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

CMakeLists.txt

@@ -98,6 +98,7 @@ lite_option(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK  "Enable light-weight framework" OF
 lite_option(LITE_WITH_PROFILE  "Enable profile mode in lite framework"  OFF)
 lite_option(LITE_WITH_PRECISION_PROFILE "Enable precision profile in profile mode ON in lite" OFF)
 lite_option(LITE_WITH_LOG "Enable log printing or not." ON)
+lite_option(LITE_WITH_EXCEPTION "Enable throwing the exception when error occurs in lite" OFF)
 lite_option(LITE_WITH_NVTX "Enable nvtx or not, please enable LITE_WITH_CUDA first." OFF)
 lite_option(LITE_ON_TINY_PUBLISH "Publish tiny predictor lib." OFF)
 lite_option(LITE_ON_MODEL_OPTIMIZE_TOOL "Build the model optimize tool" OFF)

cmake/configure.cmake

@@ -190,6 +190,10 @@ if (LITE_WITH_LOG)
   add_definitions("-DLITE_WITH_LOG")
 endif()
 
+if (LITE_WITH_EXCEPTION)
+  add_definitions("-DLITE_WITH_EXCEPTION")
+endif()
+
 if (LITE_ON_TINY_PUBLISH)
   add_definitions("-DLITE_ON_TINY_PUBLISH")
 endif()

cmake/cross_compiling/android.cmake

@@ -80,6 +80,17 @@ if (ARM_TARGET_LANG STREQUAL "clang")
     elseif(ARM_TARGET_ARCH_ABI STREQUAL "armv7")
         set(triple arm-v7a-linux-android)
         set(LITE_WITH_OPENMP OFF CACHE STRING "Due to libomp's bug(For ARM64, it has been fixed by https://reviews.llvm.org/D19879, but still exists on ARM32), disable OpenMP on armv7 when cross-compiling using Clang" FORCE)
+        if(ANDROID_STL_TYPE MATCHES "^c\\+\\+_")
+            # Use CMAKE_CXX_STANDARD_LIBRARIES_INIT to ensure libunwind and libc++ is linked in the right order
+            set(CMAKE_CXX_STANDARD_LIBRARIES_INIT "${CMAKE_CXX_STANDARD_LIBRARIES_INIT} ${ANDROID_NDK}/sources/cxx-stl/llvm-libc++/libs/${ANDROID_ARCH_ABI}/libunwind.a")
+            if(ANDROID_STL_TYPE STREQUAL "c++_shared")
+                set(CMAKE_CXX_STANDARD_LIBRARIES_INIT "${CMAKE_CXX_STANDARD_LIBRARIES_INIT} ${ANDROID_NDK}/sources/cxx-stl/llvm-libc++/libs/${ANDROID_ARCH_ABI}/libc++_shared.so")
+            elseif(ANDROID_STL_TYPE STREQUAL "c++_static")
+                set(CMAKE_CXX_STANDARD_LIBRARIES_INIT "${CMAKE_CXX_STANDARD_LIBRARIES_INIT} ${ANDROID_NDK}/sources/cxx-stl/llvm-libc++/libs/${ANDROID_ARCH_ABI}/libc++_static.a")
+            else()
+                message(FATAL_ERROR "Invalid Android STL TYPE: ${ANDROID_STL_TYPE}.")
+            endif()
+        endif()
     else()
         message(FATAL_ERROR "Clang do not support this ${ARM_TARGET_ARCH_ABI}, use armv8 or armv7")
     endif()

cmake/cross_compiling/postproject.cmake

@@ -23,6 +23,21 @@ if(ANDROID)
     
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -llog -fPIC")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -llog -fPIC")
+
+    # Don't re-export libgcc symbols
+    set(REMOVE_ATOMIC_GCC_SYMBOLS "-Wl,--exclude-libs,libatomic.a -Wl,--exclude-libs,libgcc.a")
+    set(CMAKE_SHARED_LINKER_FLAGS "${REMOVE_ATOMIC_GCC_SYMBOLS} ${CMAKE_SHARED_LINKER_FLAGS}")
+    set(CMAKE_MODULE_LINKER_FLAGS "${REMOVE_ATOMIC_GCC_SYMBOLS} ${CMAKE_MODULE_LINKER_FLAGS}")
+    set(CMAKE_EXE_LINKER_FLAGS "${REMOVE_ATOMIC_GCC_SYMBOLS} ${CMAKE_EXE_LINKER_FLAGS}")
+
+    # Only the libunwind.a from clang(with libc++) provide C++ exception handling support for 32-bit ARM
+    # Refer to https://android.googlesource.com/platform/ndk/+/master/docs/BuildSystemMaintainers.md#Unwinding
+    if (ARM_TARGET_LANG STREQUAL "clang" AND ARM_TARGET_ARCH_ABI STREQUAL "armv7" AND ANDROID_STL_TYPE MATCHES "^c\\+\\+_")
+        set(REMOVE_UNWIND_SYMBOLS "-Wl,--exclude-libs,libunwind.a")
+        set(CMAKE_SHARED_LINKER_FLAGS "${REMOVE_UNWIND_SYMBOLS} ${CMAKE_SHARED_LINKER_FLAGS}")
+        set(CMAKE_MODULE_LINKER_FLAGS "${REMOVE_UNWIND_SYMBOLS} ${CMAKE_MODULE_LINKER_FLAGS}")
+        set(CMAKE_EXE_LINKER_FLAGS "${REMOVE_UNWIND_SYMBOLS} ${CMAKE_EXE_LINKER_FLAGS}")
+    endif()
 endif()
 
 if(ARMLINUX)
@@ -59,14 +74,13 @@ function(check_linker_flag)
 endfunction()
 
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+if((LITE_WITH_OPENCL AND (ARM_TARGET_LANG STREQUAL "clang")) OR LITE_WITH_PYTHON OR LITE_WITH_EXCEPTION OR (NOT LITE_ON_TINY_PUBLISH))
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fexceptions -fasynchronous-unwind-tables -funwind-tables")
+else ()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-exceptions -fno-asynchronous-unwind-tables -fno-unwind-tables")
+endif()
 if (LITE_ON_TINY_PUBLISH)
-    if((NOT LITE_WITH_PYTHON))
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-exceptions")
-    endif()
-    if(LITE_WITH_OPENCL AND (ARM_TARGET_LANG STREQUAL "clang"))
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fexceptions")
-    endif()
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -ffast-math -Ofast -Os -fomit-frame-pointer -fno-asynchronous-unwind-tables -fno-unwind-tables")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -ffast-math -Ofast -Os -fomit-frame-pointer")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden -ffunction-sections")
     check_linker_flag(-Wl,--gc-sections)
 endif()

cmake/device/npu.cmake

@@ -54,6 +54,11 @@ find_library(NPU_DDK_IR_BUILD_FILE NAMES hiai_ir_build
   PATHS ${NPU_DDK_ROOT}/${NPU_SUB_LIB_PATH}
   NO_DEFAULT_PATH)
 
+# Added in HiAI DDK 320 or later version
+find_library(NPU_DDK_HCL_FILE NAMES hcl
+  PATHS ${NPU_DDK_ROOT}/${NPU_SUB_LIB_PATH}
+  NO_DEFAULT_PATH)
+
 if(NOT NPU_DDK_HIAI_FILE)
   message(FATAL_ERROR "Can not find NPU_DDK_HIAI_FILE in ${NPU_DDK_ROOT}")
 else()
@@ -78,5 +83,13 @@ else()
   set_property(TARGET npu_ddk_ir_build PROPERTY IMPORTED_LOCATION ${NPU_DDK_IR_BUILD_FILE})
 endif()
 
-set(npu_runtime_libs npu_ddk_hiai CACHE INTERNAL "npu ddk runtime libs")
+if(NOT NPU_DDK_HCL_FILE)
+# message(FATAL_ERROR "Can not find NPU_DDK_HCL_FILE in ${NPU_DDK_ROOT}")
+else()
+  message(STATUS "Found NPU_DDK HCL Library: ${NPU_DDK_HCL_FILE}")
+  add_library(npu_ddk_hcl SHARED IMPORTED GLOBAL)
+  set_property(TARGET npu_ddk_hcl PROPERTY IMPORTED_LOCATION ${NPU_DDK_HCL_FILE})
+endif()
+
+set(npu_runtime_libs npu_ddk_hiai npu_ddk_hcl CACHE INTERNAL "npu ddk runtime libs")
 set(npu_builder_libs npu_ddk_ir npu_ddk_ir_build CACHE INTERNAL "npu ddk builder libs")

lite/CMakeLists.txt

@@ -45,6 +45,7 @@ if (WITH_TESTING)
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "resnet50.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "inception_v4_simple.tar.gz")
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "MobileNetV1_quant.tar.gz")
+        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "transformer_with_mask_fp32.tar.gz")
     endif()
     if(NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL} "GoogleNet_inference.tar.gz")

lite/api/cxx_api.cc

@@ -37,8 +37,7 @@ void Predictor::SaveModel(const std::string &dir,
   if (!program_) {
     GenRuntimeProgram();
   }
-  program_->SaveOpInfosToProgram(program_desc_.get());
-  program_->UpdateVarsOfProgram(program_desc_.get());
+  program_->SaveToProgram(program_desc_);
   switch (model_type) {
     case lite_api::LiteModelType::kProtobuf:
       SaveModelPb(dir, *program_->exec_scope(), *program_desc_.get(), true);
@@ -58,18 +57,22 @@ void Predictor::SaveModel(const std::string &dir,
 void Predictor::SaveOpKernelInfo(const std::string &model_dir) {
   std::set<std::string> ops_info;
   std::set<std::string> kernels_info;
-  const auto &instructions_ = program_->instructions();
-  for (auto &node : instructions_) {
+  auto block_size = program_->block_size();
+  for (size_t block_idx = 0; block_idx < block_size; ++block_idx) {
+    const auto &insts = program_->instructions(block_idx);
+    for (auto &inst : insts) {
       // parse op type infomation
-    auto op = node.op()->op_info();
+      auto op = inst.op()->op_info();
       ops_info.insert(op->Type());
       // parse kernel type information
       std::string kernel_type_str =
-        node.kernel()->op_type() + "," + TargetRepr(node.kernel()->target()) +
-        "," + PrecisionRepr(node.kernel()->precision()) + "," +
-        DataLayoutRepr(node.kernel()->layout()) + "," + node.kernel()->alias();
+          inst.kernel()->op_type() + "," + TargetRepr(inst.kernel()->target()) +
+          "," + PrecisionRepr(inst.kernel()->precision()) + "," +
+          DataLayoutRepr(inst.kernel()->layout()) + "," +
+          inst.kernel()->alias();
       kernels_info.insert(kernel_type_str);
     }
+  }
 
   // get souce_file name from op type and kernel type
   auto op2pathmap = OpKernelInfoCollector::Global().GetOp2PathDict();
@@ -170,9 +173,9 @@ void Predictor::PrepareFeedFetch() {
 
   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();
+  const auto &insts = program_->instructions(kRootBlockIdx);
+  for (auto &inst : insts) {
+    const auto &op = inst.op()->op_info();
     if (op->Type() == "feed") {
       feeds.push_back(op);
     } else if (op->Type() == "fetch") {
@@ -255,7 +258,6 @@ void Predictor::Build(const lite_api::CxxConfig &config,
   } else {
     LOG(INFO) << "Load model from file.";
   }
-
   Build(model_path,
         model_file,
         param_file,
@@ -296,10 +298,10 @@ void Predictor::Build(const std::string &model_path,
   Build(program_desc_, valid_places, passes);
 }
 
-void Predictor::Build(const std::shared_ptr<cpp::ProgramDesc> &desc,
+void Predictor::Build(const std::shared_ptr<cpp::ProgramDesc> &program_desc,
                       const std::vector<Place> &valid_places,
                       const std::vector<std::string> &passes) {
-  program_desc_ = desc;
+  program_desc_ = program_desc;
   // `inner_places` is used to optimize passes
   std::vector<Place> inner_places = valid_places;
   for (auto &valid_place : valid_places) {
@@ -336,7 +338,7 @@ void Predictor::Build(const std::shared_ptr<cpp::ProgramDesc> &desc,
                         Place{TARGET(kARM), PRECISION(kInt8)});
   }
 
-  Program program(*desc.get(), scope_, inner_places);
+  Program program(program_desc_, scope_, inner_places);
   valid_places_ = inner_places;
 
   core::KernelPickFactor factor;

lite/api/cxx_api.h

@@ -58,13 +58,12 @@ class LITE_API Predictor {
   // Create a predictor with the weight variable scope set.
   explicit Predictor(const std::shared_ptr<lite::Scope>& root_scope)
       : scope_(root_scope) {}
-  Predictor(const std::shared_ptr<cpp::ProgramDesc>& desc,
-            const std::shared_ptr<Scope>& root,
+  Predictor(const std::shared_ptr<cpp::ProgramDesc>& program_desc,
+            const std::shared_ptr<Scope>& root_scope,
             const std::vector<Place>& valid_places,
-            const std::vector<std::string>& var_names = {})
-      : program_desc_(desc), scope_(root) {
-    Program program(*desc.get(), scope_, valid_places, var_names);
-    // TODO(wilber): rethink a new way to associate config and passes.
+            const std::vector<std::string>& vars_to_clone = {})
+      : program_desc_(program_desc), scope_(root_scope) {
+    Program program(program_desc_, scope_, valid_places, vars_to_clone);
     optimizer_ = Optimizer(std::move(program), valid_places);
     exec_scope_ = optimizer_.exec_scope();
     valid_places_ = valid_places;
@@ -86,30 +85,28 @@ class LITE_API Predictor {
       lite_api::LiteModelType model_type = lite_api::LiteModelType::kProtobuf,
       bool memory_from_memory = false);
 
-  void Build(const std::shared_ptr<cpp::ProgramDesc>& desc,
+  void Build(const std::shared_ptr<cpp::ProgramDesc>& program_desc,
              const std::vector<Place>& valid_places,
              const std::vector<std::string>& passes = {});
 
   std::shared_ptr<Predictor> Clone() const {
-    auto predictor =
-        std::make_shared<Predictor>(program_desc_, scope_, valid_places_);
-    return predictor;
+    return std::make_shared<Predictor>(program_desc_, scope_, valid_places_);
   }
 
   std::shared_ptr<Predictor> Clone(
-      const std::vector<std::string>& var_names) const {
+      const std::vector<std::string>& vars_to_clone) const {
     CHECK(program_desc_) << "Both program and scope of current predicotr "
                             "should be not be nullptr in Clone mode.";
     CHECK(scope_) << "Both program and scope of current predicotr should be "
                      "not be nullptr in Clone mode.";
     auto predictor = std::make_shared<Predictor>(
-        program_desc_, scope_, valid_places_, var_names);
+        program_desc_, scope_, valid_places_, vars_to_clone);
 
-    for (auto i : var_names) {
-      predictor->exec_scope_->LocalVar(i);
-      auto* tensor = predictor->scope_->Var(i)->GetMutable<lite::Tensor>();
+    for (auto var_name : vars_to_clone) {
+      predictor->exec_scope_->LocalVar(var_name);
+      auto* tensor = predictor->scope_->Var(var_name)->GetMutable<Tensor>();
       auto* sub_tensor =
-          predictor->exec_scope_->Var(i)->GetMutable<lite::Tensor>();
+          predictor->exec_scope_->Var(var_name)->GetMutable<Tensor>();
       sub_tensor->CopyDataFrom(*tensor);
     }
     return predictor;
@@ -147,6 +144,7 @@ class LITE_API Predictor {
   // get a const tensor according to its name
   const lite::Tensor* GetTensor(const std::string& name) const;
   const RuntimeProgram& runtime_program() const;
+  Scope* scope() { return scope_.get(); }
 
   // This method is disabled in mobile, for unnecessary dependencies required.
   void SaveModel(

lite/api/cxx_api_impl.cc

@@ -75,8 +75,10 @@ void CxxPaddleApiImpl::Init(const lite_api::CxxConfig &config) {
   mode_ = config.power_mode();
   threads_ = config.threads();
 #ifdef LITE_WITH_NPU
+  // Store the model-level configuration into scope for kernels, and use
+  // exe_scope to store the execution-level configuration
   Context<TargetType::kNPU>::SetSubgraphModelCacheDir(
-      config.subgraph_model_cache_dir());
+      raw_predictor_->scope(), config.subgraph_model_cache_dir());
 #endif
 #if (defined LITE_WITH_X86) && (defined PADDLE_WITH_MKLML) && \
     !(defined LITE_ON_MODEL_OPTIMIZE_TOOL)

lite/api/light_api.cc

@@ -22,16 +22,16 @@ namespace lite {
 void LightPredictor::Build(const std::string& lite_model_file,
                            bool model_from_memory) {
   if (model_from_memory) {
-    LoadModelNaiveFromMemory(lite_model_file, scope_.get(), &cpp_program_desc_);
+    LoadModelNaiveFromMemory(
+        lite_model_file, scope_.get(), program_desc_.get());
   } else {
-    LoadModelNaiveFromFile(lite_model_file, scope_.get(), &cpp_program_desc_);
+    LoadModelNaiveFromFile(lite_model_file, scope_.get(), program_desc_.get());
   }
 
   // For weight quantization of post training, load the int8/16 weights
   // for optimized model, and dequant it to fp32.
   DequantizeWeight();
-
-  BuildRuntimeProgram(cpp_program_desc_);
+  BuildRuntimeProgram(program_desc_);
   PrepareFeedFetch();
 }
 
@@ -43,15 +43,15 @@ void LightPredictor::Build(const std::string& model_dir,
   switch (model_type) {
 #ifndef LITE_ON_TINY_PUBLISH
     case lite_api::LiteModelType::kProtobuf:
-      LoadModelPb(model_dir, "", "", scope_.get(), &cpp_program_desc_);
+      LoadModelPb(model_dir, "", "", scope_.get(), program_desc_.get());
       break;
 #endif
     case lite_api::LiteModelType::kNaiveBuffer: {
       if (model_from_memory) {
         LoadModelNaiveFromMemory(
-            model_buffer, param_buffer, scope_.get(), &cpp_program_desc_);
+            model_buffer, param_buffer, scope_.get(), program_desc_.get());
       } else {
-        LoadModelNaive(model_dir, scope_.get(), &cpp_program_desc_);
+        LoadModelNaive(model_dir, scope_.get(), program_desc_.get());
       }
       break;
     }
@@ -60,7 +60,7 @@ void LightPredictor::Build(const std::string& model_dir,
   }
 
   DequantizeWeight();
-  BuildRuntimeProgram(cpp_program_desc_);
+  BuildRuntimeProgram(program_desc_);
   PrepareFeedFetch();
 }
 
@@ -109,15 +109,17 @@ std::vector<std::string> LightPredictor::GetOutputNames() {
 }
 // append the names of inputs and outputs into input_names_ and output_names_
 void LightPredictor::PrepareFeedFetch() {
-  auto current_block = cpp_program_desc_.GetBlock<cpp::BlockDesc>(0);
-  std::vector<cpp::OpDesc*> feeds;
-  std::vector<cpp::OpDesc*> fetchs;
-  for (size_t i = 0; i < current_block->OpsSize(); i++) {
-    auto op = current_block->GetOp<cpp::OpDesc>(i);
-    if (op->Type() == "feed") {
-      feeds.push_back(op);
-    } else if (op->Type() == "fetch") {
-      fetchs.push_back(op);
+  std::vector<const cpp::OpDesc*> feeds;
+  std::vector<const cpp::OpDesc*> fetchs;
+  std::shared_ptr<const cpp::ProgramDesc> program_desc = program_desc_;
+  auto main_block = program_desc->GetBlock<cpp::BlockDesc>(kRootBlockIdx);
+  auto op_size = main_block->OpsSize();
+  for (size_t op_idx = 0; op_idx < op_size; ++op_idx) {
+    auto op_desc = main_block->GetOp<cpp::OpDesc>(op_idx);
+    if (op_desc->Type() == "feed") {
+      feeds.push_back(op_desc);
+    } else if (op_desc->Type() == "fetch") {
+      fetchs.push_back(op_desc);
     }
   }
   input_names_.resize(feeds.size());
@@ -132,54 +134,35 @@ void LightPredictor::PrepareFeedFetch() {
   }
 }
 
-void LightPredictor::BuildRuntimeProgram(const cpp::ProgramDesc& prog) {
-  std::vector<Instruction> insts;
-  // 1. Create op first
-  Program program(prog, scope_, {});
-
-// 2. Create Instructs
-#ifdef LITE_WITH_OPENCL
-  using OpenCLContext = Context<TargetType::kOpenCL>;
-  std::unique_ptr<KernelContext> local_ctx(new KernelContext());
-  local_ctx->As<OpenCLContext>().InitOnce();
-#endif
-
-  // 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());
-
-#ifdef LITE_WITH_OPENCL
-    if ((*it)->target() == TARGET(kOpenCL)) {
-      std::unique_ptr<KernelContext> ctx(new KernelContext());
-      (*local_ctx).As<OpenCLContext>().CopySharedTo(&ctx->As<OpenCLContext>());
-      (*it)->SetContext(std::move(ctx));
+void LightPredictor::BuildRuntimeProgram(
+    const std::shared_ptr<const cpp::ProgramDesc>& program_desc) {
+  auto* exe_scope = &scope_->NewScope();
+  // Prepare workspace
+  scope_->Var("feed")->GetMutable<std::vector<lite::Tensor>>();
+  scope_->Var("fetch")->GetMutable<std::vector<lite::Tensor>>();
+  CHECK(program_desc);
+  auto block_size = program_desc->BlocksSize();
+  CHECK(block_size);
+  for (size_t block_idx = 0; block_idx < block_size; ++block_idx) {
+    auto block_desc = program_desc->GetBlock<cpp::BlockDesc>(block_idx);
+    auto var_size = block_desc->VarsSize();
+    for (size_t var_idx = 0; var_idx < var_size; ++var_idx) {
+      auto var_desc = block_desc->GetVar<cpp::VarDesc>(var_idx);
+      if (!var_desc->Persistable()) {
+        exe_scope->Var(var_desc->Name());
       } else {
-      (*it)->SetContext(ContextScheduler::Global().NewContext((*it)->target()));
+        if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") continue;
+        scope_->Var(var_desc->Name());
       }
-#else
-    (*it)->SetContext(ContextScheduler::Global().NewContext((*it)->target()));
-#endif
-
-    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());
+  }
+  // Only extracting the ops and generate the runtime program from the main
+  // block desc
+  program_.reset(new RuntimeProgram(program_desc, exe_scope, kRootBlockIdx));
 }
 
 void LightPredictor::DequantizeWeight() {
+  std::shared_ptr<const cpp::ProgramDesc> program_desc = program_desc_;
 #define PROCESS_CONV2D_DATA()                                             \
   for (int64_t i = 0; i < ch; ++i) {                                      \
     for (int64_t j = 0; j < offset; ++j) {                                \
@@ -205,10 +188,9 @@ void LightPredictor::DequantizeWeight() {
     }
     return result;
   };
-
   Tensor tmp_tensor;
-  for (size_t i = 0; i < cpp_program_desc_.BlocksSize(); i++) {
-    auto* block = cpp_program_desc_.GetBlock<cpp::BlockDesc>(i);
+  for (size_t i = 0; i < program_desc->BlocksSize(); i++) {
+    auto* block = program_desc->GetBlock<cpp::BlockDesc>(i);
     for (size_t k = 0; k < block->OpsSize(); ++k) {
       auto* op_desc = block->GetOp<cpp::OpDesc>(k);
       if (is_weight_quantized_op(op_desc)) {

lite/api/light_api.h

@@ -46,6 +46,7 @@ class LITE_API LightPredictor {
   LightPredictor(const std::string& lite_model_file,
                  bool model_from_memory = false) {
     scope_ = std::make_shared<Scope>();
+    program_desc_ = std::make_shared<cpp::ProgramDesc>();
     Build(lite_model_file, model_from_memory);
   }
 
@@ -57,6 +58,7 @@ class LITE_API LightPredictor {
                  lite_api::LiteModelType model_type =
                      lite_api::LiteModelType::kNaiveBuffer) {
     scope_ = std::make_shared<Scope>();
+    program_desc_ = std::make_shared<cpp::ProgramDesc>();
     Build(model_dir, model_buffer, param_buffer, model_type, model_from_memory);
   }
 
@@ -78,6 +80,7 @@ class LITE_API LightPredictor {
   std::vector<std::string> GetInputNames();
   std::vector<std::string> GetOutputNames();
   void PrepareFeedFetch();
+  Scope* scope() { return scope_.get(); }
 
  private:
   void Build(const std::string& lite_model_file,
@@ -91,14 +94,15 @@ class LITE_API LightPredictor {
       lite_api::LiteModelType model_type = lite_api::LiteModelType::kProtobuf,
       bool model_from_memory = false);
 
-  void BuildRuntimeProgram(const cpp::ProgramDesc& prog);
+  void BuildRuntimeProgram(
+      const std::shared_ptr<const cpp::ProgramDesc>& program_desc);
 
   void DequantizeWeight();
 
  private:
   std::shared_ptr<Scope> scope_;
   std::unique_ptr<RuntimeProgram> program_;
-  cpp::ProgramDesc cpp_program_desc_;
+  std::shared_ptr<cpp::ProgramDesc> program_desc_;
   std::vector<std::string> input_names_;
   std::vector<std::string> output_names_;
 };

lite/api/light_api_impl.cc

@@ -38,8 +38,10 @@ void LightPredictorImpl::Init(const lite_api::MobileConfig& config) {
   threads_ = config.threads();
 
 #ifdef LITE_WITH_NPU
+  // Store the model-level configuration into scope for kernels, and use
+  // exe_scope to store the execution-level configuration
   Context<TargetType::kNPU>::SetSubgraphModelCacheDir(
-      config.subgraph_model_cache_dir());
+      raw_predictor_->scope(), config.subgraph_model_cache_dir());
 #endif
 }
 

lite/api/paddle_use_passes.h

@@ -28,6 +28,7 @@ USE_MIR_PASS(graph_visualize_pass);
 
 USE_MIR_PASS(remove_tf_redundant_ops_pass);
 USE_MIR_PASS(lite_conv_bn_fuse_pass);
+USE_MIR_PASS(lite_conv_conv_fuse_pass);
 USE_MIR_PASS(lite_fc_fuse_pass);
 USE_MIR_PASS(lite_shuffle_channel_fuse_pass);
 USE_MIR_PASS(lite_transpose_softmax_transpose_fuse_pass);
@@ -53,6 +54,7 @@ USE_MIR_PASS(mlu_postprocess_pass);
 USE_MIR_PASS(weight_quantization_preprocess_pass);
 USE_MIR_PASS(apu_subgraph_pass);
 USE_MIR_PASS(quantized_op_attributes_inference_pass);
+USE_MIR_PASS(control_flow_op_unused_inputs_and_outputs_eliminate_pass)
 USE_MIR_PASS(lite_scale_activation_fuse_pass);
 USE_MIR_PASS(__xpu__resnet_fuse_pass);
 USE_MIR_PASS(__xpu__resnet_cbam_fuse_pass);

lite/backends/arm/math/beam_search.cc

@@ -234,7 +234,7 @@ void beam_search(const Tensor *pre_ids,
   selected_ids->Resize(dims);
   selected_scores->Resize(dims);
   if (parent_idx) {
-    parent_idx->Resize(dims);
+    parent_idx->Resize({static_cast<int64_t>(num_instances)});
   }
   auto *selected_ids_data = selected_ids->mutable_data<int64_t>();
   auto *selected_scores_data = selected_scores->mutable_data<float>();

lite/backends/arm/math/conv_block_utils.h

@@ -139,6 +139,71 @@ static bool conv_trans_weights_numc(const dtype* din,
   }
   return true;
 }
+// for example: m = 4, n = 4
+// din = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9 , 10 ,11], [12, 13, 14, 15]]
+// dout = [[0, 4, 8, 12], [1, 5, 9, 13], [2, 6, 10, 14], [3, 7, 11, 15]]
+/*
+  m = 8 n = 8: 0 1 2 3 4 5 6 7           0 8 16 24 32 40 48 56
+               16 17 18 19 20 21 22 23   2 10 18 26 34 42 50 58
+               24 25 26 27 28 29 30 31   3 11 19 27 35 43 51 59
+               32 33 34 35 36 37 38 39   4 12 20 28 36 44 52 60           ...
+    }
+  }
+*/
+template <typename Dtype>
+void local_transpose(const Dtype* din, Dtype* dout, int m, int n) {
+  // n % 4 == 0 && m % 4 == 0
+  // n * m ==> n * m data trans
+  int offset_m = m << 2;
+  const Dtype* din_ptr = din;
+  Dtype* dout_ptr = dout;
+  for (int i = 0; i < n; i += 4) {
+    Dtype* out_ptr0 = dout_ptr;
+    Dtype* out_ptr1 = dout_ptr + m;
+    Dtype* out_ptr2 = out_ptr1 + m;
+    Dtype* out_ptr3 = out_ptr2 + m;
+    const Dtype* in_ptr0 = din_ptr;
+    const Dtype* in_ptr1 = din_ptr + m;
+    const Dtype* in_ptr2 = in_ptr1 + m;
+    const Dtype* in_ptr3 = in_ptr2 + m;
+    for (int j = 0; j < m; j += 4) {
+      float32x4_t vin0 = vld1q_f32(in_ptr0);
+      float32x4_t vin1 = vld1q_f32(in_ptr1);
+      float32x4_t vin2 = vld1q_f32(in_ptr2);
+      float32x4_t vin3 = vld1q_f32(in_ptr3);
+      // a00 b00 a02 b02 a01 b01 a03 b03
+      float32x4x2_t tmp0 = vtrnq_f32(vin0, vin1);
+      // c00 d00 c02 d02 c01 d01 c03 d03
+      float32x4x2_t tmp2 = vtrnq_f32(vin2, vin3);
+      in_ptr0 = in_ptr3 + m;
+      in_ptr1 = in_ptr3 + 2 * m;
+      float tmp_val1 = tmp0.val[0][2];
+      float tmp_val2 = tmp0.val[0][3];
+      tmp0.val[0][2] = tmp2.val[0][0];
+      tmp0.val[0][3] = tmp2.val[0][1];
+      float tmp_val3 = tmp0.val[1][2];
+      float tmp_val4 = tmp0.val[1][3];
+      tmp2.val[0][0] = tmp_val1;
+      tmp2.val[0][1] = tmp_val2;
+      tmp0.val[1][2] = tmp2.val[1][0];
+      tmp0.val[1][3] = tmp2.val[1][1];
+      tmp2.val[1][0] = tmp_val3;
+      tmp2.val[1][1] = tmp_val4;
+      in_ptr2 = in_ptr1 + m;
+      in_ptr3 = in_ptr1 + 2 * m;
+      vst1q_f32(out_ptr0, tmp0.val[0]);
+      vst1q_f32(out_ptr1, tmp0.val[1]);
+      out_ptr0 += 4;
+      out_ptr1 += 4;
+      vst1q_f32(out_ptr2, tmp2.val[0]);
+      vst1q_f32(out_ptr3, tmp2.val[1]);
+      out_ptr2 += 4;
+      out_ptr3 += 4;
+    }
+    dout_ptr += offset_m;
+    din_ptr += 4;
+  }
+}
 template <typename Dtype>
 void transpose(const Dtype* din, Dtype* dout, int m, int n) {
   // nxm == mxn

lite/backends/arm/math/elementwise.cc

@@ -747,6 +747,16 @@ void elementwise_mul<int>(const int* dinx,
   }
 }
 
+template <>
+void elementwise_mul<int64_t>(const int64_t* dinx,
+                              const int64_t* diny,
+                              int64_t* dout,
+                              int num) {
+  for (int i = 0; i < num; i++) {
+    dout[i] = dinx[i] * diny[i];
+  }
+}
+
 template <>
 void elementwise_mul_relu<float>(const float* dinx,
                                  const float* diny,
@@ -801,6 +811,17 @@ void elementwise_mul_relu<float>(const float* dinx,
   }
 }
 
+template <>
+void elementwise_mul_relu<int64_t>(const int64_t* dinx,
+                                   const int64_t* diny,
+                                   int64_t* dout,
+                                   int num) {
+  for (int i = 0; i < num; i++) {
+    int64_t tmp = dinx[i] * diny[i];
+    dout[i] = tmp > 0 ? tmp : 0;
+  }
+}
+
 template <>
 void elementwise_mul_broadcast<float>(const float* dinx,
                                       const float* diny,
@@ -935,6 +956,29 @@ void elementwise_mul_broadcast<int>(const int* dinx,
   }
 }
 
+template <>
+void elementwise_mul_broadcast<int64_t>(const int64_t* dinx,
+                                        const int64_t* diny,
+                                        int64_t* dout,
+                                        int batch,
+                                        int channels,
+                                        int num) {
+#pragma omp parallel for collapse(2)
+  for (int i = 0; i < batch; ++i) {
+    for (int j = 0; j < channels; ++j) {
+      int offset = (i * channels + j) * num;
+      const int64_t* dinx_ptr = dinx + offset;
+      const int64_t diny_data = diny[j];
+      int64_t* dout_ptr = dout + offset;
+      for (int k = 0; k < num; ++k) {
+        *dout_ptr = *dinx_ptr * diny_data;
+        dout_ptr++;
+        dinx_ptr++;
+      }
+    }
+  }
+}
+
 template <>
 void elementwise_mul_relu_broadcast<float>(const float* dinx,
                                            const float* diny,
@@ -1014,6 +1058,30 @@ void elementwise_mul_relu_broadcast<float>(const float* dinx,
   }
 }
 
+template <>
+void elementwise_mul_relu_broadcast<int64_t>(const int64_t* dinx,
+                                             const int64_t* diny,
+                                             int64_t* dout,
+                                             int batch,
+                                             int channels,
+                                             int num) {
+#pragma omp parallel for collapse(2)
+  for (int i = 0; i < batch; ++i) {
+    for (int j = 0; j < channels; ++j) {
+      int offset = (i * channels + j) * num;
+      const int64_t* dinx_ptr = dinx + offset;
+      const int64_t diny_data = diny[j];
+      int64_t* dout_ptr = dout + offset;
+      for (int k = 0; k < num; ++k) {
+        int64_t tmp = *dinx_ptr * diny_data;
+        *dout_ptr = tmp > 0 ? tmp : 0;
+        dout_ptr++;
+        dinx_ptr++;
+      }
+    }
+  }
+}
+
 template <>
 void elementwise_max<float>(const float* dinx,
                             const float* diny,

lite/backends/arm/math/prior_box.cc

@@ -21,7 +21,7 @@ namespace lite {
 namespace arm {
 namespace math {
 
-const int MALLOC_ALIGN = 64;
+const int MALLOC_ALIGN = 16;
 
 void* fast_malloc(size_t size) {
   size_t offset = sizeof(void*) + MALLOC_ALIGN - 1;

lite/backends/host/target_wrapper.cc

@@ -19,7 +19,7 @@
 namespace paddle {
 namespace lite {
 
-const int MALLOC_ALIGN = 64;
+const int MALLOC_ALIGN = 16;
 
 void* TargetWrapper<TARGET(kHost)>::Malloc(size_t size) {
   size_t offset = sizeof(void*) + MALLOC_ALIGN - 1;
@@ -30,7 +30,6 @@ void* TargetWrapper<TARGET(kHost)>::Malloc(size_t size) {
   void* r = reinterpret_cast<void*>(reinterpret_cast<size_t>(p + offset) &
                                     (~(MALLOC_ALIGN - 1)));
   static_cast<void**>(r)[-1] = p;
-  memset(r, 0, size);
   return r;
 }
 void TargetWrapper<TARGET(kHost)>::Free(void* ptr) {

lite/backends/npu/device.cc

@@ -33,7 +33,7 @@ std::shared_ptr<hiai::AiModelMngerClient> Device::Load(
   // Check HiAI DDK version
   const char* ddk_version = model_client->GetVersion();
   if (ddk_version) {
-    LOG(INFO) << "[NPU] HiAI DDK version: " << ddk_version;
+    VLOG(3) << "[NPU] HiAI DDK version: " << ddk_version;
   } else {
     LOG(WARNING) << "[NPU] Unable to get HiAI DDK version!";
   }

lite/backends/xpu/debug.h

@@ -19,7 +19,7 @@
 #include <memory>
 #include <string>
 #include <type_traits>
-#include "lite/backends/xpu/xpu_header_sitter.h"
+#include "lite/backends/xpu/target_wrapper.h"
 
 namespace paddle {
 namespace lite {
@@ -82,8 +82,8 @@ void DumpXPUMem(const T* ptr,
                 size_t item_per_line = 30) {
   size_t after_stride_len = (len + stride - 1) / stride;
   std::unique_ptr<T[]> cpu_mem(new T[len]);
-  xpu_memcpy(
-      cpu_mem.get(), ptr, len * sizeof(T), XPUMemcpyKind::XPU_DEVICE_TO_HOST);
+  XPU_CALL(xpu_memcpy(
+      cpu_mem.get(), ptr, len * sizeof(T), XPUMemcpyKind::XPU_DEVICE_TO_HOST));
   std::unique_ptr<T[]> after_stride(new T[after_stride_len]);
   for (size_t i = 0; i < after_stride_len; ++i) {
     after_stride[i] = cpu_mem[i * stride];

lite/backends/xpu/target_wrapper.cc

@@ -19,11 +19,11 @@ namespace lite {
 
 void* TargetWrapperXPU::Malloc(size_t size) {
   void* ptr{nullptr};
-  xpu_malloc(&ptr, size);
+  XPU_CALL(xpu_malloc(&ptr, size));
   return ptr;
 }
 
-void TargetWrapperXPU::Free(void* ptr) { xpu_free(ptr); }
+void TargetWrapperXPU::Free(void* ptr) { XPU_CALL(xpu_free(ptr)); }
 
 void TargetWrapperXPU::MemcpySync(void* dst,
                                   const void* src,
@@ -31,10 +31,10 @@ void TargetWrapperXPU::MemcpySync(void* dst,
                                   IoDirection dir) {
   switch (dir) {
     case IoDirection::HtoD:
-      xpu_memcpy(dst, src, size, XPU_HOST_TO_DEVICE);
+      XPU_CALL(xpu_memcpy(dst, src, size, XPU_HOST_TO_DEVICE));
       break;
     case IoDirection::DtoH:
-      xpu_memcpy(dst, src, size, XPU_DEVICE_TO_HOST);
+      XPU_CALL(xpu_memcpy(dst, src, size, XPU_DEVICE_TO_HOST));
       break;
     default:
       LOG(FATAL) << "Unsupported IoDirection " << static_cast<int>(dir);
@@ -49,7 +49,7 @@ XPUScratchPadGuard TargetWrapperXPU::MallocScratchPad(size_t size,
   } else {
     ptr = TargetWrapperXPU::Malloc(size);
   }
-  CHECK(ptr != nullptr);
+  CHECK(ptr != nullptr) << "size = " << size << ", use_l3 = " << use_l3;
   return XPUScratchPadGuard(new XPUScratchPad(ptr, use_l3));
 }
 

lite/backends/xpu/target_wrapper.h

@@ -16,11 +16,23 @@
 
 #include <memory>                                 // std::unique_ptr
 #include "lite/backends/xpu/xpu_header_sitter.h"  // xpu_free
-#include "lite/core/target_wrapper.h"
+#include "lite/core/target_wrapper.h"             // TargetWrapper
+#include "lite/utils/cp_logging.h"                // CHECK_EQ
+
+#define XPU_CALL(func)                                        \
+  {                                                           \
+    auto e = (func);                                          \
+    CHECK_EQ(e, 0) << "XPU: (" << #func << ") returns " << e; \
+  }
 
 namespace paddle {
 namespace lite {
 
+// MAX(lod.size()) = 64
+const int XPU_MAX_LOD_SIZE = 64;
+// MAX(lod[i + 1] - lod[i]) = 512
+const int XPU_MAX_LOD_SEQ_LEN = 512;
+
 using TargetWrapperXPU = TargetWrapper<TARGET(kXPU)>;
 
 struct XPUScratchPad {
@@ -33,7 +45,7 @@ struct XPUScratchPad {
 struct XPUScratchPadDeleter {
   void operator()(XPUScratchPad* sp) const {
     if (!sp->is_l3_) {
-      xpu_free(sp->addr_);
+      XPU_CALL(xpu_free(sp->addr_));
     }
     delete sp;
   }
@@ -55,7 +67,7 @@ class TargetWrapper<TARGET(kXPU)> {
                          size_t size,
                          IoDirection dir);
 
-  static XPUScratchPadGuard MallocScratchPad(size_t size, bool use_l3 = true);
+  static XPUScratchPadGuard MallocScratchPad(size_t size, bool use_l3 = false);
 
   static xdnn::Context* GetRawContext() {
     if (tls_raw_ctx_ == nullptr) {
@@ -77,11 +89,10 @@ class TargetWrapper<TARGET(kXPU)> {
   static void SetDev(int dev_no = 0) {
     const char* dev_env = getenv("LITE_XPU_DEV");
     if (dev_env) {
-      xpu_set_device(atoi(dev_env));
-      return;
+      dev_no = atoi(dev_env);
     }
 
-    xpu_set_device(dev_no);
+    XPU_CALL(xpu_set_device(dev_no));
   }
 
   static std::string multi_encoder_precision;  // NOLINT

lite/core/arena/framework.cc

@@ -32,25 +32,27 @@ void TestCase::CreateInstruction() {
 #endif
   if (enable_subgraph_op) {
     // Create a new block desc to wrap the original op desc
+    auto sub_program_desc = std::make_shared<cpp::ProgramDesc>();
     int sub_block_idx = 0;
-    auto sub_block_desc = new cpp::BlockDesc();
+    auto sub_block_desc = sub_program_desc->AddBlock<cpp::BlockDesc>();
     sub_block_desc->ClearOps();
     sub_block_desc->ClearVars();
-    auto sub_block_op_desc = sub_block_desc->AddOp<cpp::OpDesc>();
-    *sub_block_op_desc = *op_desc_;
+    auto sub_op_desc = sub_block_desc->AddOp<cpp::OpDesc>();
+    *sub_op_desc = *op_desc_;
     // Add the block desc into the subgraph op which used to replace the
     // original op
     op_desc_.reset(new cpp::OpDesc());
     op_desc_->SetType("subgraph");
     op_desc_->SetAttr<int32_t>("sub_block", sub_block_idx);
-    auto in_names = sub_block_op_desc->input_vars();
-    auto out_names = sub_block_op_desc->output_vars();
+    auto in_names = sub_op_desc->input_vars();
+    auto out_names = sub_op_desc->output_vars();
     op_desc_->SetInput("Inputs", in_names);
     op_desc_->SetOutput("Outputs", out_names);
     op_desc_->SetAttr<std::vector<std::string>>("input_data_names", in_names);
     op_desc_->SetAttr<std::vector<std::string>>("output_data_names", out_names);
     op = LiteOpRegistry::Global().Create(op_desc().Type());
-    static_cast<operators::SubgraphOp*>(op.get())->SetSubBlock(sub_block_desc);
+    static_cast<operators::SubgraphOp*>(op.get())->SetProgramDesc(
+        sub_program_desc);
   } else {
     op = LiteOpRegistry::Global().Create(op_desc().Type());
   }
@@ -60,7 +62,7 @@ void TestCase::CreateInstruction() {
   // filter out the target kernel
   CHECK(!kernels.empty()) << "No kernel found for place "
                           << place_.DebugString();
-  auto it = std::remove_if(
+  auto it = std::find_if(
       kernels.begin(), kernels.end(), [&](std::unique_ptr<KernelBase>& k) {
         return k->alias() == alias_;
       });
@@ -234,19 +236,6 @@ bool TestCase::CheckPrecision(const std::string& var_name,
   return success;
 }
 
-TestCase::~TestCase() {
-  if (op_desc_->Type() == "subgraph") {
-    // Release the subblock desc of Subgraph op
-    auto subgraph_op = const_cast<operators::SubgraphOp*>(
-        static_cast<const operators::SubgraphOp*>(instruction_->op()));
-    CHECK(subgraph_op);
-    auto sub_block_desc = subgraph_op->GetSubBlock();
-    if (sub_block_desc) {
-      delete sub_block_desc;
-    }
-  }
-}
-
 }  // namespace arena
 }  // namespace lite
 }  // namespace paddle

lite/core/arena/framework.h

@@ -46,7 +46,7 @@ class TestCase {
         base_scope_(new Scope) {
     ctx_ = ContextScheduler::Global().NewContext(place_.target);
   }
-  virtual ~TestCase();
+  virtual ~TestCase() {}
 
   void Prepare() {
     PrepareData();

lite/core/context.cc

@@ -17,10 +17,6 @@
 namespace paddle {
 namespace lite {
 
-#ifdef LITE_WITH_NPU
-std::string Context<TargetType::kNPU>::subgraph_model_cache_dir_{""};  // NOLINT
-#endif
-
 #ifdef LITE_WITH_MLU
 int Context<TargetType::kMLU>::next_queue_id_{0};
 std::map<int, int> Context<TargetType::kMLU>::queue_id_map_;

lite/core/context.h

@@ -39,6 +39,7 @@
 #include <utility>
 #include <vector>
 #include "lite/core/device_info.h"
+#include "lite/core/scope.h"
 #include "lite/core/target_wrapper.h"
 #include "lite/core/tensor.h"
 #include "lite/utils/all.h"
@@ -84,15 +85,19 @@ class Context<TargetType::kNPU> {
   NPUContext& operator=(const NPUContext& ctx) {}
   std::string name() const { return "NPUContext"; }
 
-  static void SetSubgraphModelCacheDir(std::string subgraph_model_cache_dir) {
-    subgraph_model_cache_dir_ = subgraph_model_cache_dir;
+  static void SetSubgraphModelCacheDir(Scope* scope,
+                                       std::string subgraph_model_cache_dir) {
+    auto var = scope->Var("SUBGRAPH_MODEL_CACHE_DIR");
+    CHECK(var);
+    auto data = var->GetMutable<std::string>();
+    CHECK(data);
+    *data = subgraph_model_cache_dir;
   }
-  static std::string SubgraphModelCacheDir() {
-    return subgraph_model_cache_dir_;
+  static std::string SubgraphModelCacheDir(Scope* scope) {
+    auto var = scope->FindVar("SUBGRAPH_MODEL_CACHE_DIR");
+    if (!var) return "";
+    return var->Get<std::string>();
   }
-
- private:
-  static std::string subgraph_model_cache_dir_;
 };
 #endif
 

lite/core/mir/CMakeLists.txt

@@ -18,6 +18,7 @@ lite_cc_library(mir_passes
       fusion/conv_activation_fuse_pass.cc
       fusion/var_conv_2d_activation_fuse_pass.cc
       fusion/conv_bn_fuse_pass.cc
+      fusion/conv_conv_fuse_pass.cc
       fusion/elementwise_add_activation_fuse_pass.cc
       fusion/quant_dequant_fuse_pass.cc
       fusion/sequence_pool_concat_fuse_pass.cc
@@ -32,6 +33,7 @@ lite_cc_library(mir_passes
       elimination/identity_dropout_eliminate_pass.cc
       elimination/elementwise_mul_constant_eliminate_pass.cc
       elimination/remove_tf_redundant_ops_pass.cc
+      elimination/control_flow_op_unused_inputs_and_outputs_eliminate_pass.cc
       static_kernel_pick_pass.cc
       variable_place_inference_pass.cc
       type_target_cast_pass.cc

lite/core/mir/elimination/control_flow_op_unused_inputs_and_outputs_eliminate_pass.cc

+// 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.
+
+#include "lite/core/mir/elimination/control_flow_op_unused_inputs_and_outputs_eliminate_pass.h"
+#include <algorithm>
+#include <list>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+#include "lite/core/mir/pass_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace mir {
+
+// Remove all of the unused nodes from the contorl flow op and update the inputs
+// and outputs of the op info The unused nodes are defined as the nodes which
+// are only linked to the control flow op nodes but nerver linked to the other
+// op nodes.
+//
+// For example:
+// graph[0]: main block
+//                      in_x
+//             in_f      |   in_z(unused node)
+//                  \    |    /
+//                   \   |   /
+//        in_w ------- while ------- in_y(unused_node)
+//                    /  |
+//                   /   |
+// (unused node)out_y    |
+//                     out_x
+//
+// graph[1]: sub block
+//                     in_x
+//                       |
+//                       |
+//                     conv2d----in_f
+//                       |
+//                       |
+//                      fc ------in_w
+//                       |
+//                       |
+//                     softmax
+//                       |
+//                       |
+//                     out_x
+//
+// After the pass is applied:
+//                      in_x
+//             in_f      |
+//                  \    |
+//                   \   |
+//        in_w ------- while
+//                       |
+//                       |
+//                       |
+//                     out_x
+
+// Remove the var node from var2rm if it is recursively referred to any op in
+// the subblock
+void CollectUnusedInputOutputNodes(
+    int block_idx,
+    std::vector<std::unique_ptr<mir::SSAGraph>>* graphs,
+    const std::unordered_set<std::string>& control_flow_op_types,
+    std::unordered_map<std::string, Node*>* in_vars2rm,
+    std::unordered_map<std::string, Node*>* out_vars2rm) {
+  auto block_size = graphs->size();
+  for (auto& op_node : (*graphs)[block_idx]->StmtTopologicalOrder()) {
+    if (!op_node->IsStmt()) continue;
+    auto op_info = op_node->AsStmt().op_info();
+    auto op_type = op_info->Type();
+    if (control_flow_op_types.count(op_type)) {
+      int sub_block_idx = op_info->GetAttr<int32_t>("sub_block");
+      CHECK(block_idx >= 0 && block_idx < block_size);
+      CollectUnusedInputOutputNodes(sub_block_idx,
+                                    graphs,
+                                    control_flow_op_types,
+                                    in_vars2rm,
+                                    out_vars2rm);
+    } else {
+      for (auto& var_node : op_node->inlinks) {
+        auto& var_name = var_node->AsArg().name;
+        if (in_vars2rm->count(var_name)) {
+          in_vars2rm->erase(var_name);
+        }
+      }
+      for (auto& var_node : op_node->outlinks) {
+        auto& var_name = var_node->AsArg().name;
+        // Tensor array may be only used as the output vars in the sublock
+        if (in_vars2rm->count(var_name)) {
+          in_vars2rm->erase(var_name);
+        }
+        if (out_vars2rm->count(var_name)) {
+          out_vars2rm->erase(var_name);
+        }
+      }
+    }
+  }
+}
+
+// Remove the unused var nodes from the graph and update the op_info of the
+// control flow op
+void RemoveNodesFromGraphAndUpdateOpInfo(
+    SSAGraph* graph,
+    Node* op_node,
+    const std::unordered_map<std::string, Node*>& in_vars2rm,
+    const std::unordered_map<std::string, Node*>& out_vars2rm) {
+  auto op_info = op_node->AsStmt().mutable_op_info();
+  auto op_type = op_info->Type();
+  // Unlink the in_vars2rm and out_vars2rm from the control flow op node, and
+  // remove them if nerver used.
+  for (auto& var_node : in_vars2rm) {
+    VLOG(3) << "in var node '" << var_node.first << "' is unlinked to "
+            << op_type;
+    RemoveDirectedLink(var_node.second, op_node);
+  }
+  for (auto& var_node : out_vars2rm) {
+    VLOG(3) << "out var node '" << var_node.first << "' is unlinked from "
+            << op_type;
+    RemoveDirectedLink(op_node, var_node.second);
+    // Unlink from all of the out op nodes.
+    std::unordered_set<Node*> out_op_nodes;
+    for (auto* out_op_node : var_node.second->outlinks) {
+      if (!out_op_nodes.count(out_op_node)) {
+        out_op_nodes.insert(out_op_node);
+      }
+    }
+    for (auto* out_op_node : out_op_nodes) {
+      RemoveDirectedLink(var_node.second, out_op_node);
+    }
+  }
+  // Remove the unused nodes from the graph if their inlinks and outlinks are
+  // empty
+  std::unordered_set<const Node*> removed_var_nodes;
+  for (auto& var_node : in_vars2rm) {
+    if (var_node.second->inlinks.empty() && var_node.second->outlinks.empty() &&
+        !removed_var_nodes.count(var_node.second)) {
+      removed_var_nodes.insert(var_node.second);
+      graph->RemoveNode(var_node.second);
+      VLOG(3) << "in var node " << var_node.first << " is removed";
+    }
+  }
+  for (auto& var_node : out_vars2rm) {
+    if (var_node.second->inlinks.empty() && var_node.second->outlinks.empty() &&
+        !removed_var_nodes.count(var_node.second)) {
+      removed_var_nodes.insert(var_node.second);
+      graph->RemoveNode(var_node.second);
+      VLOG(3) << "out var node " << var_node.first << " is removed";
+    }
+  }
+  // Update the op info of the control flow op
+  for (auto& input : *op_info->mutable_inputs()) {
+    for (auto var = input.second.begin(); var != input.second.end();) {
+      if (in_vars2rm.count(*var)) {
+        var = input.second.erase(var);
+      } else {
+        ++var;
+      }
+    }
+  }
+  for (auto& output : *op_info->mutable_outputs()) {
+    for (auto var = output.second.begin(); var != output.second.end();) {
+      if (out_vars2rm.count(*var)) {
+        var = output.second.erase(var);
+      } else {
+        ++var;
+      }
+    }
+  }
+}
+
+void ControlFlowOpUnusedInputsAndOutputsEliminatePass::SetAllGraphs(
+    std::vector<std::unique_ptr<mir::SSAGraph>>* graphs) {
+  CHECK(graphs && !graphs->empty());
+  graphs_ = graphs;
+}
+
+void ControlFlowOpUnusedInputsAndOutputsEliminatePass::Apply(
+    const std::unique_ptr<SSAGraph>& graph) {
+  // Remove the unused input and output nodes from the control flow op nodes
+  // Which are only linked to the control flow op nodes but nerver linked to the
+  // other op nodes
+  const std::unordered_set<std::string> control_flow_op_types = {
+      "while", "conditional_block"};
+  auto block_size = graphs_->size();
+  for (auto& op_node : graph->StmtTopologicalOrder()) {
+    if (!op_node->IsStmt()) continue;
+    auto op_info = op_node->AsStmt().mutable_op_info();
+    auto op_type = op_info->Type();
+    if (!control_flow_op_types.count(op_type)) continue;
+    int sub_block_idx = op_info->GetAttr<int32_t>("sub_block");
+    CHECK(sub_block_idx >= 0 && sub_block_idx < block_size);
+    // Initialize the unused nodes with all of the input and output nodes
+    std::unordered_map<std::string, Node *> in_vars2rm, out_vars2rm;
+    for (auto* var_node : op_node->inlinks) {
+      auto& var_name = var_node->AsArg().name;
+      if (!in_vars2rm.count(var_name)) {
+        in_vars2rm.insert(std::pair<std::string, Node*>(var_name, var_node));
+      }
+    }
+    for (auto* var_node : op_node->outlinks) {
+      auto& var_name = var_node->AsArg().name;
+      if (!out_vars2rm.count(var_name)) {
+        out_vars2rm.insert(std::pair<std::string, Node*>(var_name, var_node));
+      }
+    }
+    // Remove the nodes which used in subblock recursively, and the remaining
+    // nodes are the unused one.
+    CollectUnusedInputOutputNodes(sub_block_idx,
+                                  graphs_,
+                                  control_flow_op_types,
+                                  &in_vars2rm,
+                                  &out_vars2rm);
+    if (in_vars2rm.size() > 0 || out_vars2rm.size() > 0) {
+      // Remove the unused nodes from graph, and update the op info of the
+      // control flow op
+      RemoveNodesFromGraphAndUpdateOpInfo(
+          graph.get(), op_node, in_vars2rm, out_vars2rm);
+    }
+  }
+}
+
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_MIR_PASS(
+    control_flow_op_unused_inputs_and_outputs_eliminate_pass,
+    paddle::lite::mir::ControlFlowOpUnusedInputsAndOutputsEliminatePass)
+    .BindTargets({TARGET(kNPU)});

lite/core/mir/elimination/control_flow_op_unused_inputs_and_outputs_eliminate_pass.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
+
+#include <limits>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+#include "lite/core/mir/pass.h"
+#include "lite/core/types.h"
+
+namespace paddle {
+namespace lite {
+namespace mir {
+
+class ControlFlowOpUnusedInputsAndOutputsEliminatePass : public mir::StmtPass {
+ public:
+  void Apply(const std::unique_ptr<SSAGraph> &graph) override;
+  void SetAllGraphs(std::vector<std::unique_ptr<mir::SSAGraph>> *graphs);
+
+ private:
+  std::vector<std::unique_ptr<mir::SSAGraph>> *graphs_;
+};
+
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle

lite/core/mir/fusion/CMakeLists.txt

@@ -16,6 +16,9 @@ lite_cc_library(fuse_var_conv_activation
 lite_cc_library(fuse_conv_bn
         SRCS conv_bn_fuser.cc
         DEPS pattern_matcher_high_api)
+lite_cc_library(fuse_conv_conv
+        SRCS conv_conv_fuser.cc
+        DEPS pattern_matcher_high_api)     
 lite_cc_library(fuse_elementwise_add_activation
         SRCS elementwise_add_activation_fuser.cc
         DEPS pattern_matcher_high_api)
@@ -42,6 +45,7 @@ set(mir_fusers
     fuse_conv_activation
     fuse_var_conv_activation
     fuse_conv_bn
+    fuse_conv_conv
     fuse_quant_dequant
     fuse_elementwise_add_activation
     fuse_transpose_softmax_transpose

lite/core/mir/fusion/__xpu__multi_encoder_fuse_pass.cc

@@ -383,10 +383,10 @@ class XPUSingleEncoderFuser : public FuseBase {
     op_desc.SetAttr<std::string>("act_type", act_type_);
 
     auto fake_subgraph_op = LiteOpRegistry::Global().Create("subgraph");
-    // XXX: memleak?
-    auto sub_block_desc = new cpp::BlockDesc();
+    auto sub_program_desc = std::make_shared<cpp::ProgramDesc>();
+    sub_program_desc->AddBlock<cpp::BlockDesc>();
     static_cast<operators::SubgraphOp*>(fake_subgraph_op.get())
-        ->SetSubBlock(sub_block_desc);
+        ->SetProgramDesc(sub_program_desc);
     auto* single_encoder_stmt = matched.at("q_mul")->stmt();
     fake_subgraph_op->Attach(op_desc, single_encoder_stmt->op()->scope());
     fake_subgraph_op->SetValidPlaces(single_encoder_stmt->op()->valid_places());

lite/core/mir/fusion/__xpu__resnet_cbam_fuse_pass.cc

@@ -373,10 +373,10 @@ class XPUResNetCbamBlock0Fuser : public FuseBase {
     auto block0_stmt = matched.at("left_conv1")->stmt();
     // block0_stmt->ResetOp(op_desc, graph->valid_places());
     auto fake_subgraph_op = LiteOpRegistry::Global().Create("subgraph");
-    // XXX: memleak?
-    auto sub_block_desc = new cpp::BlockDesc();
+    auto sub_program_desc = std::make_shared<cpp::ProgramDesc>();
+    sub_program_desc->AddBlock<cpp::BlockDesc>();
     static_cast<operators::SubgraphOp*>(fake_subgraph_op.get())
-        ->SetSubBlock(sub_block_desc);
+        ->SetProgramDesc(sub_program_desc);
     fake_subgraph_op->Attach(op_desc, block0_stmt->op()->scope());
     fake_subgraph_op->SetValidPlaces(block0_stmt->op()->valid_places());
     block0_stmt->SetOp(fake_subgraph_op);
@@ -693,10 +693,10 @@ class XPUResNetCbamBlock1Fuser : public FuseBase {
 
     auto block1_stmt = matched.at("right_conv1")->stmt();
     auto fake_subgraph_op = LiteOpRegistry::Global().Create("subgraph");
-    // XXX: memleak?
-    auto sub_block_desc = new cpp::BlockDesc();
+    auto sub_program_desc = std::make_shared<cpp::ProgramDesc>();
+    sub_program_desc->AddBlock<cpp::BlockDesc>();
     static_cast<operators::SubgraphOp*>(fake_subgraph_op.get())
-        ->SetSubBlock(sub_block_desc);
+        ->SetProgramDesc(sub_program_desc);
     fake_subgraph_op->Attach(op_desc, block1_stmt->op()->scope());
     fake_subgraph_op->SetValidPlaces(block1_stmt->op()->valid_places());
     block1_stmt->SetOp(fake_subgraph_op);
@@ -932,10 +932,10 @@ class XPUResNetCbamBlock2Fuser : public FuseBase {
         << "Y of last fc must have been transposed";
 
     auto fake_subgraph_op = LiteOpRegistry::Global().Create("subgraph");
-    // XXX: memleak?
-    auto sub_block_desc = new cpp::BlockDesc();
+    auto sub_program_desc = std::make_shared<cpp::ProgramDesc>();
+    sub_program_desc->AddBlock<cpp::BlockDesc>();
     static_cast<operators::SubgraphOp*>(fake_subgraph_op.get())
-        ->SetSubBlock(sub_block_desc);
+        ->SetProgramDesc(sub_program_desc);
     fake_subgraph_op->Attach(op_desc, scope);
     fake_subgraph_op->SetValidPlaces(block2_stmt->op()->valid_places());
     block2_stmt->SetOp(fake_subgraph_op);

lite/core/mir/fusion/__xpu__resnet_fuse_pass.cc

@@ -315,10 +315,10 @@ class XPUResNetBlock0Fuser : public FuseBase {
     auto block0_stmt = matched.at("left_conv1")->stmt();
     // block0_stmt->ResetOp(op_desc, graph->valid_places());
     auto fake_subgraph_op = LiteOpRegistry::Global().Create("subgraph");
-    // XXX: memleak?
-    auto sub_block_desc = new cpp::BlockDesc();
+    auto sub_program_desc = std::make_shared<cpp::ProgramDesc>();
+    sub_program_desc->AddBlock<cpp::BlockDesc>();
     static_cast<operators::SubgraphOp*>(fake_subgraph_op.get())
-        ->SetSubBlock(sub_block_desc);
+        ->SetProgramDesc(sub_program_desc);
     fake_subgraph_op->Attach(op_desc, block0_stmt->op()->scope());
     fake_subgraph_op->SetValidPlaces(block0_stmt->op()->valid_places());
     block0_stmt->SetOp(fake_subgraph_op);
@@ -577,10 +577,10 @@ class XPUResNetBlock1Fuser : public FuseBase {
 
     auto block1_stmt = matched.at("right_conv1")->stmt();
     auto fake_subgraph_op = LiteOpRegistry::Global().Create("subgraph");
-    // XXX: memleak?
-    auto sub_block_desc = new cpp::BlockDesc();
+    auto sub_program_desc = std::make_shared<cpp::ProgramDesc>();
+    sub_program_desc->AddBlock<cpp::BlockDesc>();
     static_cast<operators::SubgraphOp*>(fake_subgraph_op.get())
-        ->SetSubBlock(sub_block_desc);
+        ->SetProgramDesc(sub_program_desc);
     fake_subgraph_op->Attach(op_desc, block1_stmt->op()->scope());
     fake_subgraph_op->SetValidPlaces(block1_stmt->op()->valid_places());
     block1_stmt->SetOp(fake_subgraph_op);

lite/core/mir/fusion/conv_conv_fuse_pass.cc

+// 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.
+
+#include "lite/core/mir/fusion/conv_conv_fuse_pass.h"
+#include <memory>
+#include <vector>
+#include "lite/core/mir/fusion/conv_conv_fuser.h"
+#include "lite/core/mir/graph_visualize_pass.h"
+#include "lite/core/mir/pass_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace mir {
+
+void ConvConvFusePass::Apply(const std::unique_ptr<SSAGraph>& graph) {
+  // initialze fuser params
+  std::vector<bool> conv_has_bias_cases{true, false};
+  std::vector<std::string> conv_type_cases{"conv2d", "depthwise_conv2d"};
+  bool has_arm = false;
+  for (auto& place : graph->valid_places()) {
+    if (place.target == TARGET(kARM) && place.precision == PRECISION(kFloat)) {
+      has_arm = true;
+      break;
+    }
+  }
+  if (!has_arm) {
+    return;
+  }
+  // only support fp32 fusion
+  for (auto conv_has_bias0 : conv_has_bias_cases) {
+    for (auto conv_has_bias1 : conv_has_bias_cases) {
+      for (auto conv_type0 : conv_type_cases) {
+        for (auto conv_type1 : conv_type_cases) {
+          VLOG(4) << "conv_has_bias0:" << conv_has_bias0
+                  << " conv_type0:" << conv_type0;
+          VLOG(4) << "conv_has_bias1:" << conv_has_bias1
+                  << " conv_type1:" << conv_type1;
+          fusion::ConvConvFuser fuser(
+              conv_type0, conv_type1, conv_has_bias0, conv_has_bias1);
+          fuser(graph.get());
+        }
+      }
+    }
+  }
+}
+
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_MIR_PASS(lite_conv_conv_fuse_pass, paddle::lite::mir::ConvConvFusePass)
+    .BindTargets({TARGET(kARM)});

lite/kernels/xpu/utils.h → lite/core/mir/fusion/conv_conv_fuse_pass.h

@@ -14,18 +14,19 @@
 
 #pragma once
 
-#include "lite/backends/xpu/xpu_header_sitter.h"
+#include <memory>
+#include <string>
+#include "lite/core/mir/pass.h"
 
 namespace paddle {
 namespace lite {
-namespace kernels {
-namespace xpu {
+namespace mir {
 
-struct XPUFreeDeleter {
-  void operator()(void* p) const { xpu_free(p); }
+class ConvConvFusePass : public ProgramPass {
+ public:
+  void Apply(const std::unique_ptr<SSAGraph>& graph) override;
 };
 
-}  // namespace xpu
-}  // namespace kernels
+}  // namespace mir
 }  // namespace lite
 }  // namespace paddle

lite/core/mir/fusion/conv_conv_fuser.cc

+// 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.
+
+#include "lite/core/mir/fusion/conv_conv_fuser.h"
+#include <memory>
+#include <set>
+#include <vector>
+
+namespace paddle {
+namespace lite {
+namespace mir {
+namespace fusion {
+
+void ConvConvFuser::BuildPattern() {
+  auto* conv_input0 = VarNode("conv_input0")
+                          ->assert_is_op_input(conv_type0_, "Input")
+                          ->AsInput();
+  auto* conv_weight0 = VarNode("conv_weight0")
+                           ->assert_is_op_input(conv_type0_, "Filter")
+                           ->AsInput();
+  auto* conv0 = OpNode("conv2d0", conv_type0_)->assert_is_op(conv_type0_);
+  auto* conv_out0 = VarNode("conv_out0")
+                        ->assert_is_op_output(conv_type0_, "Output")
+                        ->assert_is_op_input(conv_type1_, "Input")
+                        ->AsIntermediate();
+
+  auto* conv_weight1 = VarNode("conv_weight1")
+                           ->assert_is_op_input(conv_type1_, "Filter")
+                           ->AsIntermediate();
+  auto* conv1 = OpNode("conv2d1", conv_type1_)
+                    ->assert_is_op(conv_type1_)
+                    ->assert_op_attr<int>("groups", 1)
+                    ->AsIntermediate();
+
+  auto* conv_out1 = VarNode("conv_out1")
+                        ->assert_is_op_output(conv_type1_, "Output")
+                        ->AsOutput();
+
+  if (conv_has_bias0_) {
+    if (conv_has_bias1_) {
+      auto* conv_bias0 = VarNode("conv_bias0")
+                             ->assert_is_op_input(conv_type0_, "Bias")
+                             ->AsIntermediate();
+      auto* conv_bias1 = VarNode("conv_bias1")
+                             ->assert_is_op_input(conv_type1_, "Bias")
+                             ->AsInput();
+      conv0->LinksFrom({conv_input0, conv_weight0, conv_bias0})
+          .LinksTo({conv_out0});
+      conv1->LinksFrom({conv_out0, conv_weight1, conv_bias1})
+          .LinksTo({conv_out1});
+    } else {
+      auto* conv_bias0 = VarNode("conv_bias0")
+                             ->assert_is_op_input(conv_type0_, "Bias")
+                             ->AsIntermediate();
+      conv0->LinksFrom({conv_input0, conv_weight0, conv_bias0})
+          .LinksTo({conv_out0});
+      conv1->LinksFrom({conv_out0, conv_weight1}).LinksTo({conv_out1});
+    }
+  } else {
+    conv0->LinksFrom({conv_input0, conv_weight0}).LinksTo({conv_out0});
+    if (conv_has_bias1_) {
+      auto* conv_bias1 = VarNode("conv_bias1")
+                             ->assert_is_op_input(conv_type1_, "Bias")
+                             ->AsInput();
+      conv1->LinksFrom({conv_out0, conv_weight1, conv_bias1})
+          .LinksTo({conv_out1});
+    } else {
+      conv1->LinksFrom({conv_out0, conv_weight1}).LinksTo({conv_out1});
+    }
+  }
+}
+
+void ConvConvFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
+  auto conv_instruct = matched.at("conv2d0")->stmt();
+  auto conv_op_desc = conv_instruct->mutable_op_info();
+  auto conv = conv_instruct->op();
+  auto* scope = conv->scope();
+  auto conv_op_desc1 = matched.at("conv2d1")->stmt()->mutable_op_info();
+
+  // conv0
+  auto weight0_t = scope->FindVar(matched.at("conv_weight0")->arg()->name)
+                       ->GetMutable<lite::Tensor>();
+
+  // conv1
+  auto weight1_t = scope->FindVar(matched.at("conv_weight1")->arg()->name)
+                       ->GetMutable<lite::Tensor>();
+  // auto groups0 = conv_op_desc->GetAttr<int>("groups");
+  auto groups1 = conv_op_desc1->GetAttr<int>("groups");
+  auto strides1 = conv_op_desc1->GetAttr<std::vector<int>>("strides");
+  auto paddings1 = conv_op_desc1->GetAttr<std::vector<int>>("paddings");
+  auto dilations1 = conv_op_desc1->GetAttr<std::vector<int>>("dilations");
+
+  bool enable0_int8 = conv_op_desc->HasAttr("enable_int8") ? true : false;
+  bool enable1_int8 = conv_op_desc1->HasAttr("enable_int8") ? true : false;
+  int kw = weight1_t->dims()[2];
+  int kh = weight1_t->dims()[3];
+  if (!(kw == 1 && kh == 1)) {
+    return;
+  }
+  CHECK_EQ(enable0_int8, enable1_int8) << "The Conv compute type must be same";
+  CHECK_EQ(groups1, 1) << "The groups of weight1_dim must be 1";
+  CHECK_EQ(weight0_t->dims()[0], weight1_t->dims()[1])
+      << "weight0_dims[0] == weight1_dim[1]";
+  for (int i = 0; i < strides1.size(); i++) {
+    CHECK_EQ(strides1[i], 1) << "strides[" << i << "]: " << strides1[i]
+                             << " must be 1";
+  }
+  for (int i = 0; i < paddings1.size(); i++) {
+    CHECK_EQ(paddings1[i], 0) << "paddings1[" << i << "]: " << paddings1[i]
+                              << " must be 0";
+  }
+  for (int i = 0; i < dilations1.size(); i++) {
+    CHECK_EQ(dilations1[i], 1) << "dilations1[" << i << "]: " << dilations1[i]
+                               << " must be 1";
+  }
+  // comupte new_wight and new bias
+  ///////////////////////////////////////////////////////////////////////////////
+  // Compute ConvConvFuser
+  // Before fusion
+  //
+  //   conv(x) = conv(x) = kx + z = y
+  //   conv(y) = ay + b
+  //
+  // After fusion:
+  //
+  //   conv(conv(x)) = a(kx + z) + b = akx + az + b
+  //
+  //   new_weights = ak
+  //   new_bias = az + b
+  ///////////////////////////////////////////////////////////////////////////////
+  if (enable0_int8) {
+    LOG(FATAL) << "it doesn't support";
+    return;
+  } else {
+    // compute new conv_weight
+    Tensor weight_tensor;
+    auto in_dims = weight0_t->dims();
+    auto weight_dims = weight1_t->dims();
+    const float* din = weight0_t->data<float>();
+    const float* weights = weight1_t->data<float>();
+    int oc0 = in_dims[0];
+    int ic = in_dims[1];
+    int ih = in_dims[2];
+    int iw = in_dims[3];
+    int oc = weight_dims[0];
+    weight_tensor.Resize({oc, ic, ih, iw});
+    float* dout = weight_tensor.mutable_data<float>();
+    ComputeNewWeight(dout, din, weights, oc0, ic, ih, iw, oc);
+    weight0_t->CopyDataFrom(weight_tensor);
+  }
+  // compute new conv_bias
+  if (conv_has_bias0_ && conv_op_desc->HasInput("Bias") &&
+      conv_op_desc->Input("Bias").size() > 0) {
+    auto bias_t0 = scope->FindVar(matched.at("conv_bias0")->arg()->name)
+                       ->GetMutable<lite::Tensor>();
+    if (conv_has_bias1_ && conv_op_desc1->HasInput("Bias") &&
+        conv_op_desc1->Input("Bias").size() > 0) {
+      auto bias_t1 = scope->FindVar(matched.at("conv_bias1")->arg()->name)
+                         ->GetMutable<lite::Tensor>();
+      Tensor bias;
+      bias.CopyDataFrom(*bias_t1);
+      auto bias_data = bias.mutable_data<float>();
+      ComputeNewBias(bias_data, bias_t0, weight1_t, bias_t1);
+      bias_t1->CopyDataFrom(bias);
+      conv_op_desc->SetInput(
+          "Bias", {matched.at("conv_bias1")->arg()->name});  // conv_bias
+      IR_NODE_LINK_TO(matched.at("conv_bias1"), matched.at("conv2d0"));
+    } else {
+      Tensor bias;
+      auto weight_dims = weight1_t->dims();
+      bias.Resize({weight_dims[0]});
+      auto bias_d = bias.mutable_data<float>();
+      ComputeNewBias(bias_d, bias_t0, weight1_t, nullptr);
+      bias_t0->CopyDataFrom(bias);
+      conv_op_desc->SetInput(
+          "Bias", {matched.at("conv_bias0")->arg()->name});  // conv_bias
+    }
+  } else {
+    if (conv_has_bias1_ && conv_op_desc1->HasInput("Bias") &&
+        conv_op_desc1->Input("Bias").size() > 0) {
+      conv_op_desc->SetInput(
+          "Bias", {matched.at("conv_bias1")->arg()->name});  // conv_bias
+      IR_NODE_LINK_TO(matched.at("conv_bias1"), matched.at("conv2d0"));
+    }
+  }
+  conv_op_desc->SetType(conv_type0_);
+  conv_op_desc->SetInput("Input", {matched.at("conv_input0")->arg()->name});
+  conv_op_desc->SetInput("Filter", {matched.at("conv_weight0")->arg()->name});
+  conv_op_desc->SetOutput("Output", {matched.at("conv_out1")->arg()->name});
+
+  auto update_conv_desc = *conv_instruct->mutable_op_info();
+  conv_instruct->ResetOp(update_conv_desc, graph->valid_places());
+
+  IR_OP_VAR_LINK(matched.at("conv2d0"), matched.at("conv_out1"));
+}
+
+}  // namespace fusion
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle

lite/core/mir/fusion/conv_conv_fuser.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
+
+#include <cmath>
+#include <memory>
+#include <string>
+#include "lite/core/mir/pattern_matcher_high_api.h"
+#include "lite/utils/cp_logging.h"
+
+namespace paddle {
+namespace lite {
+namespace mir {
+namespace fusion {
+
+class ConvConvFuser : public FuseBase {
+ public:
+  explicit ConvConvFuser(const std::string& conv_type0,
+                         const std::string& conv_type1,
+                         const bool conv_has_bias0,
+                         const bool conv_has_bias1)
+      : conv_type0_(conv_type0),
+        conv_type1_(conv_type1),
+        conv_has_bias0_(conv_has_bias0),
+        conv_has_bias1_(conv_has_bias1) {}
+  void BuildPattern() override;
+  void InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) override;
+
+ private:
+  void ComputeNewWeight(float* dout,
+                        const float* din,
+                        const float* weights,
+                        int oc0,
+                        int ic,
+                        int ih,
+                        int iw,
+                        int oc1) {
+    // input conv_weight0_t weights conv_weight1_t
+    // output weight_tensor
+    // ksize = 1
+    int in_size = ih * iw;
+    int in_channel_size = ic * in_size;
+    // out = w1[j, i, ih, iw] * w2[k, j, kw, kh]
+    // out_dim = [oc1, ic, kh, kw], din_dim = [oc0, ic, kh, kw]
+    // weight_dim = [oc1, oc0, kh, kw]
+    for (int k = 0; k < oc1; k++) {
+      const float* weights_ptr = weights + k * oc0;
+      float* out_ptr = dout + k * in_channel_size;
+      for (int c = 0; c < ic; c++) {
+        float* out_ptr_channel = out_ptr + c * in_size;
+        const float* din_ptr = din + c * in_size;
+        for (int i = 0; i < in_size; i++) {
+          float sum = 0.f;
+          for (int j = 0; j < oc0; j++) {
+            sum += din_ptr[j * in_channel_size] * weights_ptr[j];
+          }
+          *out_ptr_channel++ = sum;
+        }
+      }
+    }
+  }
+
+  void ComputeNewBias(float* dout,
+                      Tensor* bias0_tensor,
+                      Tensor* weight_tensor,
+                      Tensor* bias1_tensor) {
+    // input bias0_tensor weight_tensor bias1_tensor
+    // output bias_tensor
+    auto in_dims = bias0_tensor->dims();
+    auto weight_dims = weight_tensor->dims();
+    const float* din = bias0_tensor->data<float>();
+    const float* weights = weight_tensor->data<float>();
+    int ic = in_dims[0];
+    int oc = weight_dims[0];
+    // out_k = b0[num, j, 1, 1] * w2[k, j, 1, 1]
+    if (bias1_tensor) {
+      const float* din2 = bias1_tensor->data<float>();
+      for (int k = 0; k < oc; k++) {
+        const float* weights_ptr = weights + k * ic;
+        float sum = 0.f;
+        for (int j = 0; j < ic; j++) {
+          sum += din[j] * weights_ptr[j];
+        }
+        dout[k] = sum + din2[k];
+      }
+    } else {
+      for (int k = 0; k < oc; k++) {
+        const float* weights_ptr = weights + k * ic;
+        float sum = 0.f;
+        for (int j = 0; j < ic; j++) {
+          sum += din[j] * weights_ptr[j];
+        }
+        dout[k] = sum;
+      }
+    }
+  }
+
+ private:
+  std::string conv_type0_{"conv2d"};
+  std::string conv_type1_{"conv2d"};
+  bool conv_has_bias0_{false};
+  bool conv_has_bias1_{false};
+};
+
+}  // namespace fusion
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle

lite/core/mir/fusion/quant_dequant_op_fuser.cc

@@ -175,7 +175,11 @@ void DequantOpFuser::InsertNewNode(SSAGraph* graph,
   for (int i = 0; i < weight_scale_size; i++) {
     weight_scale.push_back(whole_weight_scale);
   }
+
+  // Arm CPU does not support conv2d_transpose
+  if (quantized_op_type_ != "conv2d_transpose") {
     op_desc.SetAttr("enable_int8", true);
+  }
   op_desc.SetInputScale(weight_name, weight_scale);
 
   // change the weight from the float type to int8 type.
@@ -280,6 +284,7 @@ void ChannelWiseDequantOpFuser::InsertNewNode(SSAGraph* graph,
     op_desc.SetInput("X", {quantized_op_input->arg()->name});
     op_desc.SetOutput("Out", {dequant_op_out->arg()->name});
   }
+  // Arm CPU does not support conv2d_transpose
   if (quantized_op_type_ != "conv2d_transpose") {
     op_desc.SetAttr("enable_int8", true);
   }

lite/core/mir/generate_program_pass.cc

@@ -39,6 +39,7 @@ void GenerateProgramPass::Apply(const std::unique_ptr<SSAGraph>& graph) {
     nodes_in_order = graph->StmtTopologicalOrder();
   }
 
+  insts_.emplace_back();
   for (auto& item : nodes_in_order) {
     if (item->IsStmt()) {
       auto& stmt = item->AsStmt();
@@ -57,7 +58,7 @@ void GenerateProgramPass::Apply(const std::unique_ptr<SSAGraph>& graph) {
             .SetSyncStreams(stmt.sync_streams_);
       }
 #endif
-      insts_.emplace_back(stmt.op(), std::move(stmt.kernels().front()));
+      insts_.back().emplace_back(stmt.op(), std::move(stmt.kernels().front()));
     }
   }
 }

lite/core/mir/generate_program_pass.h

@@ -42,7 +42,7 @@ class GenerateProgramPass : public ProgramPass {
   }
 
  private:
-  std::vector<Instruction> insts_;
+  std::vector<std::vector<Instruction>> insts_;
 };
 
 }  // namespace mir

lite/core/mir/mlu_postprocess_pass.cc

@@ -284,13 +284,19 @@ void MLUPostprocessPass::InsertBefore(SSAGraph* graph,
                 head_node->AsArg().name,
                 cur_node->AsArg().name);
   // for subgraph op, modify the BlockDesc
-  auto* sub_block_desc = dynamic_cast<paddle::lite::operators::SubgraphOp*>(
+  auto sub_program_desc = dynamic_cast<paddle::lite::operators::SubgraphOp*>(
                               inst_node->AsStmt().op().get())
-                             ->GetSubBlock();
-  for (size_t i = 0; i < sub_block_desc->OpsSize(); ++i) {
-    auto* sub_block_op_desc = sub_block_desc->GetOp<cpp::OpDesc>(i);
-    UpdateInputTo(
-        sub_block_op_desc, head_node->AsArg().name, cur_node->AsArg().name);
+                              ->GetProgramDesc();
+  CHECK(sub_program_desc);
+  int sub_block_idx =
+      inst_node->AsStmt().op()->op_info()->GetAttr<int32_t>("sub_block");
+  auto* sub_block_desc =
+      sub_program_desc->GetBlock<cpp::BlockDesc>(sub_block_idx);
+  for (size_t sub_op_idx = 0; sub_op_idx < sub_block_desc->OpsSize();
+       ++sub_op_idx) {
+    auto* sub_op_desc = const_cast<cpp::OpDesc*>(
+        sub_block_desc->GetOp<cpp::OpDesc>(sub_op_idx));
+    UpdateInputTo(sub_op_desc, head_node->AsArg().name, cur_node->AsArg().name);
   }
 
   // recreate the op
@@ -444,21 +450,27 @@ void MLUPostprocessPass::InsertAfter(SSAGraph* graph,
                  tail_node->AsArg().name,
                  cur_node->AsArg().name);
   // for subgraph op, modify the BlockDesc
-  auto* sub_block_desc = dynamic_cast<paddle::lite::operators::SubgraphOp*>(
+  auto sub_program_desc = dynamic_cast<paddle::lite::operators::SubgraphOp*>(
                               inst_node->AsStmt().op().get())
-                             ->GetSubBlock();
-  for (size_t i = 0; i < sub_block_desc->OpsSize(); ++i) {
-    auto* sub_block_op_desc = sub_block_desc->GetOp<cpp::OpDesc>(i);
+                              ->GetProgramDesc();
+  CHECK(sub_program_desc);
+  int sub_block_idx =
+      inst_node->AsStmt().op()->op_info()->GetAttr<int32_t>("sub_block");
+  auto* sub_block_desc =
+      sub_program_desc->GetBlock<cpp::BlockDesc>(sub_block_idx);
+  for (size_t sub_op_idx = 0; sub_op_idx < sub_block_desc->OpsSize();
+       ++sub_op_idx) {
+    auto* sub_op_desc = const_cast<cpp::OpDesc*>(
+        sub_block_desc->GetOp<cpp::OpDesc>(sub_op_idx));
     UpdateOutputTo(
-        sub_block_op_desc, tail_node->AsArg().name, cur_node->AsArg().name);
+        sub_op_desc, tail_node->AsArg().name, cur_node->AsArg().name);
     /* graph like this
      *        subgraph_op_0
      *          /       \
      *         /         \
      * subgraph_op_1   host_op
      */
-    UpdateInputTo(
-        sub_block_op_desc, tail_node->AsArg().name, cur_node->AsArg().name);
+    UpdateInputTo(sub_op_desc, tail_node->AsArg().name, cur_node->AsArg().name);
   }
 
   // recreate the op
@@ -482,15 +494,22 @@ void MLUPostprocessPass::RecreateOp(Node* inst_node, SSAGraph* graph) {
   }
 }
 
-bool MLUPostprocessPass::IsFirstConvInSubgraph(Node* arg_node, Node* inst) {
-  auto* block_desc =
-      static_cast<operators::SubgraphOp*>(inst->AsStmt().op().get())
-          ->GetSubBlock();
-  for (size_t op_idx = 0; op_idx < block_desc->OpsSize(); op_idx++) {
-    auto op_desc = block_desc->GetOp<cpp::OpDesc>(op_idx);
-    CHECK(op_desc);
-    if (op_desc->Type() == "conv2d") {
-      for (auto& names : op_desc->inputs()) {
+bool MLUPostprocessPass::IsFirstConvInSubgraph(Node* arg_node,
+                                               Node* inst_node) {
+  auto sub_program_desc = dynamic_cast<paddle::lite::operators::SubgraphOp*>(
+                              inst_node->AsStmt().op().get())
+                              ->GetProgramDesc();
+  CHECK(sub_program_desc);
+  int sub_block_idx =
+      inst_node->AsStmt().op()->op_info()->GetAttr<int32_t>("sub_block");
+  auto* sub_block_desc =
+      sub_program_desc->GetBlock<cpp::BlockDesc>(sub_block_idx);
+  for (size_t sub_op_idx = 0; sub_op_idx < sub_block_desc->OpsSize();
+       sub_op_idx++) {
+    auto sub_op_desc = sub_block_desc->GetOp<cpp::OpDesc>(sub_op_idx);
+    CHECK(sub_op_desc);
+    if (sub_op_desc->Type() == "conv2d") {
+      for (auto& names : sub_op_desc->inputs()) {
         if (std::find(names.second.begin(),
                       names.second.end(),
                       arg_node->AsArg().name) != names.second.end()) {
@@ -746,19 +765,23 @@ std::pair<bool, std::string> CheckOutputAndInsert(
 // insert cast op on mlu, to avoid cast on cpu
 void MLUPostprocessPass::AdjustSubgraph(Node* subgraph_node,
                                         const Type* subgraph_type) {
-  auto subgraph_op = subgraph_node->AsStmt().op();
-  CHECK_EQ(subgraph_op->Type(), "subgraph");
-  auto op = dynamic_cast<operators::SubgraphOp*>(subgraph_op.get());
-  CHECK(op);
-  auto block_desc = op->GetSubBlock();
+  CHECK_EQ(subgraph_node->AsStmt().op()->Type(), "subgraph");
+  auto subgraph_op =
+      dynamic_cast<operators::SubgraphOp*>(subgraph_node->AsStmt().op().get());
+  CHECK(subgraph_op);
+  auto sub_program_desc = subgraph_op->GetProgramDesc();
+  CHECK(sub_program_desc);
+  int sub_block_idx = subgraph_op->op_info()->GetAttr<int32_t>("sub_block");
+  auto* sub_block_desc = const_cast<cpp::BlockDesc*>(
+      sub_program_desc->GetBlock<cpp::BlockDesc>(sub_block_idx));
 
   // create a new block desc to keep op sequence correct
-  cpp::BlockDesc* new_block_desc = new cpp::BlockDesc();
-  new_block_desc->ClearOps();
-  new_block_desc->ClearVars();
-  new_block_desc->SetIdx(block_desc->Idx());
-  new_block_desc->SetParentIdx(block_desc->ParentIdx());
-  new_block_desc->SetForwardBlockIdx(block_desc->ForwardBlockIdx());
+  cpp::BlockDesc new_block_desc;
+  new_block_desc.ClearOps();
+  new_block_desc.ClearVars();
+  new_block_desc.SetIdx(sub_block_desc->Idx());
+  new_block_desc.SetParentIdx(sub_block_desc->ParentIdx());
+  new_block_desc.SetForwardBlockIdx(sub_block_desc->ForwardBlockIdx());
 
   // find all IO that is not weight or persist
   std::list<std::string> i_names, o_names;
@@ -769,8 +792,8 @@ void MLUPostprocessPass::AdjustSubgraph(Node* subgraph_node,
     auto input_name = input->AsArg().name;
     if (!(input->AsArg().is_weight || input->AsArg().is_persist)) {
       i_names.emplace_back(input_name);
-      auto ret = CheckInputAndInsert(op->scope(),
-                                     new_block_desc,
+      auto ret = CheckInputAndInsert(subgraph_op->scope(),
+                                     &new_block_desc,
                                      input_name,
                                      input->AsArg().type,
                                      subgraph_type);
@@ -783,8 +806,8 @@ void MLUPostprocessPass::AdjustSubgraph(Node* subgraph_node,
     auto output_name = output->AsArg().name;
     if (!(output->AsArg().is_weight || output->AsArg().is_persist)) {
       o_names.emplace_back(output_name);
-      auto ret = CheckOutputAndInsert(op->scope(),
-                                      block_desc,
+      auto ret = CheckOutputAndInsert(subgraph_op->scope(),
+                                      sub_block_desc,
                                       output_name,
                                       output->AsArg().type,
                                       subgraph_type);
@@ -795,46 +818,48 @@ void MLUPostprocessPass::AdjustSubgraph(Node* subgraph_node,
   }
 
   // update input and output
-  for (size_t op_idx = 0; op_idx < block_desc->OpsSize(); ++op_idx) {
-    auto desc = block_desc->GetOp<cpp::OpDesc>(op_idx);
-    auto new_desc = new_block_desc->AddOp<cpp::OpDesc>();
-    *new_desc = *desc;
-
-    if (desc->Type() != "layout" && desc->Type() != "cast") {
-      auto op_input_args = new_desc->InputArgumentNames();
+  for (size_t sub_op_idx = 0; sub_op_idx < sub_block_desc->OpsSize();
+       ++sub_op_idx) {
+    auto sub_op_desc = sub_block_desc->GetOp<cpp::OpDesc>(sub_op_idx);
+    auto new_op_desc = new_block_desc.AddOp<cpp::OpDesc>();
+    *new_op_desc = *sub_op_desc;
+
+    if (sub_op_desc->Type() != "layout" && sub_op_desc->Type() != "cast") {
+      auto op_input_args = new_op_desc->InputArgumentNames();
       for (auto& input_arg : op_input_args) {
-        auto op_input = new_desc->Input(input_arg);
+        auto op_input = new_op_desc->Input(input_arg);
         for (auto& it : i_names) {
           auto index = std::find(op_input.begin(), op_input.end(), it);
           if (index != op_input.end() &&
               node_replace.find(it) != node_replace.end()) {
             index = op_input.erase(index);
             op_input.emplace(index, node_replace.at(it));
-            VLOG(4) << new_desc->Type() << "] change input from " << it
+            VLOG(4) << new_op_desc->Type() << "] change input from " << it
                     << " to " << node_replace.at(it);
           }
         }
-        new_desc->SetInput(input_arg, op_input);
+        new_op_desc->SetInput(input_arg, op_input);
       }
 
-      auto op_output_args = new_desc->OutputArgumentNames();
+      auto op_output_args = new_op_desc->OutputArgumentNames();
       for (auto& output_arg : op_output_args) {
-        auto op_output = new_desc->Output(output_arg);
+        auto op_output = new_op_desc->Output(output_arg);
         for (auto& it : o_names) {
           auto index = std::find(op_output.begin(), op_output.end(), it);
           if (index != op_output.end() &&
               node_replace.find(it) != node_replace.end()) {
             index = op_output.erase(index);
             op_output.emplace(index, node_replace.at(it));
-            VLOG(4) << new_desc->Type() << "] change output from " << it
+            VLOG(4) << new_op_desc->Type() << "] change output from " << it
                     << " to " << node_replace.at(it);
           }
         }
-        new_desc->SetOutput(output_arg, op_output);
+        new_op_desc->SetOutput(output_arg, op_output);
       }
     }
   }
-  op->SetSubBlock(new_block_desc);
+
+  *sub_block_desc = new_block_desc;
 }
 
 void ModifyValidPlaces(SSAGraph* graph, bool use_mlu_cast) {

lite/core/mir/ssa_graph.cc

@@ -153,60 +153,61 @@ Node *SSAGraph::GraphCreateInstructNode(
 }
 
 void SSAGraph::Build(const Program &program,
-                     const std::vector<Place> &valid_places) {
+                     const std::vector<Place> &valid_places,
+                     int block_idx) {
   CHECK(node_storage_.empty());
 
-  auto weights_name = program.weights();
-  auto is_weights = [&](const std::string &name) -> bool {
-    auto it = std::find(weights_name.begin(), weights_name.end(), name);
-    if (it == weights_name.end()) return false;
+  auto weights = program.weights();
+  auto is_weight = [&](const std::string &name) -> bool {
+    auto it = std::find(weights.begin(), weights.end(), name);
+    if (it == weights.end()) return false;
     return true;
   };
 
-  std::map<std::string, PrecisionType> var_types = program.var_data_type();
-
-  std::map<std::string, mir::Node *> arg_update_node_map_;
-  for (auto &op : program.ops()) {
+  auto var_type_map = program.var_type_map();
+  std::map<std::string, mir::Node *> arg_update_node_map;
+  for (auto &op : program.ops(block_idx)) {
     VLOG(3) << op->op_info()->Type();
     auto *op_node = GraphCreateInstructNode(op, valid_places);
-    for (const std::string &name : op->op_info()->input_names()) {
+    auto *op_info = op->op_info();
+    const auto &op_type = op_info->Type();
+    for (const auto &var_name : op_info->input_names()) {
       mir::Node *arg_node = nullptr;
-      if (arg_update_node_map_.count(name)) {
-        arg_node = arg_update_node_map_.at(name);
+      if (arg_update_node_map.count(var_name)) {
+        arg_node = arg_update_node_map.at(var_name);
       } else {
         node_storage_.emplace_back();
         arg_node = &node_storage_.back();
-        arg_node->AsArg(name, node_storage_.size() - 1);
-        arg_update_node_map_[name] = arg_node;
+        arg_node->AsArg(var_name, node_storage_.size() - 1);
+        arg_update_node_map[var_name] = arg_node;
       }
-      if (var_types.count(name)) {
+      if (var_type_map.count(var_name)) {
         if (!arg_node->arg()->type) {
-          arg_node->arg()->type = LiteType::GetTensorTy(
-              TARGET(kUnk), var_types[name], DATALAYOUT(kUnk));
+          arg_node->arg()->type = var_type_map[var_name];
         }
         // Store the original data type of the output tensors for
         // type_precision_cast_pass, to keep the consistency between the
         // output types of original graph and optimized graph's
-        if (op->op_info()->Type() == "fetch") {
+        if (op_type == "fetch") {
           op->mutable_op_info()->SetAttr<int>(
-              "data_type", static_cast<int>(var_types[name]));
+              "data_type",
+              static_cast<int>(var_type_map[var_name]->precision()));
         }
       }
-      if (is_weights(name)) arg_node->AsArg().is_weight = true;
+      if (is_weight(var_name)) arg_node->AsArg().is_weight = true;
       CHECK(arg_node->IsRoleSet());
       DirectedLink(arg_node, op_node);
     }
-    for (const std::string &name : op->op_info()->output_names()) {
+    for (const auto &var_name : op->op_info()->output_names()) {
       node_storage_.emplace_back();
       auto *arg_node = &node_storage_.back();
-      arg_node->AsArg(name, node_storage_.size() - 1);
-      arg_update_node_map_[name] = arg_node;
-      if (var_types.count(name) && !arg_node->arg()->type) {
-        arg_node->arg()->type = LiteType::GetTensorTy(
-            TARGET(kUnk), var_types[name], DATALAYOUT(kUnk));
+      arg_node->AsArg(var_name, node_storage_.size() - 1);
+      arg_update_node_map[var_name] = arg_node;
+      if (var_type_map.count(var_name) && !arg_node->arg()->type) {
+        arg_node->arg()->type = var_type_map[var_name];
       }
 
-      if (is_weights(name)) arg_node->AsArg().is_weight = true;
+      if (is_weight(var_name)) arg_node->AsArg().is_weight = true;
       CHECK(arg_node->IsRoleSet());
       DirectedLink(op_node, arg_node);
     }

lite/core/mir/ssa_graph.h

@@ -35,9 +35,13 @@ class GraphBase {};
 
 class SSAGraph : GraphBase {
  public:
-  // @param program: the op program
+  // @param program: the target program with vars and ops
   // @param valid_places: the valid places user set for the system.
-  void Build(const Program &program, const std::vector<Place> &valid_places);
+  // @param block_idx: the block index in the target program, default is 0(main
+  // block)
+  void Build(const Program &program,
+             const std::vector<Place> &valid_places,
+             int block_idx = kRootBlockIdx);
   void RemoveNode(const mir::Node *node);
 
   std::vector<mir::Node *> StmtTopologicalOrder();

lite/core/mir/subgraph/subgraph_detector.cc

@@ -411,16 +411,17 @@ void SubgraphFuser::InsertNewNode(SSAGraph *graph,
   cpp::OpDesc subgraph_op_desc;
   subgraph_op_desc.SetType("subgraph");
 
-  // Create a new sub block desc for storing all of Ops and Vars of the target
-  // subgraph and sub_block_idx is set as a attribute of subgraph op,
-  // sub_block_idx < 0 means it's a new subgraph op
-  int sub_block_idx = -(subgraph_idx + 1);
-  auto sub_block_desc = new cpp::BlockDesc();
+  // Create a program desc and a block desc for storing all of Ops and Vars of
+  // the target subgraph and sub_block_idx is set as a attribute of subgraph op,
+  // sub_block_idx = 0 means it's a new subgraph op
+  auto sub_program_desc = std::make_shared<cpp::ProgramDesc>();
+  int sub_block_idx = 0;
+  auto sub_block_desc = sub_program_desc->AddBlock<cpp::BlockDesc>();
   sub_block_desc->ClearOps();
   sub_block_desc->ClearVars();
   for (auto &op_node : subgraph_nodes) {
-    auto sub_block_op_desc = sub_block_desc->AddOp<cpp::OpDesc>();
-    *sub_block_op_desc = *op_node->AsStmt().op_info();
+    auto sub_op_desc = sub_block_desc->AddOp<cpp::OpDesc>();
+    *sub_op_desc = *op_node->AsStmt().op_info();
   }
   subgraph_op_desc.SetAttr<int32_t>("sub_block", sub_block_idx);
 
@@ -437,13 +438,13 @@ void SubgraphFuser::InsertNewNode(SSAGraph *graph,
                        &local_var_nodes,
                        &unused_var_nodes);
   // A simplified model without the original weight/local/unused nodes on the
-  // subgraph ops will be saved only if 'SUBGRAPH_DISABLE_ONLINE_MODE' is set to
-  // true and Predictor->Run(...), Predictor->Save(...) is called.
+  // subgraph ops will be saved only if 'SUBGRAPH_ONLINE_MODE' is set to
+  // true(default) and Predictor->Run(...), Predictor->Save(...) is called.
   std::set<Node *> input_var_nodes(idata_var_nodes.begin(),
                                    idata_var_nodes.end());
   std::set<Node *> output_var_nodes(odata_var_nodes.begin(),
                                     odata_var_nodes.end());
-  if (!GetBoolFromEnv(SUBGRAPH_DISABLE_ONLINE_MODE)) {
+  if (GetBoolFromEnv(SUBGRAPH_ONLINE_MODE, true)) {
     input_var_nodes.insert(weight_var_nodes.begin(), weight_var_nodes.end());
     output_var_nodes.insert(local_var_nodes.begin(), local_var_nodes.end());
     output_var_nodes.insert(unused_var_nodes.begin(), unused_var_nodes.end());
@@ -476,7 +477,7 @@ void SubgraphFuser::InsertNewNode(SSAGraph *graph,
   subgraph_op_desc.SetOutput("Outputs", output_var_names);
   auto subgraph_op = LiteOpRegistry::Global().Create("subgraph");
   static_cast<operators::SubgraphOp *>(subgraph_op.get())
-      ->SetSubBlock(sub_block_desc);
+      ->SetProgramDesc(sub_program_desc);
   auto any_op = (*subgraph_nodes.begin())->AsStmt().op();
   subgraph_op->Attach(subgraph_op_desc, any_op->scope());
 

lite/core/mir/subgraph/subgraph_detector_test.cc

@@ -141,12 +141,11 @@ std::vector<std::string> AddFetchDesc(
 }
 
 TEST(Subgraph, detect_simple_model) {
-  cpp::ProgramDesc program_desc;
+  auto program_desc = std::make_shared<cpp::ProgramDesc>();
   std::vector<Place> valid_places{{TARGET(kHost), PRECISION(kFloat)}};
   auto scope = std::make_shared<Scope>();
   // Build a simple network
-  program_desc.ClearBlocks();
-  auto* block_desc = program_desc.AddBlock<cpp::BlockDesc>();
+  auto* block_desc = program_desc->AddBlock<cpp::BlockDesc>();
   block_desc->ClearOps();
   block_desc->ClearVars();
   auto* var_desc = block_desc->AddVar<cpp::VarDesc>();
@@ -181,13 +180,13 @@ TEST(Subgraph, detect_custom_model) {
                  "the path of model files.";
     return;
   }
-  cpp::ProgramDesc program_desc;
+  auto program_desc = std::make_shared<cpp::ProgramDesc>();
   auto scope = std::make_shared<Scope>();
   LoadModelPb(FLAGS_model_dir,
               FLAGS_model_file,
               FLAGS_params_file,
               scope.get(),
-              &program_desc,
+              program_desc.get(),
               !FLAGS_model_file.empty() && !FLAGS_params_file.empty(),
               false);
   std::vector<Place> valid_places({

lite/core/mir/type_precision_cast_pass.cc

@@ -36,14 +36,20 @@ void UpdateInputsForSubgraph(OpLite* op,
       op_desc->GetAttr<std::vector<std::string>>("input_data_names");
   std::replace(input_data_names.begin(), input_data_names.end(), from, to);
   op_desc->SetAttr("input_data_names", input_data_names);
-  auto* subblock_desc = static_cast<operators::SubgraphOp*>(op)->GetSubBlock();
-  CHECK(subblock_desc);
-  for (size_t i = 0; i < subblock_desc->OpsSize(); i++) {
-    auto* subblock_op_desc = subblock_desc->GetOp<cpp::OpDesc>(i);
-    for (auto& subblock_op_input : *subblock_op_desc->mutable_inputs()) {
-      for (auto& subblock_var_name : subblock_op_input.second) {
-        if (subblock_var_name == from) {
-          subblock_var_name = to;
+  auto sub_program_desc =
+      static_cast<operators::SubgraphOp*>(op)->GetProgramDesc();
+  CHECK(sub_program_desc);
+  int sub_block_idx = op_desc->GetAttr<int32_t>("sub_block");
+  auto sub_block_desc =
+      sub_program_desc->GetBlock<cpp::BlockDesc>(sub_block_idx);
+  for (size_t sub_op_idx = 0; sub_op_idx < sub_block_desc->OpsSize();
+       sub_op_idx++) {
+    auto sub_op_desc = const_cast<cpp::OpDesc*>(
+        sub_block_desc->GetOp<cpp::OpDesc>(sub_op_idx));
+    for (auto& sub_op_input : *sub_op_desc->mutable_inputs()) {
+      for (auto& sub_var_name : sub_op_input.second) {
+        if (sub_var_name == from) {
+          sub_var_name = to;
         }
       }
     }

lite/core/mir/variable_place_inference_pass.h

@@ -59,25 +59,46 @@ class VariablePlaceInferencePass : public DebugPass {
   }
 
   // Set the type of the weight
-  void SetWeightType(Node* w,
+  void SetWeightType(Node* weight_node,
                      const LiteType& type,
-                     const std::map<std::string, bool>& lite_with_targets) {
+                     const std::map<std::string, bool>& with_targets) {
     VLOG(4) << "type.precision():" << PrecisionRepr(type.precision());
-    if (lite_with_targets.at("kFPGA")) {
-      w->AsArg().type = LiteType::GetTensorTy(
+    if (with_targets.at("kFPGA")) {
+      weight_node->AsArg().type = LiteType::GetTensorTy(
           TARGET(kHost), PRECISION(kFloat), DATALAYOUT(kNCHW));
-    } else if (lite_with_targets.at("kOpenCL")) {
-      w->AsArg().type = LiteType::GetTensorTy(
+    } else if (with_targets.at("kOpenCL")) {
+      weight_node->AsArg().type = LiteType::GetTensorTy(
           TARGET(kHost), PRECISION(kFloat), DATALAYOUT(kNCHW));
-    } else if (lite_with_targets.at("kCUDA")) {
-      w->AsArg().type = LiteType::GetTensorTy(
+    } else if (with_targets.at("kCUDA")) {
+      weight_node->AsArg().type = LiteType::GetTensorTy(
           TARGET(kHost), PRECISION(kFloat), DATALAYOUT(kNCHW));
     } else {
-      w->AsArg().type = LiteType::GetTensorTy(
+      weight_node->AsArg().type = LiteType::GetTensorTy(
           TARGET(kHost), type.precision(), DATALAYOUT(kNCHW));
     }
   }
 
+  // Update a's kUnk fields from b's fields.
+  void UpdateTypeFrom(const Type** a, const Type* b) {
+    auto target = (*a)->target();
+    auto precision = (*a)->precision();
+    auto layout = (*a)->layout();
+    if (target == TARGET(kUnk)) {
+      target = b->target();
+    }
+    if (precision == PRECISION(kUnk)) {
+      precision = b->precision();
+    }
+    if (layout == DATALAYOUT(kUnk)) {
+      layout = b->layout();
+    }
+    if ((*a)->IsTensor() && b->IsTensor()) {
+      *a = LiteType::GetTensorTy(target, precision, layout);
+    } else if ((*a)->IsTensorList() && b->IsTensorList()) {
+      *a = LiteType::GetTensorListTy(target, precision, layout);
+    }
+  }
+
   void InferenceArgumentPlace(SSAGraph* graph) {
     auto& valid_places = graph->valid_places();
     auto valid_places_has_target = [&](TargetType t) -> bool {
@@ -88,122 +109,90 @@ class VariablePlaceInferencePass : public DebugPass {
       }
       return false;
     };
-    std::map<std::string, bool> lite_with_targets{
+    std::map<std::string, bool> with_targets{
         {"kOpenCL", valid_places_has_target(TARGET(kOpenCL))},
         {"kCUDA", valid_places_has_target(TARGET(kCUDA))},
         {"kFPGA", valid_places_has_target(TARGET(kFPGA))}};
-    VLOG(4) << "lite_with_targets['kOpenCL']:" << lite_with_targets["kOpenCL"];
-    VLOG(4) << "lite_with_targets['kFPGA']:" << lite_with_targets["kFPGA"];
+    VLOG(4) << "with_targets['kOpenCL']:" << with_targets["kOpenCL"];
+    VLOG(4) << "with_targets['kFPGA']:" << with_targets["kFPGA"];
 
     VLOG(3) << "param-type-registry:\n" << ParamTypeRegistry::Global();
-    for (auto& x : graph->StmtTopologicalOrder()) {
-      auto& inst = x->AsStmt();
+    for (auto& node : graph->StmtTopologicalOrder()) {
+      auto& inst = node->AsStmt();
+      const auto* op_info = inst.op_info();
+      const auto& op_type = op_info->Type();
+      auto& kernel = inst.picked_kernel();
+
       // The IoCopyOp is a tool operator, it won't support the type inference.
       // in fpga, we has io_copy+cali+layout tool ops, so we need type inference
-      // for
-      // tool operator
-      if ((!lite_with_targets["kFPGA"]) && (!lite_with_targets["kOpenCL"])) {
-        VLOG(3) << "inst.op_type() == 'io_copy', continue";
-        if (inst.op_type() == "io_copy") continue;
-      }
-      // deal with inputs
-      VLOG(4) << "Infering op " << inst.op_info()->Repr();
-      // TODO(zhaolong): Add check if the node's name in op's arguments.
-
-      auto get_argname = [&](
-          const std::string& node_name,
-          const std::map<std::string, std::vector<std::string>>& argname_map)
-          -> std::string {
-            for (auto& ele : argname_map) {
-              auto it =
-                  std::find(ele.second.begin(), ele.second.end(), node_name);
-              if (it != ele.second.end()) return ele.first;
-            }
-            return "";
-          };
+      // for tool operator
+      if ((!with_targets["kFPGA"]) && (!with_targets["kOpenCL"])) {
+        VLOG(3) << "skip 'io_copy' if target is FPGA and OpenCL";
+        if (op_type == "io_copy") continue;
+      }
 
-      for (auto* x_in : x->inlinks) {
-        std::string node_name = x_in->AsArg().name;
-        std::string arg_name = get_argname(node_name, inst.op_info()->inputs());
-        CHECK(arg_name.size() > 0) << "can not found op arguments for node "
-                                   << node_name;
-        VLOG(4) << "-- input arg_name:" << arg_name << " "
-                << "-- node name:" << node_name;
-        auto type = inst.picked_kernel().GetInputDeclType(arg_name);
-        if (!x_in->AsArg().type) {
-          VLOG(4) << "set type " << *type << " " << x_in->AsArg().name;
-          if (x_in->AsArg().is_weight) {
-            SetWeightType(x_in, *type, lite_with_targets);
+      // Infering the input and output variable's place according to the
+      // declaration of I/O arguments of the picked kernel of the op
+      VLOG(4) << "Op " << op_info->Repr();
+      for (auto* in_node : node->inlinks) {
+        auto& var = in_node->AsArg();
+        const auto& var_name = var.name;
+        auto* var_type = &var.type;
+        std::string arg_name;
+        CHECK(op_info->GetInputArgname(var_name, &arg_name))
+            << "Can not find the input argument for var " << var_name;
+        VLOG(4) << " - input arg name:" << arg_name << " var name:" << var_name;
+        const auto* decl_type = kernel.GetInputDeclType(arg_name);
+        if (!(*var_type)) {
+          VLOG(4) << "set type " << *decl_type << " " << var_name;
+          if (var.is_weight) {
+            SetWeightType(in_node, *decl_type, with_targets);
           } else {
-            x_in->AsArg().type = type;
+            *var_type = decl_type;
           }
-        } else if (x_in->AsArg().type->target() == TARGET(kUnk) &&
-                   x_in->AsArg().type->precision() != PRECISION(kUnk) &&
-                   x_in->AsArg().type->layout() == DATALAYOUT(kUnk)) {
+        } else if (!(*var_type)->place().is_valid()) {
           // If is quantization, infer the Int8 type.
-          if (type->precision() == PRECISION(kInt8)) {
-            x_in->AsArg().type = type;
+          if (decl_type->precision() == PRECISION(kInt8)) {
+            *var_type = decl_type;
           } else {
-            PrecisionType tmp_ptype = x_in->AsArg().type->precision();
-            x_in->AsArg().type = LiteType::GetTensorTy(
-                type->target(), tmp_ptype, type->layout());
-          }
-        }
-      }
-
-      VLOG(4) << "inst " << inst.op_info()->Repr();
-      for (auto* x_out : x->outlinks) {
-        std::string node_name = x_out->AsArg().name;
-        std::string arg_name =
-            get_argname(node_name, inst.op_info()->outputs());
-        CHECK(arg_name.size() > 0) << "can not found op arguments for node "
-                                   << node_name << " in Inst "
-                                   << inst.op_type();
-        VLOG(4) << "-- output arg_name " << arg_name;
-        auto type = inst.picked_kernel().GetOutputDeclType(arg_name);
-        if (!x_out->AsArg().type) {
-          VLOG(4) << "set type " << *type << " " << x_out->AsArg().name;
-          if (x_out->AsArg().is_weight) {
-            SetWeightType(x_out, *type, lite_with_targets);
+            UpdateTypeFrom(var_type, decl_type);
+          }
+        }
+      }
+      for (auto* out_node : node->outlinks) {
+        auto& var = out_node->AsArg();
+        const auto& var_name = var.name;
+        auto* var_type = &var.type;
+        std::string arg_name;
+        CHECK(op_info->GetOutputArgname(var_name, &arg_name))
+            << "Can not find the output argument for var " << var_name;
+        VLOG(4) << " - output arg name:" << arg_name
+                << " var name:" << var_name;
+        const auto* decl_type = kernel.GetOutputDeclType(arg_name);
+        if (!(*var_type)) {
+          VLOG(4) << "set type " << *decl_type << " " << var_name;
+          if (var.is_weight) {
+            SetWeightType(out_node, *decl_type, with_targets);
           } else {
-            x_out->AsArg().type = type;
+            *var_type = decl_type;
           }
-        } else if (x_out->AsArg().type->target() == TARGET(kUnk) &&
-                   x_out->AsArg().type->precision() != PRECISION(kUnk) &&
-                   x_out->AsArg().type->layout() == DATALAYOUT(kUnk)) {
+        } else if (!(*var_type)->place().is_valid()) {
           // If is quantization, infer the Int8 type.
-          if (type->precision() == PRECISION(kInt8)) {
-            x_out->AsArg().type = type;
-          } else if (type->precision() == PRECISION(kFP16) &&
-                     type->target() != TARGET(kOpenCL)) {
-            x_out->AsArg().type = type;
+          if (decl_type->precision() == PRECISION(kInt8) ||
+              (decl_type->precision() == PRECISION(kFP16) &&
+               decl_type->target() != TARGET(kOpenCL))) {
+            *var_type = decl_type;
           } else {
-            PrecisionType tmp_ptype = x_out->AsArg().type->precision();
-            x_out->AsArg().type = LiteType::GetTensorTy(
-                type->target(), tmp_ptype, type->layout());
-          }
+            UpdateTypeFrom(var_type, decl_type);
           }
         }
       }
     }
-
-  // Update me's kUnk fields by other's fields.
-  void UpdatePlace(Place* me, const Place& other) {
-    CHECK(other.is_valid());
-    if (me->target == TARGET(kUnk)) {
-      me->target = other.target;
-    }
-    if (me->precision == PRECISION(kUnk)) {
-      me->precision = other.precision;
-    }
-    if (me->layout == DATALAYOUT(kUnk)) {
-      me->layout = other.layout;
-    }
   }
 
  private:
-  // The default target for arguments, e.g. load weights to CPU memory for CUDA
-  // computation by default.
+  // The default target for arguments, e.g. load weights to CPU memory for
+  // CUDA computation by default.
   TargetType argument_default_target_{TARGET(kHost)};
 };
 

lite/core/op_lite.h

@@ -99,7 +99,7 @@ class OpLite : public Registry {
   std::vector<std::unique_ptr<KernelBase>> CreateKernels(
       const std::vector<Place> &places, const std::string &kernel_type = "");
 
-  lite::Scope *scope() { return scope_; }
+  Scope *scope() { return scope_; }
 
   // Assign op param to kernel.
   virtual void AttachKernel(KernelBase *kernel) = 0;
@@ -169,7 +169,7 @@ class OpLite : public Registry {
   }
 
  protected:
-  lite::Scope *scope_{nullptr};
+  Scope *scope_{nullptr};
   std::unique_ptr<KernelBase> kernel_;
   std::string op_type_;
   std::vector<Place> valid_places_;

lite/core/optimizer.h

@@ -19,6 +19,7 @@
 #include <string>
 #include <utility>
 #include <vector>
+#include "lite/core/mir/elimination/control_flow_op_unused_inputs_and_outputs_eliminate_pass.h"
 #include "lite/core/mir/generate_program_pass.h"
 #include "lite/core/mir/pass_manager.h"
 #include "lite/core/mir/pass_utils.h"
@@ -36,6 +37,9 @@ namespace lite {
  * lite::Optimizer optimize a program. It utilize the mir passes to analysis the
  * program and export an optimized program.
  */
+// TODO(hong1986032) Support the following passes for the subblocks
+const std::set<std::string> kSubblockUnsupportedPasses(
+    {"memory_optimize_pass"});
 class Optimizer {
  public:
   Optimizer() {}
@@ -60,14 +64,20 @@ class Optimizer {
     program_ = &program;
     valid_places_ = valid_places;
     CHECK(!valid_places.empty()) << "At least one valid_place should be set";
-    CHECK(!graph_) << "duplicate optimize found";
-
-    graph_.reset(new mir::SSAGraph);
-    graph_->Build(program, valid_places);
-    graph_->SetValidPlaces(valid_places);
+    CHECK(graphs_.empty()) << "duplicate optimize found";
+
+    auto block_size = program.block_size();
+    for (size_t block_idx = 0; block_idx < block_size; ++block_idx) {
+      std::unique_ptr<mir::SSAGraph> graph;
+      graph.reset(new mir::SSAGraph);
+      graph->Build(program, valid_places, block_idx);
+      graph->SetValidPlaces(valid_places);
+      graphs_.emplace_back(std::move(graph));
+    }
 
     SpecifyKernelPickTactic(kernel_pick_factor);
     InitTargetTypeTransformPass();
+    InitControlFlowOpUnusedInputsAndOutputsEliminatePass();
 
     if (passes.empty() || passes.size() == 1) {
       std::vector<std::string> passes_local{
@@ -76,6 +86,7 @@ class Optimizer {
            "lite_conv_elementwise_fuse_pass",      // conv-elemwise-bn
            "lite_conv_bn_fuse_pass",               //
            "lite_conv_elementwise_fuse_pass",      // conv-bn-elemwise
+           "lite_conv_conv_fuse_pass",             //
            // TODO(Superjomn) Refine the fusion related design to select fusion
            // kernels for devices automatically.
            "lite_conv_activation_fuse_pass",              //
@@ -111,6 +122,7 @@ class Optimizer {
            "apu_subgraph_pass",
            "rknpu_subgraph_pass",
            "mlu_subgraph_pass",
+           "control_flow_op_unused_inputs_and_outputs_eliminate_pass",
            "static_kernel_pick_pass",  // pick original kernel from graph
 
            "remove_tf_redundant_ops_pass",
@@ -175,62 +187,15 @@ class Optimizer {
     exec_scope_ = program.exec_scope();
   }
 
-  const lite::Scope* exec_scope() const { return exec_scope_; }
-
-  // Set shape(dims) infos of var descs to scope var.
-  //  developer can write pass using input / output tensor dims of op.
-  //
-  // Example: If you have node `Node* softmax_node`,
-  //   you can get dims of output tensor in passes:
-  //
-  //   auto* scope = softmax_node->AsStmt().op()->scope();
-  //   auto softmax_out_arg_name =
-  //             softmax_node->outlinks.front()->AsArg().name;
-  //   auto softmax_out_tensor =
-  //             scope->FindVar(softmax_out_arg_name)->Get<lite::Tensor>();
-  //   softmax_out_dims = softmax_out_tensor.dims();
-  void SetVarDescShapeToScopeVar() {
-    auto dims_to_str_func = [](std::vector<int64_t> shape) -> std::string {
-      std::string str_res;
-      for (size_t i = 0; i < shape.size(); ++i) {
-        str_res += std::to_string(shape[i]);
-        if (i != shape.size() - 1) {
-          str_res += "x";
-        }
-      }
-      return str_res;
-    };
-
-    auto* program_desc = program_->program_desc();
-    VLOG(5) << "program_desc->BlocksSize():" << program_desc->BlocksSize();
-    auto blocks_desc = program_desc->GetBlocks();
-    for (size_t bidx = 0; bidx < blocks_desc.size(); ++bidx) {
-      auto block_desc = blocks_desc[bidx];
-      auto vars_desc = block_desc.GetVars();
-      for (size_t vidx = 0; vidx < vars_desc.size(); ++vidx) {
-        auto var_desc = vars_desc[vidx];
-        VLOG(5) << var_desc.Name() << " "
-                << dims_to_str_func(var_desc.GetShape());
-        if (var_desc.Name() == "feed" || var_desc.Name() == "fetch") continue;
-        auto* var = program_->exec_scope()->FindVar(var_desc.Name());
-        auto tensor = var->GetMutable<lite::Tensor>();
-        if (tensor->dims().size() == 0 && var_desc.GetShape().size() != 0) {
-          VLOG(5) << "var_desc.Name():" << var_desc.Name()
-                  << " shape:" << dims_to_str_func(var_desc.GetShape());
-          tensor->Resize(var_desc.GetShape());
-        }
-        VLOG(5) << "var_desc.Name():" << var_desc.Name()
-                << " shape:" << dims_to_str_func(var_desc.GetShape())
-                << " tensor:" << tensor->dims();
-      }
-    }
-  }
+  const Scope* exec_scope() const { return exec_scope_; }
 
   // Generate a new program based on the mir graph.
   std::unique_ptr<RuntimeProgram> GenRuntimeProgram() {
     auto pass = mir::PassManager::Global().LookUp<mir::GenerateProgramPass>(
         "generate_program_pass");
-    pass->Apply(graph_);
+    for (auto& graph : graphs_) {
+      pass->Apply(graph);
+    }
     auto program = pass->GenProgram();
     CHECK(exec_scope_);
     program->set_exec_scope(exec_scope_);
@@ -246,27 +211,38 @@ class Optimizer {
     pass->SetValidPlaces(valid_places_);
   }
 
+  void InitControlFlowOpUnusedInputsAndOutputsEliminatePass() {
+    auto* pass =
+        mir::PassManager::Global()
+            .LookUp<mir::ControlFlowOpUnusedInputsAndOutputsEliminatePass>(
+                "control_flow_op_unused_inputs_and_outputs_eliminate_pass");
+    CHECK(pass);
+    CHECK(!graphs_.empty());
+    pass->SetAllGraphs(&graphs_);
+  }
+
   // Generate C++ code which combines the inference program, model and weights.
   void GenCode(const std::string& code_dir);
 
-  const mir::SSAGraph& ssa_graph() const {
-    CHECK(graph_);
-    return *graph_;
+  const mir::SSAGraph& ssa_graph(int block_idx = kRootBlockIdx) const {
+    CHECK(!graphs_.empty());
+    CHECK(graphs_[block_idx]);
+    return *graphs_[block_idx];
   }
 
-  mir::SSAGraph* mutable_ssa_graph() {
-    CHECK(graph_);
-    return graph_.get();
+  mir::SSAGraph* mutable_ssa_graph(int block_idx = kRootBlockIdx) {
+    CHECK(!graphs_.empty());
+    CHECK(graphs_[block_idx]);
+    return graphs_[block_idx].get();
   }
 
-  lite::Scope* exec_scope() { return exec_scope_; }
+  Scope* exec_scope() { return exec_scope_; }
 
  protected:
   void SpecifyKernelPickTactic(core::KernelPickFactor factor);
 
   // Specify the passes and run them.
   void RunPasses(const std::vector<std::string>& passes) {
-    SetVarDescShapeToScopeVar();
     for (auto& x : passes) {
       LOG(INFO) << "== Running pass: " << x;
       mir::Pass* pass = mir::PassManager::Global().LookUp(x);
@@ -284,16 +260,23 @@ class Optimizer {
         LOG(INFO) << "   - Skip " << x
                   << " because the target or kernel does not match.";
       } else {
-        pass->Apply(graph_);
+        // Check the pass whether it is supported for processing subblocks
+        if (kSubblockUnsupportedPasses.count(x)) {
+          pass->Apply(graphs_[kRootBlockIdx]);
+        } else {
+          for (auto& graph : graphs_) {
+            pass->Apply(graph);
+          }
+        }
         LOG(INFO) << "== Finished running: " << x;
       }
     }
   }
 
  private:
-  std::unique_ptr<mir::SSAGraph> graph_;
+  std::vector<std::unique_ptr<mir::SSAGraph>> graphs_;
   std::vector<Place> valid_places_;
-  lite::Scope* exec_scope_{};
+  Scope* exec_scope_{};
   Program* program_{};
 };
 

lite/core/program.h

@@ -41,61 +41,72 @@ static const char kKernelTypeAttr[] = "__@kernel_type_attr@__";
 // - scope: which contains all the weights
 struct Program {
  public:
-  explicit Program(const std::shared_ptr<Scope>& root) { scope_ = root; }
-  Program(const cpp::ProgramDesc& desc,
-          const std::shared_ptr<Scope>& root,
+  explicit Program(const std::shared_ptr<Scope>& root_scope) {
+    scope_ = root_scope;
+  }
+  Program(const std::shared_ptr<cpp::ProgramDesc>& program_desc,
+          const std::shared_ptr<Scope>& root_scope,
           const std::vector<Place>& valid_places,
-          const std::vector<std::string>& var_names = {})
-      : scope_(root), valid_places_(valid_places) {
-    desc_.CopyFrom(desc);
+          const std::vector<std::string>& vars_to_clone = {})
+      : scope_(root_scope),
+        valid_places_(valid_places),
+        program_desc_(program_desc) {
     CHECK(scope_) << "scope should be init first";
     VLOG(4) << "prepare work";
-    PrepareWorkspace(desc, var_names);
+    PrepareWorkspace(program_desc_, vars_to_clone);
     VLOG(4) << "build desc";
-    Build(desc);
+    Build(program_desc_);
     VLOG(4) << "build desc finished";
   }
 
   std::unique_ptr<Program> Clone() const {
-    std::unique_ptr<Program> res(new Program(desc_, scope_, valid_places_));
-    return res;
+    return std::unique_ptr<Program>(
+        new Program(program_desc_, scope_, valid_places_));
   }
 
   const std::list<std::string>& weights() const { return weights_; }
-  const std::list<std::string>& tmp_vars() const { return tmp_vars_; }
+  const std::list<std::string>& vars() const { return vars_; }
   std::list<std::string>* mutable_weights() { return &weights_; }
-  std::list<std::string>* mutable_tmp_vars() { return &tmp_vars_; }
+  std::list<std::string>* mutable_vars() { return &vars_; }
 
-  const std::list<std::shared_ptr<OpLite>>& ops() const { return ops_; }
-  std::list<std::shared_ptr<OpLite>>* mutable_ops() { return &ops_; }
+  const std::list<std::shared_ptr<OpLite>>& ops(
+      int block_idx = kRootBlockIdx) const {
+    return ops_[block_idx];
+  }
+  std::list<std::shared_ptr<OpLite>>* mutable_ops(
+      int block_idx = kRootBlockIdx) {
+    return &ops_[block_idx];
+  }
 
-  lite::Scope* exec_scope() { return exec_scope_; }
-  lite::Scope* scope() { return scope_.get(); }
+  size_t block_size() { return ops_.size(); }
 
-  cpp::ProgramDesc* program_desc() { return &desc_; }
+  Scope* exec_scope() { return exec_scope_; }
+  Scope* scope() { return scope_.get(); }
 
-  const std::map<std::string, PrecisionType>& var_data_type() const {
-    return var_data_type_;
+  cpp::ProgramDesc* program_desc() { return program_desc_.get(); }
+
+  const std::map<std::string, const Type*>& var_type_map() const {
+    return var_type_map_;
   }
 
  private:
   // Build from a program and scope.
-  void Build(const cpp::ProgramDesc& program);
+  void Build(const std::shared_ptr<cpp::ProgramDesc>& program_desc);
   // Create temporary variables.
-  void PrepareWorkspace(const cpp::ProgramDesc& program,
-                        const std::vector<std::string>& var_names = {});
+  void PrepareWorkspace(const std::shared_ptr<cpp::ProgramDesc>& program_desc,
+                        const std::vector<std::string>& vars_to_clone = {});
 
  private:
-  std::map<std::string, PrecisionType> var_data_type_;
-  std::list<std::string> tmp_vars_;
+  std::map<std::string, const Type*> var_type_map_;
+  std::list<std::string> vars_;
   std::list<std::string> weights_;
-  std::list<std::shared_ptr<OpLite>> ops_;
+  std::vector<std::list<std::shared_ptr<OpLite>>> ops_;
   // the scope to run the kernels, NOTE this is the execution scope.
-  std::shared_ptr<lite::Scope> scope_;
+  std::shared_ptr<Scope> scope_;
   std::vector<Place> valid_places_;
   // Runtime scope.
-  lite::Scope* exec_scope_{};
-  cpp::ProgramDesc desc_;
+  Scope* exec_scope_{};
+  std::shared_ptr<cpp::ProgramDesc> program_desc_;
 };
 
 struct Instruction {
@@ -179,8 +190,22 @@ struct Instruction {
  */
 class LITE_API RuntimeProgram {
  public:
-  explicit RuntimeProgram(std::vector<Instruction>&& insts)
+  explicit RuntimeProgram(std::vector<std::vector<Instruction>>&& insts)
       : instructions_(std::move(insts)) {
+    Init();
+  }
+  explicit RuntimeProgram(
+      const std::shared_ptr<const cpp::ProgramDesc>& program_desc,
+      Scope* exec_scope,
+      int block_idx = kRootBlockIdx);
+  ~RuntimeProgram() {
+#ifdef LITE_WITH_PROFILE
+    LOG(INFO) << "\n" << profiler_.Summary(profile::Type::kCreate);
+    LOG(INFO) << "\n" << profiler_.Summary(profile::Type::kDispatch);
+#endif  // LITE_WITH_PROFILE
+  }
+
+  void Init() {
     if (instructions_.empty()) {
       LOG(FATAL) << "no instructions";
     }
@@ -189,7 +214,7 @@ class LITE_API RuntimeProgram {
 #endif
 #ifdef LITE_WITH_NVTX
     const NVTXAnnotator& annotator = NVTXAnnotator::Global();
-    for (auto& inst : instructions_) {
+    for (auto& inst : instructions_[kRootBlockIdx]) {
       NVTXRangeAnnotation annotation = annotator.AnnotateBlock();
       register_layer_names_.push_back(annotator.RegisterString(
           const_cast<paddle::lite::OpLite*>(inst.op())->Type().c_str()));
@@ -197,30 +222,27 @@ class LITE_API RuntimeProgram {
     register_layer_names_.push_back(annotator.RegisterString("one_loop"));
 #endif
   }
-  ~RuntimeProgram() {
-#ifdef LITE_WITH_PROFILE
-    LOG(INFO) << "\n" << profiler_.Summary(profile::Type::kCreate);
-    LOG(INFO) << "\n" << profiler_.Summary(profile::Type::kDispatch);
-#endif  // LITE_WITH_PROFILE
-  }
 
   void Run();
 
-  void set_exec_scope(lite::Scope* x) { exec_scope_ = x; }
-  lite::Scope* exec_scope() { return exec_scope_; }
+  void set_exec_scope(Scope* x) { exec_scope_ = x; }
+  Scope* exec_scope() { return exec_scope_; }
 
-  size_t num_instructions() const { return instructions_.size(); }
+  const std::vector<Instruction>& instructions(
+      int block_idx = kRootBlockIdx) const {
+    return instructions_[block_idx];
+  }
 
-  const std::vector<Instruction>& instructions() const { return instructions_; }
+  std::vector<Instruction>* mutable_instructions(
+      int block_idx = kRootBlockIdx) {
+    return &instructions_[block_idx];
+  }
 
-  // `SaveOpInfosToProgram` will update the op list(ops_) of the block 0
-  // in ProgramDesc.
-  void SaveOpInfosToProgram(cpp::ProgramDesc* desc);
+  size_t block_size() { return instructions_.size(); }
 
-  // `UpdateVarsOfProgram` will update the var list(vars_) of the block 0 in
-  // ProgramDesc. Namely, if a new var created in some passes, its var_desc will
-  // be added in vars_.
-  void UpdateVarsOfProgram(cpp::ProgramDesc* desc);
+  // Update the ops and vars of all of blocks to the given program_desc
+  // according to the instructions
+  void SaveToProgram(std::shared_ptr<cpp::ProgramDesc> program_desc);
 
 #ifdef LITE_WITH_CUDA
   // UpdateCudaContext will update the exec stream and io stream of all kernels
@@ -230,14 +252,14 @@ class LITE_API RuntimeProgram {
 
  private:
   RuntimeProgram(const RuntimeProgram&) = delete;
-  std::vector<Instruction> instructions_;
-  lite::Scope* exec_scope_{};
+  std::vector<std::vector<Instruction>> instructions_;
+  Scope* exec_scope_{};
 
 #ifdef LITE_WITH_PROFILE
   profile::Profiler profiler_;
   void set_profiler() {
-    for (auto i = instructions_.begin(); i != instructions_.end(); ++i) {
-      i->set_profiler(&profiler_);
+    for (auto& inst : instructions_[kRootBlockIdx]) {
+      inst.set_profiler(&profiler_);
     }
   }
 #endif

lite/kernels/apu/subgraph_compute.cc

@@ -37,7 +37,7 @@ bool SubgraphEngine::BuildDeviceProgram() {
   subgraph::apu::Graph graph;
   int neuron_errCode = NeuronModel_create(&model_);
   if (NEURON_NO_ERROR != neuron_errCode) {
-    LOG(WARNING) << "Fail to create model";
+    LOG(WARNING) << "[APU] Failed to create the neuron model!";
     return false;
   }
   graph.set_model(model_);
@@ -46,11 +46,12 @@ bool SubgraphEngine::BuildDeviceProgram() {
 
   // Convert all of ops and their input vars and weights and added into the APU
   // NIR graph
-  if (origin_program_.empty()) {
+  if (!origin_program_) {
     BuildOriginProgram();
   }
   const auto& bridges = subgraph::Registry::Instance();
-  for (auto& inst : origin_program_) {
+  const auto& insts = origin_program_->instructions(kRootBlockIdx);
+  for (auto& inst : insts) {
     auto op = const_cast<OpLite*>(inst.op());
     CHECK(op);
     op->CheckShape();
@@ -70,55 +71,38 @@ bool SubgraphEngine::BuildDeviceProgram() {
     }
   }
 
-  // Get input tensor
-  std::vector<uint32_t> ins;
-  origin_itensors_.resize(input_names_.size());
-  origin_idims_.resize(input_names_.size());
+  // Get the index of input tensors
+  std::vector<uint32_t> input_indices;
   for (int i = 0; i < input_names_.size(); i++) {
-    origin_itensors_[i] = scope_->FindMutableTensor(input_names_[i]);
-    CHECK(origin_itensors_[i]);
-    origin_idims_[i] = origin_itensors_[i]->dims();
-    VLOG(3) << "subgraph input name: " << i << ", " << input_names_[i] << ":"
-            << origin_idims_[i].production();
-    // Get input index
-    int idx;
-    if (graph.Has(input_names_[i])) {
-      ins.push_back(graph.Get(input_names_[i])->index());
-      VLOG(3) << "input idx: " << graph.Get(input_names_[i])->index();
-    } else {
-      LOG(WARNING) << "Fail to find input: " << input_names_[i];
-      return false;
-    }
+    CHECK(graph.Has(input_names_[i])) << "[APU] Failed to find input node "
+                                      << input_names_[i];
+    auto index = graph.Get(input_names_[i])->index();
+    input_indices.push_back(index);
+    VLOG(3) << "[APU] Input[" << i << "] name " << input_names_[i] << " dims "
+            << origin_itensors_[i]->dims() << " index " << index;
   }
 
-  // Get output tensor
-  std::vector<uint32_t> outs;
-  origin_otensors_.resize(output_names_.size());
-  origin_odims_.resize(output_names_.size());
+  // Get the index of output tensors
+  std::vector<uint32_t> output_indices;
   for (int i = 0; i < output_names_.size(); i++) {
-    origin_otensors_[i] = scope_->FindMutableTensor(output_names_[i]);
-    CHECK(origin_otensors_[i]);
-    origin_odims_[i] = origin_otensors_[i]->dims();
-    VLOG(3) << "subgraph output name: " << i << ", " << output_names_[i] << ":"
-            << origin_odims_[i].production();
+    CHECK(graph.Has(output_names_[i])) << "[APU] Failed to find output node "
+                                       << output_names_[i];
     origin_otensors_[i]->mutable_data<int8_t>();
-    // Get input index
-    if (graph.Has(output_names_[i])) {
-      outs.push_back(graph.Get(output_names_[i])->index());
-      VLOG(3) << "output idx: " << graph.Get(output_names_[i])->index();
-    } else {
-      LOG(WARNING) << "Fail to find output: " << output_names_[i];
-      return false;
-    }
-  }
-
-  VLOG(3) << "ins size: " << ins.size() << " outs size:" << outs.size();
-  // Set subgraph input/output
-  NeuronModel_identifyInputsAndOutputs(
-      model_, ins.size(), &ins[0], outs.size(), &outs[0]);
+    auto index = graph.Get(output_names_[i])->index();
+    output_indices.push_back(index);
+    VLOG(3) << "[APU] Output[" << i << "] name " << output_names_[i] << " dims "
+            << origin_otensors_[i]->dims() << " index " << index;
+  }
+
+  // Indentify the input and output tensors of the neuron model
+  NeuronModel_identifyInputsAndOutputs(model_,
+                                       input_indices.size(),
+                                       &input_indices[0],
+                                       output_indices.size(),
+                                       &output_indices[0]);
   neuron_errCode = NeuronModel_finish(model_);
   if (NEURON_NO_ERROR != neuron_errCode) {
-    LOG(WARNING) << "Fail to create NIR model:" << neuron_errCode;
+    LOG(WARNING) << "[APU] Fail to create NIR model:" << neuron_errCode;
     return false;
   }
   VLOG(3) << "[APU] APU NIR model created!";
@@ -207,11 +191,11 @@ SubgraphEngine::~SubgraphEngine() {
 void SubgraphCompute::PrepareForRun() {
   auto& param = this->Param<param_t>();
   engine_.reset(new SubgraphEngine(ctx_.get(),
-                                   param.sub_block_idx,
-                                   param.sub_block_desc,
+                                   param.block_idx,
+                                   param.program_desc,
+                                   param.exec_scope,
                                    param.input_data_names,
-                                   param.output_data_names,
-                                   param.scope));
+                                   param.output_data_names));
   CHECK(engine_);
 }
 

lite/kernels/apu/subgraph_compute.h

@@ -31,12 +31,16 @@ class SubgraphEngine : public subgraph::Engine {
  public:
   SubgraphEngine(KernelContext *ctx,
                  int block_idx,
-                 cpp::BlockDesc *block_desc,
+                 const std::shared_ptr<const cpp::ProgramDesc> &program_desc,
+                 Scope *exec_scope,
                  const std::vector<std::string> &input_names,
-                 const std::vector<std::string> &output_names,
-                 Scope *scope)
-      : subgraph::Engine(
-            ctx, block_idx, block_desc, input_names, output_names, scope) {}
+                 const std::vector<std::string> &output_names)
+      : subgraph::Engine(ctx,
+                         block_idx,
+                         program_desc,
+                         exec_scope,
+                         input_names,
+                         output_names) {}
 
   ~SubgraphEngine();
 

lite/kernels/arm/CMakeLists.txt

@@ -75,7 +75,6 @@ add_kernel(generate_proposals_compute_arm ARM extra SRCS generate_proposals_comp
 add_kernel(roi_align_compute_arm ARM extra SRCS roi_align_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(box_clip_compute_arm ARM extra SRCS box_clip_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(assign_value_compute_arm ARM basic SRCS assign_value_compute.cc DEPS ${lite_kernel_deps} math_arm)
-add_kernel(conditional_block_compute_arm ARM extra SRCS conditional_block_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(collect_fpn_proposals_compute_arm ARM extra SRCS collect_fpn_proposals_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(distribute_fpn_proposals_compute_arm ARM extra SRCS distribute_fpn_proposals_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(clip_compute_arm ARM extra SRCS clip_compute.cc DEPS ${lite_kernel_deps} math_arm)
@@ -87,7 +86,6 @@ add_kernel(beam_search_decode_compute_arm ARM extra SRCS beam_search_decode_comp
 add_kernel(lookup_table_compute_arm ARM extra SRCS lookup_table_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(lookup_table_dequant_compute_arm ARM extra SRCS lookup_table_dequant_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(sequence_softmax_compute_arm ARM extra SRCS sequence_softmax_compute.cc DEPS ${lite_kernel_deps} math_arm)
-add_kernel(while_compute_arm ARM extra SRCS while_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(topk_compute_arm ARM extra SRCS topk_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(increment_compute_arm ARM extra SRCS increment_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(beam_search_compute_arm ARM extra SRCS beam_search_compute.cc DEPS ${lite_kernel_deps} math_arm)

lite/kernels/arm/conditional_block_compute.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
-#include <algorithm>
-#include <memory>
-#include <utility>
-#include <vector>
-#include "lite/core/kernel.h"
-#include "lite/core/op_registry.h"
-#include "lite/core/program.h"
-#include "lite/operators/conditional_block_op.h"
-#ifdef LITE_WITH_PROFILE
-#include "lite/core/profile/basic_profiler.h"
-#include "lite/core/profile/precision_profiler.h"
-#include "lite/core/profile/profiler.h"
-#endif
-
-namespace paddle {
-namespace lite {
-namespace kernels {
-namespace arm {
-
-class CondExecutor {
-  typedef std::shared_ptr<OpLite> OpPtr;
-
- public:
-  CondExecutor(cpp::BlockDesc *block, Scope *scope, Place place)
-      : scope_(scope), place_(place) {
-    int32_t op_size = block->OpsSize();
-    for (int32_t i = 0; i < op_size; ++i) {
-      auto &op_desc = *block->template GetOp<cpp::OpDesc>(i);
-      auto op_type = op_desc.Type();
-      auto op_handler = lite::LiteOpRegistry::Global().Create(op_desc.Type());
-      op_handler->Attach(op_desc, scope);
-
-      auto hostplace = place_;
-      hostplace.target = TARGET(kHost);
-      auto kernels = op_handler->CreateKernels({place_, hostplace});
-      CHECK_GT(kernels.size(), 0) << "cannot create kernel";
-      op_handler->AttachKernel(kernels[0].get());
-      op_handler->SetKernel(kernels);
-      ops_of_block_.push_back(op_handler);
-    }
-  }
-
-  void Run() {
-#ifdef LITE_WITH_PROFILE
-#ifdef LITE_WITH_PRECISION_PROFILE
-    lite::profile::Profiler profiler;
-#endif  // LITE_WITH_PRECISION_PROFILE
-#endif  // LITE_WITH_PROFILE
-    for (auto &op_handler : ops_of_block_) {
-      op_handler->CheckShape();
-      op_handler->InferShape();
-#ifdef LITE_WITH_PROFILE
-#ifdef LITE_WITH_PRECISION_PROFILE
-      std::unique_ptr<KernelBase> kernel(op_handler->GetKernel());
-      Instruction inst(op_handler, std::move(kernel));
-      inst.set_profiler(&profiler);
-#endif  // LITE_WITH_PRECISION_PROFILE
-#endif  // LITE_WITH_PROFILE
-      op_handler->Run();
-#ifdef LITE_WITH_PROFILE
-#ifdef LITE_WITH_PRECISION_PROFILE
-      LITE_PRECISION_PROFILE(inst)
-#endif  // LITE_WITH_PRECISION_PROFILE
-#endif  // LITE_WITH_PROFILE
-    }
-  }
-
- private:
-  Scope *scope_;
-  Place place_;
-  std::vector<OpPtr> ops_of_block_;
-};
-
-class ConditionalBlockCompute
-    : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
- public:
-  using param_t = operators::ConditionalBlockParam;
-
-  void PrepareForRun() override;
-  void Run() override;
-
-  virtual ~ConditionalBlockCompute() = default;
-
- private:
-  std::shared_ptr<CondExecutor> executor_;
-};
-
-}  // namespace arm
-}  // namespace kernels
-}  // namespace lite
-}  // namespace paddle

lite/kernels/arm/elementwise_compute.cc

@@ -202,17 +202,13 @@ void ElementwiseMulCompute<T, PType>::Run() {
   }
 }
 
-template <>
-void ElementwiseMulCompute<int64_t, PRECISION(kInt64)>::Run() {
-  auto& param = this->template Param<operators::ElementwiseParam>();
-  lite::arm::math::elementwise_compute_basic<int64_t>(param, "mul", "");
-}
-
-void ElementwiseMulActivationCompute::Run() {
-  auto& param = Param<operators::FusionElementwiseActivationParam>();
-  const float* x_data = param.X->data<float>();
-  const float* y_data = param.Y->data<float>();
-  float* out_data = param.Out->mutable_data<float>();
+template <typename T, PrecisionType PType>
+void ElementwiseMulActivationCompute<T, PType>::Run() {
+  auto& param =
+      this->template Param<operators::FusionElementwiseActivationParam>();
+  auto* x_data = param.X->template data<T>();
+  auto* y_data = param.Y->template data<T>();
+  auto* out_data = param.Out->template mutable_data<T>();
   int axis = param.axis;
   std::string act_type = param.act_type;
   auto x_dims = param.X->dims();
@@ -221,21 +217,21 @@ void ElementwiseMulActivationCompute::Run() {
   if (x_dims.size() < y_dims.size() &&
       is_broadcast(y_dims, x_dims, axis, &pre, &n, &post)) {
     if (act_type == "relu") {
-      lite::arm::math::elementwise_mul_relu_broadcast<float>(
+      lite::arm::math::elementwise_mul_relu_broadcast<T>(
           y_data, x_data, out_data, pre, n, post);
     } else {
       LOG(FATAL) << "unsupported Activation type: " << act_type;
     }
   } else if (is_broadcast(x_dims, y_dims, axis, &pre, &n, &post)) {
     if (act_type == "relu") {
-      lite::arm::math::elementwise_mul_relu_broadcast(
+      lite::arm::math::elementwise_mul_relu_broadcast<T>(
           x_data, y_data, out_data, pre, n, post);
     } else {
       LOG(FATAL) << "unsupported Activation type: " << act_type;
     }
   } else {
     if (act_type == "relu") {
-      lite::arm::math::elementwise_mul_relu(
+      lite::arm::math::elementwise_mul_relu<T>(
           x_data, y_data, out_data, x_dims.production());
     } else {
       LOG(FATAL) << "unsupported Activation type: " << act_type;
@@ -426,46 +422,60 @@ REGISTER_LITE_KERNEL(
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
 
-using elementwise_mul_float =
+using elementwise_mul_float_t =
     paddle::lite::kernels::arm::ElementwiseMulCompute<float, PRECISION(kFloat)>;
 REGISTER_LITE_KERNEL(
-    elementwise_mul, kARM, kFloat, kNCHW, elementwise_mul_float, def)
+    elementwise_mul, kARM, kFloat, kNCHW, elementwise_mul_float_t, def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
 
-using elementwise_mul_int32 =
+using elementwise_mul_int32_t =
     paddle::lite::kernels::arm::ElementwiseMulCompute<int, PRECISION(kInt32)>;
 REGISTER_LITE_KERNEL(
-    elementwise_mul, kARM, kInt32, kNCHW, elementwise_mul_int32, def)
+    elementwise_mul, kARM, kInt32, kNCHW, elementwise_mul_int32_t, def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
     .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
     .Finalize();
 
-using elementwise_mul_int64 =
+using elementwise_mul_int64_t =
     paddle::lite::kernels::arm::ElementwiseMulCompute<int64_t,
                                                       PRECISION(kInt64)>;
 REGISTER_LITE_KERNEL(
-    elementwise_mul, kARM, kInt64, kNCHW, elementwise_mul_int64, def)
+    elementwise_mul, kARM, kInt64, kNCHW, elementwise_mul_int64_t, def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
     .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
     .Finalize();
 
-REGISTER_LITE_KERNEL(
-    fusion_elementwise_mul_activation,
+using fusion_elementwise_mul_activation_float_t = paddle::lite::kernels::arm::
+    ElementwiseMulActivationCompute<float, PRECISION(kFloat)>;
+REGISTER_LITE_KERNEL(fusion_elementwise_mul_activation,
                      kARM,
                      kFloat,
                      kNCHW,
-    paddle::lite::kernels::arm::ElementwiseMulActivationCompute,
+                     fusion_elementwise_mul_activation_float_t,
                      def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
 
+using fusion_elementwise_mul_activation_int64_t = paddle::lite::kernels::arm::
+    ElementwiseMulActivationCompute<int64_t, PRECISION(kInt64)>;
+REGISTER_LITE_KERNEL(fusion_elementwise_mul_activation,
+                     kARM,
+                     kInt64,
+                     kNCHW,
+                     fusion_elementwise_mul_activation_int64_t,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
+    .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
+    .Finalize();
+
 REGISTER_LITE_KERNEL(elementwise_max,
                      kARM,
                      kFloat,
@@ -489,22 +499,22 @@ REGISTER_LITE_KERNEL(
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
 
-using elementwise_div_fp32 =
+using elementwise_div_fp32_t =
     paddle::lite::kernels::arm::ElementwiseDivCompute<float, PRECISION(kFloat)>;
 
 REGISTER_LITE_KERNEL(
-    elementwise_div, kARM, kFloat, kNCHW, elementwise_div_fp32, def)
+    elementwise_div, kARM, kFloat, kNCHW, elementwise_div_fp32_t, def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
 
-using elementwise_div_int64 =
+using elementwise_div_int64_t =
     paddle::lite::kernels::arm::ElementwiseDivCompute<int64_t,
                                                       PRECISION(kInt64)>;
 
 REGISTER_LITE_KERNEL(
-    elementwise_div, kARM, kInt64, kNCHW, elementwise_div_int64, def)
+    elementwise_div, kARM, kInt64, kNCHW, elementwise_div_int64_t, def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
     .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
@@ -522,11 +532,11 @@ REGISTER_LITE_KERNEL(
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
 
-using elementwise_mod_int64 =
+using elementwise_mod_int64_t =
     paddle::lite::kernels::arm::ElementwiseModCompute<int64_t,
                                                       PRECISION(kInt64)>;
 REGISTER_LITE_KERNEL(
-    elementwise_mod, kARM, kInt64, kNCHW, elementwise_mod_int64, def)
+    elementwise_mod, kARM, kInt64, kNCHW, elementwise_mod_int64_t, def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
     .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})

lite/kernels/arm/elementwise_compute.h

@@ -62,8 +62,8 @@ class ElementwiseMulCompute : public KernelLite<TARGET(kARM), PType> {
   virtual ~ElementwiseMulCompute() = default;
 };
 
-class ElementwiseMulActivationCompute
-    : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
+template <typename T, PrecisionType PType>
+class ElementwiseMulActivationCompute : public KernelLite<TARGET(kARM), PType> {
  public:
   void Run() override;
 

lite/kernels/arm/elementwise_compute_test.cc

@@ -533,13 +533,15 @@ TEST(fusion_elementwise_mul_activation_arm, retrive_op) {
 }
 
 TEST(fusion_elementwise_mul_activation_arm, init) {
-  ElementwiseMulActivationCompute fusion_elementwise_mul_activation;
+  ElementwiseMulActivationCompute<float, PRECISION(kFloat)>
+      fusion_elementwise_mul_activation;
   ASSERT_EQ(fusion_elementwise_mul_activation.precision(), PRECISION(kFloat));
   ASSERT_EQ(fusion_elementwise_mul_activation.target(), TARGET(kARM));
 }
 
 TEST(fusion_elementwise_mul_activation_arm, compute) {
-  ElementwiseMulActivationCompute fusion_elementwise_mul_activation;
+  ElementwiseMulActivationCompute<float, PRECISION(kFloat)>
+      fusion_elementwise_mul_activation;
   operators::FusionElementwiseActivationParam param;
   lite::Tensor x, y, output, output_ref;
 

lite/kernels/arm/gather_compute.cc

@@ -20,44 +20,45 @@ namespace lite {
 namespace kernels {
 namespace arm {
 
-template <typename T>
+template <typename IndexType, typename DataType>
 void GatherFunc(const operators::GatherParam& param) {
   auto src_dims = param.X->dims();
   auto index_size = param.Index->dims()[0];
-  auto* p_src = param.X->data<T>();
-  const int* p_index = param.Index->data<int>();
-  auto* p_output = param.Out->mutable_data<T>();
+  auto* p_src = param.X->data<DataType>();
+  const IndexType* p_index = param.Index->data<IndexType>();
+  auto* p_output = param.Out->mutable_data<DataType>();
 
   int slice_size = 1;
   for (size_t i = 1; i < src_dims.size(); ++i) {
     slice_size *= src_dims[i];
   }
   for (int i = 0; i < index_size; ++i) {
-    int index_ = p_index[i];
+    IndexType index_ = p_index[i];
     memcpy(p_output + i * slice_size,
            p_src + index_ * slice_size,
-           slice_size * sizeof(T));
+           slice_size * sizeof(DataType));
   }
 }
 
-void GatherCompute::Run() {
-  auto& param = this->Param<operators::GatherParam>();
+template <typename IndexType>
+void GatherCompute<IndexType>::Run() {
+  auto& param = this->template Param<operators::GatherParam>();
 
   switch (param.X->precision()) {
     case PRECISION(kFloat):
-      GatherFunc<float>(param);
+      GatherFunc<IndexType, float>(param);
       break;
     case PRECISION(kInt8):
-      GatherFunc<int8_t>(param);
+      GatherFunc<IndexType, int8_t>(param);
       break;
     case PRECISION(kInt16):
-      GatherFunc<int16_t>(param);
+      GatherFunc<IndexType, int16_t>(param);
       break;
     case PRECISION(kInt32):
-      GatherFunc<int32_t>(param);
+      GatherFunc<IndexType, int32_t>(param);
       break;
     case PRECISION(kInt64):
-      GatherFunc<int64_t>(param);
+      GatherFunc<IndexType, int64_t>(param);
       break;
     default:
       LOG(FATAL) << "Gather does not implement for the "
@@ -70,9 +71,26 @@ void GatherCompute::Run() {
 }  // namespace lite
 }  // namespace paddle
 
-REGISTER_LITE_KERNEL(
-    gather, kARM, kAny, kNCHW, paddle::lite::kernels::arm::GatherCompute, def)
+REGISTER_LITE_KERNEL(gather,
+                     kARM,
+                     kAny,
+                     kNCHW,
+                     paddle::lite::kernels::arm::GatherCompute<int32_t>,
+                     def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
-    .BindInput("Index", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
+    .BindInput("Index",
+               {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
+    .Finalize();
+
+REGISTER_LITE_KERNEL(gather,
+                     kARM,
+                     kAny,
+                     kNCHW,
+                     paddle::lite::kernels::arm::GatherCompute<int64_t>,
+                     def_int64_idx)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
+    .BindInput("Index",
+               {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
     .Finalize();

lite/kernels/arm/gather_compute.h

@@ -23,6 +23,7 @@ namespace lite {
 namespace kernels {
 namespace arm {
 
+template <typename IndexType>
 class GatherCompute : public KernelLite<TARGET(kARM), PRECISION(kAny)> {
  public:
   void Run() override;

lite/kernels/arm/sequence_conv_compute.cc

@@ -102,10 +102,14 @@ void SequenceConvCompute::Run() {
           1,
           1,  // stride_h, stride_w, dilation_h, dilation_w
           tmp_data);
-      local_naive_transpose(tmp_data,
-                            sub_col_data,
-                            kernel_size * hidden_dim,
-                            input_row_end - input_row_begin);
+      int cols = kernel_size * hidden_dim;
+      int rows = input_row_end - input_row_begin;
+      if (cols % 4 == 0 && rows % 4 == 0) {
+        paddle::lite::arm::math::local_transpose(
+            tmp_data, sub_col_data, cols, rows);
+      } else {
+        local_naive_transpose(tmp_data, sub_col_data, cols, rows);
+      }
     }
   }
 

lite/kernels/bm/subgraph_compute.cc

@@ -28,36 +28,17 @@ namespace lite {
 namespace kernels {
 namespace bm {
 
-bool SubgraphEngine::PrepareWorkspaceForDeviceProgram() {
-  // Obtain the origin input tensors, and create the origin output
-  // tensors(Don't try to access them before launch the device program or the
-  // origin program)
-  PrepareWorkspaceForOriginProgram();
-  // Create the device input and output tensors, but don't initialize them
-  // with the dimensions
-  device_inputs_.resize(input_names_.size());
-  for (int i = 0; i < input_names_.size(); i++) {
-    device_inputs_[i].reset(new hiai::AiTensor);
-    CHECK(device_inputs_[i]);
-  }
-  device_outputs_.resize(output_names_.size());
-  for (int i = 0; i < output_names_.size(); i++) {
-    device_outputs_[i].reset(new hiai::AiTensor);
-    CHECK(device_outputs_[i]);
-  }
-  return true;
-}
-
 bool SubgraphEngine::BuildDeviceProgram() {
   int status = 0;
   subgraph::bm::Graph graph;
   const auto& bridges = subgraph::Registry::Instance();
   graph.CreateCompilerHandle();
   auto& ctx = this->ctx_->template As<BMContext>();
-  if (origin_program_.empty()) {
+  if (!origin_program_) {
     BuildOriginProgram();
   }
-  for (auto& inst : origin_program_) {
+  const auto& insts = origin_program_->instructions(kRootBlockIdx);
+  for (auto& inst : insts) {
     auto op = const_cast<OpLite*>(inst.op());
     CHECK(op);
     op->CheckShape();
@@ -93,13 +74,11 @@ bool SubgraphEngine::BuildDeviceProgram() {
   net_info_ = bmrt_get_network_info(bmrt_hd_, net_names_[0]);
   auto& stage = net_info_->stages[0];
   // input
-  origin_idims_.resize(input_names_.size());
-  origin_itensors_.resize(input_names_.size());
   device_inputs_.resize(input_names_.size());
   for (size_t i = 0; i < input_names_.size(); i++) {
-    origin_itensors_[i] = scope_->FindMutableTensor(net_info_->input_names[i]);
+    origin_itensors_[i] =
+        exec_scope_->FindMutableTensor(net_info_->input_names[i]);
     CHECK(origin_itensors_[i]);
-    origin_idims_[i] = origin_itensors_[i]->dims();
     bm_device_mem_t* p_mem =
         static_cast<bm_device_mem_t*>(malloc(sizeof(bm_device_mem_t)));
     CHECK(p_mem != nullptr);
@@ -112,8 +91,6 @@ bool SubgraphEngine::BuildDeviceProgram() {
                             stage.input_shapes[i]);
   }
   // output
-  origin_odims_.resize(output_names_.size());
-  origin_otensors_.resize(output_names_.size());
   device_outputs_.resize(net_info_->output_num);
   int out_index = 0;
   for (int i = 0; i < output_names_.size(); i++) {
@@ -121,14 +98,13 @@ bool SubgraphEngine::BuildDeviceProgram() {
   }
 
   for (int i = 0; i < net_info_->output_num; i++) {
-    Tensor* t_cur = scope_->FindMutableTensor(net_info_->output_names[i]);
+    Tensor* t_cur = exec_scope_->FindMutableTensor(net_info_->output_names[i]);
     CHECK(t_cur != nullptr);
     bm_device_mem_t* p_mem =
         static_cast<bm_device_mem_t*>(malloc(sizeof(bm_device_mem_t)));
     CHECK(p_mem != nullptr);
     if (outname_map_.find(net_info_->output_names[i]) != outname_map_.end()) {
       origin_otensors_[out_index] = t_cur;
-      origin_odims_[out_index] = origin_otensors_[out_index]->dims();
       origin_otensors_[out_index]->mutable_data<float>();
       out_index += 1;
     }
@@ -173,11 +149,11 @@ bool SubgraphEngine::LaunchDeviceProgram() {
 void SubgraphCompute::PrepareForRun() {
   auto& param = this->Param<param_t>();
   engine_.reset(new SubgraphEngine(ctx_.get(),
-                                   param.sub_block_idx,
-                                   param.sub_block_desc,
+                                   param.block_idx,
+                                   param.program_desc,
+                                   param.exec_scope,
                                    param.input_data_names,
-                                   param.output_data_names,
-                                   param.scope));
+                                   param.output_data_names));
   CHECK(engine_);
 }
 

lite/kernels/bm/subgraph_compute.h

@@ -36,15 +36,18 @@ class SubgraphEngine : public subgraph::Engine {
  public:
   SubgraphEngine(KernelContext *ctx,
                  int block_idx,
-                 cpp::BlockDesc *block_desc,
+                 const std::shared_ptr<const cpp::ProgramDesc> &program_desc,
+                 Scope *exec_scope,
                  const std::vector<std::string> &input_names,
-                 const std::vector<std::string> &output_names,
-                 Scope *scope)
-      : subgraph::Engine(
-            ctx, block_idx, block_desc, input_names, output_names, scope) {}
+                 const std::vector<std::string> &output_names)
+      : subgraph::Engine(ctx,
+                         block_idx,
+                         program_desc,
+                         exec_scope,
+                         input_names,
+                         output_names) {}
 
  protected:
-  bool PrepareWorkspaceForDeviceProgram() override;
   bool BuildDeviceProgram() override;
   bool LaunchDeviceProgram() override;
 

lite/kernels/host/CMakeLists.txt

@@ -18,6 +18,9 @@ add_kernel(read_from_array_compute_host Host extra SRCS read_from_array_compute.
 add_kernel(assign_compute_host Host extra SRCS assign_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(retinanet_detection_output_compute_host Host extra SRCS retinanet_detection_output_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(where_index_compute_host Host extra SRCS where_index_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(print_compute_host Host extra SRCS print_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(while_compute_host Host extra SRCS while_compute.cc DEPS ${lite_kernel_deps} program)
+add_kernel(conditional_block_compute_host Host extra SRCS conditional_block_compute.cc DEPS ${lite_kernel_deps} program)
 add_kernel(activation_grad_compute_host Host train SRCS activation_grad_compute.cc DEPS ${lite_kernel_deps})
 
 if(LITE_BUILD_EXTRA)

lite/kernels/host/assign_compute.cc

@@ -51,3 +51,19 @@ REGISTER_LITE_KERNEL(
                                        PRECISION(kAny),
                                        DATALAYOUT(kAny))})
     .Finalize();
+
+REGISTER_LITE_KERNEL(assign,
+                     kHost,
+                     kAny,
+                     kAny,
+                     paddle::lite::kernels::host::AssignCompute,
+                     def_tensor_array)
+    .BindInput("X",
+               {LiteType::GetTensorListTy(TARGET(kHost),
+                                          PRECISION(kAny),
+                                          DATALAYOUT(kAny))})
+    .BindOutput("Out",
+                {LiteType::GetTensorListTy(TARGET(kHost),
+                                           PRECISION(kAny),
+                                           DATALAYOUT(kAny))})
+    .Finalize();

lite/kernels/arm/conditional_block_compute.cc → lite/kernels/host/conditional_block_compute.cc

@@ -12,28 +12,21 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "lite/kernels/arm/conditional_block_compute.h"
-#include <memory>
-#include <string>
-#include <vector>
-#include "lite/backends/arm/math/funcs.h"
-#include "lite/core/tensor.h"
-#include "lite/core/type_system.h"
+#include "lite/kernels/host/conditional_block_compute.h"
 
 namespace paddle {
 namespace lite {
 namespace kernels {
-namespace arm {
+namespace host {
 
 void ConditionalBlockCompute::PrepareForRun() {
-  auto& param = Param<operators::ConditionalBlockParam>();
-  auto cur_scope = param.scope;
-
-  executor_ =
-      std::make_shared<CondExecutor>(param.sub_block, cur_scope, place());
+  auto& param = this->Param<param_t>();
+  program_.reset(new RuntimeProgram(
+      param.program_desc, param.exec_scope, param.block_idx));
 }
+
 void ConditionalBlockCompute::Run() {
-  auto& param = Param<operators::ConditionalBlockParam>();
+  auto& param = this->Param<param_t>();
   for (auto& out : param.outs) {
     out->clear();
   }
@@ -43,32 +36,40 @@ void ConditionalBlockCompute::Run() {
     auto* cond_data = cond->data<bool>();
     need_run = cond_data[0];
   } else {
-    auto x = param.x;
-    for (auto pt : x) {
-      if (pt == nullptr || !pt->IsInitialized() || pt->dims().empty()) {
+    for (auto input : param.inputs) {
+      if (input == nullptr || !input->IsInitialized() ||
+          input->dims().empty()) {
         need_run = false;
         break;
       }
     }
   }
   if (need_run) {
-    executor_->Run();
+    program_->Run();
   }
 }
 
-}  // namespace arm
+}  // namespace host
 }  // namespace kernels
 }  // namespace lite
 }  // namespace paddle
 
 REGISTER_LITE_KERNEL(conditional_block,
-                     kARM,
-                     kFloat,
-                     kNCHW,
-                     paddle::lite::kernels::arm::ConditionalBlockCompute,
+                     kHost,
+                     kAny,
+                     kAny,
+                     paddle::lite::kernels::host::ConditionalBlockCompute,
                      def)
-    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindInput("Cond", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kBool))})
-    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindOutput("Scope", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindInput("Input",
+               {LiteType::GetTensorListTy(
+                   TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)})
+    .BindInput("Cond",
+               {LiteType::GetTensorTy(
+                   TARGET(kHost), PRECISION(kBool), DATALAYOUT(kAny), -1)})
+    .BindOutput("Out",
+                {LiteType::GetTensorListTy(
+                    TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)})
+    .BindOutput("Scope",
+                {LiteType::GetTensorTy(
+                    TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)})
     .Finalize();

lite/kernels/host/conditional_block_compute.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
+#include <algorithm>
+#include <memory>
+#include <string>
+#include <vector>
+#include "lite/core/kernel.h"
+#include "lite/core/op_registry.h"
+#include "lite/core/program.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace host {
+
+class ConditionalBlockCompute
+    : public KernelLite<TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny)> {
+ public:
+  using param_t = operators::ConditionalBlockParam;
+
+  void PrepareForRun() override;
+  void Run() override;
+
+ private:
+  std::unique_ptr<RuntimeProgram> program_;
+};
+
+}  // namespace host
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

lite/kernels/host/print_compute.cc

+// 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.
+
+#include "lite/kernels/host/print_compute.h"
+
+#include <mutex>  // NOLINT
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace host {
+
+const char kForward[] = "FORWARD";
+const char kBackward[] = "BACKWARD";
+const char kBoth[] = "BOTH";
+
+class TensorFormatter {
+ public:
+  TensorFormatter() {}
+
+  std::string Format(const Tensor& print_tensor,
+                     const std::string& tensor_name = "",
+                     const std::string& message = "") {
+    std::stringstream log_stream;
+    if (!tensor_name.empty()) {
+      log_stream << "Variable: " << tensor_name << std::endl;
+    }
+
+    if (!message.empty()) {
+      log_stream << "  - message: " << message << std::endl;
+    }
+
+    if (print_tensor_lod_) {
+      log_stream << "  - lod: {";
+      const LoD& lod = print_tensor.lod();
+      for (auto level : lod) {
+        log_stream << "{";
+        bool is_first = true;
+        for (auto i : level) {
+          if (is_first) {
+            log_stream << i;
+            is_first = false;
+          } else {
+            log_stream << ", " << i;
+          }
+        }
+        log_stream << "}";
+      }
+      log_stream << "}" << std::endl;
+    }
+
+    log_stream << "  - place: " << TargetToStr(print_tensor.target())
+               << std::endl;  // TODO(hong19860320) always kHost
+
+    if (print_tensor_shape_) {
+      log_stream << "  - shape: " << print_tensor.dims().repr() << std::endl;
+    }
+
+    if (print_tensor_layout_) {
+      log_stream << "  - layout: "
+                 << DataLayoutToStr(
+                        DATALAYOUT(kNCHW))  // TODO(hong19860320) Query the data
+                                            // layout from target tensor
+                 << std::endl;
+    }
+
+    auto dtype = print_tensor.precision();
+    if (print_tensor_type_) {
+      log_stream << "  - dtype: " << PrecisionToStr(dtype) << std::endl;
+    }
+
+    if (dtype == PRECISION(kBool)) {
+      FormatData<bool>(print_tensor, log_stream);
+    } else if (dtype == PRECISION(kInt8)) {
+      FormatData<int8_t>(print_tensor, log_stream);
+    } else if (dtype == PRECISION(kInt16)) {
+      FormatData<int16_t>(print_tensor, log_stream);
+    } else if (dtype == PRECISION(kInt32)) {
+      FormatData<int32_t>(print_tensor, log_stream);
+    } else if (dtype == PRECISION(kInt64)) {
+      FormatData<int64_t>(print_tensor, log_stream);
+    } else if (dtype == PRECISION(kFloat)) {
+      FormatData<float>(print_tensor, log_stream);
+    } else {
+      log_stream << "\tdata: unprintable type: " << PrecisionToStr(dtype)
+                 << std::endl;
+    }
+    return log_stream.str();
+  }
+
+  void Print(const Tensor& print_tensor,
+             const std::string& tensor_name = "",
+             const std::string& message = "") {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+    std::cout << Format(print_tensor, tensor_name, message);
+  }
+
+  void SetPrintTensorType(bool print_tensor_type) {
+    print_tensor_type_ = print_tensor_type;
+  }
+  void SetPrintTensorShape(bool print_tensor_shape) {
+    print_tensor_shape_ = print_tensor_shape;
+  }
+  void SetPrintTensorLod(bool print_tensor_lod) {
+    print_tensor_lod_ = print_tensor_lod;
+  }
+  void SetPrintTensorLayout(bool print_tensor_layout) {
+    print_tensor_layout_ = print_tensor_layout;
+  }
+  void SetSummarize(int64_t summarize) { summarize_ = summarize; }
+
+ private:
+  template <typename T>
+  void FormatData(const Tensor& print_tensor, std::stringstream& log_stream) {
+    int64_t print_size = summarize_ == -1
+                             ? print_tensor.numel()
+                             : std::min(summarize_, print_tensor.numel());
+    const T* data = print_tensor.data<T>();  // Always kHost, so unnessary to
+                                             // copy the data from device
+    log_stream << "  - data: [";
+    if (print_size > 0) {
+      log_stream << data[0];
+      for (int64_t i = 1; i < print_size; ++i) {
+        log_stream << " " << data[i];
+      }
+    }
+    log_stream << "]" << std::endl;
+  }
+
+  int64_t summarize_ = -1;
+  bool print_tensor_type_ = true;
+  bool print_tensor_shape_ = true;
+  bool print_tensor_lod_ = true;
+  bool print_tensor_layout_ = true;
+};
+
+void PrintCompute::Run() {
+  auto& param = Param<param_t>();
+  param.out->CopyDataFrom(*param.in);
+
+  if ((param.is_forward && param.print_phase == kBackward) ||
+      (!param.is_forward && param.print_phase == kForward)) {
+    return;
+  }
+
+  int first_n = param.first_n;
+  if (first_n > 0 && ++times_ > first_n) return;
+
+  TensorFormatter formatter;
+  const std::string& name = param.print_tensor_name ? param.name : "";
+  formatter.SetPrintTensorType(param.print_tensor_type);
+  formatter.SetPrintTensorShape(param.print_tensor_shape);
+  formatter.SetPrintTensorLod(param.print_tensor_lod);
+  formatter.SetPrintTensorLayout(param.print_tensor_layout);
+  formatter.SetSummarize(static_cast<int64_t>(param.summarize));
+  formatter.Print(*param.in, name, param.message);
+}
+
+}  // namespace host
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_KERNEL(
+    print, kHost, kAny, kAny, paddle::lite::kernels::host::PrintCompute, def)
+    .BindInput("In",
+               {LiteType::GetTensorTy(TARGET(kHost),
+                                      PRECISION(kAny),
+                                      DATALAYOUT(kAny))})
+    .BindOutput("Out",
+                {LiteType::GetTensorTy(TARGET(kHost),
+                                       PRECISION(kAny),
+                                       DATALAYOUT(kAny))})
+    .Finalize();

lite/kernels/host/print_compute.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
+#include <algorithm>
+
+#include "lite/core/kernel.h"
+#include "lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace host {
+
+class PrintCompute
+    : public KernelLite<TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny)> {
+ public:
+  using param_t = operators::PrintParam;
+
+  void Run() override;
+
+  virtual ~PrintCompute() = default;
+
+ private:
+  mutable int times_{0};
+};
+
+}  // namespace host
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle