
+#endif
 }
 
 std::unique_ptr<lite_api::Tensor> CxxPaddleApiImpl::GetInput(int i) {

lite/api/model_optimize_tool.cc

@@ -90,6 +90,10 @@ std::vector<Place> ParserValidPlaces() {
           TARGET(kARM));  // enable kARM CPU kernel when no opencl kernel
     } else if (target_repr == "x86") {
       valid_places.emplace_back(TARGET(kX86));
+    } else if (target_repr == "npu") {
+      valid_places.emplace_back(TARGET(kNPU));
+    } else if (target_repr == "xpu") {
+      valid_places.emplace_back(TARGET(kXPU));
     } else {
       LOG(FATAL) << lite::string_format(
           "Wrong target '%s' found, please check the command flag "

lite/api/model_test.cc

@@ -72,10 +72,6 @@ void Run(const std::vector<std::vector<int64_t>>& input_shapes,
          const int thread_num,
          const int repeat,
          const int warmup_times = 0) {
-#ifdef LITE_WITH_PROFILE
-  lite::profile::BasicProfiler<lite::profile::BasicTimer>::Global().SetWarmup(
-      warmup_times);
-#endif
   lite_api::MobileConfig config;
   config.set_model_dir(model_dir);
   config.set_power_mode(power_mode);

lite/api/paddle_api.h

@@ -133,6 +133,7 @@ class LITE_API CxxConfig : public ConfigBase {
   std::string model_file_;
   std::string param_file_;
   bool model_from_memory_{false};
+  int cpu_math_library_math_threads_ = 1;
 
  public:
   void set_valid_places(const std::vector<Place>& x) { valid_places_ = x; }
@@ -151,6 +152,13 @@ class LITE_API CxxConfig : public ConfigBase {
   std::string model_file() const { return model_file_; }
   std::string param_file() const { return param_file_; }
   bool model_from_memory() const { return model_from_memory_; }
+
+  void set_cpu_math_library_math_threads(int threads) {
+    cpu_math_library_math_threads_ = threads;
+  }
+  int cpu_math_library_num_threads() const {
+    return cpu_math_library_math_threads_;
+  }
 };
 
 /// MobileConfig is the config for the light weight predictor, it will skip

lite/api/paddle_place.cc

@@ -77,7 +77,8 @@ const std::string& PrecisionToStr(PrecisionType precision) {
 }
 
 const std::string& DataLayoutToStr(DataLayoutType layout) {
-  static const std::string datalayout2string[] = {"unk", "NCHW", "any", "NHWC"};
+  static const std::string datalayout2string[] = {
+      "unk", "NCHW", "any", "NHWC", "ImageDefault", "ImageFolder", "ImageNW"};
   auto x = static_cast<int>(layout);
   CHECK_LT(x, static_cast<int>(DATALAYOUT(NUM)));
   return datalayout2string[x];
@@ -115,8 +116,13 @@ const std::string& PrecisionRepr(PrecisionType precision) {
 }
 
 const std::string& DataLayoutRepr(DataLayoutType layout) {
-  static const std::string datalayout2string[] = {
-      "kUnk", "kNCHW", "kAny", "kNHWC"};
+  static const std::string datalayout2string[] = {"kUnk",
+                                                  "kNCHW",
+                                                  "kAny",
+                                                  "kNHWC",
+                                                  "kImageDefault",
+                                                  "kImageFolder",
+                                                  "kImageNW"};
   auto x = static_cast<int>(layout);
   CHECK_LT(x, static_cast<int>(DATALAYOUT(NUM)));
   return datalayout2string[x];
@@ -146,8 +152,12 @@ std::set<PrecisionType> ExpandValidPrecisions(PrecisionType precision) {
 }
 
 std::set<DataLayoutType> ExpandValidLayouts(DataLayoutType layout) {
-  static const std::set<DataLayoutType> valid_set(
-      {DATALAYOUT(kNCHW), DATALAYOUT(kAny), DATALAYOUT(kNHWC)});
+  static const std::set<DataLayoutType> valid_set({DATALAYOUT(kNCHW),
+                                                   DATALAYOUT(kAny),
+                                                   DATALAYOUT(kNHWC),
+                                                   DATALAYOUT(kImageDefault),
+                                                   DATALAYOUT(kImageFolder),
+                                                   DATALAYOUT(kImageNW)});
   if (layout == DATALAYOUT(kAny)) {
     return valid_set;
   }

lite/api/paddle_place.h

@@ -71,8 +71,11 @@ enum class DataLayoutType : int {
   kUnk = 0,
   kNCHW = 1,
   kNHWC = 3,
-  kAny = 2,  // any data layout
-  NUM = 4,   // number of fields.
+  kImageDefault = 4,  // for opencl image2d
+  kImageFolder = 5,   // for opencl image2d
+  kImageNW = 6,       // for opencl image2d
+  kAny = 2,           // any data layout
+  NUM = 7,            // number of fields.
 };
 
 typedef enum {

lite/api/paddle_use_passes.h

@@ -20,12 +20,6 @@ USE_MIR_PASS(static_kernel_pick_pass);
 USE_MIR_PASS(variable_place_inference_pass);
 USE_MIR_PASS(type_target_cast_pass);
 USE_MIR_PASS(generate_program_pass);
-#ifdef LITE_WITH_NPU
-USE_MIR_PASS(generate_npu_program_pass);
-#endif
-#ifdef LITE_WITH_XPU
-USE_MIR_PASS(generate_xpu_program_pass);
-#endif
 
 USE_MIR_PASS(io_copy_kernel_pick_pass);
 USE_MIR_PASS(argument_type_display_pass);
@@ -40,8 +34,12 @@ USE_MIR_PASS(lite_interpolate_fuse_pass);
 USE_MIR_PASS(identity_scale_eliminate_pass);
 USE_MIR_PASS(lite_conv_elementwise_fuse_pass);
 USE_MIR_PASS(lite_conv_activation_fuse_pass);
+USE_MIR_PASS(lite_var_conv_2d_activation_fuse_pass);
 USE_MIR_PASS(lite_elementwise_add_activation_fuse_pass);
 USE_MIR_PASS(lite_quant_dequant_fuse_pass);
 USE_MIR_PASS(type_precision_cast_pass);
 USE_MIR_PASS(type_layout_cast_pass);
 USE_MIR_PASS(memory_optimize_pass);
+USE_MIR_PASS(elementwise_mul_constant_eliminate_pass)
+USE_MIR_PASS(npu_subgraph_pass);
+USE_MIR_PASS(xpu_subgraph_pass);

lite/api/python/pybind/CMakeLists.txt

@@ -4,3 +4,6 @@ if (NOT LITE_ON_TINY_PUBLISH)
 endif()
 
 lite_cc_library(lite_pybind SHARED SRCS pybind.cc DEPS ${PYBIND_DEPS})
+if (LITE_ON_TINY_PUBLISH)
+   set_target_properties(lite_pybind PROPERTIES COMPILE_FLAGS "-flto -fdata-sections")
+endif()

lite/api/python/pybind/pybind.cc

@@ -165,6 +165,9 @@ void BindLitePlace(py::module *m) {
   py::enum_<DataLayoutType>(*m, "DataLayoutType")
       .value("NCHW", DataLayoutType::kNCHW)
       .value("NHWC", DataLayoutType::kNHWC)
+      .value("ImageDefault", DataLayoutType::kImageDefault)
+      .value("ImageFolder", DataLayoutType::kImageFolder)
+      .value("ImageNW", DataLayoutType::kImageNW)
       .value("Any", DataLayoutType::kAny);
 
   // Place

lite/api/test_step_rnn_lite_x86.cc

@@ -30,6 +30,7 @@ TEST(Step_rnn, test_step_rnn_lite_x86) {
   std::string model_dir = FLAGS_model_dir;
   lite_api::CxxConfig config;
   config.set_model_dir(model_dir);
+  config.set_cpu_math_library_math_threads(10);
   config.set_valid_places({lite_api::Place{TARGET(kX86), PRECISION(kInt64)},
                            lite_api::Place{TARGET(kX86), PRECISION(kFloat)},
                            lite_api::Place{TARGET(kHost), PRECISION(kFloat)}});

lite/backends/arm/math/CMakeLists.txt

@@ -120,5 +120,7 @@ if (NOT HAS_ARM_MATH_LIB_DIR)
       stack.cc
       affine_channel.cc
       anchor_generator.cc
+      split_merge_lod_tenosr.cc
+      reduce_prod.cc
       DEPS ${lite_kernel_deps} context tensor)
 endif()

lite/backends/arm/math/conv3x3s1_direct_fp32.cc

@@ -76,6 +76,7 @@ void conv_3x3s1_direct_fp32(const float* i_data,
   const int threads = ctx->threads();
   int l2_size = ctx->llc_size() / sizeof(float);
   auto paddings = *param.paddings;
+  auto act_param = param.activation_param;
 
   const int pad_h = paddings[0];
   const int pad_w = paddings[2];
@@ -469,7 +470,8 @@ void conv_3x3s1_direct_fp32(const float* i_data,
                                 oh,
                                 ow,
                                 flag_relu,
-                                ptr_write);
+                                ptr_write,
+                                &act_param);
       }
       const float* weight_remain_ptr = weights + c_round_down * w_stride;
 #pragma omp parallel for num_threads(threads)
@@ -780,7 +782,8 @@ void conv_3x3s1_direct_fp32(const float* i_data,
                                 oh,
                                 ow,
                                 flag_relu,
-                                ptr_write);
+                                ptr_write,
+                                &act_param);
       }
     }
   }

lite/backends/arm/math/conv3x3s2_direct_fp32.cc

@@ -75,6 +75,7 @@ void conv_3x3s2_direct_fp32(const float* i_data,
   //! prepack input to tmp buffer
   //! write output to tmp buffer
   auto paddings = *param.paddings;
+  auto act_param = param.activation_param;
   const int threads = ctx->threads();
   int l2_size = ctx->llc_size() / sizeof(float);
   const int pad_w = paddings[2];
@@ -510,7 +511,8 @@ void conv_3x3s2_direct_fp32(const float* i_data,
                                 oh,
                                 ow,
                                 flag_relu,
-                                ptr_write);
+                                ptr_write,
+                                &act_param);
       }
 
 #pragma omp parallel for num_threads(threads)
@@ -839,7 +841,8 @@ void conv_3x3s2_direct_fp32(const float* i_data,
                                 oh,
                                 ow,
                                 flag_relu,
-                                ptr_write);
+                                ptr_write,
+                                &act_param);
       }
     }
   }

lite/backends/arm/math/conv_depthwise.h

@@ -37,6 +37,7 @@ void conv_3x3s1_depthwise_fp32(const float* i_data,
                                const float* weights,
                                const float* bias,
                                const operators::ConvParam& param,
+                               const operators::ActivationParam act_param,
                                ARMContext* ctx);
 
 void conv_3x3s2_depthwise_fp32(const float* i_data,
@@ -67,6 +68,7 @@ void conv_depthwise_3x3s1_fp32(const float* din,
                                int pad,
                                bool flag_bias,
                                bool flag_relu,
+                               const operators::ActivationParam act_param,
                                ARMContext* ctx);
 
 void conv_depthwise_3x3s2_fp32(const float* din,

lite/backends/arm/math/conv_impl.cc

@@ -579,6 +579,7 @@ void conv_depthwise_3x3_fp32(const void* din,
                              ARMContext* ctx,
                              const float* scale) {
   auto paddings = *param.paddings;
+  auto act_param = param.activation_param;
   const int pad_h = paddings[0];
   const int pad_w = paddings[2];
   int stride = param.strides[1];
@@ -603,6 +604,7 @@ void conv_depthwise_3x3_fp32(const void* din,
                                 pad,
                                 flag_bias,
                                 flag_relu,
+                                act_param,
                                 ctx);
     } else {
       conv_3x3s1_depthwise_fp32(reinterpret_cast<const float*>(din),
@@ -617,6 +619,7 @@ void conv_depthwise_3x3_fp32(const void* din,
                                 reinterpret_cast<const float*>(weights),
                                 bias,
                                 param,
+                                act_param,
                                 ctx);
     }
 

lite/backends/fpga/KD/pe.hpp

@@ -32,6 +32,5 @@ class PE {
 
   virtual ~PE() {}
 };
-
 }  // namespace zynqmp
 }  // namespace paddle