!2067 get hbm and ddr size adaptively

Merge pull request !2067 from lichun/r1.5.0

.gitmodules

@@ -1,8 +1,8 @@
 [submodule "parser"]
 	path = parser
 	url = https://gitee.com/ascend/parser.git
-	branch = master
+	branch = r1.5.0
 [submodule "metadef"]
 	path = metadef
 	url = https://gitee.com/ascend/metadef.git
-	branch = master
+	branch = r1.5.0

CMakeLists.txt

@@ -95,6 +95,7 @@ else ()
             #find_module(ascendcl_static libascendcl.a ${GE_LIB_PATH})
         else()
             find_module(slog libalog.so ${ASCEND_ATC_DIR})
+            find_module(opt_feature libopt_feature.so ${ASCEND_ATC_DIR})
             find_module(static_mmpa libmmpa.a ${ASCEND_ATC_DIR})
             if(PLATFORM STREQUAL "train")
                 find_module(adump_server libadump_server.a ${ASCEND_RUNTIME_DIR})

build.sh

@@ -355,13 +355,13 @@ generate_package()
 
   if [ "x${PLATFORM}" = "xtrain" ]
   then
-    tar -cf graphengine_lib.tar fwkacllib
+    tar -zcf graphengine_lib.tar fwkacllib
   elif [ "x${PLATFORM}" = "xinference" ]
   then
-    tar -cf graphengine_lib.tar acllib atc
+    tar -zcf graphengine_lib.tar acllib atc
   elif [ "x${PLATFORM}" = "xall" ]
   then
-    tar -cf graphengine_lib.tar fwkacllib acllib atc
+    tar -zcf graphengine_lib.tar fwkacllib acllib atc
   fi
 }
 
@@ -371,6 +371,6 @@ elif [ "X$MINDSPORE_MODE" = "Xon" ]
 then
   cd "${OUTPUT_PATH}"
   find ./ -name graphengine_lib.tar -exec rm {} \;
-  tar -cf graphengine_lib.tar lib
+  tar -zcf graphengine_lib.tar lib
 fi
 echo "---------------- GraphEngine package archive generated ----------------"

cmake/external_libs/protobuf_shared.cmake

@@ -11,14 +11,14 @@ if ((${CMAKE_INSTALL_PREFIX} STREQUAL /usr/local) OR
     message(STATUS "No install prefix selected, default to ${CMAKE_INSTALL_PREFIX}.")
 endif()
 if (GE_PB_PKG)
-    set(REQ_URL "${GE_PB_PKG}/libs/protobuf/v3.8.0.tar.gz")
+    set(REQ_URL "${GE_PB_PKG}/libs/protobuf/v3.13.0.tar.gz")
 else()
     if (ENABLE_GITEE)
-        set(REQ_URL "https://gitee.com/mirrors/protobuf_source/repository/archive/v3.8.0.tar.gz")
-        set(MD5 "eba86ae9f07ba5cfbaf8af3bc4e84236")
+        set(REQ_URL "https://gitee.com/mirrors/protobuf_source/repository/archive/v3.13.0.tar.gz")
+        set(MD5 "f4489cb88922ad9c58cbe3308d59cee5")
     else()
-        set(REQ_URL "https://github.com/protocolbuffers/protobuf/archive/v3.8.0.tar.gz")
-        set(MD5 "3d9e32700639618a4d2d342c99d4507a")
+        set(REQ_URL "https://github.com/protocolbuffers/protobuf/archive/v3.13.0.tar.gz")
+        set(MD5 "1a6274bc4a65b55a6fa70e264d796490")
     endif ()
 endif()
 
@@ -58,7 +58,7 @@ target_include_directories(ascend_protobuf INTERFACE ${PROTOBUF_SHARED_PKG_DIR}/
 set(INSTALL_BASE_DIR "")
 set(INSTALL_LIBRARY_DIR lib)
 
-install(FILES ${PROTOBUF_SHARED_PKG_DIR}/${CMAKE_INSTALL_LIBDIR}/ascend_protobuf.so.3.8.0.0 OPTIONAL
+install(FILES ${PROTOBUF_SHARED_PKG_DIR}/${CMAKE_INSTALL_LIBDIR}/ascend_protobuf.so.3.13.0.0 OPTIONAL
         DESTINATION ${INSTALL_LIBRARY_DIR})
 install(FILES ${PROTOBUF_SHARED_PKG_DIR}/${CMAKE_INSTALL_LIBDIR}/ascend_protobuf.so OPTIONAL
         DESTINATION ${INSTALL_LIBRARY_DIR})

cmake/external_libs/protobuf_static.cmake

@@ -13,14 +13,14 @@ if ((${CMAKE_INSTALL_PREFIX} STREQUAL /usr/local) OR
 endif()
 
 if(GE_PB_PKG)
-    set(REQ_URL "${GE_PB_PKG}/libs/protobuf/v3.8.0.tar.gz")
+    set(REQ_URL "${GE_PB_PKG}/libs/protobuf/v3.13.0.tar.gz")
 else()
     if (ENABLE_GITEE)
-        set(REQ_URL "https://gitee.com/mirrors/protobuf_source/repository/archive/v3.8.0.tar.gz")
-        set(MD5 "eba86ae9f07ba5cfbaf8af3bc4e84236")
+        set(REQ_URL "https://gitee.com/mirrors/protobuf_source/repository/archive/v3.13.0.tar.gz")
+        set(MD5 "f4489cb88922ad9c58cbe3308d59cee5")
     else()
-        set(REQ_URL "https://github.com/protocolbuffers/protobuf/archive/v3.8.0.tar.gz")
-        set(MD5 "3d9e32700639618a4d2d342c99d4507a")
+        set(REQ_URL "https://github.com/protocolbuffers/protobuf/archive/v3.13.0.tar.gz")
+        set(MD5 "1a6274bc4a65b55a6fa70e264d796490")
     endif ()
 endif()
 
@@ -29,8 +29,6 @@ set(protobuf_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
 set(PROTOBUF_STATIC_PKG_DIR ${CMAKE_INSTALL_PREFIX}/protobuf_static)
 ExternalProject_Add(protobuf_static_build
                     URL ${REQ_URL}
-                    #URL /home/txd/workspace/linux_cmake/pkg/protobuf-3.8.0.tar.gz
-                    #SOURCE_DIR ${METADEF_DIR}/../../third_party/protobuf/src/protobuf-3.8.0
                     TLS_VERIFY OFF
                     CONFIGURE_COMMAND ${CMAKE_COMMAND}
                     -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}

cmake/external_libs/protoc.cmake

@@ -13,14 +13,14 @@ if ((${CMAKE_INSTALL_PREFIX} STREQUAL /usr/local) OR
 endif()
 
 if(GE_PB_PKG)
-    set(REQ_URL "${GE_PB_PKG}/libs/protobuf/v3.8.0.tar.gz")
+    set(REQ_URL "${GE_PB_PKG}/libs/protobuf/v3.13.0.tar.gz")
 else()
     if (ENABLE_GITEE)
-        set(REQ_URL "https://gitee.com/mirrors/protobuf_source/repository/archive/v3.8.0.tar.gz")
-        set(MD5 "eba86ae9f07ba5cfbaf8af3bc4e84236")
+        set(REQ_URL "https://gitee.com/mirrors/protobuf_source/repository/archive/v3.13.0.tar.gz")
+        set(MD5 "f4489cb88922ad9c58cbe3308d59cee5")
     else()
-        set(REQ_URL "https://github.com/protocolbuffers/protobuf/archive/v3.8.0.tar.gz")
-        set(MD5 "3d9e32700639618a4d2d342c99d4507a")
+        set(REQ_URL "https://github.com/protocolbuffers/protobuf/archive/v3.13.0.tar.gz")
+        set(MD5 "1a6274bc4a65b55a6fa70e264d796490")
     endif ()
 endif()
 
@@ -28,8 +28,6 @@ set(protobuf_CXXFLAGS "-Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -fst
 set(protobuf_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
 ExternalProject_Add(protoc_build
                     URL ${REQ_URL}
-                    #URL /home/txd/workspace/linux_cmake/pkg/protobuf-3.8.0.tar.gz
-                    #SOURCE_DIR ${GE_CODE_DIR}/../third_party/protobuf/src/protobuf-3.8.0
                     TLS_VERIFY OFF
                     CONFIGURE_COMMAND ${CMAKE_COMMAND} -Dprotobuf_WITH_ZLIB=OFF -Dprotobuf_BUILD_TESTS=OFF -DBUILD_SHARED_LIBS=OFF -DCMAKE_CXX_FLAGS=${protobuf_CXXFLAGS} -DCMAKE_CXX_LDFLAGS=${protobuf_LDFLAGS} -DCMAKE_INSTALL_PREFIX=${CMAKE_INSTALL_PREFIX}/protoc <SOURCE_DIR>/cmake
                     BUILD_COMMAND $(MAKE)

ge/CMakeLists.txt

@@ -174,6 +174,7 @@ set(TRAIN_SRC_LIST
     "graph/load/model_manager/task_info/model_exit_task_info.cc"
     "graph/load/model_manager/task_info/event_record_task_info.cc"
     "graph/load/model_manager/task_info/event_wait_task_info.cc"
+    "graph/load/model_manager/task_info/ffts_task_info.cc"
     "graph/load/model_manager/task_info/fusion_start_task_info.cc"
     "graph/load/model_manager/task_info/fusion_stop_task_info.cc"
     "graph/load/model_manager/task_info/hccl_task_info.cc"
@@ -433,6 +434,7 @@ set(TRAIN_SRC_LIST
     "graph/build/memory/max_block_mem_assigner.cc"
     "graph/build/memory/var_mem_assign_util.cc"
     "graph/build/memory/buffer_pool_mem_assigner.cc"
+    "ge_opt_info/ge_opt_info.cc"
 )
 
 set(INFER_SRC_LIST
@@ -662,6 +664,7 @@ set(INFER_SRC_LIST
     "graph/load/model_manager/task_info/task_info.cc"
     "graph/load/model_manager/task_info/event_record_task_info.cc"
     "graph/load/model_manager/task_info/event_wait_task_info.cc"
+    "graph/load/model_manager/task_info/ffts_task_info.cc"
     "graph/load/model_manager/task_info/fusion_start_task_info.cc"
     "graph/load/model_manager/task_info/fusion_stop_task_info.cc"
     "graph/load/model_manager/task_info/kernel_ex_task_info.cc"
@@ -709,6 +712,7 @@ set(INFER_SRC_LIST
     "graph/build/memory/max_block_mem_assigner.cc"
     "graph/build/memory/var_mem_assign_util.cc"
     "graph/build/memory/buffer_pool_mem_assigner.cc"
+    "ge_opt_info/ge_opt_info.cc"
 )
 
 if (NOT ENABLE_D AND NOT ENABLE_ACL AND NOT ENABLE_MS_TESTCASES)
@@ -770,11 +774,13 @@ target_include_directories(ge_runner SYSTEM PRIVATE
     ${GE_CODE_DIR}/../inc/cce
     ${GE_CODE_DIR}/../toolchain/ide/ide-daemon/external
     ${GE_CODE_DIR}/../abl/adump/external
+    ${GE_CODE_DIR}/../abl/licctrl
     #### blue zone
     ${ASCEND_DIR}/driver/include
     ${ASCEND_DIR}/fwkacllib/include
     ${GE_CODE_DIR}/third_party/fwkacllib/inc
     ${GE_CODE_DIR}/third_party/fwkacllib/inc/toolchain
+    ${GE_CODE_DIR}/third_party/fwkacllib/inc/opt_info
 )
 
 target_link_options(ge_runner PRIVATE
@@ -797,6 +803,7 @@ target_link_libraries(ge_runner PRIVATE
     runtime
     error_manager
     ascend_hal_stub
+    opt_feature
     -Wl,--as-needed
     json
     -lrt
@@ -851,11 +858,13 @@ target_include_directories(ge_compiler SYSTEM PRIVATE
     ${GE_CODE_DIR}/../inc/cce
     ${GE_CODE_DIR}/../toolchain/ide/ide-daemon/external
     ${GE_CODE_DIR}/../abl/adump/external
+    ${GE_CODE_DIR}/../abl/licctrl
     #### blue zone ####
     ${ASCEND_DIR}/driver/include
     ${ASCEND_DIR}/fwkacllib/include
     ${GE_CODE_DIR}/third_party/fwkacllib/inc
     ${GE_CODE_DIR}/third_party/fwkacllib/inc/toolchain
+    ${GE_CODE_DIR}/third_party/fwkacllib/inc/opt_info
 )
 
 target_link_options(ge_compiler PRIVATE
@@ -875,6 +884,7 @@ target_link_libraries(ge_compiler PRIVATE
     error_manager
     slog
     runtime_compile
+    opt_feature
     -Wl,--as-needed
     json
     -lrt

ge/client/proto/ge_api.proto

@@ -1 +0,0 @@
-../../proto/ge_api.proto

ge/client/proto/ge_ir.proto

@@ -1,193 +0,0 @@
-syntax = "proto3";
-
-package ge.proto;
-
-enum DataType
-{
-    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
-    DT_FLOAT     = 1;  // float type
-    DT_FLOAT16   = 2;  // fp16 type
-    DT_INT8      = 3;  // int8 type
-    DT_UINT8     = 4;  // uint8 type
-    DT_INT16     = 5;  // int16 type
-    DT_UINT16    = 6;  // uint16 type
-    DT_INT32     = 7;  //
-    DT_INT64     = 8;  // int64 type
-    DT_UINT32    = 9;  // unsigned int32
-    DT_UINT64    = 10;  // unsigned int64
-    DT_BOOL      = 11;  // bool type
-    DT_DOUBLE    = 12; // double type
-    DT_STRING = 13;            // string type
-    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
-    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
-    DT_COMPLEX64 = 16;         // complex64 type
-    DT_COMPLEX128 = 17;        // complex128 type
-    DT_QINT8 = 18;             // qint8 type
-    DT_QINT16 = 19;            // qint16 type
-    DT_QINT32 = 20;            // qint32 type
-    DT_QUINT8 = 21;            // quint8 type
-    DT_QUINT16 = 22;           // quint16 type
-    DT_RESOURCE  = 23;         // resource type
-    DT_STRING_REF = 24;        // string_ref type
-    DT_DUAL      = 25;              /**< dual output type */
-    DT_VARIANT = 26;           // variant type
-    DT_BF16 = 27;              // bf16 type
-    DT_INT4 = 28;              // int4 type
-}
-
-message AttrDef
-{
-    message ListValue
-    {
-        enum ListValueType{
-          VT_LIST_NONE = 0;
-          VT_LIST_STRING = 1;
-          VT_LIST_INT = 2;
-          VT_LIST_FLOAT = 3;
-          VT_LIST_BOOL = 4;
-          VT_LIST_BYTES = 5;
-          VT_LIST_TENSOR_DESC = 6;
-          VT_LIST_TENSOR = 7;
-          VT_LIST_GRAPH = 8;
-          VT_LIST_NAMED_ATTRS = 9;
-          VT_LIST_DATA_TYPE = 10;
-        }
-        repeated bytes s             = 2;                    // "list(string)"
-        repeated int64 i             = 3;  // "list(int)"
-        repeated float f             = 4;   // "list(float)"
-        repeated bool  b             = 5;  // "list(bool)"
-        repeated bytes bt            = 7;
-        repeated TensorDescriptor td = 8;
-        repeated TensorDef t         = 9;
-        repeated GraphDef g          = 10;
-	    repeated NamedAttrs na       = 11;
-	    repeated int64 dt            = 12; // list ge::DataType
-
-	    ListValueType val_type       = 20;
-    }
-
-    message ListListInt{
-        message ListInt{
-            repeated int64 list_i             = 1; // list int
-        }
-        repeated ListInt list_list_i             = 1; // list list int
-    }
-
-    oneof value
-    {
-        bytes            s    = 2;  // "string"
-        int64            i    = 3;  // "int"
-        float            f    = 4;  // "float"
-        bool             b    = 5;  // "bool"
-        bytes            bt   = 7;
-        ListValue        list = 1;   // any "list(...)"
-        NamedAttrs       func = 10;  // Used to support attr nesting
-        TensorDescriptor td   = 11;  // GeTensorDesc type
-        TensorDef        t    = 12;  // GeTensor type
-        GraphDef         g    = 13;  // Graph type
-        ListListInt      list_list_int  = 14;  // List List Int type
-        int64            dt   = 15; // ge::DataType
-    }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs
-{
-    string               name = 1;
-    map<string, AttrDef> attr = 2;
-}
-
-// Shape / dimension description, using row-major order
-message ShapeDef
-{
-    repeated int64 dim = 1;  // Size of each dimension
-}
-
-// Multidimensional data description
-message TensorDescriptor
-{
-    string   name   = 1;  // Optional parameter, tensor name
-
-    DataType dtype  = 2;  // tensor datatype
-    ShapeDef shape  = 3;  // Shape / dimension
-    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"
-
-    bool has_out_attr = 9;
-    int64 size = 10;
-    int64 weight_size = 11;
-    bool reuse_input = 12;
-    bool output_tensor = 13;
-    string device_type = 14;
-    bool input_tensor =15;
-    int64 real_dim_cnt = 16;
-    int64 reuse_input_index = 17;
-    int64 data_offset = 18;
-    int64 cmps_size = 19;
-    string cmps_tab = 20;
-    int64 cmps_tab_offset = 21;
-
-	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
-}
-
-// GeTensor definition
-message TensorDef
-{
-    TensorDescriptor desc = 1;  // Tensor description
-    bytes            data = 2;  // Tensor data
-}
-
-
-// Operator description
-message OpDef
-{
-    string name = 1;  // name
-    string type = 2;  // type
-
-    repeated string input = 5;  // input original op name + outgoing index. op_name：index
-
-    map<string, AttrDef> attr = 10;  // Set of operator parameter fields
-
-    bool has_out_attr = 20;
-    int64 id = 21;
-    int64 stream_id =22;
-    repeated string input_name = 23;
-    repeated string src_name = 24;
-    repeated int64 src_index = 25;
-    repeated string dst_name = 26;
-    repeated int64 dst_index = 27;
-    repeated int64 input_i = 28;
-    repeated int64 output_i = 29;
-    repeated int64 workspace = 30;
-    repeated int64 workspace_bytes = 31;
-    repeated bool is_input_const = 32;
-    repeated TensorDescriptor input_desc = 33;
-    repeated TensorDescriptor output_desc = 34;
-    repeated string subgraph_name = 35;
-}
-
-// Graph definition
-message GraphDef
-{
-    string name   = 1;   //  name
-
-    repeated string input  = 4;  // Graph input
-    repeated string output = 5;  // Graph output
-
-    repeated OpDef op      = 6;  // List of operators
-
-	map<string, AttrDef> attr = 11;  // Extended field
-}
-
-// model definition
-message ModelDef
-{
-	string name         = 1;  // name
-	uint32 version      = 2;  // IR Proto verion
-	string custom_version = 3;  // User model version number, passed in by user
-
-    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef
-
-    map<string, AttrDef> attr = 11;  // Extended field
-}
-

ge/client/proto/insert_op.proto

@@ -1,140 +0,0 @@
-syntax = "proto3";
-
-package domi;
-
-message InsertNewOps {
-	repeated AippOpParams aipp_op = 1;
-	repeated MultiShapeOpParams multi_shape_op = 2;
-}
-
-message AippOpParams {
-	enum InputFormat {
-		UNDEFINED = 0;
-		YUV420SP_U8 = 1;
-		XRGB8888_U8 = 2;
-		RGB888_U8   = 3;
-		YUV400_U8   = 4;
-		NC1HWC0DI_FP16 = 5;
-		NC1HWC0DI_S8 = 6;
-		ARGB8888_U8 = 7;
-		YUYV_U8 = 8;
-		YUV422SP_U8 = 9;
-		AYUV444_U8 = 10;
-		RAW10 = 11;
-		RAW12 = 12;
-		RAW16 = 13;
-		RAW24 = 14;
-		RGB16 = 15;
-		RGB20 = 16;
-		RGB24 = 17;
-		RGB8_IR = 18;
-		RGB16_IR = 19;
-		RGB24_IR = 20;
-	}
-
-	enum AippMode {
-		undefined = 0;
-		static = 1;
-		dynamic = 2;
-	}
-
-	// AIPP模式，区分静态AIPP和动态AIPP
-	AippMode aipp_mode = 1;
-
-	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
-	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
-	uint32 related_input_rank = 2;
-
-        // related_input_name is optional and the top name of data node which inserts aipp
-        string related_input_name = 6;
-
-	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
-	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
-	// 配置值 <= Data算子输出边的个数。
-	repeated uint32 input_edge_idx = 3;
-
-	// [Begin] 动态AIPP参数，配置静态AIPP时无效
-	uint32 max_src_image_size = 4;
-
-	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
-	bool support_rotation = 5;
-
-	// [End] 动态AIPP参数
-
-
-	// [Begin] 静态AIPP参数，配置动态AIPP时无效
-	InputFormat input_format = 51;
-	bool csc_switch = 52;
-	float cpadding_value = 53;
-	bool rbuv_swap_switch = 54;
-	bool ax_swap_switch = 55;
-	bool single_line_mode = 56;
-
-	int32 src_image_size_w = 57;
-	int32 src_image_size_h = 58;
-
-	bool crop = 59;
-	int32 load_start_pos_w = 60;
-	int32 load_start_pos_h = 61;
-	int32 crop_size_w = 62;
-	int32 crop_size_h = 63;
-
-	bool resize = 64;
-	int32 resize_output_w = 65;
-	int32 resize_output_h = 66;
-
-	bool padding = 67;
-	int32 left_padding_size = 68;
-	int32 right_padding_size = 69;
-	int32 top_padding_size = 70;
-	int32 bottom_padding_size = 71;
-	float padding_value = 72;
-
-	int32 mean_chn_0 = 10;
-	int32 mean_chn_1 = 11;
-	int32 mean_chn_2 = 12;
-	int32 mean_chn_3 = 19;
-	float min_chn_0 = 13;
-	float min_chn_1 = 14;
-	float min_chn_2 = 15;
-	float min_chn_3 = 20;
-	repeated float var_reci_chn_0 = 16;
-	repeated float var_reci_chn_1 = 17;
-	repeated float var_reci_chn_2 = 18;
-	repeated float var_reci_chn_3 = 21;
-
-	repeated int32 matrix_r0c0 = 30;
-	repeated int32 matrix_r0c1 = 31;
-	repeated int32 matrix_r0c2 = 32;
-	repeated int32 matrix_r1c0 = 33;
-	repeated int32 matrix_r1c1 = 34;
-	repeated int32 matrix_r1c2 = 35;
-	repeated int32 matrix_r2c0 = 36;
-	repeated int32 matrix_r2c1 = 37;
-	repeated int32 matrix_r2c2 = 38;
-	repeated int32 output_bias_0 = 39;
-	repeated int32 output_bias_1 = 40;
-	repeated int32 output_bias_2 = 41;
-	repeated int32 input_bias_0 = 42;
-	repeated int32 input_bias_1 = 43;
-	repeated int32 input_bias_2 = 44;
-
-	// [End] 静态AIPP参数
-
-       // The n number that is used for raw/rgbir data into f16 transformation.
-       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
-       uint32 raw_rgbir_to_f16_n = 45;
-}
-
-message MultiShapeOpParams {
-	enum MultiShapeMode {
-		batch      = 0;             //动态batch
-		resolution = 1;             //动态分辨率，扩展用
-	}
-
-	MultiShapeMode    mode                      = 1;        //算子模式
-	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入
-
-
-	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
-}

ge/client/proto/om.proto

@@ -1,396 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-enum TargetType
-{
-    MINI = 0;
-    TINY = 1;
-    LITE = 2;
-}
-
-// offline model
-message ModelDef {
-    string name = 1;
-    uint32 version = 2;
-
-    uint64 memory_size = 10;
-    uint32 stream_num = 11;
-    uint32 event_num = 12;
-    uint64 weight_size = 13;
-    uint32 label_num = 15;
-    repeated OpDef op = 20;
-    TargetType target_type = 23;
-
-    map<string, AttrDef> attr = 30;
-};
-
-// operator define
-message OpDef {
-    string name = 1;
-    string type = 2;
-
-    uint32 id = 3;
-    uint32 stream_id = 4;
-
-    repeated string input_name = 5;
-
-    repeated string src_name = 8;
-    repeated int32 src_index = 9;
-    repeated int64 input = 10;
-    repeated int64 output = 11;
-    repeated TensorDescriptor input_desc = 12;
-    repeated TensorDescriptor output_desc = 13;
-    repeated WeightDef weights = 14;
-    repeated string dst_name = 15;
-    repeated int32 dst_index = 16;
-
-    repeated int64 workspace = 20;
-    repeated uint32 workspace_bytes = 21;
-
-    repeated string weight_name = 22;
-    repeated bool is_input_const = 23;
-
-    map<string, AttrDef> attr = 30;
-
-    QuantizeFactorParams quantize_factor = 31;
-
-    oneof op_params {
-        // start at 100 here
-        SendOpParams sender_param = 100;
-        RecvOpParams receiver_param = 200;
-        ConvolutionOpParams convolution_param = 300;
-        PoolingOpParams pooling_param = 400;
-        EltwiseOpParams eltwise_param = 500;
-        BatchNormOpParams batchnorm_param = 600;
-        ScaleOpParams scale_param = 700;
-        FullConnectionOpParams full_connection_param = 800;
-        SoftmaxOpParams softmax_param = 900;
-        ActivationOpParams activation_param = 1000;
-        ReshapeOpParams reshape_param = 1100;
-    }
-};
-
-message SendOpParams {
-    uint32 event_id = 1;
-};
-
-message RecvOpParams {
-    uint32 event_id = 1;
-};
-
-enum QuantizeScaleType
-{
-    VECTOR_SCALE = 0;
-    SCALAR_SCALE = 1;
-}
-
-enum QuantizeScaleMode
-{
-    NORMAL_MODE = 0;
-    SQRT_MODE = 1;
-}
-
-enum QuantizeAlgorithm
-{
-    NON_OFFSET_ALGO = 0;
-    HALF_OFFSET_ALGO = 1;
-    ALL_OFFSET_ALGO = 2;
-}
-message QuantizeFactor
-{
-    QuantizeScaleMode scale_mode = 1;
-    bytes scale_value = 2;
-    int64 scale_offset = 3;
-    bytes offset_data_value = 4;
-    int64 offset_data_offset = 5;
-    bytes offset_weight_value = 6;
-    int64 offset_weight_offset = 7;
-    bytes offset_pad_value = 8;
-    int64 offset_pad_offset = 9;
-};
-
-message QuantizeCalcFactor
-{
-    bytes offsetw = 1;
-    int64 offsetw_offset = 2;
-    bytes offsetd = 3;
-    int64 offsetd_offset = 4;
-    bytes scalereq = 5;
-    int64 scaledreq_offset = 6;
-    bytes offsetdnext = 7;
-    int64 offsetdnext_offset = 8;
-}
-
-message QuantizeFactorParams
-{
-    QuantizeAlgorithm quantize_algo = 1;
-    QuantizeScaleType scale_type = 2;
-    QuantizeFactor quantize_param = 3;
-    QuantizeFactor dequantize_param = 4;
-    QuantizeFactor requantize_param = 5;
-    QuantizeCalcFactor quantizecalc_param = 6;
-};
-
-message ConvolutionOpParams {
-    int32 mode = 1;
-    int32 algo = 2;
-    int32 pad_mode = 3;
-    uint32 group = 4;
-    uint32 num_output = 5;
-
-    repeated uint32 pad = 10;
-    repeated uint32 stride = 11;
-    repeated uint32 dilation = 12;
-    repeated uint32 kernel = 13;
-
-    float alpha = 20;
-    float beta = 21;
-
-    WeightDef filter = 40;
-    WeightDef bias = 41;
-
-    bool relu_flag = 62;
-    repeated uint32 adj = 70;
-    repeated uint32 target_shape = 71;
-    repeated uint32 before_pad = 72;
-};
-
-message PoolingOpParams {
-    int32 mode = 1;
-    int32 nan_opt = 2;
-    int32 pad_mode = 3;
-    bool global_pooling = 4;
-
-    repeated uint32 window = 10;
-    repeated uint32 pad = 11;
-    repeated uint32 stride = 12;
-    bool ceil_mode = 13;
-    int32 data_mode  = 14;
-
-    float alpha = 20;
-    float beta = 21;
-    repeated uint32 before_pad = 22;
-};
-
-message EltwiseOpParams {
-    int32 mode = 1;
-    repeated float coeff = 2;
-    float alpha = 3;
-    float beta = 4;
-    repeated WeightDef weight = 5;
-    bool relu_flag = 6;
-};
-
-message ActivationOpParams {
-    int32 mode = 1;
-    float coef = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message BatchNormOpParams {
-    int32 mode = 1;
-
-    float alpha = 2;
-    float beta = 3;
-    double epsilon = 4;//optinal,[default = 1e-5]
-    bool use_global_stats = 5; //optinal,by default true,testing mode
-    float  moving_average_fraction = 6; //optinal,[default = .999];
-
-    WeightDef estimated_mean = 7;
-    WeightDef estimated_variance = 8;
-
-    WeightDef scale = 9;
-    WeightDef bias = 10;
-};
-
-message ScaleOpParams {
-    WeightDef scale = 1;
-    WeightDef bias = 2;
-};
-
-message ReshapeOpParams {
-    float alpha = 1;
-    float beta = 2;
-    ShapeDef shape = 3;
-    int32 axis = 4;
-    int32 num_axes = 5;
-    int32 format = 6;
-};
-
-message SoftmaxOpParams {
-    int32 algo = 1;
-    int32 mode = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message FullConnectionOpParams {
-    WeightDef filter = 1;
-    WeightDef bias = 2;
-    uint32 num_output = 3;
-    bool relu_flag = 12;
-};
-
-message FlattenOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 start_axis = 3;
-    int32 end_axis = 4;
-}
-
-message AddLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message AddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message SubOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message BiasAddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    WeightDef bias = 10;
-};
-
-message MatMulOpParams {
-    float alpha = 1;
-    float beta = 2;
-    bool transposeX = 3;
-    bool transposeW = 4;
-
-    WeightDef filter = 10;
-    WeightDef bias = 12;
-};
-
-message RsqrtOpParams {
-    float alpha = 1;
-    float beta = 2;
-};
-
-
-message WeightDef {
-    int32 format = 1;
-    int32 data_type = 2;
-    ShapeDef shape = 3;
-    bytes data = 4;
-    int64 data_offset = 5;
-    uint32 cmps_size = 6;
-    bytes cmps_tab = 7;
-    int64 cmps_tab_offset = 10;
-    CompressInfo cmps_info = 8;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
-}
-
-message ShapeDef {
-    repeated int64 dim = 1;
-}
-
-enum DeviceType {
-  NPU = 0;                   // In default, we will use NPU.
-  CPU = 1;                   // CPU
-}
-
-message AllOffsetQuantizeInfo {
-	float scale  = 1;
-	int32 offset = 2;
-} 
-
-message TensorDescriptor {
-    int32 format = 1;
-    int32 data_type = 2;
-    repeated int64 dim = 3;
-    uint32 size = 4;
-    bool reuse_input = 5;
-    bool output_tensor = 7;
-    DeviceType device_type = 8;
-    bool input_tensor = 9;
-    uint32 real_dim_cnt = 10;
-    uint32 reuse_input_index = 11;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
-}
-
-message CompressInfo {
-    int32 blockRow = 1;                             // block row
-    int32 blockCol = 2;                             // block col
-    int32 fractalK = 3;                             // fractal K
-    int32 fractalN = 4;                             // fractal N
-    int32 lastFractalK = 5;                         // K of last fractal
-    int32 lastFractalN = 6;                         // N of last fractal
-    int32 cubeSize = 7;                             // cube's length
-    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
-}
-
-message AttrDef {
-  message ListValue {
-    repeated string s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated uint32 u = 6 [packed = true];         // "list(uint)"
-    repeated bytes bt = 7;
-  }
-
-  oneof value {
-    string s = 2;                // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    uint32 u = 6;                // "uint32"
-    bytes bt = 7;
-    ListValue list = 1;          // any "list(...)"
-    NamedAttrs func = 10;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs {
-  string name = 1;
-  map<string, AttrDef> attr = 2;
-}
-

ge/client/proto/task.proto

@@ -1,179 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-message ModelTaskDef {
-    string version = 1;
-
-    map<string, string> attr = 9; // Extended field
-    repeated TaskDef task = 10;
-
-    uint64 memory_size = 11;
-    uint32 stream_num = 12;
-    uint32 event_num = 13;
-    uint64 weight_size = 14;
-
-    repeated bytes op = 15; // input/output opdef in bytes
-
-    uint64 base_addr = 16;    // base addr
-    uint64 weight_addr = 17;  // weight addr
-    uint32 batch_num = 18;
-}
-
-
-message TaskDef {
-    uint32 id = 1;
-    uint32 type = 2;
-
-    uint32 stream_id = 10;
-    uint32 event_id = 11;
-
-    KernelDef kernel = 20;
-    KernelExDef kernel_ex = 21;
-    KernelHcclDef kernel_hccl = 25;
-    EventExDef event_ex = 26;
-    LogTimeStampDef log_timestamp = 28;
-
-    uint32 label_id = 30;
-
-    MemcpyAsyncDef memcpy_async = 31;
-    StreamSwitchDef stream_switch = 32;
-    StreamActiveDef stream_active = 33;
-    bytes private_def = 34;
-    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
-    StreamSwitchNDef stream_switch_n = 36;
-
-    LabelSetDef label_set = 37;
-    LabelGotoExDef label_goto_ex = 38;
-    LabelSwitchByIndexDef label_switch_by_index = 39;
-    KernelDefWithHandle kernel_with_handle = 40;
-}
-
-message KernelDef {
-    KernelContext context = 1;
-
-    string stub_func = 10;
-    uint32 block_dim = 11;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes sm_desc = 14;
-    bytes flowtable = 15;
-    string so_name = 16;
-    string kernel_name = 17;
-    bytes kernel_ext_info = 18;
-    uint32 kernel_ext_info_size = 19;
-}
-
-message KernelDefWithHandle {
-    KernelContext context = 1;
-
-    uint64 handle = 10;
-    string dev_func = 11;
-    uint32 block_dim = 12;
-    uint32 args_size = 13;
-    bytes args = 14;
-    bytes sm_desc = 15;
-    string original_kernel_key = 16;
-    string node_info = 17;
-}
-
-message KernelContext {
-    uint32 kernel_type = 1;
-    uint32 op_id = 2;                              // OP type in CCE
-    uint32 kernel_func_id = 3;
-    uint32 op_index = 4;                           // TE/Custom operator
-    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
-    bytes args_offset = 6;                         // args offset information
-    uint32 args_count = 7;                         // args count
-    repeated uint32 origin_op_index = 8;
-}
-
-
-message KernelExDef {
-    uint32 flags = 1;
-
-    uint32 op_index = 4;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
-    uint32 task_info_size = 15;
-    bytes kernel_ext_info = 16;
-    uint32 kernel_ext_info_size = 17;
-}
-
-
-message KernelHcclDef {
-    uint32 op_index = 8;
-    string hccl_type = 9;
-}
-
-
-message EventExDef {
-    uint32 op_index = 1;
-    uint32 event_type = 2;
-}
-
-message LogTimeStampDef {
-    uint64 logid = 1;
-    bool notify = 2;
-    uint32 flat = 3;
-}
-
-message MemcpyAsyncDef {
-    uint64 dst = 1;
-    uint64 dst_max = 2;
-    uint64 src = 3;
-    uint64 count = 4;
-    uint32 kind = 5;
-    uint32 op_index = 6;
-}
-
-message StreamSwitchDef {
-    uint32 op_index = 1;
-    uint32 true_stream_id = 2;
-    int64 value = 3;
-    uint64 value_ptr = 4;
-    uint32 data_type = 5;
-}
-
-message StreamActiveDef {
-    uint32 op_index = 1;
-    uint32 active_stream_id = 2;
-}
-
-message StreamSwitchNDef {
-    uint32 op_index = 1;
-    uint32 size = 2;
-    repeated int64 target_value = 3;
-    repeated uint32 true_stream_id = 4;
-    uint32 element_size = 5;
-    uint32 data_type = 6;
-}
-
-message LabelSetDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelGotoExDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelSwitchByIndexDef {
-    uint32 op_index = 1;
-    uint32 label_max = 2;
-}

ge/common/CMakeLists.txt

@@ -106,6 +106,7 @@ target_link_libraries(ge_common PRIVATE
     c_sec
     error_manager
     slog
+    opt_feature
     -Wl,--as-needed
     json
     $<$<NOT:$<STREQUAL:${TARGET_SYSTEM_NAME},Android>>:-lrt>

ge/common/dump/dump_manager.cc

@@ -33,7 +33,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY DumpManager &DumpManager::GetIn
 
 bool DumpManager::NeedDoDump(const DumpConfig &dump_config, DumpProperties &dump_properties) {
   if (dump_config.dump_status.empty() && dump_config.dump_debug.empty()) {
-    dump_properties_map_.emplace(kInferSessionId, dump_properties);
+    dump_properties_map_[kInferSessionId] = dump_properties;
     GELOGI("Dump does not open");
     return false;
   }
@@ -41,7 +41,7 @@ bool DumpManager::NeedDoDump(const DumpConfig &dump_config, DumpProperties &dump
   if ((dump_config.dump_status == kDumpoff || dump_config.dump_status == kDumpOFF) &&
        dump_config.dump_debug == kDumpoff) {
     dump_properties.ClearDumpPropertyValue();
-    dump_properties_map_.emplace(kInferSessionId, dump_properties);
+    dump_properties_map_[kInferSessionId] = dump_properties;
     return false;
   }
   if (dump_config.dump_status == kDumpOn && dump_config.dump_debug == kDumpOn) {

ge/common/dump/exception_dumper.cc

@@ -161,6 +161,7 @@ Status ExceptionDumper::DumpExceptionInfo(const std::vector<rtExceptionInfo> &ex
 
       uint64_t proto_size = dump_data.ByteSizeLong();
       std::unique_ptr<char[]> proto_msg(new (std::nothrow) char[proto_size]);
+      GE_CHECK_NOTNULL(proto_msg);
       bool ret = dump_data.SerializeToArray(proto_msg.get(), proto_size);
       if (!ret || proto_size == 0) {
         REPORT_INNER_ERROR("E19999", "Serialize proto to string fail");

ge/common/formats/format_transfers/format_transfer_fractal_nz.cc

@@ -185,6 +185,7 @@ Status TransFormatFromNdToFracNz(const TransArgs &args, TransResult &result, con
         auto src_offset = (src_h_head + w1_idx * w0) * size;
         auto protected_size = dst_size - dst_offset < static_cast<int64_t>(SECUREC_MEM_MAX_LEN) ?
                               dst_size - dst_offset : static_cast<int64_t>(SECUREC_MEM_MAX_LEN);
+        GE_CHECK_GE(protected_size, 0);
         auto ret = memcpy_s(dst.get() + dst_offset, static_cast<size_t>(protected_size), args.data + src_offset,
                             static_cast<size_t>(size * w0));
         if (ret != EOK) {
@@ -202,6 +203,7 @@ Status TransFormatFromNdToFracNz(const TransArgs &args, TransResult &result, con
         auto src_offset = (src_h_head + src_w_idx) * size;
         auto protected_size = dst_size - dst_offset < static_cast<int64_t>(SECUREC_MEM_MAX_LEN) ?
                               dst_size - dst_offset : static_cast<int64_t>(SECUREC_MEM_MAX_LEN);
+        GE_CHECK_GE(protected_size, 0);
         auto ret = memcpy_s(dst.get() + dst_offset, static_cast<size_t>(protected_size), args.data + src_offset,
                             static_cast<size_t>(size));
         if (ret != EOK) {
@@ -267,6 +269,7 @@ Status TransFormatFromFracNzToNd(const TransArgs &args, TransResult &result, con
         auto dst_offset = (dst_h_head + w1_idx * w0) * size;
         auto protected_size = dst_size - dst_offset < static_cast<int64_t>(SECUREC_MEM_MAX_LEN) ?
                               dst_size - dst_offset : static_cast<int64_t>(SECUREC_MEM_MAX_LEN);
+        GE_CHECK_GE(protected_size, 0);
         ret = memcpy_s(dst.get() + dst_offset, static_cast<size_t>(protected_size), args.data + src_offset,
                        static_cast<size_t>(size * w0));
         if (ret != EOK) {
@@ -285,6 +288,7 @@ Status TransFormatFromFracNzToNd(const TransArgs &args, TransResult &result, con
         auto dst_offset = (dst_h_head + dst_w_idx) * size;
         auto protected_size = dst_size - dst_offset < static_cast<int64_t>(SECUREC_MEM_MAX_LEN) ?
                               dst_size - dst_offset : static_cast<int64_t>(SECUREC_MEM_MAX_LEN);
+        GE_CHECK_GE(protected_size, 0);
         ret = memcpy_s(dst.get() + dst_offset, static_cast<size_t>(protected_size), args.data + src_offset,
                        static_cast<size_t>(size));
         if (ret != EOK) {

ge/common/formats/format_transfers/format_transfer_fractal_zz.cc

@@ -193,6 +193,7 @@ Status TransFormatFromNdToFracZz(const TransArgs &args, TransResult &result, con
           auto protected_size = dst_size - dst_offset < static_cast<int64_t>(SECUREC_MEM_MAX_LEN)
                                   ? dst_size - dst_offset
                                   : static_cast<int64_t>(SECUREC_MEM_MAX_LEN);
+          GE_CHECK_GE(protected_size, 0);
           auto ret = memcpy_s(dst.get() + dst_offset, static_cast<size_t>(protected_size), args.data + src_offset,
                               static_cast<size_t>(size * w0));
           if (ret != EOK) {
@@ -213,6 +214,7 @@ Status TransFormatFromNdToFracZz(const TransArgs &args, TransResult &result, con
           auto protected_size = dst_size - dst_offset < static_cast<int64_t>(SECUREC_MEM_MAX_LEN)
                                   ? dst_size - dst_offset
                                   : static_cast<int64_t>(SECUREC_MEM_MAX_LEN);
+          GE_CHECK_GE(protected_size, 0);
           auto ret = memcpy_s(dst.get() + dst_offset, static_cast<size_t>(protected_size), args.data + src_offset,
                               static_cast<size_t>(size));
           if (ret != EOK) {
@@ -284,6 +286,7 @@ Status TransFormatFromFracZzToNd(const TransArgs &args, TransResult &result, con
           auto protected_size = dst_size - dst_offset < static_cast<int64_t>(SECUREC_MEM_MAX_LEN)
                                   ? dst_size - dst_offset
                                   : static_cast<int64_t>(SECUREC_MEM_MAX_LEN);
+          GE_CHECK_GE(protected_size, 0);
           auto ret = memcpy_s(dst.get() + dst_offset, static_cast<size_t>(protected_size), args.data + src_offset,
                               static_cast<size_t>(size * w0));
           if (ret != EOK) {
@@ -304,6 +307,7 @@ Status TransFormatFromFracZzToNd(const TransArgs &args, TransResult &result, con
           auto protected_size = dst_size - dst_offset < static_cast<int64_t>(SECUREC_MEM_MAX_LEN)
                                   ? dst_size - dst_offset
                                   : static_cast<int64_t>(SECUREC_MEM_MAX_LEN);
+          GE_CHECK_GE(protected_size, 0);
           auto ret = memcpy_s(dst.get() + dst_offset, static_cast<size_t>(protected_size), args.data + src_offset,
                               static_cast<size_t>(size));
           if (ret != EOK) {

ge/common/ge/tbe_plugin_manager.cc

@@ -104,7 +104,15 @@ void TBEPluginManager::ProcessSoFullName(vector<string> &file_list, string &caff
   }
 }
 
-void TBEPluginManager::FindParserSo(const string &path, vector<string> &file_list, string &caffe_parser_path) {
+void TBEPluginManager::FindParserSo(const string &path, vector<string> &file_list,
+                                    string &caffe_parser_path, int recursive_depth) {
+  static const int kMaxRecursiveDepth = 20; // For recursive depth protection
+
+  if (recursive_depth >= kMaxRecursiveDepth) {
+    GELOGW("Recursive depth is become %d, Please check input!", recursive_depth);
+    return;
+  }
+
   // Path, change to absolute path
   string real_path = RealPath(path.c_str());
   // Plugin path does not exist
@@ -138,7 +146,7 @@ void TBEPluginManager::FindParserSo(const string &path, vector<string> &file_lis
           ProcessSoFullName(file_list, caffe_parser_path, full_name, caffe_parser_so_suff, aicpu_so_suff,
                             aicpu_host_so_suff);
       } else {
-          FindParserSo(full_name, file_list, caffe_parser_path);
+          FindParserSo(full_name, file_list, caffe_parser_path, recursive_depth + 1);
       }
   }
   mmScandirFree(entries, ret);

ge/common/ge/tbe_plugin_manager.h

@@ -57,7 +57,8 @@ class TBEPluginManager {
   static void ProcessSoFullName(vector<string> &file_list, string &caffe_parser_path, string &full_name,
                                 const string &caffe_parser_so_suff, const string &aicpu_so_suff,
                                 const string &aicpu_host_so_suff);
-  static void FindParserSo(const string &path, vector<string> &file_list, string &caffe_parser_path);
+  static void FindParserSo(const string &path, vector<string> &file_list, string &caffe_parser_path,
+                           int recursive_depth = 0);
   static void GetPluginSoFileList(const string &path, vector<string> &file_list, string &caffe_parser_path);
   static void GetCustomOpPath(std::string &customop_path);
   void LoadCustomOpLib();

ge/common/helper/model_cache_helper.cc

@@ -1679,6 +1679,13 @@ Status ModelCacheHelper::LoadOmModelFromCache(GeModelPtr &ge_model) const {
     GELOGW("LoadOmModelFromCache: Load model from file failed. ret = %u", ret);
     return ret;
   }
+  std::function<void()> callback = [&]() {
+    if (model_data.model_data != nullptr) {
+      delete[] reinterpret_cast<char *>(model_data.model_data);
+      model_data.model_data = nullptr;
+    }
+  };
+  GE_MAKE_GUARD(release, callback);
 
   ModelHelper model_helper;
   ret = model_helper.LoadModel(model_data);

ge/common/profiling/ge_profiling.cc

@@ -22,6 +22,7 @@
 #include "graph/load/graph_loader.h"
 #include "init/gelib.h"
 #include "framework/common/ge_inner_error_codes.h"
+#include "model/ge_model.h"
 
 namespace {
 const uint32_t kDeviceListIndex = 3;
@@ -42,6 +43,10 @@ const std::map<ProfCommandHandleType, std::string> kProfCommandTypeMap = {
     {kProfCommandhandleFinalize, kProfilingFinalize},
     {kProfCommandhandleModelSubscribe, kProfModelSubscribe},
     {kProfCommandhandleModelUnsubscribe, kProfModelUnsubscribe}};
+
+const uint64_t kModelId = ge::INVALID_MODEL_ID;
+const uint16_t kStepStart = 0;
+const uint16_t kStepEnd = 1;
 }  // namespace
 
 bool TransProfConfigToParam(const ProfCommandHandleData &profCommand, vector<string> &prof_config_params) {
@@ -216,3 +221,36 @@ ge::Status ProfCommandHandle(ProfCommandHandleType type, void *data, uint32_t le
   return ge::SUCCESS;
 }
 
+ge::Status ProfSetStepInfo(uint64_t index_id, uint16_t tag_id, rtStream_t stream) {
+  static bool is_first_run = true;
+  int32_t device_id = 0;
+  rtError_t rt_ret = rtGetDevice(&device_id);
+  if (rt_ret != RT_ERROR_NONE) {
+    GELOGE(rt_ret, "[Get][LogicDeviceId]Failed, ret 0x%X", rt_ret);
+    REPORT_CALL_ERROR("E19999", "Get logic device id failed, ret 0x%X", rt_ret);
+    return ge::FAILED;
+  }
+  if (is_first_run && tag_id == kStepStart) {
+    GE_CHK_STATUS_RET_NOLOG(ge::ProfilingManager::Instance().ProfileStepInfo(index_id,
+                                                                             kModelId,
+                                                                             tag_id,
+                                                                             stream,
+                                                                             device_id));
+    is_first_run = false;
+    return ge::SUCCESS;
+  }
+  if (!is_first_run && tag_id == kStepEnd) {
+    GE_CHK_STATUS_RET_NOLOG(ge::ProfilingManager::Instance().ProfileStepInfo(index_id,
+                                                                             kModelId,
+                                                                             tag_id,
+                                                                             stream,
+                                                                             device_id));
+    is_first_run = true;
+    return ge::SUCCESS;
+  }
+  GELOGE(ge::FAILED, "Param tag_id:%u invalid when is_first_run is %d", tag_id, is_first_run);
+  REPORT_INPUT_ERROR("E10001", std::vector<std::string>({"value", "parameter", "reason"}),
+                     std::vector<std::string>({std::to_string(tag_id), "tag_id",
+                                               "tag id must be 0 when first run, must be 1 when second run"}));
+  return ge::FAILED;
+}

ge/common/proto/ge_ir.proto

@@ -1,193 +0,0 @@
-syntax = "proto3";
-
-package ge.proto;
-
-enum DataType
-{
-    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
-    DT_FLOAT     = 1;  // float type
-    DT_FLOAT16   = 2;  // fp16 type
-    DT_INT8      = 3;  // int8 type
-    DT_UINT8     = 4;  // uint8 type
-    DT_INT16     = 5;  // int16 type
-    DT_UINT16    = 6;  // uint16 type
-    DT_INT32     = 7;  //
-    DT_INT64     = 8;  // int64 type
-    DT_UINT32    = 9;  // unsigned int32
-    DT_UINT64    = 10;  // unsigned int64
-    DT_BOOL      = 11;  // bool type
-    DT_DOUBLE    = 12; // double type
-    DT_STRING = 13;            // string type
-    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
-    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
-    DT_COMPLEX64 = 16;         // complex64 type
-    DT_COMPLEX128 = 17;        // complex128 type
-    DT_QINT8 = 18;             // qint8 type
-    DT_QINT16 = 19;            // qint16 type
-    DT_QINT32 = 20;            // qint32 type
-    DT_QUINT8 = 21;            // quint8 type
-    DT_QUINT16 = 22;           // quint16 type
-    DT_RESOURCE  = 23;         // resource type
-    DT_STRING_REF = 24;        // string_ref type
-    DT_DUAL      = 25;              /**< dual output type */
-    DT_VARIANT = 26;           // variant type
-    DT_BF16 = 27;              // bf16 type
-    DT_INT4 = 28;              // int4 type
-}
-
-message AttrDef
-{
-    message ListValue
-    {
-        enum ListValueType{
-          VT_LIST_NONE = 0;
-          VT_LIST_STRING = 1;
-          VT_LIST_INT = 2;
-          VT_LIST_FLOAT = 3;
-          VT_LIST_BOOL = 4;
-          VT_LIST_BYTES = 5;
-          VT_LIST_TENSOR_DESC = 6;
-          VT_LIST_TENSOR = 7;
-          VT_LIST_GRAPH = 8;
-          VT_LIST_NAMED_ATTRS = 9;
-          VT_LIST_DATA_TYPE = 10;
-        }
-        repeated bytes s             = 2;                    // "list(string)"
-        repeated int64 i             = 3;  // "list(int)"
-        repeated float f             = 4;   // "list(float)"
-        repeated bool  b             = 5;  // "list(bool)"
-        repeated bytes bt            = 7;
-        repeated TensorDescriptor td = 8;
-        repeated TensorDef t         = 9;
-        repeated GraphDef g          = 10;
-	    repeated NamedAttrs na       = 11;
-	    repeated int64 dt            = 12; // list ge::DataType
-
-	    ListValueType val_type       = 20;
-    }
-
-    message ListListInt{
-        message ListInt{
-            repeated int64 list_i             = 1; // list int
-        }
-        repeated ListInt list_list_i             = 1; // list list int
-    }
-
-    oneof value
-    {
-        bytes            s    = 2;  // "string"
-        int64            i    = 3;  // "int"
-        float            f    = 4;  // "float"
-        bool             b    = 5;  // "bool"
-        bytes            bt   = 7;
-        ListValue        list = 1;   // any "list(...)"
-        NamedAttrs       func = 10;  // Used to support attr nesting
-        TensorDescriptor td   = 11;  // GeTensorDesc type
-        TensorDef        t    = 12;  // GeTensor type
-        GraphDef         g    = 13;  // Graph type
-        ListListInt      list_list_int  = 14;  // List List Int type
-        int64            dt   = 15; // ge::DataType
-    }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs
-{
-    string               name = 1;
-    map<string, AttrDef> attr = 2;
-}
-
-// Shape / dimension description, using row-major order
-message ShapeDef
-{
-    repeated int64 dim = 1;  // Size of each dimension
-}
-
-// Multidimensional data description
-message TensorDescriptor
-{
-    string   name   = 1;  // Optional parameter, tensor name
-
-    DataType dtype  = 2;  // tensor datatype
-    ShapeDef shape  = 3;  // Shape / dimension
-    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"
-
-    bool has_out_attr = 9;
-    int64 size = 10;
-    int64 weight_size = 11;
-    bool reuse_input = 12;
-    bool output_tensor = 13;
-    string device_type = 14;
-    bool input_tensor =15;
-    int64 real_dim_cnt = 16;
-    int64 reuse_input_index = 17;
-    int64 data_offset = 18;
-    int64 cmps_size = 19;
-    string cmps_tab = 20;
-    int64 cmps_tab_offset = 21;
-
-	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
-}
-
-// GeTensor definition
-message TensorDef
-{
-    TensorDescriptor desc = 1;  // Tensor description
-    bytes            data = 2;  // Tensor data
-}
-
-
-// Operator description
-message OpDef
-{
-    string name = 1;  // name
-    string type = 2;  // type
-
-    repeated string input = 5;  // input original op name + outgoing index. op_name：index
-
-    map<string, AttrDef> attr = 10;  // Set of operator parameter fields
-
-    bool has_out_attr = 20;
-    int64 id = 21;
-    int64 stream_id =22;
-    repeated string input_name = 23;
-    repeated string src_name = 24;
-    repeated int64 src_index = 25;
-    repeated string dst_name = 26;
-    repeated int64 dst_index = 27;
-    repeated int64 input_i = 28;
-    repeated int64 output_i = 29;
-    repeated int64 workspace = 30;
-    repeated int64 workspace_bytes = 31;
-    repeated bool is_input_const = 32;
-    repeated TensorDescriptor input_desc = 33;
-    repeated TensorDescriptor output_desc = 34;
-    repeated string subgraph_name = 35;
-}
-
-// Graph definition
-message GraphDef
-{
-    string name   = 1;   //  name
-
-    repeated string input  = 4;  // Graph input
-    repeated string output = 5;  // Graph output
-
-    repeated OpDef op      = 6;  // List of operators
-
-	map<string, AttrDef> attr = 11;  // Extended field
-}
-
-// model definition
-message ModelDef
-{
-	string name         = 1;  // name
-	uint32 version      = 2;  // IR Proto verion
-	string custom_version = 3;  // User model version number, passed in by user
-
-    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef
-
-    map<string, AttrDef> attr = 11;  // Extended field
-}
-

ge/common/proto/insert_op.proto

@@ -1,140 +0,0 @@
-syntax = "proto3";
-
-package domi;
-
-message InsertNewOps {
-	repeated AippOpParams aipp_op = 1;
-	repeated MultiShapeOpParams multi_shape_op = 2;
-}
-
-message AippOpParams {
-	enum InputFormat {
-		UNDEFINED = 0;
-		YUV420SP_U8 = 1;
-		XRGB8888_U8 = 2;
-		RGB888_U8   = 3;
-		YUV400_U8   = 4;
-		NC1HWC0DI_FP16 = 5;
-		NC1HWC0DI_S8 = 6;
-		ARGB8888_U8 = 7;
-		YUYV_U8 = 8;
-		YUV422SP_U8 = 9;
-		AYUV444_U8 = 10;
-		RAW10 = 11;
-		RAW12 = 12;
-		RAW16 = 13;
-		RAW24 = 14;
-		RGB16 = 15;
-		RGB20 = 16;
-		RGB24 = 17;
-		RGB8_IR = 18;
-		RGB16_IR = 19;
-		RGB24_IR = 20;
-	}
-
-	enum AippMode {
-		undefined = 0;
-		static = 1;
-		dynamic = 2;
-	}
-
-	// AIPP模式，区分静态AIPP和动态AIPP
-	AippMode aipp_mode = 1;
-
-	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
-	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
-	uint32 related_input_rank = 2;
-
-        // related_input_name is optional and the top name of data node which inserts aipp
-        string related_input_name = 6;
-
-	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
-	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
-	// 配置值 <= Data算子输出边的个数。
-	repeated uint32 input_edge_idx = 3;
-
-	// [Begin] 动态AIPP参数，配置静态AIPP时无效
-	uint32 max_src_image_size = 4;
-
-	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
-	bool support_rotation = 5;
-
-	// [End] 动态AIPP参数
-
-
-	// [Begin] 静态AIPP参数，配置动态AIPP时无效
-	InputFormat input_format = 51;
-	bool csc_switch = 52;
-	float cpadding_value = 53;
-	bool rbuv_swap_switch = 54;
-	bool ax_swap_switch = 55;
-	bool single_line_mode = 56;
-
-	int32 src_image_size_w = 57;
-	int32 src_image_size_h = 58;
-
-	bool crop = 59;
-	int32 load_start_pos_w = 60;
-	int32 load_start_pos_h = 61;
-	int32 crop_size_w = 62;
-	int32 crop_size_h = 63;
-
-	bool resize = 64;
-	int32 resize_output_w = 65;
-	int32 resize_output_h = 66;
-
-	bool padding = 67;
-	int32 left_padding_size = 68;
-	int32 right_padding_size = 69;
-	int32 top_padding_size = 70;
-	int32 bottom_padding_size = 71;
-	float padding_value = 72;
-
-	int32 mean_chn_0 = 10;
-	int32 mean_chn_1 = 11;
-	int32 mean_chn_2 = 12;
-	int32 mean_chn_3 = 19;
-	float min_chn_0 = 13;
-	float min_chn_1 = 14;
-	float min_chn_2 = 15;
-	float min_chn_3 = 20;
-	repeated float var_reci_chn_0 = 16;
-	repeated float var_reci_chn_1 = 17;
-	repeated float var_reci_chn_2 = 18;
-	repeated float var_reci_chn_3 = 21;
-
-	repeated int32 matrix_r0c0 = 30;
-	repeated int32 matrix_r0c1 = 31;
-	repeated int32 matrix_r0c2 = 32;
-	repeated int32 matrix_r1c0 = 33;
-	repeated int32 matrix_r1c1 = 34;
-	repeated int32 matrix_r1c2 = 35;
-	repeated int32 matrix_r2c0 = 36;
-	repeated int32 matrix_r2c1 = 37;
-	repeated int32 matrix_r2c2 = 38;
-	repeated int32 output_bias_0 = 39;
-	repeated int32 output_bias_1 = 40;
-	repeated int32 output_bias_2 = 41;
-	repeated int32 input_bias_0 = 42;
-	repeated int32 input_bias_1 = 43;
-	repeated int32 input_bias_2 = 44;
-
-	// [End] 静态AIPP参数
-
-       // The n number that is used for raw/rgbir data into f16 transformation.
-       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
-       uint32 raw_rgbir_to_f16_n = 45;
-}
-
-message MultiShapeOpParams {
-	enum MultiShapeMode {
-		batch      = 0;             //动态batch
-		resolution = 1;             //动态分辨率，扩展用
-	}
-
-	MultiShapeMode    mode                      = 1;        //算子模式
-	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入
-
-
-	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
-}

ge/common/proto/om.proto

@@ -1,396 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-enum TargetType
-{
-    MINI = 0;
-    TINY = 1;
-    LITE = 2;
-}
-
-// offline model
-message ModelDef {
-    string name = 1;
-    uint32 version = 2;
-
-    uint64 memory_size = 10;
-    uint32 stream_num = 11;
-    uint32 event_num = 12;
-    uint64 weight_size = 13;
-    uint32 label_num = 15;
-    repeated OpDef op = 20;
-    TargetType target_type = 23;
-
-    map<string, AttrDef> attr = 30;
-};
-
-// operator define
-message OpDef {
-    string name = 1;
-    string type = 2;
-
-    uint32 id = 3;
-    uint32 stream_id = 4;
-
-    repeated string input_name = 5;
-
-    repeated string src_name = 8;
-    repeated int32 src_index = 9;
-    repeated int64 input = 10;
-    repeated int64 output = 11;
-    repeated TensorDescriptor input_desc = 12;
-    repeated TensorDescriptor output_desc = 13;
-    repeated WeightDef weights = 14;
-    repeated string dst_name = 15;
-    repeated int32 dst_index = 16;
-
-    repeated int64 workspace = 20;
-    repeated uint32 workspace_bytes = 21;
-
-    repeated string weight_name = 22;
-    repeated bool is_input_const = 23;
-
-    map<string, AttrDef> attr = 30;
-
-    QuantizeFactorParams quantize_factor = 31;
-
-    oneof op_params {
-        // start at 100 here
-        SendOpParams sender_param = 100;
-        RecvOpParams receiver_param = 200;
-        ConvolutionOpParams convolution_param = 300;
-        PoolingOpParams pooling_param = 400;
-        EltwiseOpParams eltwise_param = 500;
-        BatchNormOpParams batchnorm_param = 600;
-        ScaleOpParams scale_param = 700;
-        FullConnectionOpParams full_connection_param = 800;
-        SoftmaxOpParams softmax_param = 900;
-        ActivationOpParams activation_param = 1000;
-        ReshapeOpParams reshape_param = 1100;
-    }
-};
-
-message SendOpParams {
-    uint32 event_id = 1;
-};
-
-message RecvOpParams {
-    uint32 event_id = 1;
-};
-
-enum QuantizeScaleType
-{
-    VECTOR_SCALE = 0;
-    SCALAR_SCALE = 1;
-}
-
-enum QuantizeScaleMode
-{
-    NORMAL_MODE = 0;
-    SQRT_MODE = 1;
-}
-
-enum QuantizeAlgorithm
-{
-    NON_OFFSET_ALGO = 0;
-    HALF_OFFSET_ALGO = 1;
-    ALL_OFFSET_ALGO = 2;
-}
-message QuantizeFactor
-{
-    QuantizeScaleMode scale_mode = 1;
-    bytes scale_value = 2;
-    int64 scale_offset = 3;
-    bytes offset_data_value = 4;
-    int64 offset_data_offset = 5;
-    bytes offset_weight_value = 6;
-    int64 offset_weight_offset = 7;
-    bytes offset_pad_value = 8;
-    int64 offset_pad_offset = 9;
-};
-
-message QuantizeCalcFactor
-{
-    bytes offsetw = 1;
-    int64 offsetw_offset = 2;
-    bytes offsetd = 3;
-    int64 offsetd_offset = 4;
-    bytes scalereq = 5;
-    int64 scaledreq_offset = 6;
-    bytes offsetdnext = 7;
-    int64 offsetdnext_offset = 8;
-}
-
-message QuantizeFactorParams
-{
-    QuantizeAlgorithm quantize_algo = 1;
-    QuantizeScaleType scale_type = 2;
-    QuantizeFactor quantize_param = 3;
-    QuantizeFactor dequantize_param = 4;
-    QuantizeFactor requantize_param = 5;
-    QuantizeCalcFactor quantizecalc_param = 6;
-};
-
-message ConvolutionOpParams {
-    int32 mode = 1;
-    int32 algo = 2;
-    int32 pad_mode = 3;
-    uint32 group = 4;
-    uint32 num_output = 5;
-
-    repeated uint32 pad = 10;
-    repeated uint32 stride = 11;
-    repeated uint32 dilation = 12;
-    repeated uint32 kernel = 13;
-
-    float alpha = 20;
-    float beta = 21;
-
-    WeightDef filter = 40;
-    WeightDef bias = 41;
-
-    bool relu_flag = 62;
-    repeated uint32 adj = 70;
-    repeated uint32 target_shape = 71;
-    repeated uint32 before_pad = 72;
-};
-
-message PoolingOpParams {
-    int32 mode = 1;
-    int32 nan_opt = 2;
-    int32 pad_mode = 3;
-    bool global_pooling = 4;
-
-    repeated uint32 window = 10;
-    repeated uint32 pad = 11;
-    repeated uint32 stride = 12;
-    bool ceil_mode = 13;
-    int32 data_mode  = 14;
-
-    float alpha = 20;
-    float beta = 21;
-    repeated uint32 before_pad = 22;
-};
-
-message EltwiseOpParams {
-    int32 mode = 1;
-    repeated float coeff = 2;
-    float alpha = 3;
-    float beta = 4;
-    repeated WeightDef weight = 5;
-    bool relu_flag = 6;
-};
-
-message ActivationOpParams {
-    int32 mode = 1;
-    float coef = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message BatchNormOpParams {
-    int32 mode = 1;
-
-    float alpha = 2;
-    float beta = 3;
-    double epsilon = 4;//optinal,[default = 1e-5]
-    bool use_global_stats = 5; //optinal,by default true,testing mode
-    float  moving_average_fraction = 6; //optinal,[default = .999];
-
-    WeightDef estimated_mean = 7;
-    WeightDef estimated_variance = 8;
-
-    WeightDef scale = 9;
-    WeightDef bias = 10;
-};
-
-message ScaleOpParams {
-    WeightDef scale = 1;
-    WeightDef bias = 2;
-};
-
-message ReshapeOpParams {
-    float alpha = 1;
-    float beta = 2;
-    ShapeDef shape = 3;
-    int32 axis = 4;
-    int32 num_axes = 5;
-    int32 format = 6;
-};
-
-message SoftmaxOpParams {
-    int32 algo = 1;
-    int32 mode = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message FullConnectionOpParams {
-    WeightDef filter = 1;
-    WeightDef bias = 2;
-    uint32 num_output = 3;
-    bool relu_flag = 12;
-};
-
-message FlattenOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 start_axis = 3;
-    int32 end_axis = 4;
-}
-
-message AddLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message AddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message SubOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message BiasAddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    WeightDef bias = 10;
-};
-
-message MatMulOpParams {
-    float alpha = 1;
-    float beta = 2;
-    bool transposeX = 3;
-    bool transposeW = 4;
-
-    WeightDef filter = 10;
-    WeightDef bias = 12;
-};
-
-message RsqrtOpParams {
-    float alpha = 1;
-    float beta = 2;
-};
-
-
-message WeightDef {
-    int32 format = 1;
-    int32 data_type = 2;
-    ShapeDef shape = 3;
-    bytes data = 4;
-    int64 data_offset = 5;
-    uint32 cmps_size = 6;
-    bytes cmps_tab = 7;
-    int64 cmps_tab_offset = 10;
-    CompressInfo cmps_info = 8;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
-}
-
-message ShapeDef {
-    repeated int64 dim = 1;
-}
-
-enum DeviceType {
-  NPU = 0;                   // In default, we will use NPU.
-  CPU = 1;                   // CPU
-}
-
-message AllOffsetQuantizeInfo {
-	float scale  = 1;
-	int32 offset = 2;
-} 
-
-message TensorDescriptor {
-    int32 format = 1;
-    int32 data_type = 2;
-    repeated int64 dim = 3;
-    uint32 size = 4;
-    bool reuse_input = 5;
-    bool output_tensor = 7;
-    DeviceType device_type = 8;
-    bool input_tensor = 9;
-    uint32 real_dim_cnt = 10;
-    uint32 reuse_input_index = 11;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
-}
-
-message CompressInfo {
-    int32 blockRow = 1;                             // block row
-    int32 blockCol = 2;                             // block col
-    int32 fractalK = 3;                             // fractal K
-    int32 fractalN = 4;                             // fractal N
-    int32 lastFractalK = 5;                         // K of last fractal
-    int32 lastFractalN = 6;                         // N of last fractal
-    int32 cubeSize = 7;                             // cube's length
-    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
-}
-
-message AttrDef {
-  message ListValue {
-    repeated string s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated uint32 u = 6 [packed = true];         // "list(uint)"
-    repeated bytes bt = 7;
-  }
-
-  oneof value {
-    string s = 2;                // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    uint32 u = 6;                // "uint32"
-    bytes bt = 7;
-    ListValue list = 1;          // any "list(...)"
-    NamedAttrs func = 10;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs {
-  string name = 1;
-  map<string, AttrDef> attr = 2;
-}
-

ge/common/proto/op_mapping.proto

@@ -1,75 +0,0 @@
-syntax = "proto3";
-package toolkit.aicpu.dump;
-
-message Shape {
-    repeated uint64 dim = 1;
-}
-
-message Output {
-    int32 data_type = 1;
-    int32 format = 2;
-    Shape shape = 3;
-    uint64 address = 4;
-    string original_name = 5;
-    int32 original_output_index = 6;
-    int32 original_output_data_type = 7;
-    int32 original_output_format = 8;
-    uint64 size = 9;
-    Shape origin_shape = 10;
-}
-
-message Input {
-    int32 data_type =1;
-    int32 format = 2;
-    Shape shape = 3;
-    uint64 address = 4;
-    uint64 size = 5;
-    Shape origin_shape = 6;
-}
-
-enum BufferType {
-    L1 = 0;
-}
-
-message OpBuffer {
-    BufferType buffer_type = 1;
-    uint64 address = 2;
-    uint64 size = 3;
-}
-
-message Op {
-    string op_name = 1;
-    string op_type = 2;
-}
-
-message Task {
-    uint32 task_id = 1;
-    uint32 stream_id = 2;
-    Op op = 3;
-    repeated Output output = 4;
-    bool end_graph = 5;
-    repeated Input input = 6;
-    repeated OpBuffer buffer = 7;
-}
-
-message OpMappingInfo {
-    string dump_path = 1;
-    oneof model_name_param {
-        string model_name = 2;
-    }
-    oneof model_id_param {
-        uint32 model_id = 3;
-    }
-    oneof step_id {
-        uint64 step_id_addr = 4;
-    }
-    oneof iterations_per_loop {
-        uint64 iterations_per_loop_addr = 5;
-    }
-    oneof loop_cond {
-        uint64 loop_cond_addr = 6;
-    }
-    uint32 flag = 7; // 0x01 load, 0x00 unload
-    repeated Task task = 8;
-    string dump_step = 9;
-}

ge/common/proto/task.proto

@@ -1,179 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-message ModelTaskDef {
-    string version = 1;
-
-    map<string, string> attr = 9; // Extended field
-    repeated TaskDef task = 10;
-
-    uint64 memory_size = 11;
-    uint32 stream_num = 12;
-    uint32 event_num = 13;
-    uint64 weight_size = 14;
-
-    repeated bytes op = 15; // input/output opdef in bytes
-
-    uint64 base_addr = 16;    // base addr
-    uint64 weight_addr = 17;  // weight addr
-    uint32 batch_num = 18;
-}
-
-
-message TaskDef {
-    uint32 id = 1;
-    uint32 type = 2;
-
-    uint32 stream_id = 10;
-    uint32 event_id = 11;
-
-    KernelDef kernel = 20;
-    KernelExDef kernel_ex = 21;
-    KernelHcclDef kernel_hccl = 25;
-    EventExDef event_ex = 26;
-    LogTimeStampDef log_timestamp = 28;
-
-    uint32 label_id = 30;
-
-    MemcpyAsyncDef memcpy_async = 31;
-    StreamSwitchDef stream_switch = 32;
-    StreamActiveDef stream_active = 33;
-    bytes private_def = 34;
-    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
-    StreamSwitchNDef stream_switch_n = 36;
-
-    LabelSetDef label_set = 37;
-    LabelGotoExDef label_goto_ex = 38;
-    LabelSwitchByIndexDef label_switch_by_index = 39;
-    KernelDefWithHandle kernel_with_handle = 40;
-}
-
-message KernelDef {
-    KernelContext context = 1;
-
-    string stub_func = 10;
-    uint32 block_dim = 11;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes sm_desc = 14;
-    bytes flowtable = 15;
-    string so_name = 16;
-    string kernel_name = 17;
-    bytes kernel_ext_info = 18;
-    uint32 kernel_ext_info_size = 19;
-}
-
-message KernelDefWithHandle {
-    KernelContext context = 1;
-
-    uint64 handle = 10;
-    string dev_func = 11;
-    uint32 block_dim = 12;
-    uint32 args_size = 13;
-    bytes args = 14;
-    bytes sm_desc = 15;
-    string original_kernel_key = 16;
-    string node_info = 17;
-}
-
-message KernelContext {
-    uint32 kernel_type = 1;
-    uint32 op_id = 2;                              // OP type in CCE
-    uint32 kernel_func_id = 3;
-    uint32 op_index = 4;                           // TE/Custom operator
-    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
-    bytes args_offset = 6;                         // args offset information
-    uint32 args_count = 7;                         // args count
-    repeated uint32 origin_op_index = 8;
-}
-
-
-message KernelExDef {
-    uint32 flags = 1;
-
-    uint32 op_index = 4;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
-    uint32 task_info_size = 15;
-    bytes kernel_ext_info = 16;
-    uint32 kernel_ext_info_size = 17;
-}
-
-
-message KernelHcclDef {
-    uint32 op_index = 8;
-    string hccl_type = 9;
-}
-
-
-message EventExDef {
-    uint32 op_index = 1;
-    uint32 event_type = 2;
-}
-
-message LogTimeStampDef {
-    uint64 logid = 1;
-    bool notify = 2;
-    uint32 flat = 3;
-}
-
-message MemcpyAsyncDef {
-    uint64 dst = 1;
-    uint64 dst_max = 2;
-    uint64 src = 3;
-    uint64 count = 4;
-    uint32 kind = 5;
-    uint32 op_index = 6;
-}
-
-message StreamSwitchDef {
-    uint32 op_index = 1;
-    uint32 true_stream_id = 2;
-    int64 value = 3;
-    uint64 value_ptr = 4;
-    uint32 data_type = 5;
-}
-
-message StreamActiveDef {
-    uint32 op_index = 1;
-    uint32 active_stream_id = 2;
-}
-
-message StreamSwitchNDef {
-    uint32 op_index = 1;
-    uint32 size = 2;
-    repeated int64 target_value = 3;
-    repeated uint32 true_stream_id = 4;
-    uint32 element_size = 5;
-    uint32 data_type = 6;
-}
-
-message LabelSetDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelGotoExDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelSwitchByIndexDef {
-    uint32 op_index = 1;
-    uint32 label_max = 2;
-}

ge/common/proto/tensorflow/attr_value.proto

@@ -1,70 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "AttrValueProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensor.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing the value for an attr used to configure an Op.
-// Comment indicates the corresponding attr type.  Only the field matching the
-// attr type may be filled.
-message AttrValue {
-  // LINT.IfChange
-  message ListValue {
-    repeated bytes s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated DataType type = 6 [packed = true];  // "list(type)"
-    repeated TensorShapeProto shape = 7;         // "list(shape)"
-    repeated TensorProto tensor = 8;             // "list(tensor)"
-    repeated NameAttrList func = 9;              // "list(attr)"
-  }
-  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
-
-  oneof value {
-    bytes s = 2;                 // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    DataType type = 6;           // "type"
-    TensorShapeProto shape = 7;  // "shape"
-    TensorProto tensor = 8;      // "tensor"
-    ListValue list = 1;          // any "list(...)"
-
-    // "func" represents a function. func.name is a function's name or
-    // a primitive op's name. func.attr.first is the name of an attr
-    // defined for that function. func.attr.second is the value for
-    // that attr in the instantiation.
-    NameAttrList func = 10;
-
-    // This is a placeholder only used in nodes defined inside a
-    // function.  It indicates the attr value will be supplied when
-    // the function is instantiated.  For example, let us suppose a
-    // node "N" in function "FN". "N" has an attr "A" with value
-    // placeholder = "foo". When FN is instantiated with attr "foo"
-    // set to "bar", the instantiated node N's attr A will have been
-    // given the value "bar".
-    string placeholder = 9;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NameAttrList {
-  string name = 1;
-  map<string, AttrValue> attr = 2;
-}

ge/common/proto/tensorflow/function.proto

@@ -1,108 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "FunctionProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "node_def.proto";
-import "op_def.proto";
-
-// A library is a set of named functions.
-message FunctionDefLibrary {
-  repeated FunctionDef function = 1;
-  repeated GradientDef gradient = 2;
-}
-
-// A function can be instantiated when the runtime can bind every attr
-// with a value. When a GraphDef has a call to a function, it must
-// have binding for every attr defined in the signature.
-//   * device spec, etc.
-message FunctionDef {
-  // The definition of the function's name, arguments, return values,
-  // attrs etc.
-  OpDef signature = 1;
-
-  // Attributes specific to this function definition.
-  map<string, AttrValue> attr = 5;
-
-  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
-  reserved 2;
-
-  // In both of the following fields, there is the need to specify an
-  // output that is used as either the input to another node (in
-  // `node_def`) or as a return value of the function (in `ret`).
-  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
-  // list in some cases (instead of just single outputs).  Also, we
-  // need to be able to deal with lists of unknown length (so the
-  // output index may not be known at function definition time).  So
-  // we use the following format instead:
-  // * "fun_in" where "fun_in" is the name of a function input arg in
-  //   the `signature` field above.  This represents that input, whether
-  //   it is a single tensor or a list.
-  // * "fun_in:0" gives the first element of a function input arg (a
-  //   non-list input is considered a list of length 1 for these
-  //   purposes).
-  // * "node:out" where "node" is the name of a node in `node_def` and
-  //   "out" is the name one of its op's output arguments (the name
-  //   comes from the OpDef of the node's op). This represents that
-  //   node's output, whether it is a single tensor or a list.
-  //   Note: We enforce that an op's output arguments are never
-  //   renamed in the backwards-compatibility test.
-  // * "node:out:0" gives the first element of a node output arg (a
-  //   non-list output is considered a list of length 1 for these
-  //   purposes).
-  //
-  // NOT CURRENTLY SUPPORTED (but may be in the future):
-  // * "node:out:-1" gives last element in a node output list
-  // * "node:out:1:" gives a list with all but the first element in a
-  //   node output list
-  // * "node:out::-1" gives a list with all but the last element in a
-  //   node output list
-
-  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
-  // may have values of type `placeholder` and the `input` field uses
-  // the "output" format above.
-
-  // By convention, "op" in node_def is resolved by consulting with a
-  // user-defined library first. If not resolved, "func" is assumed to
-  // be a builtin op.
-  repeated NodeDef node_def = 3;
-
-  // A mapping from the output arg names from `signature` to the
-  // outputs from `node_def` that should be returned by the function.
-  map<string, string> ret = 4;
-}
-
-// GradientDef defines the gradient function of a function defined in
-// a function library.
-//
-// A gradient function g (specified by gradient_func) for a function f
-// (specified by function_name) must follow the following:
-//
-// The function 'f' must be a numerical function which takes N inputs
-// and produces M outputs. Its gradient function 'g', which is a
-// function taking N + M inputs and produces N outputs.
-//
-// I.e. if we have
-//    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
-// then, g is
-//    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
-//                                      dL/dy1, dL/dy2, ..., dL/dy_M),
-// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
-// loss function). dL/dx_i is the partial derivative of L with respect
-// to x_i.
-message GradientDef {
-  string function_name = 1;  // The function name.
-  string gradient_func = 2;  // The gradient function's name.
-}

ge/common/proto/tensorflow/graph.proto

@@ -1,64 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "GraphProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "node_def.proto";
-import "function.proto";
-import "versions.proto";
-
-// Represents the graph of operations
-message GraphDef {
-  repeated NodeDef node = 1;
-
-  // Compatibility versions of the graph.  See core/public/version.h for version
-  // history.  The GraphDef version is distinct from the TensorFlow version, and
-  // each release of TensorFlow will support a range of GraphDef versions.
-  VersionDef versions = 4;
-
-  // Deprecated single version field; use versions above instead.  Since all
-  // GraphDef changes before "versions" was introduced were forward
-  // compatible, this field is entirely ignored.
-  int32 version = 3 [deprecated = true];
-
-  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-  //
-  // "library" provides user-defined functions.
-  //
-  // Naming:
-  //   * library.function.name are in a flat namespace.
-  //     NOTE: We may need to change it to be hierarchical to support
-  //     different orgs. E.g.,
-  //     { "/google/nn", { ... }},
-  //     { "/google/vision", { ... }}
-  //     { "/org_foo/module_bar", { ... }}
-  //     map<string, FunctionDefLib> named_lib;
-  //   * If node[i].op is the name of one function in "library",
-  //     node[i] is deemed as a function call. Otherwise, node[i].op
-  //     must be a primitive operation supported by the runtime.
-  //
-  //
-  // Function call semantics:
-  //
-  //   * The callee may start execution as soon as some of its inputs
-  //     are ready. The caller may want to use Tuple() mechanism to
-  //     ensure all inputs are ready in the same time.
-  //
-  //   * The consumer of return values may start executing as soon as
-  //     the return values the consumer depends on are ready.  The
-  //     consumer may want to use Tuple() mechanism to ensure the
-  //     consumer does not start until all return values of the callee
-  //     function are ready.
-  FunctionDefLibrary library = 2;
-};

ge/common/proto/tensorflow/graph_library.proto

@@ -1,22 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-
-import "graph.proto";
-
-message GeGraphDef {
-  string name = 1;
-  GraphDef graph = 2;
-}
-
-message GraphDefLibrary {
-  repeated GeGraphDef graph_def = 1;
-};

ge/common/proto/tensorflow/node_def.proto

@@ -1,71 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "NodeProto";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-
-message NodeDef {
-  // The name given to this operator. Used for naming inputs,
-  // logging, visualization, etc.  Unique within a single GraphDef.
-  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-  string name = 1;
-
-  // The operation name.  There may be custom parameters in attrs.
-  // Op names starting with an underscore are reserved for internal use.
-  string op = 2;
-
-  // Each input is "node:src_output" with "node" being a string name and
-  // "src_output" indicating which output tensor to use from "node". If
-  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-  // may optionally be followed by control inputs that have the format
-  // "^node".
-  repeated string input = 3;
-
-  // A (possibly partial) specification for the device on which this
-  // node should be placed.
-  // The expected syntax for this string is as follows:
-  //
-  // DEVICE_SPEC ::= PARTIAL_SPEC
-  //
-  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-  // CONSTRAINT ::= ("job:" JOB_NAME)
-  //              | ("replica:" [1-9][0-9]*)
-  //              | ("task:" [1-9][0-9]*)
-  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
-  //
-  // Valid values for this string include:
-  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
-  // * "/job:worker/device:GPU:3"                   (partial specification)
-  // * ""                                    (no specification)
-  //
-  // If the constraints do not resolve to a single device (or if this
-  // field is empty or not present), the runtime will attempt to
-  // choose a device automatically.
-  string device = 4;
-
-  // Operation-specific graph-construction-time configuration.
-  // Note that this should include all attrs defined in the
-  // corresponding OpDef, including those with a value matching
-  // the default -- this allows the default to change and makes
-  // NodeDefs easier to interpret on their own.  However, if
-  // an attr with a default is not specified in this list, the
-  // default will be used.
-  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-  // one of the names from the corresponding OpDef's attr field).
-  // The values must have a type matching the corresponding OpDef
-  // attr's type field.
-  // Add some examples here showing best practices.
-  map<string, AttrValue> attr = 5;
-};

ge/common/proto/tensorflow/op_def.proto

@@ -1,172 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "OpDefProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "types.proto";
-
-// Defines an operation. A NodeDef in a GraphDef specifies an Op by
-// using the "op" field which should match the name of a OpDef.
-// LINT.IfChange
-message OpDef {
-  // Op names starting with an underscore are reserved for internal use.
-  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-  string name = 1;
-
-  // For describing inputs and outputs.
-  message ArgDef {
-    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-    string name = 1;
-
-    // Human readable description.
-    string description = 2;
-
-    // Describes the type of one or more tensors that are accepted/produced
-    // by this input/output arg.  The only legal combinations are:
-    // * For a single tensor: either the "type" field is set or the
-    //   "type_attr" field is set to the name of an attr with type "type".
-    // * For a sequence of tensors with the same type: the "number_attr"
-    //   field will be set to the name of an attr with type "int", and
-    //   either the "type" or "type_attr" field will be set as for
-    //   single tensors.
-    // * For a sequence of tensors, the "type_list_attr" field will be set
-    //   to the name of an attr with type "list(type)".
-    DataType type = 3;
-    string type_attr = 4;    // if specified, attr must have type "type"
-    string number_attr = 5;  // if specified, attr must have type "int"
-    // If specified, attr must have type "list(type)", and none of
-    // type, type_attr, and number_attr may be specified.
-    string type_list_attr = 6;
-
-    // For inputs: if true, the inputs are required to be refs.
-    //   By default, inputs can be either refs or non-refs.
-    // For outputs: if true, outputs are refs, otherwise they are not.
-    bool is_ref = 16;
-  };
-
-  // Description of the input(s).
-  repeated ArgDef input_arg = 2;
-
-  // Description of the output(s).
-  repeated ArgDef output_arg = 3;
-
-  // Description of the graph-construction-time configuration of this
-  // Op.  That is to say, this describes the attr fields that will
-  // be specified in the NodeDef.
-  message AttrDef {
-    // A descriptive name for the argument.  May be used, e.g. by the
-    // Python client, as a keyword argument name, and so should match
-    // the regexp "[a-z][a-z0-9_]+".
-    string name = 1;
-
-    // One of the type names from attr_value.proto ("string", "list(string)",
-    // "int", etc.).
-    string type = 2;
-
-    // A reasonable default for this attribute if the user does not supply
-    // a value.  If not specified, the user must supply a value.
-    AttrValue default_value = 3;
-
-    // Human-readable description.
-    string description = 4;
-
-
-    // --- Constraints ---
-    // These constraints are only in effect if specified.  Default is no
-    // constraints.
-
-    // For type == "int", this is a minimum value.  For "list(___)"
-    // types, this is the minimum length.
-    bool has_minimum = 5;
-    int64 minimum = 6;
-
-    // The set of allowed values.  Has type that is the "list" version
-    // of the "type" field above (uses the "list" field of AttrValue).
-    // If type == "type" or "list(type)" above, then the "type" field
-    // of "allowed_values.list" has the set of allowed DataTypes.
-    // If type == "string" or "list(string)", then the "s" field of
-    // "allowed_values.list" has the set of allowed strings.
-    AttrValue allowed_values = 7;
-  }
-  repeated AttrDef attr = 4;
-
-  // Optional deprecation based on GraphDef versions.
-  OpDeprecation deprecation = 8;
-
-  // One-line human-readable description of what the Op does.
-  string summary = 5;
-
-  // Additional, longer human-readable description of what the Op does.
-  string description = 6;
-
-  // -------------------------------------------------------------------------
-  // Which optimizations this operation can participate in.
-
-  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-  bool is_commutative = 18;
-
-  // If is_aggregate is true, then this operation accepts N >= 2
-  // inputs and produces 1 output all of the same type.  Should be
-  // associative and commutative, and produce output with the same
-  // shape as the input.  The optimizer may replace an aggregate op
-  // taking input from multiple devices with a tree of aggregate ops
-  // that aggregate locally within each device (and possibly within
-  // groups of nearby devices) before communicating.
-  bool is_aggregate = 16;  // for things like add
-
-  // Other optimizations go here, like
-  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.
-
-  // -------------------------------------------------------------------------
-  // Optimization constraints.
-
-  // Ops are marked as stateful if their behavior depends on some state beyond
-  // their input tensors (e.g. variable reading op) or if they have
-  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
-  // must always produce the same output for the same input and have
-  // no side-effects.
-  //
-  // By default Ops may be moved between devices.  Stateful ops should
-  // either not be moved, or should only be moved if that state can also
-  // be moved (e.g. via some sort of save / restore).
-  // Stateful ops are guaranteed to never be optimized away by Common
-  // Subexpression Elimination (CSE).
-  bool is_stateful = 17;  // for things like variables, queue
-
-  // -------------------------------------------------------------------------
-  // Non-standard options.
-
-  // By default, all inputs to an Op must be initialized Tensors.  Ops
-  // that may initialize tensors for the first time should set this
-  // field to true, to allow the Op to take an uninitialized Tensor as
-  // input.
-  bool allows_uninitialized_input = 19;  // for Assign, etc.
-};
-// LINT.ThenChange(
-//     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)
-
-// Information about version-dependent deprecation of an op
-message OpDeprecation {
-  // First GraphDef version at which the op is disallowed.
-  int32 version = 1;
-
-  // Explanation of why it was deprecated and what to use instead.
-  string explanation = 2;
-};
-
-// A collection of OpDefs
-message OpList {
-  repeated OpDef op = 1;
-};

ge/common/proto/tensorflow/resource_handle.proto

@@ -1,37 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "ResourceHandle";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Protocol buffer representing a handle to a tensorflow resource. Handles are
-// not valid across executions, but can be serialized back and forth from within
-// a single run.
-message ResourceHandleProto {
-  // Unique name for the device containing the resource.
-  string device = 1;
-
-  // Container in which this resource is placed.
-  string container = 2;
-
-  // Unique name of this resource.
-  string name = 3;
-
-  // Hash code for the type of the resource. Is only valid in the same device
-  // and in the same execution.
-  uint64 hash_code = 4;
-
-  // For debug-only, the name of the type pointed to by this handle, if
-  // available.
-  string maybe_type_name = 5;
-};

ge/common/proto/tensorflow/tensor.proto

@@ -1,102 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "resource_handle.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing a tensor.
-message TensorProto {
-  DataType dtype = 1;
-
-  // Shape of the tensor.
-  TensorShapeProto tensor_shape = 2;
-
-  // Only one of the representations below is set, one of "tensor_contents" and
-  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
-  // contain repeated fields it would require another extra set of messages.
-
-  // Version number.
-  //
-  // In version 0, if the "repeated xxx" representations contain only one
-  // element, that element is repeated to fill the shape.  This makes it easy
-  // to represent a constant Tensor with a single value.
-  int32 version_number = 3;
-
-  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
-  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
-  // can be used for all tensor types. The purpose of this representation is to
-  // reduce serialization overhead during RPC call by avoiding serialization of
-  // many repeated small items.
-  bytes tensor_content = 4;
-
-  // Type specific representations that make it easy to create tensor protos in
-  // all languages.  Only the representation corresponding to "dtype" can
-  // be set.  The values hold the flattened representation of the tensor in
-  // row major order.
-
-  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
-  // have some pointless zero padding for each value here.
-  repeated int32 half_val = 13 [packed = true];
-
-  // DT_FLOAT.
-  repeated float float_val = 5 [packed = true];
-
-  // DT_DOUBLE.
-  repeated double double_val = 6 [packed = true];
-
-  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
-  repeated int32 int_val = 7 [packed = true];
-
-  // DT_STRING
-  repeated bytes string_val = 8;
-
-  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
-  // and imaginary parts of i-th single precision complex.
-  repeated float scomplex_val = 9 [packed = true];
-
-  // DT_INT64
-  repeated int64 int64_val = 10 [packed = true];
-
-  // DT_BOOL
-  repeated bool bool_val = 11 [packed = true];
-
-  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
-  // and imaginary parts of i-th double precision complex.
-  repeated double dcomplex_val = 12 [packed = true];
-
-  // DT_RESOURCE
-  repeated ResourceHandleProto resource_handle_val = 14;
-
-  // DT_VARIANT
-  repeated VariantTensorDataProto variant_val = 15;
-
-  // DT_UINT32
-  repeated uint32 uint32_val = 16 [packed = true];
-
-  // DT_UINT64
-  repeated uint64 uint64_val = 17 [packed = true];
-};
-
-// Protocol buffer representing the serialization format of DT_VARIANT tensors.
-message VariantTensorDataProto {
-  // Name of the type of objects being serialized.
-  string type_name = 1;
-  // Portions of the object that are not Tensors.
-  bytes metadata = 2;
-  // Tensors contained within objects being serialized.
-  repeated TensorProto tensors = 3;
-}

ge/common/proto/tensorflow/tensor_shape.proto

@@ -1,53 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-// Protocol buffer representing the shape of tensors.
-
-syntax = "proto3";
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorShapeProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-package domi.tensorflow;
-
-// Dimensions of a tensor.
-message TensorShapeProto {
-  // One dimension of the tensor.
-  message Dim {
-    // Size of the tensor in that dimension.
-    // This value must be >= -1, but values of -1 are reserved for "unknown"
-    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
-    // that work with TensorShapeProto may fail at runtime when deserializing
-    // a TensorShapeProto containing a dim value of -1.
-    int64 size = 1;
-
-    // Optional name of the tensor dimension.
-    string name = 2;
-  };
-
-  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
-  // for a 30 x 40 2D tensor.  If an entry has size -1, this
-  // corresponds to a dimension of unknown size. The names are
-  // optional.
-  //
-  // The order of entries in "dim" matters: It indicates the layout of the
-  // values in the tensor in-memory representation.
-  //
-  // The first entry in "dim" is the outermost dimension used to layout the
-  // values, the last entry is the innermost dimension.  This matches the
-  // in-memory layout of RowMajor Eigen tensors.
-  //
-  // If "dim.size()" > 0, "unknown_rank" must be false.
-  repeated Dim dim = 2;
-
-  // If true, the number of dimensions in the shape is unknown.
-  //
-  // If true, "dim.size()" must be 0.
-  bool unknown_rank = 3;
-};

ge/common/proto/tensorflow/types.proto

@@ -1,82 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TypesProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// LINT.IfChange
-enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0;
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1;
-  DT_DOUBLE = 2;
-  DT_INT32 = 3;
-  DT_UINT8 = 4;
-  DT_INT16 = 5;
-  DT_INT8 = 6;
-  DT_STRING = 7;
-  DT_COMPLEX64 = 8;  // Single-precision complex
-  DT_INT64 = 9;
-  DT_BOOL = 10;
-  DT_QINT8 = 11;     // Quantized int8
-  DT_QUINT8 = 12;    // Quantized uint8
-  DT_QINT32 = 13;    // Quantized int32
-  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
-  DT_QINT16 = 15;    // Quantized int16
-  DT_QUINT16 = 16;   // Quantized uint16
-  DT_UINT16 = 17;
-  DT_COMPLEX128 = 18;  // Double-precision complex
-  DT_HALF = 19;
-  DT_RESOURCE = 20;
-  DT_VARIANT = 21;  // Arbitrary C++ data types
-  DT_UINT32 = 22;
-  DT_UINT64 = 23;
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101;
-  DT_DOUBLE_REF = 102;
-  DT_INT32_REF = 103;
-  DT_UINT8_REF = 104;
-  DT_INT16_REF = 105;
-  DT_INT8_REF = 106;
-  DT_STRING_REF = 107;
-  DT_COMPLEX64_REF = 108;
-  DT_INT64_REF = 109;
-  DT_BOOL_REF = 110;
-  DT_QINT8_REF = 111;
-  DT_QUINT8_REF = 112;
-  DT_QINT32_REF = 113;
-  DT_BFLOAT16_REF = 114;
-  DT_QINT16_REF = 115;
-  DT_QUINT16_REF = 116;
-  DT_UINT16_REF = 117;
-  DT_COMPLEX128_REF = 118;
-  DT_HALF_REF = 119;
-  DT_RESOURCE_REF = 120;
-  DT_VARIANT_REF = 121;
-  DT_UINT32_REF = 122;
-  DT_UINT64_REF = 123;
-}
-// LINT.ThenChange(
-//    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
-//    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)

ge/common/proto/tensorflow/versions.proto

@@ -1,39 +0,0 @@
-/**
- * This file is part of Open Source Software TensorFlow, version 1.15.0 https://github.com/tensorflow/tensorflow
- *
- * This file is included by GraphEngine so as to support model format conversion from tensorflow model to GraphEngine model.
- * This file in this distribution may have been modified by Huawei Technologies Co., Ltd ("Huawei Modifications").
- * All Huawei Modifications are Copyright 2019-2020 Huawei Technologies Co., Ltd.
- */
-
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "VersionsProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Version information for a piece of serialized data
-//
-// There are different types of versions for each type of data
-// (GraphDef, etc.), but they all have the same common shape
-// described here.
-//
-// Each consumer has "consumer" and "min_producer" versions (specified
-// elsewhere).  A consumer is allowed to consume this data if
-//
-//   producer >= min_producer
-//   consumer >= min_consumer
-//   consumer not in bad_consumers
-//
-message VersionDef {
-  // The version of the code that produced this data.
-  int32 producer = 1;
-
-  // Any consumer below this version is not allowed to consume this data.
-  int32 min_consumer = 2;
-
-  // Specific consumer versions which are disallowed (e.g. due to bugs).
-  repeated int32 bad_consumers = 3;
-};

ge/executor/CMakeLists.txt

@@ -37,6 +37,7 @@ set(SRC_LIST
     "../graph/load/model_manager/task_info/task_info.cc"
     "../graph/load/model_manager/task_info/event_record_task_info.cc"
     "../graph/load/model_manager/task_info/event_wait_task_info.cc"
+    "../graph/load/model_manager/task_info/ffts_task_info.cc"
     "../graph/load/model_manager/task_info/fusion_start_task_info.cc"
     "../graph/load/model_manager/task_info/fusion_stop_task_info.cc"
     "../graph/load/model_manager/task_info/kernel_ex_task_info.cc"

ge/executor/proto/dump_task.proto

@@ -1,113 +0,0 @@
-syntax = "proto3";
-package toolkit.dump;
-
-enum OutputDataType {
-    DT_UNDEFINED = 0;
-    DT_FLOAT = 1;
-    DT_FLOAT16 = 2;
-    DT_INT8 = 3;
-    DT_UINT8 = 4;
-    DT_INT16 = 5;
-    DT_UINT16 = 6;
-    DT_INT32 = 7;
-    DT_INT64 = 8;
-    DT_UINT32 = 9;
-    DT_UINT64 = 10;
-    DT_BOOL = 11;
-    DT_DOUBLE = 12;
-    DT_STRING = 13;
-    DT_DUAL_SUB_INT8 = 14;
-    DT_DUAL_SUB_UINT8 = 15;
-    DT_COMPLEX64 = 16;
-    DT_COMPLEX128 = 17;
-    DT_QINT8 = 18;
-    DT_QINT16 = 19;
-    DT_QINT32 = 20;
-    DT_QUINT8 = 21;
-    DT_QUINT16 = 22;
-    DT_RESOURCE = 23;
-    DT_STRING_REF = 24;
-    DT_DUAL = 25;
-    DT_VARIANT = 26;
-}
-
-enum OutputFormat {
-    FORMAT_NCHW = 0;
-    FORMAT_NHWC = 1;
-    FORMAT_ND = 2;
-    FORMAT_NC1HWC0 = 3;
-    FORMAT_FRACTAL_Z = 4;
-    FORMAT_NC1C0HWPAD = 5;
-    FORMAT_NHWC1C0 = 6;
-    FORMAT_FSR_NCHW = 7;
-    FORMAT_FRACTAL_DECONV = 8;
-    FORMAT_C1HWNC0 = 9;
-    FORMAT_FRACTAL_DECONV_TRANSPOSE = 10;
-    FORMAT_FRACTAL_DECONV_SP_STRIDE_TRANS = 11;
-    FORMAT_NC1HWC0_C04 = 12;
-    FORMAT_FRACTAL_Z_C04 = 13;
-    FORMAT_CHWN = 14;
-    FORMAT_FRACTAL_DECONV_SP_STRIDE8_TRANS = 15;
-    FORMAT_HWCN = 16;
-    FORMAT_NC1KHKWHWC0 = 17;
-    FORMAT_BN_WEIGHT = 18;
-    FORMAT_FILTER_HWCK = 19;
-    FORMAT_HASHTABLE_LOOKUP_LOOKUPS=20;
-    FORMAT_HASHTABLE_LOOKUP_KEYS = 21;
-    FORMAT_HASHTABLE_LOOKUP_VALUE = 22;
-    FORMAT_HASHTABLE_LOOKUP_OUTPUT = 23;
-    FORMAT_HASHTABLE_LOOKUP_HITS=24;
-    FORMAT_C1HWNCoC0 = 25;
-    FORMAT_MD = 26;
-    FORMAT_NDHWC = 27;
-    FORMAT_FRACTAL_ZZ = 28;
-    FORMAT_FRACTAL_NZ = 29;
-    FORMAT_RESERVED = 30;
-}
-
-message OriginalOp {
-    string name = 1;
-    uint32 output_index = 2;
-    OutputDataType data_type = 3;
-    OutputFormat format = 4;
-}
-
-message Shape {
-    repeated uint64 dim = 1;
-}
-
-message OpOutput {
-    OutputDataType data_type = 1;
-    OutputFormat format = 2;
-    Shape shape = 3;
-    OriginalOp original_op = 4;  // the original op corresponding to the output
-    bytes data = 5;
-    uint64 size = 6;
-}
-
-message OpInput {
-    OutputDataType data_type = 1;
-    OutputFormat format = 2;
-    Shape shape = 3;
-    bytes data = 4;
-    uint64 size = 5;
-}
-
-enum BufferType {
-    L1 = 0;
-}
-
-message OpBuffer {
-    BufferType buffer_type = 1;
-    bytes data = 2;
-    uint64 size = 3;
-}
-
-message DumpData{
-    string version = 1;
-    uint64 dump_time = 2;
-    repeated OpOutput output = 3;
-    repeated OpInput input = 4;
-    repeated OpBuffer buffer = 5;
-    string op_name = 6;
-}

ge/executor/proto/ge_ir.proto

@@ -1,193 +0,0 @@
-syntax = "proto3";
-
-package ge.proto;
-
-enum DataType
-{
-    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
-    DT_FLOAT     = 1;  // float type
-    DT_FLOAT16   = 2;  // fp16 type
-    DT_INT8      = 3;  // int8 type
-    DT_UINT8     = 4;  // uint8 type
-    DT_INT16     = 5;  // int16 type
-    DT_UINT16    = 6;  // uint16 type
-    DT_INT32     = 7;  //
-    DT_INT64     = 8;  // int64 type
-    DT_UINT32    = 9;  // unsigned int32
-    DT_UINT64    = 10;  // unsigned int64
-    DT_BOOL      = 11;  // bool type
-    DT_DOUBLE    = 12; // double type
-    DT_STRING = 13;            // string type
-    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
-    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
-    DT_COMPLEX64 = 16;         // complex64 type
-    DT_COMPLEX128 = 17;        // complex128 type
-    DT_QINT8 = 18;             // qint8 type
-    DT_QINT16 = 19;            // qint16 type
-    DT_QINT32 = 20;            // qint32 type
-    DT_QUINT8 = 21;            // quint8 type
-    DT_QUINT16 = 22;           // quint16 type
-    DT_RESOURCE  = 23;         // resource type
-    DT_STRING_REF = 24;        // string_ref type
-    DT_DUAL      = 25;              /**< dual output type */
-    DT_VARIANT = 26;           // variant type
-    DT_BF16 = 27;              // bf16 type
-    DT_INT4 = 28;              // int4 type
-}
-
-message AttrDef
-{
-    message ListValue
-    {
-        enum ListValueType{
-          VT_LIST_NONE = 0;
-          VT_LIST_STRING = 1;
-          VT_LIST_INT = 2;
-          VT_LIST_FLOAT = 3;
-          VT_LIST_BOOL = 4;
-          VT_LIST_BYTES = 5;
-          VT_LIST_TENSOR_DESC = 6;
-          VT_LIST_TENSOR = 7;
-          VT_LIST_GRAPH = 8;
-          VT_LIST_NAMED_ATTRS = 9;
-          VT_LIST_DATA_TYPE = 10;
-        }
-        repeated bytes s             = 2;                    // "list(string)"
-        repeated int64 i             = 3;  // "list(int)"
-        repeated float f             = 4;   // "list(float)"
-        repeated bool  b             = 5;  // "list(bool)"
-        repeated bytes bt            = 7;
-        repeated TensorDescriptor td = 8;
-        repeated TensorDef t         = 9;
-        repeated GraphDef g          = 10;
-	    repeated NamedAttrs na       = 11;
-	    repeated int64 dt            = 12; // list ge::DataType
-
-	    ListValueType val_type       = 20;
-    }
-
-    message ListListInt{
-        message ListInt{
-            repeated int64 list_i             = 1; // list int
-        }
-        repeated ListInt list_list_i             = 1; // list list int
-    }
-
-    oneof value
-    {
-        bytes            s    = 2;  // "string"
-        int64            i    = 3;  // "int"
-        float            f    = 4;  // "float"
-        bool             b    = 5;  // "bool"
-        bytes            bt   = 7;
-        ListValue        list = 1;   // any "list(...)"
-        NamedAttrs       func = 10;  // Used to support attr nesting
-        TensorDescriptor td   = 11;  // GeTensorDesc type
-        TensorDef        t    = 12;  // GeTensor type
-        GraphDef         g    = 13;  // Graph type
-        ListListInt      list_list_int  = 14;  // List List Int type
-        int64            dt   = 15; // ge::DataType
-    }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs
-{
-    string               name = 1;
-    map<string, AttrDef> attr = 2;
-}
-
-// Shape / dimension description, using row-major order
-message ShapeDef
-{
-    repeated int64 dim = 1;  // Size of each dimension
-}
-
-// Multidimensional data description
-message TensorDescriptor
-{
-    string   name   = 1;  // Optional parameter, tensor name
-
-    DataType dtype  = 2;  // tensor datatype
-    ShapeDef shape  = 3;  // Shape / dimension
-    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"
-
-    bool has_out_attr = 9;
-    int64 size = 10;
-    int64 weight_size = 11;
-    bool reuse_input = 12;
-    bool output_tensor = 13;
-    string device_type = 14;
-    bool input_tensor =15;
-    int64 real_dim_cnt = 16;
-    int64 reuse_input_index = 17;
-    int64 data_offset = 18;
-    int64 cmps_size = 19;
-    string cmps_tab = 20;
-    int64 cmps_tab_offset = 21;
-
-	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
-}
-
-// GeTensor definition
-message TensorDef
-{
-    TensorDescriptor desc = 1;  // Tensor description
-    bytes            data = 2;  // Tensor data
-}
-
-
-// Operator description
-message OpDef
-{
-    string name = 1;  // name
-    string type = 2;  // type
-
-    repeated string input = 5;  // input original op name + outgoing index. op_name：index
-
-    map<string, AttrDef> attr = 10;  // Set of operator parameter fields
-
-    bool has_out_attr = 20;
-    int64 id = 21;
-    int64 stream_id =22;
-    repeated string input_name = 23;
-    repeated string src_name = 24;
-    repeated int64 src_index = 25;
-    repeated string dst_name = 26;
-    repeated int64 dst_index = 27;
-    repeated int64 input_i = 28;
-    repeated int64 output_i = 29;
-    repeated int64 workspace = 30;
-    repeated int64 workspace_bytes = 31;
-    repeated bool is_input_const = 32;
-    repeated TensorDescriptor input_desc = 33;
-    repeated TensorDescriptor output_desc = 34;
-    repeated string subgraph_name = 35;
-}
-
-// Graph definition
-message GraphDef
-{
-    string name   = 1;   //  name
-
-    repeated string input  = 4;  // Graph input
-    repeated string output = 5;  // Graph output
-
-    repeated OpDef op      = 6;  // List of operators
-
-	map<string, AttrDef> attr = 11;  // Extended field
-}
-
-// model definition
-message ModelDef
-{
-	string name         = 1;  // name
-	uint32 version      = 2;  // IR Proto verion
-	string custom_version = 3;  // User model version number, passed in by user
-
-    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef
-
-    map<string, AttrDef> attr = 11;  // Extended field
-}
-

ge/executor/proto/insert_op.proto

@@ -1,140 +0,0 @@
-syntax = "proto3";
-
-package domi;
-
-message InsertNewOps {
-	repeated AippOpParams aipp_op = 1;
-	repeated MultiShapeOpParams multi_shape_op = 2;
-}
-
-message AippOpParams {
-	enum InputFormat {
-		UNDEFINED = 0;
-		YUV420SP_U8 = 1;
-		XRGB8888_U8 = 2;
-		RGB888_U8   = 3;
-		YUV400_U8   = 4;
-		NC1HWC0DI_FP16 = 5;
-		NC1HWC0DI_S8 = 6;
-		ARGB8888_U8 = 7;
-		YUYV_U8 = 8;
-		YUV422SP_U8 = 9;
-		AYUV444_U8 = 10;
-		RAW10 = 11;
-		RAW12 = 12;
-		RAW16 = 13;
-		RAW24 = 14;
-		RGB16 = 15;
-		RGB20 = 16;
-		RGB24 = 17;
-		RGB8_IR = 18;
-		RGB16_IR = 19;
-		RGB24_IR = 20;
-	}
-
-	enum AippMode {
-		undefined = 0;
-		static = 1;
-		dynamic = 2;
-	}
-
-	// AIPP模式，区分静态AIPP和动态AIPP
-	AippMode aipp_mode = 1;
-
-	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
-	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
-	uint32 related_input_rank = 2;
-
-        // related_input_name is optional and the top name of data node which inserts aipp
-        string related_input_name = 6;
-
-	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
-	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
-	// 配置值 <= Data算子输出边的个数。
-	repeated uint32 input_edge_idx = 3;
-
-	// [Begin] 动态AIPP参数，配置静态AIPP时无效
-	uint32 max_src_image_size = 4;
-
-	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
-	bool support_rotation = 5;
-
-	// [End] 动态AIPP参数
-
-
-	// [Begin] 静态AIPP参数，配置动态AIPP时无效
-	InputFormat input_format = 51;
-	bool csc_switch = 52;
-	float cpadding_value = 53;
-	bool rbuv_swap_switch = 54;
-	bool ax_swap_switch = 55;
-	bool single_line_mode = 56;
-
-	int32 src_image_size_w = 57;
-	int32 src_image_size_h = 58;
-
-	bool crop = 59;
-	int32 load_start_pos_w = 60;
-	int32 load_start_pos_h = 61;
-	int32 crop_size_w = 62;
-	int32 crop_size_h = 63;
-
-	bool resize = 64;
-	int32 resize_output_w = 65;
-	int32 resize_output_h = 66;
-
-	bool padding = 67;
-	int32 left_padding_size = 68;
-	int32 right_padding_size = 69;
-	int32 top_padding_size = 70;
-	int32 bottom_padding_size = 71;
-	float padding_value = 72;
-
-	int32 mean_chn_0 = 10;
-	int32 mean_chn_1 = 11;
-	int32 mean_chn_2 = 12;
-	int32 mean_chn_3 = 19;
-	float min_chn_0 = 13;
-	float min_chn_1 = 14;
-	float min_chn_2 = 15;
-	float min_chn_3 = 20;
-	repeated float var_reci_chn_0 = 16;
-	repeated float var_reci_chn_1 = 17;
-	repeated float var_reci_chn_2 = 18;
-	repeated float var_reci_chn_3 = 21;
-
-	repeated int32 matrix_r0c0 = 30;
-	repeated int32 matrix_r0c1 = 31;
-	repeated int32 matrix_r0c2 = 32;
-	repeated int32 matrix_r1c0 = 33;
-	repeated int32 matrix_r1c1 = 34;
-	repeated int32 matrix_r1c2 = 35;
-	repeated int32 matrix_r2c0 = 36;
-	repeated int32 matrix_r2c1 = 37;
-	repeated int32 matrix_r2c2 = 38;
-	repeated int32 output_bias_0 = 39;
-	repeated int32 output_bias_1 = 40;
-	repeated int32 output_bias_2 = 41;
-	repeated int32 input_bias_0 = 42;
-	repeated int32 input_bias_1 = 43;
-	repeated int32 input_bias_2 = 44;
-
-	// [End] 静态AIPP参数
-
-       // The n number that is used for raw/rgbir data into f16 transformation.
-       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
-       uint32 raw_rgbir_to_f16_n = 45;
-}
-
-message MultiShapeOpParams {
-	enum MultiShapeMode {
-		batch      = 0;             //动态batch
-		resolution = 1;             //动态分辨率，扩展用
-	}
-
-	MultiShapeMode    mode                      = 1;        //算子模式
-	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入
-
-
-	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
-}

ge/executor/proto/om.proto

@@ -1,396 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-enum TargetType
-{
-    MINI = 0;
-    TINY = 1;
-    LITE = 2;
-}
-
-// offline model
-message ModelDef {
-    string name = 1;
-    uint32 version = 2;
-
-    uint64 memory_size = 10;
-    uint32 stream_num = 11;
-    uint32 event_num = 12;
-    uint64 weight_size = 13;
-    uint32 label_num = 15;
-    repeated OpDef op = 20;
-    TargetType target_type = 23;
-
-    map<string, AttrDef> attr = 30;
-};
-
-// operator define
-message OpDef {
-    string name = 1;
-    string type = 2;
-
-    uint32 id = 3;
-    uint32 stream_id = 4;
-
-    repeated string input_name = 5;
-
-    repeated string src_name = 8;
-    repeated int32 src_index = 9;
-    repeated int64 input = 10;
-    repeated int64 output = 11;
-    repeated TensorDescriptor input_desc = 12;
-    repeated TensorDescriptor output_desc = 13;
-    repeated WeightDef weights = 14;
-    repeated string dst_name = 15;
-    repeated int32 dst_index = 16;
-
-    repeated int64 workspace = 20;
-    repeated uint32 workspace_bytes = 21;
-
-    repeated string weight_name = 22;
-    repeated bool is_input_const = 23;
-
-    map<string, AttrDef> attr = 30;
-
-    QuantizeFactorParams quantize_factor = 31;
-
-    oneof op_params {
-        // start at 100 here
-        SendOpParams sender_param = 100;
-        RecvOpParams receiver_param = 200;
-        ConvolutionOpParams convolution_param = 300;
-        PoolingOpParams pooling_param = 400;
-        EltwiseOpParams eltwise_param = 500;
-        BatchNormOpParams batchnorm_param = 600;
-        ScaleOpParams scale_param = 700;
-        FullConnectionOpParams full_connection_param = 800;
-        SoftmaxOpParams softmax_param = 900;
-        ActivationOpParams activation_param = 1000;
-        ReshapeOpParams reshape_param = 1100;
-    }
-};
-
-message SendOpParams {
-    uint32 event_id = 1;
-};
-
-message RecvOpParams {
-    uint32 event_id = 1;
-};
-
-enum QuantizeScaleType
-{
-    VECTOR_SCALE = 0;
-    SCALAR_SCALE = 1;
-}
-
-enum QuantizeScaleMode
-{
-    NORMAL_MODE = 0;
-    SQRT_MODE = 1;
-}
-
-enum QuantizeAlgorithm
-{
-    NON_OFFSET_ALGO = 0;
-    HALF_OFFSET_ALGO = 1;
-    ALL_OFFSET_ALGO = 2;
-}
-message QuantizeFactor
-{
-    QuantizeScaleMode scale_mode = 1;
-    bytes scale_value = 2;
-    int64 scale_offset = 3;
-    bytes offset_data_value = 4;
-    int64 offset_data_offset = 5;
-    bytes offset_weight_value = 6;
-    int64 offset_weight_offset = 7;
-    bytes offset_pad_value = 8;
-    int64 offset_pad_offset = 9;
-};
-
-message QuantizeCalcFactor
-{
-    bytes offsetw = 1;
-    int64 offsetw_offset = 2;
-    bytes offsetd = 3;
-    int64 offsetd_offset = 4;
-    bytes scalereq = 5;
-    int64 scaledreq_offset = 6;
-    bytes offsetdnext = 7;
-    int64 offsetdnext_offset = 8;
-}
-
-message QuantizeFactorParams
-{
-    QuantizeAlgorithm quantize_algo = 1;
-    QuantizeScaleType scale_type = 2;
-    QuantizeFactor quantize_param = 3;
-    QuantizeFactor dequantize_param = 4;
-    QuantizeFactor requantize_param = 5;
-    QuantizeCalcFactor quantizecalc_param = 6;
-};
-
-message ConvolutionOpParams {
-    int32 mode = 1;
-    int32 algo = 2;
-    int32 pad_mode = 3;
-    uint32 group = 4;
-    uint32 num_output = 5;
-
-    repeated uint32 pad = 10;
-    repeated uint32 stride = 11;
-    repeated uint32 dilation = 12;
-    repeated uint32 kernel = 13;
-
-    float alpha = 20;
-    float beta = 21;
-
-    WeightDef filter = 40;
-    WeightDef bias = 41;
-
-    bool relu_flag = 62;
-    repeated uint32 adj = 70;
-    repeated uint32 target_shape = 71;
-    repeated uint32 before_pad = 72;
-};
-
-message PoolingOpParams {
-    int32 mode = 1;
-    int32 nan_opt = 2;
-    int32 pad_mode = 3;
-    bool global_pooling = 4;
-
-    repeated uint32 window = 10;
-    repeated uint32 pad = 11;
-    repeated uint32 stride = 12;
-    bool ceil_mode = 13;
-    int32 data_mode  = 14;
-
-    float alpha = 20;
-    float beta = 21;
-    repeated uint32 before_pad = 22;
-};
-
-message EltwiseOpParams {
-    int32 mode = 1;
-    repeated float coeff = 2;
-    float alpha = 3;
-    float beta = 4;
-    repeated WeightDef weight = 5;
-    bool relu_flag = 6;
-};
-
-message ActivationOpParams {
-    int32 mode = 1;
-    float coef = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message BatchNormOpParams {
-    int32 mode = 1;
-
-    float alpha = 2;
-    float beta = 3;
-    double epsilon = 4;//optinal,[default = 1e-5]
-    bool use_global_stats = 5; //optinal,by default true,testing mode
-    float  moving_average_fraction = 6; //optinal,[default = .999];
-
-    WeightDef estimated_mean = 7;
-    WeightDef estimated_variance = 8;
-
-    WeightDef scale = 9;
-    WeightDef bias = 10;
-};
-
-message ScaleOpParams {
-    WeightDef scale = 1;
-    WeightDef bias = 2;
-};
-
-message ReshapeOpParams {
-    float alpha = 1;
-    float beta = 2;
-    ShapeDef shape = 3;
-    int32 axis = 4;
-    int32 num_axes = 5;
-    int32 format = 6;
-};
-
-message SoftmaxOpParams {
-    int32 algo = 1;
-    int32 mode = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message FullConnectionOpParams {
-    WeightDef filter = 1;
-    WeightDef bias = 2;
-    uint32 num_output = 3;
-    bool relu_flag = 12;
-};
-
-message FlattenOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 start_axis = 3;
-    int32 end_axis = 4;
-}
-
-message AddLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message AddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message SubOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message BiasAddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    WeightDef bias = 10;
-};
-
-message MatMulOpParams {
-    float alpha = 1;
-    float beta = 2;
-    bool transposeX = 3;
-    bool transposeW = 4;
-
-    WeightDef filter = 10;
-    WeightDef bias = 12;
-};
-
-message RsqrtOpParams {
-    float alpha = 1;
-    float beta = 2;
-};
-
-
-message WeightDef {
-    int32 format = 1;
-    int32 data_type = 2;
-    ShapeDef shape = 3;
-    bytes data = 4;
-    int64 data_offset = 5;
-    uint32 cmps_size = 6;
-    bytes cmps_tab = 7;
-    int64 cmps_tab_offset = 10;
-    CompressInfo cmps_info = 8;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
-}
-
-message ShapeDef {
-    repeated int64 dim = 1;
-}
-
-enum DeviceType {
-  NPU = 0;                   // In default, we will use NPU.
-  CPU = 1;                   // CPU
-}
-
-message AllOffsetQuantizeInfo {
-	float scale  = 1;
-	int32 offset = 2;
-} 
-
-message TensorDescriptor {
-    int32 format = 1;
-    int32 data_type = 2;
-    repeated int64 dim = 3;
-    uint32 size = 4;
-    bool reuse_input = 5;
-    bool output_tensor = 7;
-    DeviceType device_type = 8;
-    bool input_tensor = 9;
-    uint32 real_dim_cnt = 10;
-    uint32 reuse_input_index = 11;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
-}
-
-message CompressInfo {
-    int32 blockRow = 1;                             // block row
-    int32 blockCol = 2;                             // block col
-    int32 fractalK = 3;                             // fractal K
-    int32 fractalN = 4;                             // fractal N
-    int32 lastFractalK = 5;                         // K of last fractal
-    int32 lastFractalN = 6;                         // N of last fractal
-    int32 cubeSize = 7;                             // cube's length
-    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
-}
-
-message AttrDef {
-  message ListValue {
-    repeated string s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated uint32 u = 6 [packed = true];         // "list(uint)"
-    repeated bytes bt = 7;
-  }
-
-  oneof value {
-    string s = 2;                // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    uint32 u = 6;                // "uint32"
-    bytes bt = 7;
-    ListValue list = 1;          // any "list(...)"
-    NamedAttrs func = 10;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs {
-  string name = 1;
-  map<string, AttrDef> attr = 2;
-}
-

ge/executor/proto/op_mapping.proto

@@ -1,75 +0,0 @@
-syntax = "proto3";
-package toolkit.aicpu.dump;
-
-message Shape {
-    repeated uint64 dim = 1;
-}
-
-message Output {
-    int32 data_type = 1;
-    int32 format = 2;
-    Shape shape = 3;
-    uint64 address = 4;
-    string original_name = 5;
-    int32 original_output_index = 6;
-    int32 original_output_data_type = 7;
-    int32 original_output_format = 8;
-    uint64 size = 9;
-    Shape origin_shape = 10;
-}
-
-message Input {
-    int32 data_type =1;
-    int32 format = 2;
-    Shape shape = 3;
-    uint64 address = 4;
-    uint64 size = 5;
-    Shape origin_shape = 6;
-}
-
-enum BufferType {
-    L1 = 0;
-}
-
-message OpBuffer {
-    BufferType buffer_type = 1;
-    uint64 address = 2;
-    uint64 size = 3;
-}
-
-message Op {
-    string op_name = 1;
-    string op_type = 2;
-}
-
-message Task {
-    uint32 task_id = 1;
-    uint32 stream_id = 2;
-    Op op = 3;
-    repeated Output output = 4;
-    bool end_graph = 5;
-    repeated Input input = 6;
-    repeated OpBuffer buffer = 7;
-}
-
-message OpMappingInfo {
-    string dump_path = 1;
-    oneof model_name_param {
-        string model_name = 2;
-    }
-    oneof model_id_param {
-        uint32 model_id = 3;
-    }
-    oneof step_id {
-        uint64 step_id_addr = 4;
-    }
-    oneof iterations_per_loop {
-        uint64 iterations_per_loop_addr = 5;
-    }
-    oneof loop_cond {
-        uint64 loop_cond_addr = 6;
-    }
-    uint32 flag = 7; // 0x01 load, 0x00 unload
-    repeated Task task = 8;
-    string dump_step = 9;
-}

ge/executor/proto/task.proto

@@ -1,179 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-message ModelTaskDef {
-    string version = 1;
-
-    map<string, string> attr = 9; // Extended field
-    repeated TaskDef task = 10;
-
-    uint64 memory_size = 11;
-    uint32 stream_num = 12;
-    uint32 event_num = 13;
-    uint64 weight_size = 14;
-
-    repeated bytes op = 15; // input/output opdef in bytes
-
-    uint64 base_addr = 16;    // base addr
-    uint64 weight_addr = 17;  // weight addr
-    uint32 batch_num = 18;
-}
-
-
-message TaskDef {
-    uint32 id = 1;
-    uint32 type = 2;
-
-    uint32 stream_id = 10;
-    uint32 event_id = 11;
-
-    KernelDef kernel = 20;
-    KernelExDef kernel_ex = 21;
-    KernelHcclDef kernel_hccl = 25;
-    EventExDef event_ex = 26;
-    LogTimeStampDef log_timestamp = 28;
-
-    uint32 label_id = 30;
-
-    MemcpyAsyncDef memcpy_async = 31;
-    StreamSwitchDef stream_switch = 32;
-    StreamActiveDef stream_active = 33;
-    bytes private_def = 34;
-    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
-    StreamSwitchNDef stream_switch_n = 36;
-
-    LabelSetDef label_set = 37;
-    LabelGotoExDef label_goto_ex = 38;
-    LabelSwitchByIndexDef label_switch_by_index = 39;
-    KernelDefWithHandle kernel_with_handle = 40;
-}
-
-message KernelDef {
-    KernelContext context = 1;
-
-    string stub_func = 10;
-    uint32 block_dim = 11;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes sm_desc = 14;
-    bytes flowtable = 15;
-    string so_name = 16;
-    string kernel_name = 17;
-    bytes kernel_ext_info = 18;
-    uint32 kernel_ext_info_size = 19;
-}
-
-message KernelDefWithHandle {
-    KernelContext context = 1;
-
-    uint64 handle = 10;
-    string dev_func = 11;
-    uint32 block_dim = 12;
-    uint32 args_size = 13;
-    bytes args = 14;
-    bytes sm_desc = 15;
-    string original_kernel_key = 16;
-    string node_info = 17;
-}
-
-message KernelContext {
-    uint32 kernel_type = 1;
-    uint32 op_id = 2;                              // OP type in CCE
-    uint32 kernel_func_id = 3;
-    uint32 op_index = 4;                           // TE/Custom operator
-    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
-    bytes args_offset = 6;                         // args offset information
-    uint32 args_count = 7;                         // args count
-    repeated uint32 origin_op_index = 8;
-}
-
-
-message KernelExDef {
-    uint32 flags = 1;
-
-    uint32 op_index = 4;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
-    uint32 task_info_size = 15;
-    bytes kernel_ext_info = 16;
-    uint32 kernel_ext_info_size = 17;
-}
-
-
-message KernelHcclDef {
-    uint32 op_index = 8;
-    string hccl_type = 9;
-}
-
-
-message EventExDef {
-    uint32 op_index = 1;
-    uint32 event_type = 2;
-}
-
-message LogTimeStampDef {
-    uint64 logid = 1;
-    bool notify = 2;
-    uint32 flat = 3;
-}
-
-message MemcpyAsyncDef {
-    uint64 dst = 1;
-    uint64 dst_max = 2;
-    uint64 src = 3;
-    uint64 count = 4;
-    uint32 kind = 5;
-    uint32 op_index = 6;
-}
-
-message StreamSwitchDef {
-    uint32 op_index = 1;
-    uint32 true_stream_id = 2;
-    int64 value = 3;
-    uint64 value_ptr = 4;
-    uint32 data_type = 5;
-}
-
-message StreamActiveDef {
-    uint32 op_index = 1;
-    uint32 active_stream_id = 2;
-}
-
-message StreamSwitchNDef {
-    uint32 op_index = 1;
-    uint32 size = 2;
-    repeated int64 target_value = 3;
-    repeated uint32 true_stream_id = 4;
-    uint32 element_size = 5;
-    uint32 data_type = 6;
-}
-
-message LabelSetDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelGotoExDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelSwitchByIndexDef {
-    uint32 op_index = 1;
-    uint32 label_max = 2;
-}

ge/ge_local_engine/engine/host_cpu_engine.cc

@@ -13,15 +13,15 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "host_cpu_engine.h"
-#include "graph/common/omg_util.h"
+#include "ge_local_engine/engine/host_cpu_engine.h"
 #include "graph/utils/op_desc_utils.h"
 #include "graph/utils/tensor_adapter.h"
+#include "graph/utils/node_utils.h"
+#include "graph/utils/type_utils.h"
 #include "register/op_kernel_registry.h"
 #include "register/host_cpu_context.h"
 #include "common/ge/ge_util.h"
 #include "common/ge/plugin_manager.h"
-#include "graph/utils/type_utils.h"
 #include "common/fp16_t.h"
 #include "common/math/math_util.h"
 
@@ -123,10 +123,7 @@ bool HostCpuEngine::CheckSupported(const string &op_type) {
 }
 
 Status HostCpuEngine::FindOpKernel(const ge::NodePtr &node, std::unique_ptr<HostCpuOp> &op_kernel) {
-  std::string op_type;
-  auto status = GetOriginalType(node, op_type);
-  GE_CHK_BOOL_EXEC_NOLOG(status == SUCCESS, return status);
-
+  const std::string op_type = NodeUtils::GetNodeType(node);
   auto kernel = OpKernelRegistry::GetInstance().CreateHostCpuOp(op_type);
   if (kernel == nullptr) {
     GELOGD("Op of type %s is not supported by host cpu engine", op_type.c_str());

ge/ge_local_engine/proto/task.proto

@@ -1,179 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-message ModelTaskDef {
-    string version = 1;
-
-    map<string, string> attr = 9; // Extended field
-    repeated TaskDef task = 10;
-
-    uint64 memory_size = 11;
-    uint32 stream_num = 12;
-    uint32 event_num = 13;
-    uint64 weight_size = 14;
-
-    repeated bytes op = 15; // input/output opdef in bytes
-
-    uint64 base_addr = 16;    // base addr
-    uint64 weight_addr = 17;  // weight addr
-    uint32 batch_num = 18;
-}
-
-
-message TaskDef {
-    uint32 id = 1;
-    uint32 type = 2;
-
-    uint32 stream_id = 10;
-    uint32 event_id = 11;
-
-    KernelDef kernel = 20;
-    KernelExDef kernel_ex = 21;
-    KernelHcclDef kernel_hccl = 25;
-    EventExDef event_ex = 26;
-    LogTimeStampDef log_timestamp = 28;
-
-    uint32 label_id = 30;
-
-    MemcpyAsyncDef memcpy_async = 31;
-    StreamSwitchDef stream_switch = 32;
-    StreamActiveDef stream_active = 33;
-    bytes private_def = 34;
-    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
-    StreamSwitchNDef stream_switch_n = 36;
-
-    LabelSetDef label_set = 37;
-    LabelGotoExDef label_goto_ex = 38;
-    LabelSwitchByIndexDef label_switch_by_index = 39;
-    KernelDefWithHandle kernel_with_handle = 40;
-}
-
-message KernelDef {
-    KernelContext context = 1;
-
-    string stub_func = 10;
-    uint32 block_dim = 11;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes sm_desc = 14;
-    bytes flowtable = 15;
-    string so_name = 16;
-    string kernel_name = 17;
-    bytes kernel_ext_info = 18;
-    uint32 kernel_ext_info_size = 19;
-}
-
-message KernelDefWithHandle {
-    KernelContext context = 1;
-
-    uint64 handle = 10;
-    string dev_func = 11;
-    uint32 block_dim = 12;
-    uint32 args_size = 13;
-    bytes args = 14;
-    bytes sm_desc = 15;
-    string original_kernel_key = 16;
-    string node_info = 17;
-}
-
-message KernelContext {
-    uint32 kernel_type = 1;
-    uint32 op_id = 2;                              // OP type in CCE
-    uint32 kernel_func_id = 3;
-    uint32 op_index = 4;                           // TE/Custom operator
-    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
-    bytes args_offset = 6;                         // args offset information
-    uint32 args_count = 7;                         // args count
-    repeated uint32 origin_op_index = 8;
-}
-
-
-message KernelExDef {
-    uint32 flags = 1;
-
-    uint32 op_index = 4;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
-    uint32 task_info_size = 15;
-    bytes kernel_ext_info = 16;
-    uint32 kernel_ext_info_size = 17;
-}
-
-
-message KernelHcclDef {
-    uint32 op_index = 8;
-    string hccl_type = 9;
-}
-
-
-message EventExDef {
-    uint32 op_index = 1;
-    uint32 event_type = 2;
-}
-
-message LogTimeStampDef {
-    uint64 logid = 1;
-    bool notify = 2;
-    uint32 flat = 3;
-}
-
-message MemcpyAsyncDef {
-    uint64 dst = 1;
-    uint64 dst_max = 2;
-    uint64 src = 3;
-    uint64 count = 4;
-    uint32 kind = 5;
-    uint32 op_index = 6;
-}
-
-message StreamSwitchDef {
-    uint32 op_index = 1;
-    uint32 true_stream_id = 2;
-    int64 value = 3;
-    uint64 value_ptr = 4;
-    uint32 data_type = 5;
-}
-
-message StreamActiveDef {
-    uint32 op_index = 1;
-    uint32 active_stream_id = 2;
-}
-
-message StreamSwitchNDef {
-    uint32 op_index = 1;
-    uint32 size = 2;
-    repeated int64 target_value = 3;
-    repeated uint32 true_stream_id = 4;
-    uint32 element_size = 5;
-    uint32 data_type = 6;
-}
-
-message LabelSetDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelGotoExDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelSwitchByIndexDef {
-    uint32 op_index = 1;
-    uint32 label_max = 2;
-}

ge/ge_opt_info/ge_opt_info.cc

@@ -0,0 +1,58 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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 "ge_opt_info/ge_opt_info.h"
+
+#include <string>
+#include <map>
+#include "graph/ge_local_context.h"
+#include "ge/ge_api_types.h"
+#include "common/debug/ge_log.h"
+#include "opt_info.h"
+
+namespace ge {
+Status GeOptInfo::SetOptInfo() {
+  std::string soc_ver;
+  graphStatus ret = GetThreadLocalContext().GetOption(SOC_VERSION, soc_ver);
+  if (ret != GRAPH_SUCCESS) {
+    REPORT_CALL_ERROR("E19999", "Get soc version failed.");
+    GELOGE(FAILED, "[Get][SocVersion]Get soc version failed.");
+    return FAILED;
+  }
+  GELOGD("Soc version:%s.", soc_ver.c_str());
+  std::map<std::string, std::string> opt_info;
+  // the first arg does not work at present.
+  if (gelc::GetOptInfo(gelc::kOffline, soc_ver, opt_info) != gelc::SUCCESS) {
+    REPORT_CALL_ERROR("E19999", "Get optional information failed, is_offline:%d, soc version:%s",
+                      gelc::kOffline, soc_ver.c_str());
+    GELOGE(FAILED, "[Get][OptInfo]Get optional information failed, is_offline:%d, soc version:%s",
+           gelc::kOffline, soc_ver.c_str());
+    return FAILED;
+  }
+  // do nothing if get empty information
+  if (opt_info.empty()) {
+    GELOGI("Optional information is empty.");
+    return SUCCESS;
+  }
+  std::map<std::string, std::string> graph_options = GetThreadLocalContext().GetAllGraphOptions();
+  for (const auto &itr : opt_info) {
+    graph_options.emplace(itr.first, itr.second);
+    GELOGI("Get optional information success, key:%s, value:%s.", itr.first.c_str(), itr.second.c_str());
+  }
+  GetThreadLocalContext().SetGraphOption(graph_options);
+  return SUCCESS;
+}
+}  // namespace ge

ge/ge_opt_info/ge_opt_info.h

@@ -0,0 +1,32 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef GE_OPT_INFO_GE_OPT_INFO_H_
+#define GE_OPT_INFO_GE_OPT_INFO_H_
+
+#include "ge/ge_api_error_codes.h"
+#include "register/register_types.h"
+
+namespace ge {
+class FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY GeOptInfo {
+ public:
+  GeOptInfo() = default;
+  ~GeOptInfo() = default;
+  static Status SetOptInfo();
+};
+}  // namespace ge
+
+#endif  // GE_OPT_INFO_GE_OPT_INFO_H_

ge/ge_runtime/task/label_goto_task.cc

@@ -72,7 +72,7 @@ bool LabelGotoTask::Distribute() {
     return false;
   }
 
-  rt_ret = rtLabelListCpy((void**)label_list.data(), label_list.size(), label_info_, label_info_size);
+  rt_ret = rtLabelListCpy(reinterpret_cast<void**>(label_list.data()), label_list.size(), label_info_, label_info_size);
   if (rt_ret != RT_ERROR_NONE) {
     GELOGE(RT_FAILED, "Call rt api failed, ret: %#x", rt_ret);
     return false;

ge/generator/ge_generator.cc

@@ -674,6 +674,12 @@ Status GeGenerator::GenerateModel(const Graph &graph, const string &file_name_pr
     GELOGD("Current ctx is null.");
     ctx = nullptr;
   }
+  std::function<void()> callback = [&]() {
+    if (ctx != nullptr) {
+      (void)rtCtxSetCurrent(ctx);
+    }
+  };
+  GE_MAKE_GUARD(restore, callback);
 
   GeRootModelPtr ge_root_model = nullptr;
   GE_CHECK_NOTNULL_EXEC(impl_, return PARAM_INVALID);
@@ -712,11 +718,6 @@ Status GeGenerator::GenerateModel(const Graph &graph, const string &file_name_pr
     }
     return ret;
   }
-
-  if (ctx != nullptr) {
-    (void)rtCtxSetCurrent(ctx);
-  }
-
   return SUCCESS;
 }
 
@@ -806,7 +807,7 @@ Status GeGenerator::CheckForSingleOp(OpDescPtr &op_desc, const vector<GeTensor>
   return SUCCESS;
 }
 
-Status GeGenerator::InferFormatForSingleOp(OpDescPtr &op_desc) {
+Status GeGenerator::InferFormatForSingleOp(OpDescPtr &op_desc, Graph &graph) {
   GE_CHECK_NOTNULL(op_desc);
   if (OperatorFactoryImpl::GetInferFormatFunc(op_desc->GetType()) != nullptr) {
     auto node_op = ge::OperatorFactoryImpl::CreateOperator("node_op", op_desc->GetType());
@@ -830,7 +831,11 @@ Status GeGenerator::InferFormatForSingleOp(OpDescPtr &op_desc) {
     }
     node_op.BreakConnect();
   }
-  auto op = OpDescUtils::CreateOperatorFromOpDesc(op_desc);
+  auto comp_graph = GraphUtils::GetComputeGraph(graph);
+  GE_CHECK_NOTNULL(comp_graph);
+  auto node = comp_graph->FindNode(op_desc->GetName());
+  GE_CHECK_NOTNULL(node);
+  auto op = OpDescUtils::CreateOperatorFromNode(node);
   auto ret = op_desc->CallInferFormatFunc(op);
   if (ret != GRAPH_SUCCESS) {
     REPORT_INNER_ERROR("E19999", "call InferFormatFunc for single op:%s fail",
@@ -877,7 +882,7 @@ Status GeGenerator::BuildSingleOp(OpDescPtr &op_desc, const vector<GeTensor> &in
   Graph graph;
   GE_CHK_STATUS(BuildSingleOpGraph(op_desc, inputs, outputs, name, graph),
                 "[Build][Graph] for single op:%s fail.", op_desc->GetName().c_str());
-  GE_CHK_STATUS_RET_NOLOG(InferFormatForSingleOp(op_desc));
+  GE_CHK_STATUS_RET_NOLOG(InferFormatForSingleOp(op_desc, graph));
 
   // 2. check engine type when compile online
   if (model_file_name == kFileNameSuffix) {

ge/graph/build/label_allocator.cc

@@ -86,6 +86,11 @@ bool LabelAllocator::CollectFunctionalNode(ComputeGraphPtr &graph, std::set<Node
     return false;
   }
 
+  if (func_node->GetOpDesc() != nullptr && func_node->GetOpDesc()->HasAttr(ATTR_NAME_FFTS_SUB_GRAPH)) {
+    GELOGD("Graph[%s] is ffts subgraph, skip label allocator.", graph->GetName().c_str());
+    return true;
+  }
+
   ComputeGraphPtr owner_graph = func_node->GetOwnerComputeGraph();
   if (owner_graph == nullptr) {
     REPORT_INNER_ERROR("E19999", "ComputeGraph owner not set in node:%s(%s), graph:%s",

ge/graph/build/logical_stream_allocator.cc

@@ -474,6 +474,11 @@ Status UpdateForSkippedEnginePass::Run(ComputeGraphPtr graph, const vector<Subgr
   for (ge::NodePtr &node : graph->GetDirectNode()) {
     auto op_desc = node->GetOpDesc();
     GE_CHECK_NOTNULL(op_desc);
+    if (op_desc->HasAttr(ATTR_NAME_THREAD_SCOPE_ID)) {
+      op_desc->SetStreamId(kInvalidStream);
+      GELOGI("Ffts node %s of type %s reassign to invalid stream.", node->GetName().c_str(), node->GetType().c_str());
+      continue;
+    }
     int64_t stream_id = op_desc->GetStreamId();
     if (ops_without_label.find(op_desc) != ops_without_label.end()) {
       if (AreAllPredStreamsInvalid(node) && op_desc->GetSubgraphInstanceNames().empty()) {

ge/graph/build/model_builder.cc

@@ -707,7 +707,7 @@ Status ModelBuilder::SaveDataToModel(ge::Model &model, ge::GeModel &ge_model) {
       if (!kernel_name.empty() && (kernel_buffer.GetSize() > 0)) {
         GE_CHECK_NOTNULL(kernel_buffer.GetData());
         std::vector<char> data(kernel_buffer.GetData(), kernel_buffer.GetData() + kernel_buffer.GetSize());
-        tbe_kernel = std::make_shared<OpKernelBin>(kernel_name, std::move(data));
+        tbe_kernel = MakeShared<OpKernelBin>(kernel_name, std::move(data));
         GE_CHECK_NOTNULL(tbe_kernel);
         GELOGI("Node [%s][%s] start recovery extra attr %s from %s", node_op_desc->GetName().c_str(),
                node_op_desc->GetType().c_str(), ge::OP_EXTATTR_NAME_TBE_KERNEL, ATTR_NAME_TBE_KERNEL_NAME.c_str());

ge/graph/build/stream_allocator.cc

@@ -432,7 +432,11 @@ Status StreamAllocator::SetActiveStreamsForSubgraphs() {
 
 // Insert the send/recv event id to the graph
 Status StreamAllocator::InsertSyncEvents() {
-  for (const auto &cur_node : whole_graph_->GetNodes(whole_graph_->GetGraphUnknownFlag())) {
+  auto ffts_filter = [](const Node &node, const char *, const ComputeGraphPtr &) {
+    return !node.GetOpDesc()->HasAttr(ATTR_NAME_FFTS_SUB_GRAPH);
+  };
+
+  for (const auto &cur_node : whole_graph_->GetNodes(whole_graph_->GetGraphUnknownFlag(), nullptr, ffts_filter)) {
     // Take the adjacent points, then judge whether need to insert the event
     for (const OutDataAnchorPtr &anchor : cur_node->GetAllOutDataAnchors()) {
       for (const InDataAnchorPtr &peer_in_anchor : anchor->GetPeerInDataAnchors()) {
@@ -531,6 +535,11 @@ Status StreamAllocator::InsertOneEventInTwoNodes(const NodePtr &cur_node, const
 Status StreamAllocator::InsertEventsForSubgraph() {
   for (const auto &subgraph : whole_graph_->GetAllSubgraphs()) {
     GE_CHECK_NOTNULL(subgraph);
+    const auto parent_node = subgraph->GetParentNode();
+    if (parent_node != nullptr && parent_node->GetOpDesc()->HasAttr(ATTR_NAME_FFTS_SUB_GRAPH)) {
+      GELOGD("Skip ffts subgraph, parent node is %s.", parent_node->GetName().c_str());
+      continue;
+    }
     for (const auto &node : subgraph->GetDirectNode()) {
       auto op_desc = node->GetOpDesc();
       GE_CHECK_NOTNULL(op_desc);

ge/graph/build/task_generator.cc

@@ -354,7 +354,10 @@ Status TaskGenerator::GenerateTask(RunContext &run_context, ComputeGraphPtr &gra
   };
   GE_MAKE_GUARD(release, callback);
 
-  for (auto &node : graph->GetNodes(graph->GetGraphUnknownFlag())) {
+  auto ffts_filter = [](const Node &node, const char *, const ComputeGraphPtr &) {
+    return !node.GetOpDesc()->HasAttr(ATTR_NAME_FFTS_SUB_GRAPH);
+  };
+  for (auto &node : graph->GetNodes(graph->GetGraphUnknownFlag(), nullptr, ffts_filter)) {
     OpDescPtr op_desc = node->GetOpDesc();
     GE_CHECK_NOTNULL(op_desc);
     node_index++;
@@ -380,10 +383,8 @@ Status TaskGenerator::GenerateTask(RunContext &run_context, ComputeGraphPtr &gra
       GELOGI("Fusion node[name:%s, type:%s] do not need generate task again.", name.c_str(), type.c_str());
       continue;
     }
-    if (op_kernel_lib_name.empty()) {
-      GELOGI("Node[name:%s, type:%s] does not need to generate task.", name.c_str(), type.c_str());
-      continue;
-    }
+    GE_CHK_BOOL_EXEC_INFO(!op_kernel_lib_name.empty(), continue,
+                          "Node[name:%s, type:%s] does not need to generate task.", name.c_str(), type.c_str());
     auto kernel_info_store = ops_kernel_manager.GetOpsKernelInfoStore(op_kernel_lib_name);
     if (kernel_info_store == nullptr) {
       REPORT_INNER_ERROR("E19999", "Get ops kernel info store failed for op:%s(%s), op_kernel_name:%s",
@@ -394,6 +395,10 @@ Status TaskGenerator::GenerateTask(RunContext &run_context, ComputeGraphPtr &gra
     }
     GE_CHK_STATUS_RET(UpdateAnchorStatus(node), "[Call][UpdateAnchorStatus] node:%s(%s) failed", name.c_str(),
                       type.c_str());
+    if (node->GetOpDesc()->HasAttr(ATTR_NAME_FFTS_SUB_GRAPH)) {
+      GE_CHK_STATUS_RET(UpdateAnchorStatusForFfts(node), "[Call][UpdateAnchorStatusForFfts] node:%s(%s) failed",
+                        name.c_str(), type.c_str());
+    }
     // Profiling task
     size_t task_list_size_before = task_def_list.size();
     GE_CHK_STATUS_RET(InsertProfilingTaskBefore(op_desc, profiling_point, all_reduce_nodes, node_index, task_def_list));
@@ -571,7 +576,24 @@ Status TaskGenerator::GenerateTaskForFusionNode(FusionTaskInfo &fusion_task_info
   return ret;
 }
 
+Status TaskGenerator::UpdateAnchorStatusForFfts(const NodePtr &node) {
+  GELOGD("Start UpdateAnchorStatusForFfts for %s.", node->GetName().c_str());
+  if (!node->GetOpDesc()->GetSubgraphInstanceNames().empty()) {
+    for (size_t i = 0; i < node->GetOpDesc()->GetSubgraphInstanceNames().size(); ++i) {
+      auto sub_graph = NodeUtils::GetSubgraph(*node, i);
+      GE_CHECK_NOTNULL(sub_graph);
+      GELOGD("Start update anchor status for %s.", sub_graph->GetName().c_str());
+      for (auto &ffts_node : sub_graph->GetDirectNode()) {
+        GE_CHK_STATUS_RET(UpdateAnchorStatus(ffts_node), "[Call][UpdateAnchorStatus] node:%s(%s) failed",
+                          ffts_node->GetName().c_str(), ffts_node->GetType().c_str());
+      }
+    }
+  }
+  return SUCCESS;
+}
+
 Status TaskGenerator::UpdateAnchorStatus(const NodePtr &node) {
+  GELOGD("Start UpdateAnchorStatus for %s.", node->GetName().c_str());
   if (NodeUtils::SetAllAnchorStatus(node) != GRAPH_SUCCESS) {
     REPORT_CALL_ERROR("E19999", "SetAllAnchorStatus fail for op:%s(%s)",
                       node->GetName().c_str(), node->GetType().c_str());
@@ -771,7 +793,6 @@ Status TaskGenerator::AutoFindBpOpIndex(const ComputeGraphPtr &graph, ProfilingP
   GELOGI("Start AutoFindBpOpIndex");
   NodePtr bp_node = nullptr;
   uint32_t current_idx = 0;
-  uint32_t netoutput_idx = 0;
   for (auto &node : graph->GetNodes(graph->GetGraphUnknownFlag())) {
     OpDescPtr op_desc = node->GetOpDesc();
     GE_CHECK_NOTNULL(op_desc);
@@ -789,7 +810,6 @@ Status TaskGenerator::AutoFindBpOpIndex(const ComputeGraphPtr &graph, ProfilingP
     if (op_desc->GetName() == NODE_NAME_NET_OUTPUT) {
       if (bp_node == nullptr) {
         bp_node = node;
-        netoutput_idx = current_idx - 1;
       }
     }
     if (graph->GetNeedIteration()) {
@@ -814,34 +834,30 @@ Status TaskGenerator::AutoFindBpOpIndex(const ComputeGraphPtr &graph, ProfilingP
   if (bp_node == nullptr) {
     GELOGW("not find bp_node.");
     return SUCCESS;
-  } else if (bp_node->GetName() == NODE_NAME_NET_OUTPUT) {
-    profiling_point.bp_index = netoutput_idx;
-    GELOGI("First bp name %s, idx %u", bp_node->GetName().c_str(), netoutput_idx);
-  } else {
-    profiling_point.bp_index = FindLastBpFromBpNode(graph, bp_node);
   }
 
-  return SUCCESS;
+  return FindLastBpFromBpNode(graph, bp_node, profiling_point.bp_index);
 }
 
-uint32_t TaskGenerator::FindLastBpFromBpNode(const ComputeGraphPtr &graph, const NodePtr &bp_node) const {
-  uint32_t last_bp = 0;
+Status TaskGenerator::FindLastBpFromBpNode(const ComputeGraphPtr &graph, const NodePtr &target_node,
+                                           uint32_t &bp_index) const {
+  bp_index = 0;
+  auto target_desc = target_node->GetOpDesc();
+  GE_CHECK_NOTNULL(target_desc);
   OpDescPtr bp_op_desc = nullptr;
-  for (auto &in_anchor : bp_node->GetAllInDataAnchors()) {
-    auto out_anchor = in_anchor->GetPeerOutAnchor();
-    if (out_anchor == nullptr || out_anchor->GetOwnerNode() == nullptr) {
-      continue;
+  for (auto &in_node : target_node->GetInAllNodes()) {
+    GE_CHECK_NOTNULL(in_node);
+    auto in_node_desc = in_node->GetOpDesc();
+    GE_CHECK_NOTNULL(in_node_desc);
+    if ((bp_op_desc == nullptr || (in_node_desc->GetId() > bp_op_desc->GetId())) &&
+        (in_node_desc->GetStreamId() == target_desc->GetStreamId())){
+      bp_op_desc = in_node_desc;
     }
-    auto out_node_desc = out_anchor->GetOwnerNode()->GetOpDesc();
-    GE_CHECK_NOTNULL(out_node_desc);
-    if (bp_op_desc == nullptr || ((out_node_desc->GetId()) > (bp_op_desc->GetId()))) {
-      bp_op_desc = out_node_desc;
-    }
-    GELOGI("bp_op_desc is %s, id is %ld", bp_op_desc->GetName().c_str(), bp_op_desc->GetId());
   }
 
   if (bp_op_desc == nullptr) {
-    return last_bp;
+    GELOGI("Did not find bp node.");
+    return SUCCESS;
   }
   uint32_t current_idx = 0;
   for (auto &node : graph->GetNodes(graph->GetGraphUnknownFlag())) {
@@ -849,12 +865,14 @@ uint32_t TaskGenerator::FindLastBpFromBpNode(const ComputeGraphPtr &graph, const
     GE_CHECK_NOTNULL(op_desc);
     current_idx++;
     if (op_desc->GetName() == bp_op_desc->GetName()) {
-      last_bp = current_idx;
-      GELOGI("First bp name %s, idx %u", op_desc->GetName().c_str(), last_bp);
+      bp_index = current_idx;
+      GELOGI("Find bp name %s, idx %u", op_desc->GetName().c_str(), bp_index);
       break;
     }
   }
-  return last_bp;
+  GELOGI("Last bp node[%s], type[%s], index[%u], stream id[%ld]", bp_op_desc->GetName().c_str(),
+         bp_op_desc->GetType().c_str(), bp_index, bp_op_desc->GetStreamId());
+  return SUCCESS;
 }
 
 Status TaskGenerator::FindFpOfEnv(const ComputeGraphPtr &graph, const std::string &fp_point_str,

ge/graph/build/task_generator.h

@@ -80,6 +80,7 @@ class TaskGenerator {
   Status FindProfilingNodeIndex(const ComputeGraphPtr &graph, ProfilingPoint &profiling_point,
                                 std::vector<uint32_t> &all_reduce_nodes);
  private:
+  Status UpdateAnchorStatusForFfts(const NodePtr &node);
   Status UpdateAnchorStatus(const NodePtr &node);
 
   Status UpdateOpIsVarAttr(const OpDescPtr &op_desc, uint64_t session_id);
@@ -115,7 +116,7 @@ class TaskGenerator {
   Status AutoFindFpOpIndex(const ComputeGraphPtr &graph, ProfilingPoint &profiling_point) const;
   Status AutoFindBpOpIndex(const ComputeGraphPtr &graph, ProfilingPoint &profiling_point,
                            vector<uint32_t> &all_reduce_nodes) const;
-  uint32_t FindLastBpFromBpNode(const ComputeGraphPtr &graph, const NodePtr &bp_node) const;
+  Status FindLastBpFromBpNode(const ComputeGraphPtr &graph, const NodePtr &bp_node, uint32_t &bp_index) const;
 
   Status FindFpOfEnv(const ComputeGraphPtr &graph, const std::string &fp_point_str,
                      ProfilingPoint &profiling_point) const;

ge/graph/common/omg_util.cc

@@ -275,21 +275,6 @@ bool IsUnknownShapeTensor(const GeTensorDesc &tensor_desc) {
 }
 
 ///
-/// @brief Set Op _force_unknown_shape flag
-/// @param [in] node
-/// @param [in] force_unknown, set attribute if true
-/// @param [in] group_index, condition group index of node.
-/// @return
-///
-void MarkForceUnknownShape(const NodePtr &node, bool force_unknown, int64_t group_index) {
-  if (!force_unknown) {
-    return;
-  }
-
-  SetControlFlowGroup(node, group_index);
-}
-
-///
 /// @brief Set Op _control_flow_group flag
 /// @param [in] node
 /// @param [in] group, condition group index of node.

ge/graph/common/omg_util.h

@@ -126,15 +126,6 @@ Status GetMemorySize(const NodePtr &node, int64_t &output_size);
 bool IsUnknownShapeTensor(const GeTensorDesc &tensor_desc);
 
 ///
-/// @brief Set Op _force_unknown_shape flag
-/// @param [in] node
-/// @param [in] force_unknown, set attribute if true
-/// @param [in] group_index, condition group index of node.
-/// @return
-///
-void MarkForceUnknownShape(const NodePtr &node, bool force_unknown, int64_t group_index);
-
-///
 /// @brief Set Op _control_flow_group flag
 /// @param [in] node
 /// @param [in] group, condition group index of node.

ge/graph/load/model_manager/davinci_model.cc

@@ -99,6 +99,9 @@ const uint32_t kEndOfSequenceNew = 507005;
 const int32_t kModelAbortNormal = 0x0704000e;
 const int32_t kModelAbortNormalNew = 507024;
 const uint32_t kInteval = 2;
+const uint32_t kFftsTbeHandleElementSize = 2;
+const uint32_t kNonTailBlock = 0;
+const uint32_t kTailBlock = 1;
 const char *const kModelName = "model_name";
 const char *const kModeleId = "model_id";
 const char *const kLoadStartTime = "load_start_time";
@@ -116,14 +119,15 @@ const char *const kWorkSpaceSize = "workspace_size";
 const char *const kTotalSize = "total_size";
 const char *const kTaskCount = "task_count";
 const char *const kTaskId = "task_id";
-const char* const kRequestId = "request_id";
-const char* const kThreadId = "thread_id";
-const char* const kInputBeginTime = "input_begin_time";
-const char* const kInputEndTime = "input_end_time";
-const char* const kInferBeginTime = "infer_begin_time";
-const char* const kInferEndTime = "infer_end_time";
-const char* const kOutputBeginTime = "output_start_time";
-const char* const kOutputEndTime = "output_end_time";
+const char *const kRequestId = "request_id";
+const char *const kThreadId = "thread_id";
+const char *const kInputBeginTime = "input_begin_time";
+const char *const kInputEndTime = "input_end_time";
+const char *const kInferBeginTime = "infer_begin_time";
+const char *const kInferEndTime = "infer_end_time";
+const char *const kOutputBeginTime = "output_start_time";
+const char *const kOutputEndTime = "output_end_time";
+const char *const kStubFuncName = "_register_stub_func";
 const uint32_t kStringHeadElems = 2;
 const uint32_t kPlacementHostData = 0;
 const size_t kAlignment = 64;
@@ -902,10 +906,8 @@ Status DavinciModel::InitNodes(const ComputeGraphPtr &compute_graph) {
     SetLabelForDynamic(node);
     auto it = op_desc_handle.find(op_desc->GetType());
     if (it != op_desc_handle.end()) {
-      if ((this->*it->second)(op_desc) != SUCCESS) {
-        GELOGE(PARAM_INVALID, "[Init][Node] failed, Name:%s", op_desc->GetName().c_str());
-        return PARAM_INVALID;
-      }
+      GE_CHK_BOOL_TRUE_EXEC_WITH_LOG((this->*it->second)(op_desc) != SUCCESS, return PARAM_INVALID,
+                                     "[Init][Node] failed, Name:%s", op_desc->GetName().c_str());
       continue;
     }
 
@@ -935,7 +937,8 @@ Status DavinciModel::InitNodes(const ComputeGraphPtr &compute_graph) {
 
     GE_TIMESTAMP_RESTART(InitTbeHandle);
     if (IsTbeTask(op_desc)) {
-      Status status = InitTbeHandle(op_desc);
+      Status status =
+          op_desc->HasAttr(ATTR_NAME_THREAD_SCOPE_ID) ? InitTbeHandleWithFfts(op_desc) : InitTbeHandle(op_desc);
       if (status != SUCCESS) {
         GELOGE(status, "[Init][TbeHandle] failed. op:%s", op_desc->GetName().c_str());
         return status;
@@ -1477,6 +1480,11 @@ Status DavinciModel::GetLabelGotoAddr(uint32_t label_index, rtMemType_t mem_type
   return SUCCESS;
 }
 
+void DavinciModel::SetGlobalStep(void *global_step, uint64_t global_step_size) {
+  global_step_addr_ = global_step;
+  global_step_size_ = global_step_size;
+}
+
 /// @ingroup ge
 /// @brief LabelSet Op Initialize.
 /// @param [in] op_desc: LabelSet Op descriptor.
@@ -1539,14 +1547,16 @@ Status DavinciModel::InitLabelSet(const OpDescPtr &op_desc) {
 }
 
 Status DavinciModel::InitVariable(const OpDescPtr &op_desc, map<string, OpDescPtr> &variable_by_name) {
-  if (op_desc->GetName() == NODE_NAME_GLOBAL_STEP) {
-    const auto output_sizes = ModelUtils::GetOutputSize(op_desc);
-    if (!output_sizes.empty()) {
-      global_step_size_ = output_sizes[0];
-    }
-    const auto output_addrs = ModelUtils::GetOutputDataAddrs(runtime_param_, op_desc);
-    if (!output_addrs.empty()) {
-      global_step_addr_ = output_addrs[0];
+  if (!known_node_) {
+    if (op_desc->GetName() == NODE_NAME_GLOBAL_STEP) {
+      const auto output_sizes = ModelUtils::GetOutputSize(op_desc);
+      if (!output_sizes.empty()) {
+        global_step_size_ = output_sizes[0];
+      }
+      const auto output_addrs = ModelUtils::GetOutputDataAddrs(runtime_param_, op_desc);
+      if (!output_addrs.empty()) {
+        global_step_addr_ = output_addrs[0];
+      }
     }
   }
 
@@ -3673,6 +3683,7 @@ Status DavinciModel::InitConstant(const OpDescPtr &op_desc) {
       elem_num = 1;
     }
     uint64_t *buff = reinterpret_cast<uint64_t *>(tensor->MutableData().data());
+    GE_CHECK_NOTNULL(buff);
     if (ge::CheckInt64Uint32MulOverflow(elem_num, kBytes * kStringHeadElems) != SUCCESS) {
       GELOGE(FAILED, "[Call][CheckInt64Uint32MulOverflow] Shape size:%ld is invalid", elem_num);
       return FAILED;
@@ -3700,6 +3711,7 @@ Status DavinciModel::InitConstant(const OpDescPtr &op_desc) {
 /// @return Status
 ///
 Status DavinciModel::InitTbeHandle(const OpDescPtr &op_desc) {
+  string bin_file = op_desc->GetName();
   auto kernel = ge_model_->GetTBEKernelStore().FindKernel(op_desc->GetName());
   auto tbe_kernel = (kernel != nullptr) ? kernel : op_desc->TryGetExtAttr(OP_EXTATTR_NAME_TBE_KERNEL, TBEKernelPtr());
   if (tbe_kernel == nullptr) {
@@ -3708,12 +3720,61 @@ Status DavinciModel::InitTbeHandle(const OpDescPtr &op_desc) {
     GELOGE(INTERNAL_ERROR, "[Check][Param] TBE: %s can't find tvm bin file!", op_desc->GetName().c_str());
     return INTERNAL_ERROR;
   }
+  GE_CHK_STATUS_RET(FunctionRegister(op_desc, bin_file, tbe_kernel, false), "Function register of bin file: %s failed",
+                    bin_file.c_str());
+  return SUCCESS;
+}
 
-  std::string session_graph_model_id;
-  GetUniqueId(op_desc, session_graph_model_id);
-  const char *bin_file_key = GetRegisterStub(op_desc->GetName(), session_graph_model_id);  // from set, always valid.
-  TBEHandleStore &kernel_store = TBEHandleStore::GetInstance();
+Status DavinciModel::InitTbeHandleWithFfts(const OpDescPtr &op_desc) {
+  std::vector<OpKernelBinPtr> tbe_kernel;
+  tbe_kernel = op_desc->TryGetExtAttr(OP_EXTATTR_NAME_THREAD_TBE_KERNEL, tbe_kernel);
+  GELOGD("Kernel bin ptr vec size is %zu.", tbe_kernel.size());
+  if (tbe_kernel.size() != kFftsTbeHandleElementSize) {
+    REPORT_INNER_ERROR("E19999", "Get tbe_kernel for op:%s(%s) fail, model_id:%u",
+                       op_desc->GetName().c_str(), op_desc->GetType().c_str(), model_id_);
+    GELOGE(INTERNAL_ERROR, "[Check][Param] TBE: %s can't find tvm bin file, size is %zu when ffts",
+           op_desc->GetName().c_str(), tbe_kernel.size());
+    return INTERNAL_ERROR;
+  }
+  if (tbe_kernel[0] == nullptr || tbe_kernel[1] == nullptr) {
+    REPORT_INNER_ERROR("E19999", "Tbe kernel for op:%s is nullptr.", op_desc->GetName().c_str());
+    GELOGE(INTERNAL_ERROR, "[Check][Param] TBE: tvm bin file of %s is nullptr when ffts.", op_desc->GetName().c_str());
+    return INTERNAL_ERROR;
+  }
+  vector<string> bin_file_keys;
+  (void)AttrUtils::GetListStr(op_desc, kStubFuncName, bin_file_keys);
+  if (bin_file_keys.size() != kFftsTbeHandleElementSize) {
+    REPORT_INNER_ERROR("E19999", "Get bin_file for op:%s(%s) fail.", op_desc->GetName().c_str(),
+                       op_desc->GetType().c_str());
+    GELOGE(INTERNAL_ERROR, "[Check][Param] TBE: %s can't find bin file keys, size is %zu when ffts",
+           op_desc->GetName().c_str(), bin_file_keys.size());
+    return INTERNAL_ERROR;
+  }
+  GE_CHK_STATUS_RET(FunctionRegister(op_desc, bin_file_keys[kNonTailBlock], tbe_kernel[kNonTailBlock], true,
+                                     kNonTailBlock),
+                    "Function register of first bin file %s failed.", bin_file_keys[kNonTailBlock].c_str());
+  GE_CHK_STATUS_RET(FunctionRegister(op_desc, bin_file_keys[kTailBlock], tbe_kernel[kTailBlock], true, kTailBlock),
+                    "Function register of second bin file %s failed.", bin_file_keys[kTailBlock].c_str());
+  return SUCCESS;
+}
 
+Status DavinciModel::FunctionRegister(const OpDescPtr &op_desc, string &bin_file, OpKernelBinPtr &tbe_kernel,
+                                      bool is_ffts, size_t thread_index) {
+  if (thread_index > 1) {
+    GELOGE(INTERNAL_ERROR, "[Check][Param] failed. Thread index: %zu should less than 1.", thread_index);
+    return INTERNAL_ERROR;
+  }
+  const char *bin_file_key;
+  if (is_ffts) {
+    bin_file_key = GetRegisterStub(bin_file, "");
+    GELOGI("Node:%s inherit func name:%s directly.", op_desc->GetName().c_str(), bin_file_key);
+  } else {
+    std::string session_graph_model_id;
+    GetUniqueId(op_desc, session_graph_model_id);
+    bin_file_key = GetRegisterStub(bin_file, session_graph_model_id);  // from set, always valid.
+  }
+
+  TBEHandleStore &kernel_store = TBEHandleStore::GetInstance();
   std::lock_guard<std::mutex> lock(tvm_bin_mutex_);
   if (rtQueryFunctionRegistered(bin_file_key) != RT_ERROR_NONE) {
     void *bin_handle = nullptr;
@@ -3721,59 +3782,115 @@ Status DavinciModel::InitTbeHandle(const OpDescPtr &op_desc) {
       GELOGD("TBE: can't find the kernel_name[%s] in HandleMap", bin_file_key);
 
       rtDevBinary_t binary;
-      std::string json_string;
-      GE_IF_BOOL_EXEC(AttrUtils::GetStr(op_desc, TVM_ATTR_NAME_MAGIC, json_string),
-                      GELOGD("Get original type of session_graph_id."));
-      if (json_string == "RT_DEV_BINARY_MAGIC_ELF_AICPU") {
-        binary.magic = RT_DEV_BINARY_MAGIC_ELF_AICPU;
-      } else if (json_string == "RT_DEV_BINARY_MAGIC_ELF") {
-        binary.magic = RT_DEV_BINARY_MAGIC_ELF;
-      } else if (json_string == "RT_DEV_BINARY_MAGIC_ELF_AIVEC") {
-        binary.magic = RT_DEV_BINARY_MAGIC_ELF_AIVEC;
-      } else if (json_string == "RT_DEV_BINARY_MAGIC_ELF_AICUBE") {
-        binary.magic = RT_DEV_BINARY_MAGIC_ELF_AICUBE;
-      } else {
-        REPORT_INNER_ERROR("E19999", "Attr:%s value:%s in op:%s(%s), model_id:%u, check invalid",
-                           TVM_ATTR_NAME_MAGIC.c_str(), json_string.c_str(),
-                           op_desc->GetName().c_str(), op_desc->GetType().c_str(), model_id_);
-        GELOGE(PARAM_INVALID, "[Check][Param] Attr:%s value:%s in op:%s(%s), model_id:%u, check invalid",
-               TVM_ATTR_NAME_MAGIC.c_str(), json_string.c_str(),
-               op_desc->GetName().c_str(), op_desc->GetType().c_str(), model_id_);
-        return PARAM_INVALID;
-      }
-
+      GE_CHK_STATUS_RET(InitBinaryMagic(op_desc, is_ffts, thread_index, binary), "Init binary magic of %s failed.",
+                        op_desc->GetName().c_str());
       binary.version = 0;
       binary.data = tbe_kernel->GetBinData();
       binary.length = tbe_kernel->GetBinDataSize();
-
       GELOGD("TBE: binary.length: %lu", binary.length);
       GE_CHK_RT_RET(rtDevBinaryRegister(&binary, &bin_handle));
 
-      std::string meta_data;
-      GE_IF_BOOL_EXEC(AttrUtils::GetStr(op_desc, TVM_ATTR_NAME_METADATA, meta_data),
-                      GELOGI("Get original type of json_string"));
-      GELOGD("TBE: meta data: %s", meta_data.empty() ? "null" : meta_data.c_str());
-      GE_IF_BOOL_EXEC(!meta_data.empty(), GE_CHK_RT_RET(rtMetadataRegister(bin_handle, meta_data.c_str())));
-
+      GE_CHK_STATUS_RET(InitMetaData(op_desc, is_ffts, thread_index, bin_handle), "Init tvm meta data of %s failed.",
+                        op_desc->GetName().c_str());
       kernel_store.StoreTBEHandle(bin_file_key, bin_handle, tbe_kernel);
     } else {
       GELOGI("TBE: find the kernel_name[%s] in HandleMap", bin_file_key);
       kernel_store.ReferTBEHandle(bin_file_key);
     }
-
     std::string kernel_name;
-    GE_IF_BOOL_EXEC(AttrUtils::GetStr(op_desc, op_desc->GetName() + "_kernelname", kernel_name),
-                    GELOGD("Get original type of kernel_name"));
+    GE_CHK_STATUS_RET(InitKernelName(op_desc, is_ffts, thread_index, kernel_name), "Init kernel name of %s failed.",
+                      op_desc->GetName().c_str());
     GE_CHK_RT_RET(rtFunctionRegister(bin_handle, bin_file_key, bin_file_key, kernel_name.c_str(), 0));
     used_tbe_handle_map_[bin_file_key] = 1;  // Init used num to 1.
     return SUCCESS;
   }
-
   // Kernel registed, Increase used num in store.
   StoreTbeHandle(bin_file_key);
   return SUCCESS;
 }
 
+Status DavinciModel::InitBinaryMagic(const OpDescPtr &op_desc, bool is_ffts, size_t thread_index,
+                                     rtDevBinary_t &binary) {
+  string json_string;
+  const string &tvm_magic = is_ffts ? TVM_ATTR_NAME_THREAD_MAGIC : TVM_ATTR_NAME_MAGIC;
+  const static std::map<std::string, uint32_t> binary_magics = {
+    {"RT_DEV_BINARY_MAGIC_ELF_AICPU", RT_DEV_BINARY_MAGIC_ELF_AICPU},
+    {"RT_DEV_BINARY_MAGIC_ELF", RT_DEV_BINARY_MAGIC_ELF},
+    {"RT_DEV_BINARY_MAGIC_ELF_AIVEC", RT_DEV_BINARY_MAGIC_ELF_AIVEC},
+    {"RT_DEV_BINARY_MAGIC_ELF_AICUBE", RT_DEV_BINARY_MAGIC_ELF_AICUBE}
+  };
+  if (is_ffts) {
+    vector<string> json_list;
+    (void)AttrUtils::GetListStr(op_desc, tvm_magic, json_list);
+    if (json_list.size() != kFftsTbeHandleElementSize) {
+      GELOGE(INTERNAL_ERROR, "[Check][Param] failed. Attr is %s, thread index is %zu, json list size is %zu.",
+             tvm_magic.c_str(), thread_index, json_list.size());
+      return INTERNAL_ERROR;
+    }
+    json_string = json_list[thread_index];
+  } else {
+    (void)AttrUtils::GetStr(op_desc, tvm_magic, json_string);
+  }
+  auto iter = binary_magics.find(json_string);
+  if (iter == binary_magics.end()) {
+    REPORT_INNER_ERROR("E19999", "Attr:%s value:%s in op:%s(%s), model_id:%u, check invalid",
+                       tvm_magic.c_str(), json_string.c_str(), op_desc->GetName().c_str(),
+                       op_desc->GetType().c_str(), model_id_);
+    GELOGE(PARAM_INVALID, "[Check][Param] Attr:%s value:%s in op:%s(%s), model_id:%u, check invalid",
+           TVM_ATTR_NAME_MAGIC.c_str(), json_string.c_str(),
+           op_desc->GetName().c_str(), op_desc->GetType().c_str(), model_id_);
+    return PARAM_INVALID;
+  }
+  binary.magic = iter->second;
+  return SUCCESS;
+}
+
+Status DavinciModel::InitMetaData(const OpDescPtr &op_desc, bool is_ffts, size_t thread_index, void *bin_handle) {
+  string meta_data;
+  const string &tvm_metadata = is_ffts ? TVM_ATTR_NAME_THREAD_METADATA : TVM_ATTR_NAME_METADATA;
+  if (is_ffts) {
+    vector<string> meta_data_list;
+    (void)AttrUtils::GetListStr(op_desc, tvm_metadata, meta_data_list);
+    if (meta_data_list.size() != kFftsTbeHandleElementSize) {
+      GELOGE(INTERNAL_ERROR, "[Check][Param] failed, attr is %s, thread index is %zu, meta data list size is %zu.",
+             tvm_metadata.c_str(), thread_index, meta_data_list.size());
+      return INTERNAL_ERROR;
+    }
+    meta_data = meta_data_list[thread_index];
+  } else {
+    (void)AttrUtils::GetStr(op_desc, tvm_metadata, meta_data);
+  }
+  GELOGD("TBE: meta data: %s", meta_data.empty() ? "null" : meta_data.c_str());
+  if (!meta_data.empty()) {
+    GE_CHK_RT_RET(rtMetadataRegister(bin_handle, meta_data.c_str()));
+  }
+  return SUCCESS;
+}
+
+Status DavinciModel::InitKernelName(const OpDescPtr &op_desc, bool is_ffts, size_t thread_index, string &kernel_name) {
+  if (is_ffts) {
+    // delete prefix, eg: *sgt_graph_nodes*/loss_scale/gradient/fp32_vals/Mean_grad/Tile
+    vector<string> kernel_name_list;
+    auto pos = op_desc->GetName().find("/");
+    if (pos == std::string::npos) {
+      GELOGE(INTERNAL_ERROR, "[Check][Param] failed, subgraph node name: %s.", op_desc->GetName().c_str());
+      return INTERNAL_ERROR;
+    }
+    string attr_kernel_name = op_desc->GetName().substr(pos + 1) + "_thread_kernelname";
+    (void)AttrUtils::GetListStr(op_desc, attr_kernel_name, kernel_name_list);
+    if (kernel_name_list.size() != kFftsTbeHandleElementSize) {
+      GELOGE(INTERNAL_ERROR, "[Check][Param] failed, attr is %s, thread index is %zu, kernel name list size is %zu.",
+             attr_kernel_name.c_str(), thread_index, kernel_name_list.size());
+      return INTERNAL_ERROR;
+    }
+    kernel_name = kernel_name_list[thread_index];
+  } else {
+    string attr_kernel_name = op_desc->GetName() + "_kernelname";
+    (void)AttrUtils::GetStr(op_desc, attr_kernel_name, kernel_name);
+  }
+  return SUCCESS;
+}
+
 void DavinciModel::StoreTbeHandle(const std::string &handle_key) {
   // Online mode FE may call rtFunctionRegister.
   TBEHandleStore &kernel_store = TBEHandleStore::GetInstance();
@@ -4256,7 +4373,7 @@ void DavinciModel::SetDataDumperArgs(const ComputeGraphPtr &graph, const map<str
   data_dumper_.SetDeviceId(device_id);
 
   if (known_node_) {
-    data_dumper_.SetLoopAddr(known_shape_global_step_, nullptr, nullptr);
+    data_dumper_.SetLoopAddr(global_step_addr_, nullptr, nullptr);
   } else {
     // set loop count addr
     auto get_var_addr = [&](const string &name) -> void *{

ge/graph/load/model_manager/davinci_model.h

@@ -300,6 +300,7 @@ class DavinciModel {
     return op_list_.at(index);
   }
 
+  void SetGlobalStep(void *global_step, uint64_t global_step_size);
   void *GetGlobalStep() const { return global_step_addr_; }
 
   // get task info for profiling
@@ -498,10 +499,6 @@ class DavinciModel {
     return exception_dumper_.DumpExceptionInfo(exception_infos);
   }
 
-  void SetKnownShapeGlobalStep(void *global_step) {
-    known_shape_global_step_ = global_step;
-  }
-
   void DumperShrink() {
     data_dumper_.DumpShrink();
   }
@@ -771,6 +768,12 @@ class DavinciModel {
   /// @return Status
   ///
   Status InitTbeHandle(const OpDescPtr &op_desc);
+  Status InitTbeHandleWithFfts(const OpDescPtr &op_desc);
+  Status FunctionRegister(const OpDescPtr &op_desc, string &bin_file, OpKernelBinPtr &tbe_kernel, bool is_ffts,
+                          size_t thread_index = 0);
+  Status InitBinaryMagic(const OpDescPtr &op_desc, bool is_ffts, size_t thread_index, rtDevBinary_t &binary);
+  Status InitMetaData(const OpDescPtr &op_desc, bool is_ffts, size_t thread_index, void *bin_handle);
+  Status InitKernelName(const OpDescPtr &op_desc, bool is_ffts, size_t thread_index, string &kernel_name);
 
   void StoreTbeHandle(const string &handle_key);
   void CleanTbeHandle();
@@ -1102,9 +1105,6 @@ class DavinciModel {
   vector<InputOutputDescInfo> output_descs_;
   vector<uint32_t> output_formats_;
 
-  // known shape node for dump
-  void *known_shape_global_step_;
-
   // op name to attrs mapping
   std::map<std::string, std::map<std::string, std::vector<std::string>>> op_name_to_attrs_;
 };

ge/graph/load/model_manager/model_manager.cc

@@ -570,6 +570,7 @@ Status ModelManager::DataInputTensor(uint32_t model_id, const std::vector<ge::Te
       uint32_t length = static_cast<uint32_t>(cur_dynamic_dims.size() * sizeof(int32_t));
       GE_CHK_BOOL_EXEC(memcpy_s(data.data, length, cur_dynamic_dims.data(), length) == EOK,
                        REPORT_CALL_ERROR("E19999", "memcpy data failed, size:%u", length);
+                       delete[] reinterpret_cast<int32_t *>(data.data);
                        return INTERNAL_ERROR, "[Memcpy][Data] failed, size:%u.", length);
       data.length = length;
       input_data.blobs.push_back(data);
@@ -1378,7 +1379,9 @@ Status ModelManager::LoadCustAicpuSo(const OpDescPtr &op_desc, const string &so_
 Status ModelManager::LaunchKernelCustAicpuSo(const string &kernel_name) {
   GELOGD("Aicpu kernel launch task in, kernel name %s.", kernel_name.c_str());
   std::lock_guard<std::mutex> lock(cust_aicpu_mutex_);
-  if (cust_aicpu_so_.size() == 0) return SUCCESS;
+  if (cust_aicpu_so_.empty()) {
+    return SUCCESS;
+  }
   // get current context
   rtContext_t rt_cur_ctx = nullptr;
   auto rt_error = rtCtxGetCurrent(&rt_cur_ctx);
@@ -1394,9 +1397,19 @@ Status ModelManager::LaunchKernelCustAicpuSo(const string &kernel_name) {
     return SUCCESS;
   }
 
+  rtStream_t stream = nullptr;
   vector<void *> allocated_mem;
+  std::function<void()> callback = [&]() {
+    for (auto mem : allocated_mem) {
+      GE_CHK_RT(rtFree(mem));
+    }
+    if (stream != nullptr) {
+      GE_CHK_RT(rtStreamDestroy(stream));
+    }
+  };
+  GE_MAKE_GUARD(release, callback);
+
   rtError_t status;
-  rtStream_t stream = nullptr;
   vector<CustAicpuSoBuf> v_cust_so;
   void *args = nullptr;
 
@@ -1471,13 +1484,6 @@ Status ModelManager::LaunchKernelCustAicpuSo(const string &kernel_name) {
     GELOGE(RT_FAILED, "[Call][RtStreamSynchronize] fail, ret = 0x%X", status);
     return RT_ERROR_TO_GE_STATUS(status);
   }
-  std::function<void()> callback = [&]() {
-    for (auto mem : allocated_mem) {
-      GE_CHK_RT(rtFree(mem));
-    }
-    GE_CHK_RT(rtStreamDestroy(stream));
-  };
-  GE_MAKE_GUARD(release, callback);
   GELOGI("Cpu kernel launch task success.");
   return SUCCESS;
 }
@@ -1786,7 +1792,8 @@ Status ModelManager::LaunchKernelCheckAicpuOp(std::vector<std::string> &aicpu_op
       std::vector<char> op_name;
       op_name.clear();
       op_name.resize(kOpNameMaxSize);
-      GE_CHK_RT(rtMemcpy(op_name.data(), aicpu_info.opLen, reinterpret_cast<void *>(aicpu_info.opType),
+      GE_CHK_RT(rtMemcpy(op_name.data(), aicpu_info.opLen,
+                         reinterpret_cast<void *>(static_cast<uintptr_t>(aicpu_info.opType)),
                          aicpu_info.opLen, RT_MEMCPY_DEVICE_TO_HOST));
       std::string kernel_type =
           (static_cast<OpKernelType>(aicpu_info.kernelsType) == TF_KERNEL) ? "TF_KERNEL" : "CPU_KERNEL";

ge/graph/load/model_manager/task_info/ffts_task_info.cc

@@ -0,0 +1,393 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * 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 "graph/load/model_manager/task_info/ffts_task_info.h"
+
+#include <vector>
+
+#include "graph/load/model_manager/davinci_model.h"
+
+namespace {
+constexpr uint32_t kAddrLen = sizeof(void *);
+}
+namespace ge {
+FftsTaskInfo::~FftsTaskInfo() {
+  GE_FREE_RT_LOG(args_);
+}
+
+Status FftsTaskInfo::Init(const domi::TaskDef &task_def, DavinciModel *davinci_model) {
+  GELOGI("FftsTaskInfo Init Start.");
+  GE_CHECK_NOTNULL(davinci_model);
+  davinci_model_ = davinci_model;
+  GE_CHK_STATUS_RET_NOLOG(SetStream(task_def.stream_id(), davinci_model_->GetStreamList()));
+
+  const domi::FftsTaskDef &ffts_task_def = task_def.ffts_task();
+  OpDescPtr op_desc = davinci_model_->GetOpByIndex(ffts_task_def.op_index());
+  GE_CHECK_NOTNULL(op_desc);
+
+  if ((ffts_task_def.sub_task_size() > static_cast<int>(RT_FFTS_MAX_SUB_TASK_NUM)) ||
+      (ffts_task_def.ticket_cache_size() > static_cast<int>(RT_FFTS_MAX_TICKET_CACHE_NUM))) {
+    GELOGE(INTERNAL_ERROR, "[Check][Param] failed. Node: %s, sub task desc size: %d, ticket cache size: %d",
+           op_desc->GetName().c_str(), ffts_task_def.sub_task_size(), ffts_task_def.ticket_cache_size());
+    return INTERNAL_ERROR;
+  }
+  args_size_ = kAddrLen * ffts_task_def.addr_size();
+  GE_CHK_RT_RET(rtMalloc(&args_, args_size_, RT_MEMORY_HBM));
+  InitFftsDescInfo(ffts_task_def.ffts_desc(), sub_task_info_.fftsDesc);
+
+  sub_task_info_.fftsType = static_cast<rtFftsType_t>(ffts_task_def.ffts_type());
+  sub_task_info_.subTaskNum = ffts_task_def.sub_task_size();
+  for (int idx = 0; idx < ffts_task_def.sub_task_size(); ++idx) {
+    GE_CHK_STATUS_RET_NOLOG(InitSubTaskInfo(ffts_task_def.sub_task(idx), sub_task_info_.subTask[idx]));
+  }
+
+  sub_task_info_.tickCacheNum = ffts_task_def.ticket_cache_size();
+  for (int idx = 0; idx < ffts_task_def.ticket_cache_size(); ++idx) {
+    GE_CHK_STATUS_RET_NOLOG(InitTicketCache(ffts_task_def.ticket_cache(idx), sub_task_info_.ticketCache[idx]));
+  }
+
+  size_t data_size = kAddrLen * io_addrs_.size();
+  GE_CHK_RT_RET(rtMemcpy(args_, args_size_, io_addrs_.data(), data_size, RT_MEMCPY_HOST_TO_DEVICE));
+  GELOGI("FftsTaskInfo::Init Success. Node: %s, input/output size: %zu", op_desc->GetName().c_str(), io_addrs_.size());
+  return SUCCESS;
+}
+
+void FftsTaskInfo::InitFftsDescInfo(const domi::FftsDescInfoDef &ffts_desc_def, rtFftsDescInfo_t &ffts_desc) {
+  ffts_desc.tm = static_cast<uint8_t>(ffts_desc_def.tm());
+  ffts_desc.di = static_cast<uint8_t>(ffts_desc_def.di());
+  ffts_desc.dw = static_cast<uint8_t>(ffts_desc_def.dw());
+  ffts_desc.df = static_cast<uint8_t>(ffts_desc_def.df());
+  ffts_desc.dataSplitUnit = static_cast<uint8_t>(ffts_desc_def.data_split_unit());
+  ffts_desc.prefetchOstNum = static_cast<uint8_t>(ffts_desc_def.prefetch_ost_num());
+  ffts_desc.cacheMaintainOstNum = static_cast<uint8_t>(ffts_desc_def.cache_maintain_ost_num());
+  ffts_desc.aicPrefetchUpper = static_cast<uint8_t>(ffts_desc_def.aic_prefetch_upper());
+  ffts_desc.aicPrefetchLower = static_cast<uint8_t>(ffts_desc_def.aic_prefetch_lower());
+  ffts_desc.aivPrefetchUpper = static_cast<uint8_t>(ffts_desc_def.aiv_prefetch_upper());
+  ffts_desc.aivPrefetchLower = static_cast<uint8_t>(ffts_desc_def.aiv_prefetch_lower());
+}
+
+Status FftsTaskInfo::InitSubTaskInfo(const domi::FftsSubTaskDef &sub_task_def, rtFftsSubTaskInfo_t &sub_task_desc) {
+  if ((sub_task_def.dst_tick_cache_id_size() > static_cast<int>(RT_FFTS_MAX_TICKET_CACHE_PER_SUBTASK)) ||
+      (sub_task_def.src_tick_cache_id_size() > static_cast<int>(RT_FFTS_MAX_TICKET_CACHE_PER_SUBTASK))) {
+    GELOGE(FAILED, "[Check][Param] Invalid FftsSubTaskInfo, dst tick cache id size: %d, src tick cache id size: %d",
+           sub_task_def.dst_tick_cache_id_size(), sub_task_def.src_tick_cache_id_size());
+    return FAILED;
+  }
+
+  if (sub_task_def.has_auto_thread_aic_aiv() == sub_task_def.has_manual_thread_aic_aiv()) {
+    GELOGE(FAILED, "[Check][Param] Invalid FftsSubTaskInfo, auto thread aic/aiv: %d, manual thread aic/aiv: %d",
+           sub_task_def.has_auto_thread_aic_aiv(), sub_task_def.has_manual_thread_aic_aiv());
+    return FAILED;
+  }
+
+  thread_dim_ = sub_task_def.thread_dim();
+  GE_CHK_BOOL_RET_STATUS(thread_dim_ != 0, FAILED, "[Get][thread_dim] failed, Invalid thread dim: %u!", thread_dim_);
+
+  sub_task_desc.subTaskType = static_cast<rtFftsSubTaskType_t>(sub_task_def.sub_task_type());
+  sub_task_desc.threadDim = sub_task_def.thread_dim();
+
+  sub_task_desc.dstTickCacheVldBitmap = sub_task_def.dst_tick_cache_vld_bitmap();
+  sub_task_desc.srcTickCacheVldBitmap = sub_task_def.src_tick_cache_vld_bitmap();
+  sub_task_desc.srcDataOutOfSubGraphBitmap = sub_task_def.src_data_out_of_subgraph_bitmap();
+
+  for (int idx = 0; idx < sub_task_def.dst_tick_cache_id_size(); ++idx) {
+    sub_task_desc.dstTickCacheID[idx] = sub_task_def.dst_tick_cache_id(idx);
+  }
+
+  for (int idx = 0; idx < sub_task_def.src_tick_cache_id_size(); ++idx) {
+    sub_task_desc.srcTickCacheID[idx] = sub_task_def.src_tick_cache_id(idx);
+  }
+
+  if (sub_task_def.has_auto_thread_aic_aiv()) {
+    GE_CHK_STATUS_RET_NOLOG(InitAutoAicAiv(sub_task_def.auto_thread_aic_aiv(), sub_task_desc.custom.autoThreadAicAiv));
+  }
+
+  if (sub_task_def.has_manual_thread_aic_aiv()) {
+    GE_CHK_STATUS_RET_NOLOG(
+        InitManualAicAiv(sub_task_def.manual_thread_aic_aiv(), sub_task_desc.custom.manualThreadAicAiv));
+  }
+
+  if (sub_task_def.has_manual_thread_nop()) {
+    GE_CHK_STATUS_RET_NOLOG(InitManualNop(sub_task_def.manual_thread_nop(), sub_task_desc.custom.manualThreadNop));
+  }
+
+  return SUCCESS;
+}
+
+Status FftsTaskInfo::InitTicketCache(const domi::TicketCacheDef &ticket_cache_def, rtTicketCache_t &ticket_cache) {
+  if (ticket_cache_def.has_auto_thread_cache() == ticket_cache_def.has_manual_thread_cache()) {
+    GELOGE(FAILED, "[Check][Param] Invalid TicketCacheDef, has auto thread cache: %d, has manual thread cache: %d",
+           ticket_cache_def.has_auto_thread_cache(), ticket_cache_def.has_manual_thread_cache());
+    return FAILED;
+  }
+
+  ticket_cache.cacheOption = static_cast<rtCacheOp_t>(ticket_cache_def.cache_option());
+  ticket_cache.ticketCacheWindow = ticket_cache_def.ticket_cache_window();
+
+  if (ticket_cache_def.has_auto_thread_cache()) {
+    InitAutoCacheInfo(ticket_cache_def.auto_thread_cache(), ticket_cache.custom.autoThreadCache);
+  }
+  if (ticket_cache_def.has_manual_thread_cache()) {
+    GE_CHK_STATUS_RET_NOLOG(
+        InitManualCacheInfo(ticket_cache_def.manual_thread_cache(), ticket_cache.custom.manualThreadCache));
+  }
+
+  return SUCCESS;
+}
+
+// task_addr = {0,200,700,1000,2000, 3500}
+// task_addr_offset = {20,40,2,100,200}
+template <typename T>
+Status FftsTaskInfo::InitIoAddrs(const RuntimeParam &rts_param, const T &aic_aiv_def, uint32_t thread_dim,
+                                 uint32_t addr_count) {
+  for (uint32_t i = 0; i < addr_count; ++i) {
+    uintptr_t logic_addr = aic_aiv_def.task_addr(i)  + thread_dim * aic_aiv_def.task_addr_offset(i);
+    uint8_t *io_addr = nullptr;
+    if (ModelUtils::GetRtAddress(rts_param, logic_addr, io_addr) != SUCCESS) {
+      GELOGE(INTERNAL_ERROR, "[Check][GetRtAddress]GetRtAddress failed.");
+      return INTERNAL_ERROR;
+    }
+    GELOGD("aic_aiv_def task base addr is %ld, offset is %ld, thread is %d, logic addrs is 0x%lx, io addr is %p",
+           aic_aiv_def.task_addr(i), aic_aiv_def.task_addr_offset(i), thread_dim, logic_addr, io_addr);
+    io_addrs_.emplace_back(io_addr);
+  }
+  return SUCCESS;
+}
+
+Status FftsTaskInfo::InitAutoAicAiv(const domi::AutoThreadAicAivDef &aic_aiv_def, rtAutoThreadAicAivInfo_t &aic_aiv) {
+  if (aic_aiv_def.src_prefetch_size() > static_cast<int>(RT_FFTS_MAX_TICKET_CACHE_PER_SUBTASK)) {
+    GELOGE(FAILED, "[Check][Param] Invalid AutoThreadAicAivInfo, prefetch size: %d", aic_aiv_def.src_prefetch_size());
+    return FAILED;
+  }
+
+  aic_aiv.taskParamAddr = reinterpret_cast<uintptr_t>(args_) + kAddrLen * io_addrs_.size();
+  GELOGD("AutoThreadAicAivDef: task param addr is %lu.", aic_aiv.taskParamAddr);
+  const auto &rts_param = davinci_model_->GetRuntimeParam();
+  for (uint32_t i = 0; i < thread_dim_ - 1; ++i) {
+    GE_CHK_STATUS_RET_NOLOG(InitIoAddrs(rts_param, aic_aiv_def, i,
+                                        static_cast<uint32_t>(aic_aiv_def.task_addr_offset_size())));
+  }
+  GE_CHK_STATUS_RET_NOLOG(InitIoAddrs(rts_param, aic_aiv_def, thread_dim_ - 1, aic_aiv_def.input_output_count()));
+  int last_thread_workspace_size = aic_aiv_def.task_addr_size() - aic_aiv_def.task_addr_offset_size();
+  for (int k = 0; k < last_thread_workspace_size; ++k) {
+    uintptr_t logic_addr = aic_aiv_def.task_addr(aic_aiv_def.task_addr_offset_size() + k);
+    uint8_t *io_addr = nullptr;
+    GE_CHK_STATUS_RET_NOLOG(ModelUtils::GetRtAddress(rts_param, logic_addr, io_addr));
+    GELOGD("logic addr is 0x%lx, io addr is %p.", logic_addr, io_addr);
+    io_addrs_.emplace_back(io_addr);
+  }
+
+  aic_aiv.taskParamOffset = aic_aiv_def.task_param_offset();
+  GELOGD("args_: %p, io_addrs size: %zu, task param offset: %u.", args_, io_addrs_.size(), aic_aiv.taskParamOffset);
+  aic_aiv.satMode = aic_aiv_def.sat_mode();
+  aic_aiv.scheduleMode = aic_aiv_def.schedule_mode();
+  aic_aiv.iCachePrefetchCnt = aic_aiv_def.cache_prefetch_cnt();
+
+  aic_aiv.prefetchEnableBitmap = aic_aiv_def.prefetch_enable_bitmap();
+  aic_aiv.prefetchOnceBitmap = aic_aiv_def.prefetch_once_bitmap();
+
+  aic_aiv.tailBlkDim = aic_aiv_def.tail_blk_dim();
+  aic_aiv.nonTailBlkDim = aic_aiv_def.non_tail_blk_dim();
+
+  aic_aiv.nonTailTaskFuncStub = davinci_model_->GetRegisterStub(aic_aiv_def.non_tail_task_func_stub(), "");
+  aic_aiv.tailTaskFuncStub = davinci_model_->GetRegisterStub(aic_aiv_def.tail_task_func_stub(), "");
+
+  GELOGI("Set func name[%s][%s] succ.", aic_aiv.nonTailTaskFuncStub, aic_aiv.tailTaskFuncStub);
+  for (int idx = 0; idx < aic_aiv_def.src_prefetch_size(); ++idx) {
+    InitAutoPrefetch(aic_aiv_def.src_prefetch(idx), aic_aiv.srcPrefetch[idx]);
+  }
+
+  return SUCCESS;
+}
+
+void FftsTaskInfo::InitAutoCacheInfo(const domi::AutoThreadCacheDef &cache_def, rtAutoThreadCacheInfo_t &cache) {
+  cache.dataAddr = cache_def.data_addr();
+  cache.dataAddrOffset = cache_def.data_addr_offset();
+  cache.nonTailDataLen = cache_def.non_tail_data_len();
+  cache.tailDataLen = cache_def.tail_data_len();
+  cache.ticketCacheRefCnt = cache_def.ticket_cache_ref_cnt();
+}
+
+void FftsTaskInfo::InitAutoPrefetch(const domi::AutoThreadPrefetchDef &prefetch_def, rtAutoThreadPrefetch_t &prefetch) {
+  prefetch.dataAddr = prefetch_def.data_addr();
+  prefetch.dataAddrOffset = prefetch_def.data_addr_offset();
+  prefetch.nonTailDataLen = prefetch_def.non_tail_data_len();
+  prefetch.tailDataLen = prefetch_def.tail_data_len();
+}
+
+Status FftsTaskInfo::InitManualAicAiv(const domi::ManualThreadAicAivDef &aic_aiv_def,
+                                      rtManualThreadAicAivInfo_t &aic_aiv) {
+  if ((aic_aiv_def.thread_prefetch_dmu_idx_size() > static_cast<int>(RT_FFTS_MAX_MANUAL_THREAD_NUM)) ||
+      (aic_aiv_def.thread_blk_dim_size() > static_cast<int>(RT_FFTS_MAX_MANUAL_THREAD_NUM)) ||
+      (aic_aiv_def.thread_task_func_stub_size() > static_cast<int>(RT_FFTS_MAX_MANUAL_THREAD_NUM)) ||
+      (aic_aiv_def.src_dep_tbl_size() > static_cast<int>(RT_FFTS_MAX_TICKET_CACHE_PER_SUBTASK))) {
+    GELOGE(FAILED, "[Check][Param] Invalid ManualThreadAicAivInfo, thread prefetch dmu desc size: %d, "
+           "thread blk dim size: %d, thread task func stub size: %d, src dep tbl size: %d",
+           aic_aiv_def.thread_prefetch_dmu_idx_size(), aic_aiv_def.thread_blk_dim_size(),
+           aic_aiv_def.thread_task_func_stub_size(), aic_aiv_def.src_dep_tbl_size());
+    return FAILED;
+  }
+  aic_aiv.taskParamAddr = reinterpret_cast<uintptr_t>(args_) + kAddrLen * io_addrs_.size();
+  GELOGD("ManualThreadAicAivDef: task param addr is %lu.", aic_aiv.taskParamAddr);
+  const auto &rts_param = davinci_model_->GetRuntimeParam();
+  for (uint32_t i = 0; i < thread_dim_ - 1; ++i) {
+    GE_CHK_STATUS_RET_NOLOG(InitIoAddrs(rts_param, aic_aiv_def, i,
+                                        static_cast<uint32_t>(aic_aiv_def.task_addr_offset_size())));
+  }
+  GE_CHK_STATUS_RET_NOLOG(InitIoAddrs(rts_param, aic_aiv_def, thread_dim_ - 1, aic_aiv_def.input_output_count()));
+  int last_thread_workspace_size = aic_aiv_def.task_addr_size() - aic_aiv_def.task_addr_offset_size();
+  for (int k = 0; k < last_thread_workspace_size; ++k) {
+    uintptr_t logic_addr = aic_aiv_def.task_addr(aic_aiv_def.task_addr_offset_size() + k);
+    uint8_t *io_addr = nullptr;
+    GE_CHK_STATUS_RET_NOLOG(ModelUtils::GetRtAddress(rts_param, logic_addr, io_addr));
+    io_addrs_.emplace_back(io_addr);
+  }
+  aic_aiv.taskParamOffset = aic_aiv_def.task_param_offset();
+
+  aic_aiv.satMode = aic_aiv_def.sat_mode();
+  aic_aiv.scheduleMode = aic_aiv_def.schedule_mode();
+  aic_aiv.iCachePrefetchCnt = aic_aiv_def.cache_prefetch_cnt();
+
+  aic_aiv.prefetchEnableBitmap = aic_aiv_def.prefetch_enable_bitmap();    // 8 bit bitmap 1 0 1 0
+  aic_aiv.prefetchOnceBitmap = aic_aiv_def.prefetch_once_bitmap();   // 8 bit bitmap 1 0 1 0
+  aic_aiv.prefetchOnceDmuNum = aic_aiv_def.prefetch_once_dmu_num();
+
+  for (int idx = 0; idx < aic_aiv_def.thread_prefetch_dmu_idx_size(); ++idx) {
+    aic_aiv.threadPrefetchDmuIdx[idx] = aic_aiv_def.thread_prefetch_dmu_idx(idx);
+  }
+  for (int idx = 0; idx < aic_aiv_def.thread_blk_dim_size(); ++idx) {
+    aic_aiv.threadBlkDim[idx] = aic_aiv_def.thread_blk_dim(idx);
+  }
+  for (int idx = 0; idx < aic_aiv_def.thread_task_func_stub_size(); ++idx) {
+    aic_aiv.threadTaskFuncStub[idx] = aic_aiv_def.thread_task_func_stub(idx).c_str();
+  }
+
+  InitManualDmuInfo(aic_aiv_def, aic_aiv.prefetchList);
+  for (int idx = 0; idx < aic_aiv_def.src_dep_tbl_size(); ++idx) {
+    GE_CHK_STATUS_RET_NOLOG(InitManualDependency(aic_aiv_def.src_dep_tbl(idx), aic_aiv.srcDepTbl[idx]));
+  }
+
+  return SUCCESS;
+}
+
+Status FftsTaskInfo::InitManualCacheInfo(const domi::ManualThreadCacheDef &cache_def,
+                                         rtManualThreadCacheInfo_t &cache_info) {
+  if ((cache_def.slice_dmu_idx_size() > static_cast<int>(RT_FFTS_MAX_MANUAL_THREAD_NUM)) ||
+      (cache_def.ticket_cache_ref_cnt_tbl_size() > static_cast<int>(RT_FFTS_MAX_MANUAL_THREAD_NUM))) {
+    GELOGE(FAILED, "[Check][Param] Invalid ManualThreadCacheInfo slice dum desc index %d, ticket cache ref cnt %d",
+           cache_def.slice_dmu_idx_size(), cache_def.ticket_cache_ref_cnt_tbl_size());
+    return FAILED;
+  }
+
+  InitManualDmuInfo(cache_def, cache_info.dmuList);
+  for (int idx = 0; idx < cache_def.slice_dmu_idx_size(); ++idx) {
+    cache_info.sliceDmuIdx[idx] = cache_def.slice_dmu_idx(idx);
+  }
+
+  for (int idx = 0; idx < cache_def.ticket_cache_ref_cnt_tbl_size(); ++idx) {
+    cache_info.ticketCacheRefCntTbl[idx] = cache_def.ticket_cache_ref_cnt_tbl(idx);
+  }
+
+  return SUCCESS;
+}
+
+Status FftsTaskInfo::InitManualDependency(const domi::ManualThreadDependencyDef &dependency_def,
+                                          rtManualThreadDependency_t &dependency) {
+  if (dependency_def.dependency_size() > static_cast<int>(RT_FFTS_MANUAL_SRC_DEPEND_TBL_LEN)) {
+    GELOGE(FAILED, "[Check][Param] Invalid ManualThreadDependency size: %d", dependency_def.dependency_size());
+    return FAILED;
+  }
+
+  for (int idx = 0; idx < dependency_def.dependency_size(); ++idx) {
+    dependency.dependency[idx] = dependency_def.dependency(idx);
+  }
+
+  return SUCCESS;
+}
+
+Status FftsTaskInfo::InitManualNop(const domi::ManualThreadNopDef &nop_def, rtManualThreadNopInfo_t &nop_info) {
+  if (nop_def.src_dep_tbl_size() > static_cast<int>(RT_FFTS_MAX_TICKET_CACHE_PER_SUBTASK)) {
+    GELOGE(FAILED, "[Check][Param] Invalid ManualThreadNopInfo, src dep tbl size: %d", nop_def.src_dep_tbl_size());
+    return FAILED;
+  }
+
+  for (int idx = 0; idx < nop_def.src_dep_tbl_size(); ++idx) {
+    GE_CHK_STATUS_RET_NOLOG(InitManualDependency(nop_def.src_dep_tbl(idx), nop_info.srcDepTbl[idx]));
+  }
+
+  return SUCCESS;
+}
+
+void FftsTaskInfo::InitManualDmuInfo(const domi::ManualThreadAicAivDef &aic_aiv_def, rtManualThreadDmuInfo_t *&dmu) {
+  if (aic_aiv_def.prefetch_list().empty()) {
+    return;
+  }
+
+  std::vector<uint8_t> buffer(sizeof(rtManualThreadDmuInfo_t) * aic_aiv_def.prefetch_list_size());
+  dmu = reinterpret_cast<rtManualThreadDmuInfo_t *>(buffer.data());
+  for (int idx = 0; idx < aic_aiv_def.prefetch_list_size(); ++idx) {
+    InitManualDmuInfo(aic_aiv_def.prefetch_list(idx), dmu[idx]);
+  }
+}
+
+void FftsTaskInfo::InitManualDmuInfo(const domi::ManualThreadCacheDef &cache_def, rtManualThreadDmuInfo_t *&dmu) {
+  if (cache_def.dmu_list().empty()) {
+    return;
+  }
+
+  std::vector<uint8_t> buffer(sizeof(rtManualThreadDmuInfo_t) * cache_def.dmu_list_size());
+  dmu = reinterpret_cast<rtManualThreadDmuInfo_t *>(buffer.data());
+  for (int idx = 0; idx < cache_def.dmu_list_size(); ++idx) {
+    InitManualDmuInfo(cache_def.dmu_list(idx), dmu[idx]);
+  }
+}
+
+void FftsTaskInfo::InitManualDmuInfo(const domi::ManualThreadDmuDef &dmu_def, rtManualThreadDmuInfo_t &dmu) {
+  dmu.dataAddr = dmu_def.data_addr();
+  dmu.numOuter = dmu_def.num_outer();
+  dmu.numInner = dmu_def.num_inner();
+  dmu.strideOuter = dmu_def.stride_outer();
+  dmu.lenInner = dmu_def.len_inner();
+  dmu.strideInner = dmu_def.stride_inner();
+}
+
+Status FftsTaskInfo::CalculateArgs(const domi::TaskDef &task_def, DavinciModel *davinci_model) {
+  return SUCCESS;
+}
+
+Status FftsTaskInfo::UpdateArgs() {
+  GE_CHECK_NOTNULL(davinci_model_);
+  std::vector<void *> io_addrs = io_addrs_;
+  davinci_model_->UpdateKnownZeroCopyAddr(io_addrs);
+  auto addr_size = kAddrLen * io_addrs.size();
+  GE_CHK_RT_RET(rtMemcpy(args_, args_size_, io_addrs.data(), addr_size, RT_MEMCPY_HOST_TO_DEVICE));
+  return SUCCESS;
+}
+
+Status FftsTaskInfo::Distribute() {
+  GELOGI("FftsTaskInfo Distribute Start.");
+  rtError_t rt_ret = rtFftsTaskLaunch(&sub_task_info_, stream_);
+  if (rt_ret != RT_ERROR_NONE) {
+    GELOGE(RT_FAILED, "[Check][RT_ret] Call rtFftsTaskLaunch failed, ret: 0x%X", rt_ret);
+    return RT_ERROR_TO_GE_STATUS(rt_ret);
+  }
+
+  GELOGI("FftsTaskInfo Distribute Success.");
+  return SUCCESS;
+}
+
+REGISTER_TASK_INFO(RT_MODEL_TASK_FFTS_TASK, FftsTaskInfo);
+}  // namespace ge

ge/graph/load/model_manager/task_info/ffts_task_info.h

@@ -0,0 +1,66 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef GE_GRAPH_LOAD_NEW_MODEL_MANAGER_TASK_INFO_FFTS_TASK_INFO_H_
+#define GE_GRAPH_LOAD_NEW_MODEL_MANAGER_TASK_INFO_FFTS_TASK_INFO_H_
+
+#include "graph/load/model_manager/task_info/task_info.h"
+#include "graph/op_desc.h"
+
+namespace ge {
+class FftsTaskInfo : public TaskInfo {
+ public:
+  FftsTaskInfo() = default;
+  ~FftsTaskInfo() override;
+
+  Status Init(const domi::TaskDef &task_def, DavinciModel *davinci_model) override;
+
+  Status Distribute() override;
+
+  Status UpdateArgs() override;
+
+  Status CalculateArgs(const domi::TaskDef &task_def, DavinciModel *davinci_model) override;
+
+ private:
+  void InitFftsDescInfo(const domi::FftsDescInfoDef &ffts_desc_def, rtFftsDescInfo_t &ffts_desc);
+  Status InitSubTaskInfo(const domi::FftsSubTaskDef &task_def, rtFftsSubTaskInfo_t &task);
+  Status InitTicketCache(const domi::TicketCacheDef &cache_def, rtTicketCache_t &cache);
+
+  Status InitAutoAicAiv(const domi::AutoThreadAicAivDef &aic_aiv_def, rtAutoThreadAicAivInfo_t &aic_aiv);
+  void InitAutoCacheInfo(const domi::AutoThreadCacheDef &cache_def, rtAutoThreadCacheInfo_t &cache);
+  void InitAutoPrefetch(const domi::AutoThreadPrefetchDef &prefetch_def, rtAutoThreadPrefetch_t &prefetch);
+
+  Status InitManualAicAiv(const domi::ManualThreadAicAivDef &aic_aiv_def, rtManualThreadAicAivInfo_t &aic_aiv);
+  Status InitManualCacheInfo(const domi::ManualThreadCacheDef &cache_def, rtManualThreadCacheInfo_t &cache);
+  Status InitManualDependency(const domi::ManualThreadDependencyDef &depend_def, rtManualThreadDependency_t &depend);
+  Status InitManualNop(const domi::ManualThreadNopDef &nop_def, rtManualThreadNopInfo_t &nop);
+
+  void InitManualDmuInfo(const domi::ManualThreadDmuDef &dmu_def, rtManualThreadDmuInfo_t &dmu);
+  void InitManualDmuInfo(const domi::ManualThreadCacheDef &cache_def, rtManualThreadDmuInfo_t *&dmu);
+  void InitManualDmuInfo(const domi::ManualThreadAicAivDef &aic_aiv_def, rtManualThreadDmuInfo_t *&dmu);
+
+  template<typename T>
+  Status InitIoAddrs(const RuntimeParam &rts_param, const T &aic_aiv_def, uint32_t thread_dim, uint32_t addr_count);
+
+  DavinciModel *davinci_model_{nullptr};
+  rtFftsTaskInfo_t sub_task_info_;
+  std::vector<void *> io_addrs_;
+  uint32_t thread_dim_{0};
+  void *args_{nullptr};    // runtime args memory
+  uint32_t args_size_{0};    // runtime args memory length
+};
+}  // namespace ge
+#endif  // GE_GRAPH_LOAD_NEW_MODEL_MANAGER_TASK_INFO_FFTS_TASK_INFO_H_

ge/graph/load/model_manager/task_info/hccl_task_info.cc

@@ -329,7 +329,7 @@ void HcclTaskInfo::GetPrivateDefByTaskDef(const domi::TaskDef &task) {
   // Get privateDef and opsKernelStorePtr from taskDef and save them in taskInfo
   GELOGI("get custom info in modelTaskDef.");
   ops_kernel_store_ = nullptr;
-  void *ops_kernel_store_name_temp = reinterpret_cast<void *>(task.ops_kernel_store_ptr());
+  void *ops_kernel_store_name_temp = reinterpret_cast<void *>(static_cast<uintptr_t>(task.ops_kernel_store_ptr()));
   if (ops_kernel_store_name_temp != nullptr) {
     ops_kernel_store_ = std::move(ops_kernel_store_name_temp);
     std::string private_def_temp = task.private_def();

ge/graph/load/model_manager/task_info/kernel_task_info.cc

@@ -645,6 +645,7 @@ Status KernelTaskInfo::InitTVMTask(uint16_t offset, const domi::KernelDef &kerne
   GE_CHECK_NOTNULL(op_desc);
 
   args_addr = std::unique_ptr<uint8_t[]>(new (std::nothrow) uint8_t[args_size_]);
+  GE_CHECK_NOTNULL(args_addr);
   errno_t sec_ret = memcpy_s(args_addr.get(), args_size_, kernel_def.args().data(), args_size_);
   if (sec_ret != EOK) {
     REPORT_CALL_ERROR("E19999", "Call memcpy_s fail, size:%u, ret:0x%X", args_size_, sec_ret);
@@ -1000,6 +1001,7 @@ Status KernelTaskInfo::InitAicpuTask(uint32_t op_index, const domi::KernelDef &k
 
   // copy args to new host memory
   args_addr = std::unique_ptr<uint8_t[]>(new (std::nothrow) uint8_t[args_size_]);
+  GE_CHECK_NOTNULL(args_addr);
   GE_PRINT_DYNAMIC_MEMORY(new, "cce task physical memory.", sizeof(uint8_t) * args_size_)
   errno_t sec_ret = memcpy_s(args_addr.get(), args_size_, kernel_def.args().data(), args_size_);
   if (sec_ret != EOK) {

ge/graph/load/model_manager/task_info/memcpy_async_task_info.h

@@ -47,7 +47,7 @@ class MemcpyAsyncTaskInfo : public TaskInfo {
   uint64_t count_;
   uint32_t kind_;
   vector<void *> io_addrs_;
-  int64_t fixed_addr_offset_;
+  int64_t fixed_addr_offset_ = 0;
   DavinciModel *davinci_model_ = nullptr;
   uint32_t args_offset_ = 0;
 };

ge/graph/load/model_manager/zero_copy_offset.cc

@@ -62,7 +62,8 @@ Status ZeroCopyOffset::InitInputDataInfo(int64_t output_size, void *virtual_addr
     for (size_t index = 0; index < zero_copy_basic_offset_.size(); ++index) {
       if (zero_copy_basic_offset_.at(index) == virtual_addr_offset) {
         out_count++;
-        uint64_t out_offset = reinterpret_cast<uint64_t>(virtual_addr) + zero_copy_relative_offset_.at(index);
+        uint64_t out_offset = static_cast<uint64_t>(reinterpret_cast<uintptr_t>(virtual_addr)) +
+                              zero_copy_relative_offset_.at(index);
         data_info_.emplace_back(output_size, reinterpret_cast<void *>(static_cast<uintptr_t>(out_offset)));
         relative_offset_.emplace_back(zero_copy_relative_offset_.at(index));
         GELOGI("[ZCPY] virtual_addr: %p has been l2-fusion to %lu, need copy data_size is %ld.", basic_addr_,
@@ -117,7 +118,8 @@ Status ZeroCopyOffset::InitOutputDataInfo(const vector<int64_t> &input_size_list
     for (size_t index = 0; index < zero_copy_basic_offset_.size(); ++index) {
       if (zero_copy_basic_offset_.at(index) == virtual_addr_offset) {
         in_count++;
-        uint64_t in_offset = reinterpret_cast<uint64_t>(virtual_addr_list[idx]) + zero_copy_relative_offset_.at(index);
+        uint64_t in_offset = static_cast<uint64_t>(reinterpret_cast<uintptr_t>(virtual_addr_list[idx])) +
+                             zero_copy_relative_offset_.at(index);
         int64_t real_data_size = ModelUtils::GetInputSize(op_desc).at(idx);
         data_info_.emplace_back(real_data_size, reinterpret_cast<void *>(static_cast<uintptr_t>(in_offset)));
         relative_offset_.emplace_back(zero_copy_relative_offset_.at(index));

ge/graph/manager/graph_manager.cc

@@ -27,6 +27,7 @@
 #include "common/math/math_util.h"
 #include "common/thread_pool.h"
 #include "common/dump/dump_manager.h"
+#include "ge_opt_info/ge_opt_info.h"
 #include "analyzer/analyzer.h"
 #include "graph/common/ge_call_wrapper.h"
 #include "graph/common/local_context.h"
@@ -120,7 +121,6 @@ const char *const kCheckPointForGetVar = "CheckPointGraphForGetVar";
 const char *const kCheckPointGraph = "checkpoint_graph";
 const char *const kVectorEngine = "VectorEngine";
 const char *const kAIcoreEngine = "AIcoreEngine";
-const char *const kRunFlagOffline = "0";
 const int32_t kDynamicDimsTypeIsGetNext = 0;
 const int32_t kDynamicDimsTypeIsData = 1;
 const char *const kGetNextName = "IteratorV2";
@@ -950,7 +950,7 @@ Status GraphManager::SetRtContext(rtContext_t rt_context, rtCtxMode_t mode, uint
 
   rtError_t rt_ret = rtCtxCreate(&rt_context, mode, ge::GetContext().DeviceId());
   if (rt_ret != RT_ERROR_NONE) {
-    REPORT_CALL_ERROR("E19999", "Call rtCtxCreate faileded, session_id:%lu, graph_id:%u, mode:%d",
+    REPORT_CALL_ERROR("E19999", "Call rtCtxCreate failed, session_id:%lu, graph_id:%u, mode:%d",
                       session_id, graph_id, mode);
     GELOGE(FAILED, "[Call][RtCtxCreate] faileded, session_id:%lu, graph_id:%u, mode:%d", session_id, graph_id, mode);
     return FAILED;
@@ -1002,6 +1002,12 @@ Status GraphManager::PreRun(const GraphNodePtr &graph_node, const std::vector<Ge
     return ret;
   }
 
+  ret = GeOptInfo::SetOptInfo();
+  if (ret != SUCCESS) {
+    GELOGE(ret, "[Set][OptInfo] Set optional information failed.");
+    return ret;
+  }
+
   /// 1. BUILD_MODE_TUNING with BUILD_STEP_AFTER_UB_MATCH no need PreRunOptimizeOriginalGraph;
   /// 2. BUILD_MODE_TUNING with BUILD_STEP_AFTER_MERGE no need PreRunOptimizeOriginalGraph.
   /// 3. BUILD_MODE_TUNING with BUILD_STEP_AFTER_BUILDER_SUB no need PreRunOptimizeOriginalGraph.
@@ -1789,8 +1795,7 @@ Status GraphManager::ParseOptions(const std::map<std::string, std::string> &opti
                   return GE_GRAPH_OPTIONS_INVALID);
 
   // ge.graphType
-  ret =
-    ParseTrainGraphFlag(options_.run_graph_flag, options_.train_graph_flag);
+  ret = ParseTrainGraphFlag(options_.run_graph_flag, options_.train_graph_flag);
   GE_IF_BOOL_EXEC(ret != SUCCESS,
                   GELOGE(GE_GRAPH_OPTIONS_INVALID, "[Parse][TrainGraphFlag] Key:ge.runFlag value is invalid");
                   return GE_GRAPH_OPTIONS_INVALID);
@@ -2436,6 +2441,8 @@ Status GraphManager::RemoveIsolatedConstInThisGraph(ge::ComputeGraphPtr &compute
       continue;
     }
     if (n->GetOpDesc()->GetType() == CONSTANT || n->GetOpDesc()->GetType() == CONSTANTOP) {
+      // reset const type depend on train_flag
+      options_.train_graph_flag ? n->GetOpDesc()->SetType(CONSTANTOP) : n->GetOpDesc()->SetType(CONSTANT);
       if (n->GetOutAllNodes().empty() && n->GetInAllNodes().empty()) {
         // it is an isolated constant, just remove it
         if (GraphUtils::RemoveJustNode(compute_graph, n) != GRAPH_SUCCESS) {
@@ -2762,35 +2769,22 @@ Status GraphManager::OptimizeStage2(ge::ComputeGraphPtr &compute_graph) {
         "Please pay attention to it.");
   }
 
-  GE_CHK_STATUS_RET(ChangeConstType(compute_graph));
+  ChangeConstTypeWhenTraining(compute_graph);
 
   GELOGI("End optimize after merge sub graph.");
   return SUCCESS;
 }
 
-Status GraphManager::ChangeConstType(const ComputeGraphPtr &compute_graph) {
-  // run_flag off means offline, on means online
-  string run_flag;
-  (void)ge::GetContext().GetOption(ge::RUN_FLAG, run_flag);
-  // The constant for online is CONSTANTOP, and is CONSTANT for offline. They will be unified in future.
-  if (run_flag == kRunFlagOffline) {
-    GELOGI("Offline mode, change all Constant to Const.");
-  } else {
-    GELOGI("Online mode, change all Const to Constant.");
-  }
-  for (NodePtr &n : compute_graph->GetAllNodes()) {
-    GE_CHECK_NOTNULL(n);
-    if (n->GetType() == CONSTANT || n->GetType() == CONSTANTOP) {
-      auto op_desc = n->GetOpDesc();
-      GE_CHECK_NOTNULL(op_desc);
-      if (run_flag == kRunFlagOffline) {
-        op_desc->SetType(CONSTANT);
-      } else {
-        op_desc->SetType(CONSTANTOP);
+void GraphManager::ChangeConstTypeWhenTraining(const ComputeGraphPtr &compute_graph) {
+  // The constant for train is CONSTANTOP, and is CONSTANT for inference. They will be unified in future.
+  if (options_.train_graph_flag) {
+    for (NodePtr &n : compute_graph->GetAllNodes()) {
+      // This can ensure that n is not a null pointer
+      if (n->GetOpDesc()->GetType() == CONSTANT) {
+        n->GetOpDesc()->SetType(CONSTANTOP);
       }
     }
   }
-  return SUCCESS;
 }
 
 Status GraphManager::LoadGraphAsync(const GeRootModelPtr &ge_root_model, const GraphNodePtr &graph_node) {
@@ -3145,10 +3139,10 @@ void GraphManager::PreRunThread(GraphManager *graph_manager) {
     }
     // Avoid repeatively prerun for graphs owns same graph_id in online inference concurrency
     if (count > 1 && graph_node->GetBuildFlag()) {
-      graph_node->Lock();
       GELOGD("Avoid repeatively prerun, graph_id:%u.", args.graph_id);
       // In online inference concurrency senario, graph_node is allowed to be locked for 'count' times
       graph_node->SetSemSize(count);
+      graph_node->Lock();
       graph_manager->run_args_q_.Push(RunArgs( { graph_node, args.graph_id, args.session_id, args.error_context,
           args.input_tensor, graph_node->GetGeRootModel(), GetThreadLocalContext(), args.callback }));
       GELOGI("[PreRunThread] Loop end. Start to run with cached build model.");

ge/graph/manager/graph_manager.h

@@ -375,7 +375,7 @@ class GraphManager {
   static void ReturnError(GraphManager *graph_manager, GraphNodePtr &graph_node, RunAsyncCallback callback,
                           Status ret, const string &log);
 
-  Status ChangeConstType(const ComputeGraphPtr &compute_graph);
+  void ChangeConstTypeWhenTraining(const ComputeGraphPtr &compute_graph);
 
   Status PreRunOptimizeOriginalGraph(const GraphNodePtr &graph_node, const std::vector<GeTensor> &inputs,
                                      ge::ComputeGraphPtr &compute_graph, uint64_t session_id);

ge/graph/manager/graph_var_manager.cc

@@ -20,6 +20,7 @@
 #include "graph/manager/graph_mem_manager.h"
 #include "graph/manager/trans_var_data_utils.h"
 #include "graph/utils/type_utils.h"
+#include "graph/ge_context.h"
 
 using std::map;
 using std::string;
@@ -816,25 +817,60 @@ Status VarManager::GetChangedGraphId(const std::string &var_name, uint32_t &grap
   return var_resource_->GetChangedGraphId(var_name, graph_id);
 }
 
+Status VarManager::GetTotalMemorySize(size_t &total_mem_size) {
+  rtError_t rt_ret = rtSetDevice(GetContext().DeviceId());
+  if (rt_ret != RT_ERROR_NONE) {
+    REPORT_CALL_ERROR("E19999", "Call rtSetDevice failed, device_id:%u, ret:0x%X",
+                      GetContext().DeviceId(), rt_ret);
+    GELOGE(RT_FAILED, "[Call][RtSetDevice] failed, device_id:%u, ret:0x%X", GetContext().DeviceId(), rt_ret);
+    return RT_FAILED;
+  }
+  size_t free_mem = 0;
+  rt_ret = rtMemGetInfoEx(RT_MEMORYINFO_HBM, &free_mem, &total_mem_size);
+  if (rt_ret != RT_ERROR_NONE) {
+    REPORT_CALL_ERROR("E19999", "Call rtMemGetInfo failed, ret:0x%X", rt_ret);
+    GELOGE(RT_FAILED, "[Call][RtMemGetInfo] failed, ret:0x%X", rt_ret);
+    return RT_FAILED;
+  }
+  if (total_mem_size == 0) {
+    rt_ret = rtMemGetInfoEx(RT_MEMORYINFO_DDR, &free_mem, &total_mem_size);
+    if (rt_ret != RT_ERROR_NONE) {
+      REPORT_CALL_ERROR("E19999", "Call rtMemGetInfo failed, ret:0x%X", rt_ret);
+      GELOGE(RT_FAILED, "[Call][RtMemGetInfo] failed, ret:0x%X", rt_ret);
+      return RT_FAILED;
+    }
+  }
+  rt_ret = rtDeviceReset(GetContext().DeviceId());
+  if (rt_ret != RT_ERROR_NONE) {
+    REPORT_CALL_ERROR("E19999", "Call rtDeviceReset failed, device_id:%u, ret:0x%X",
+                      GetContext().DeviceId(), rt_ret);
+    GELOGE(RT_FAILED, "[Call][RtDeviceReset] failed, device_id:%u, ret:0x%X", GetContext().DeviceId(), rt_ret);
+    return RT_FAILED;
+  }
+  return SUCCESS;
+}
+
 Status VarManager::SetMemoryMallocSize(const map<string, string> &options) {
-  auto it = options.find(GRAPH_MEMORY_MAX_SIZE);
-  if (it == options.end()) {
-    graph_mem_max_size_ = kGraphMemoryManagerMallocMaxSize;
-  } else {
-    string graph_memory_manager_malloc_max_size = it->second;
+  size_t total_mem_size = 0;
+  GE_CHK_STATUS_RET_NOLOG(VarManager::GetTotalMemorySize(total_mem_size));
+  GEEVENT("Total memory size is %zu", total_mem_size);
+
+  graph_mem_max_size_ = floor(total_mem_size * kGraphMemoryManagerMallocRatio);
+  var_mem_max_size_ = floor(total_mem_size * kVarMemoryManagerMallocRatio);
+
+  auto it1 = options.find(GRAPH_MEMORY_MAX_SIZE);
+  if (it1 != options.end()) {
+    string graph_memory_manager_malloc_max_size = it1->second;
     ge::Status ret = ParseMemoryMallocSize(graph_memory_manager_malloc_max_size, graph_mem_max_size_);
     if (ret != SUCCESS) {
       GELOGE(ge::GE_GRAPH_OPTIONS_INVALID, "[Call][ParseMemoryMallocSize] failed, session id:%lu.", session_id_);
       return ge::GE_GRAPH_OPTIONS_INVALID;
     }
-    GELOGI("The max size for graph mem is set to %zu", graph_mem_max_size_);
   }
 
-  it = options.find(VARIABLE_MEMORY_MAX_SIZE);
-  if (it == options.end()) {
-    var_mem_max_size_ = kMemoryVarManagerMallocSize;
-  } else {
-    string memory_var_manager_malloc_size = it->second;
+  auto it2 = options.find(VARIABLE_MEMORY_MAX_SIZE);
+  if (it2 != options.end()) {
+    string memory_var_manager_malloc_size = it2->second;
     ge::Status ret = ParseMemoryMallocSize(memory_var_manager_malloc_size, var_mem_max_size_);
     if (ret != SUCCESS) {
       GELOGE(ge::GE_GRAPH_OPTIONS_INVALID, "[Call][ParseMemoryMallocSize] failed, session id:%lu.", session_id_);
@@ -842,6 +878,8 @@ Status VarManager::SetMemoryMallocSize(const map<string, string> &options) {
     }
   }
 
+  GEEVENT("The graph_mem_max_size is %zu and the var_mem_max_size is %zu", graph_mem_max_size_, var_mem_max_size_);
+
   var_mem_logic_base_ = graph_mem_max_size_ + kGraphMemoryBuffer;
   if (var_mem_logic_base_ > kMaxMemorySize) {
     REPORT_INNER_ERROR("E19999", "var_login_base:%zu can not exeed limit:%zu, session_id:%lu, check invalid",

ge/graph/manager/graph_var_manager.h

@@ -43,6 +43,8 @@ const size_t kMaxMemorySize = 256UL * 1024UL * 1024UL * 1024UL;
 const char kEnvGeuseStaticMemory[] = "GE_USE_STATIC_MEMORY";
 const uint64_t kSessionMemAlignSize = 512;
 const size_t kSessionMemAlignUnit = 2;
+const double kGraphMemoryManagerMallocRatio = 26.0 / 32.0;
+const double kVarMemoryManagerMallocRatio = 5.0 / 32.0;
 
 enum MemStatus {
   NORMAL = 0,
@@ -316,6 +318,7 @@ class FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY VarManager {
   mutable std::recursive_mutex mutex_;
 
   Status ParseMemoryMallocSize(std::string &memory_size, size_t &my_size);
+  Status GetTotalMemorySize(size_t &total_mem_size);
 };
 
 class VarManagerPool {

ge/graph/optimize/graph_optimize.cc

@@ -336,10 +336,8 @@ Status GraphOptimize::OptimizeAfterStage1(ComputeGraphPtr &compute_graph) {
         GELOGI("[OptimizeAfterStage1]: engine type will exclude:%s.", exclude_core_type.c_str());
         continue;
       }
-#ifndef ONLY_COMPILE_OPEN_SRC
       GELOGI("Begin to optimize graph after stage1 by engine %s.", iter->first.c_str());
       ret = (iter->second)->OptimizeAfterStage1(*compute_graph);
-#endif
       if (ret != SUCCESS) {
         REPORT_INNER_ERROR("E19999", "Call OptimizeAfterStage1 failed, ret:%d, engine_name:%s, "
                            "graph_name:%s.", ret, iter->first.c_str(), compute_graph->GetName().c_str());

ge/graph/partition/dynamic_shape_partition.cc

@@ -284,9 +284,6 @@ Status DynamicShapePartitioner::InitClusters() {
     auto cluster = MakeShared<Cluster>(rank++, type, node, this);
     REQUIRE_NOT_NULL(cluster, "[New][Memory] for cluster failed.");
     node_2_cluster_[node] = cluster;
-    if (cluster->IsUnknownShape()) {
-      ordered_cluster_.push_back(cluster);
-    }
 
     int64_t group_index = -1;
     if (AttrUtils::GetInt(node->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index)) {
@@ -306,7 +303,7 @@ Status DynamicShapePartitioner::InitClusters() {
   return SUCCESS;
 }
 
-Status DynamicShapePartitioner::TopologicalSortClusters() {
+Status DynamicShapePartitioner::TopologicalSortClusters(const OrderedFilter &ordered_filter) {
   ordered_cluster_.clear();
   // BFS topological sort clusters for known shape cluster
   std::queue<ClusterPtr> ready_clusters;
@@ -331,7 +328,7 @@ Status DynamicShapePartitioner::TopologicalSortClusters() {
     auto cluster = ready_clusters.front();
     ready_clusters.pop();
     cluster->UpdateRank(rank++);
-    if (cluster->IsKnownShape() || cluster->IsInputNode()) {
+    if (ordered_filter == nullptr || ordered_filter(cluster)) {
       ordered_cluster_.push_back(cluster);
     }
     for (const auto &out_cluster : cluster->Outputs()) {
@@ -364,6 +361,7 @@ static std::string ToString(const std::vector<ClusterPtr> &clusters) {
 }
 
 void DynamicShapePartitioner::MergeClustersControlFlow() {
+  std::unordered_set<ClusterPtr> all_merged_clusters;
   for (const auto &item : control_clusters_) {
     const auto &control_cluster = item.second;
     auto rit = control_cluster.rbegin();
@@ -373,12 +371,21 @@ void DynamicShapePartitioner::MergeClustersControlFlow() {
     }
 
     const auto &cluster = *rit;
+    if (all_merged_clusters.count(cluster) > 0) {
+      continue;
+    }
+
     for (++rit; rit != control_cluster.rend(); ++rit) {
       const auto &cluster_from = *rit;
+      if (all_merged_clusters.count(cluster_from) > 0) {
+        continue;
+      }
+
       auto merged_clusters = cluster->MergeAllPathFrom(cluster_from);
       GELOGD("Merge all path cluster from %lu to %lu %s.", cluster_from->Id(), cluster->Id(),
              ToString(merged_clusters).c_str());
       for (const auto &merged_cluster : merged_clusters) {
+        all_merged_clusters.emplace(merged_cluster);
         for (const auto &node : merged_cluster->Nodes()) {
           node_2_cluster_[node] = cluster;
         }
@@ -459,9 +466,19 @@ void DynamicShapePartitioner::MergeClustersInputData() {
 }
 
 Status DynamicShapePartitioner::MergeClusters() {
+  const auto filter_known = [](const ClusterPtr &cluster) {
+    return cluster->IsKnownShape() || cluster->IsInputNode();
+  };
+  const auto filter_unknown = [](const ClusterPtr &cluster) {
+    return cluster->IsUnknownShape();
+  };
+
   MergeClustersControlFlow();
+  REQUIRE_SUCCESS(TopologicalSortClusters(filter_unknown),
+                  "[TopologicalSort][Clusters] after merge control flow clusters failed.");
   MergeClustersUnknownShape();
-  REQUIRE_SUCCESS(TopologicalSortClusters(), "[TopologicalSort][Clusters] after merge unknown shape clusters failed.");
+  REQUIRE_SUCCESS(TopologicalSortClusters(filter_known),
+                  "[TopologicalSort][Clusters] after merge unknown shape clusters failed.");
   MergeClustersKnownShape();
   MergeClustersInputData();
   return SUCCESS;
@@ -703,7 +720,12 @@ void Cluster::Merge(ClusterPtr other) {
   if (other->min_ < min_) {
     min_ = other->min_;
   }
-};
+
+  if (!IsUnknownShape() && other->IsUnknownShape()) {
+    type_ = UNKNOWN_SHAPE;
+  }
+}
+
 bool Cluster::TryMerge(ClusterPtr other) {
   std::queue<ClusterPtr> forward_reached;
   forward_reached.push(other);

ge/graph/partition/dynamic_shape_partition.h

@@ -111,6 +111,8 @@ class DynamicShapePartitioner {
 
   Status Partition();
 
+  using OrderedFilter = std::function<bool(const std::shared_ptr<Cluster> &cluster)>;
+
  private:
   Status PartitionImpl();
   // Collect nodes that satisfy the unknowshape rules:
@@ -138,7 +140,7 @@ class DynamicShapePartitioner {
   // Merge clusters step3
   void MergeClustersInputData();
   // Topological sort clusters after merge unknown shape clusters.
-  Status TopologicalSortClusters();
+  Status TopologicalSortClusters(const OrderedFilter &ordered_filter);
   // Deduplicate merged clusters
   void PruneUniqueClusters();
   // Establish the input-output anchors for each partition of the cluster and record links to other clusters
@@ -161,7 +163,7 @@ class DynamicShapePartitioner {
   ge::ComputeGraphPtr root_graph_;                                        // The original graph to partition
   std::unordered_map<NodePtr, std::shared_ptr<Cluster>> node_2_cluster_;  // Record nodes and the cluster it belongs to
   // V1 control flow cluster, need merge to one Graph.
-  std::unordered_map<int64_t, std::vector<std::shared_ptr<Cluster>>> control_clusters_;
+  std::map<int64_t, std::vector<std::shared_ptr<Cluster>>> control_clusters_;
   // topological sorted clusters, this field will change with the splitting.
   // When partitioning UNKNOWN_SHAPE cluster, it is a collection of all topological sorted UNKNOWN_SHAPE clusters
   // When partitioning KNOWN_SHAPE cluster, it is a collection of all topological sorted KNOWN_SHAPE clusters

ge/graph/partition/graph_partition.cc

@@ -179,6 +179,7 @@ Status ge::GraphPartitioner::MergeAfterSubGraphOptimization(ge::ComputeGraphPtr
     GELOGE(ret, "[Merge][SubGraph] Failed, ret:%d", ret);
   }
   GE_CHECK_NOTNULL(original_compute_graph);
+  output_merged_compute_graph->SetName(original_compute_graph->GetName());
   // partition sub graph
   for (const auto &sub_graph : original_compute_graph->GetAllSubgraphs()) {
     ComputeGraphPtr merged_sub_graph = nullptr;
@@ -188,8 +189,16 @@ Status ge::GraphPartitioner::MergeAfterSubGraphOptimization(ge::ComputeGraphPtr
       GELOGE(ret, "[Merge][SubGraph] Failed, ret:%d", ret);
       continue;
     }
+    // this means subgraph added in optimize subgraph and without partitions, so just add to root graph
+    if (merged_sub_graph == sub_graph) {
+      GELOGI("Just add subgraph %s (parent node is %s) to root graph %s.", sub_graph->GetName().c_str(),
+             sub_graph->GetParentNode()->GetName().c_str(), output_merged_compute_graph->GetName().c_str());
+      sub_graph->SetParentGraph(sub_graph->GetParentNode()->GetOwnerComputeGraph());
+      GE_IF_BOOL_EXEC(output_merged_compute_graph->AddSubgraph(sub_graph->GetName(), merged_sub_graph) != SUCCESS,
+                      return FAILED;)
+      continue;
+    }
     // add sub graph
-    output_merged_compute_graph->SetName(original_compute_graph->GetName());
     merged_sub_graph->SetName(sub_graph->GetName());
     merged_sub_graph->SetInputSize(sub_graph->GetInputSize());
     merged_sub_graph->SetOutputSize(sub_graph->GetOutputSize());
@@ -245,12 +254,9 @@ Status ge::GraphPartitioner::MergeSubGraph(ge::ComputeGraphPtr &output_merged_co
   }
   if ((graph_2_graph_partition_info_.find(original_compute_graph) == graph_2_graph_partition_info_.end()) ||
       (graph_2_subgraph_list_.find(original_compute_graph) == graph_2_subgraph_list_.end())) {
-    REPORT_INNER_ERROR("E19999", "original_compute_graph:%s is not find in graph_2_graph_partition_info_.",
-                       original_compute_graph->GetName().c_str());
-    GELOGE(GE_GRAPH_NULL_INPUT,
-           "[Check][Param] original_compute_graph:%s is not find in graph_2_graph_partition_info_.",
-           original_compute_graph->GetName().c_str());
-    return FAILED;
+    GELOGW("[GraphPartition]: compute_graph has not found, just return original.");
+    output_merged_compute_graph = original_compute_graph;
+    return SUCCESS;
   }
   GraphPartitionInfo &subgraph_info = graph_2_graph_partition_info_[original_compute_graph];
   const auto &sub_graph_list = graph_2_subgraph_list_[original_compute_graph];
@@ -708,6 +714,7 @@ Status ge::GraphPartitioner::AddPartitionsToGraphNode(vector<ge::SubGraphInfoPtr
     }
     auto &engine_name = graph_info_.partitions_.at(sub_graph);
     (void)AttrUtils::SetStr(sub_graph, ATTR_NAME_PARENT_GRAPH_NAME, compute_graph->GetName());
+    (void)sub_graph->SetExtAttr("part_src_graph", compute_graph);
     GELOGD("set attr success. subgraph(%s) with parent graph(%s)", sub_graph->GetName().c_str(),
            compute_graph->GetName().c_str());
     GE_DUMP(sub_graph, sub_graph->GetName()  + "_" + mode_2_str_[graph_info_.mode_]);

ge/graph/passes/mark_force_unknown_for_cond_pass.cc

@@ -16,8 +16,6 @@
 
 #include "mark_force_unknown_for_cond_pass.h"
 
-#include <queue>
-
 #include "graph/utils/node_utils.h"
 #include "graph/common/omg_util.h"
 
@@ -26,17 +24,7 @@ namespace {
 inline bool IsMergeInLoop(const NodePtr &node) {
   const static std::set<std::string> kLoopMergeInputs{ ENTER, REFENTER, NEXTITERATION, REFNEXTITERATION };
 
-  std::string node_type;
-  (void)GetOriginalType(node, node_type);
-  return kLoopMergeInputs.count(node_type) > 0;
-}
-
-inline bool IsSwitchInLoop(const NodePtr &node) {
-  const static std::set<std::string> kLoopSwitchInputs{ MERGE, REFMERGE, LOOPCOND };
-
-  std::string node_type;
-  (void)GetOriginalType(node, node_type);
-  return kLoopSwitchInputs.count(node_type) > 0;
+  return kLoopMergeInputs.count(NodeUtils::GetNodeType(node)) > 0;
 }
 }
 
@@ -44,10 +32,7 @@ Status MarkForceUnknownForCondPass::Run(ComputeGraphPtr graph) {
   GELOGD("MarkForceUnknownForCondPass Enter");
   std::map<NodePtr, std::vector<NodePtr>> switch_groups;
   for (const auto &node : graph->GetDirectNode()) {
-    std::string node_type;
-    GE_CHK_STATUS_RET(GetOriginalType(node, node_type),
-                      "[Get][OriginalType] of node in graph:%s failed.", graph->GetName().c_str());
-    if (kMergeOpTypes.count(node_type) == 0) {
+    if (kMergeOpTypes.count(NodeUtils::GetNodeType(node)) == 0) {
       continue;
     }
 
@@ -65,6 +50,51 @@ Status MarkForceUnknownForCondPass::Run(ComputeGraphPtr graph) {
 }
 
 ///
+/// @brief Deal with Switch node for LoopCond
+/// @param [in] Switch node
+/// @param [in] dest span
+/// @param [out] Search queue
+/// @return true: Switch In while loop / false: Not in while Loop.
+///
+bool MarkForceUnknownForCondPass::DealAsLoopSwitch(const NodePtr &node, uint32_t dst_span,
+                                                   std::queue<std::pair<NodePtr, uint32_t>> &search_queue) {
+  ///                 LoopCond --->\.
+  ///                               \.
+  /// Enter-----------+              \.
+  ///                 +--> Merge --> Switch --> Exit
+  /// NextIteration---+
+  const auto is_loop_op = [](const NodePtr &n) {
+    return NodeUtils::GetNodeType(n) == LOOPCOND;
+  };
+  const auto is_exit_op = [](const NodePtr &n) {
+    return kExitOpTypes.count(NodeUtils::GetNodeType(n)) > 0;
+  };
+
+  const auto src_nodes = node->GetInAllNodes();
+  const auto dst_nodes = node->GetOutAllNodes();
+  if (std::none_of(src_nodes.begin(), src_nodes.end(), is_loop_op) &&
+      std::none_of(dst_nodes.begin(), dst_nodes.end(), is_exit_op)) {
+    return false;
+  }
+
+  for (const auto &m : src_nodes) {
+    if (kMergeOpTypes.count(NodeUtils::GetNodeType(m)) > 0) {
+      for (const auto &n : m->GetInAllNodes()) {
+        if (kNextIterationOpTypes.count(NodeUtils::GetNodeType(n)) > 0) {
+          continue;
+        }
+
+        search_queue.push({n, dst_span});
+        GELOGD("Travel in Loop: %s <-- %s <-- %s, span is: %u", node->GetName().c_str(), m->GetName().c_str(),
+               n->GetName().c_str(), dst_span);
+      }
+    }
+  }
+
+  return true;
+}
+
+///
 /// @brief Mark force unknown shape for Switch node
 /// @param [in] merge node
 /// @param [out] switch group
@@ -72,6 +102,7 @@ Status MarkForceUnknownForCondPass::Run(ComputeGraphPtr graph) {
 ///
 void MarkForceUnknownForCondPass::MarkUnknownForSwitch(const NodePtr &node, std::vector<NodePtr> &switch_group) {
   // Switch --> {Switch --> Merge} --> Merge
+  GELOGD("Search Switch node for Merge: %s", node->GetName().c_str());
   std::unordered_set<NodePtr> nodes_seen;
   std::queue<std::pair<NodePtr, uint32_t>> search_queue({{node, 0}});
   while (!search_queue.empty()) {
@@ -79,43 +110,25 @@ void MarkForceUnknownForCondPass::MarkUnknownForSwitch(const NodePtr &node, std:
     const auto dst_span = search_queue.front().second;
     search_queue.pop();
 
-    // Switch --> Identity --> Constant
-    for (const auto &in_node : dst_node->GetInControlNodes()) {
+    for (const auto &in_node : dst_node->GetInAllNodes()) {
       if (nodes_seen.count(in_node) > 0) {
         GELOGD("Travel node: %s, Skip already seen node: %s", dst_node->GetName().c_str(), in_node->GetName().c_str());
         continue;
       }
       nodes_seen.insert(in_node);
 
-      if (in_node->GetType() == IDENTITY) {
-        GELOGD("Travel node: %s, In control: %s, span is: %u", dst_node->GetName().c_str(),
-               in_node->GetName().c_str(), dst_span);
-        search_queue.push({in_node, dst_span});
-      }
-    }
-
-    for (const auto &in_node : dst_node->GetInDataNodes()) {
-      if (nodes_seen.count(in_node) > 0) {
-        GELOGD("Travel node: %s, Skip already seen node: %s", dst_node->GetName().c_str(), in_node->GetName().c_str());
-        continue;
-      }
-      nodes_seen.insert(in_node);
-
-      std::string node_type;
-      (void)GetOriginalType(in_node, node_type);
+      const std::string node_type = NodeUtils::GetNodeType(in_node);
       GELOGD("Travel node: %s, %s node: %s, span is: %u", dst_node->GetName().c_str(), node_type.c_str(),
              in_node->GetName().c_str(), dst_span);
       if (kSwitchOpTypes.count(node_type) > 0) { // Switch input node.
+        if (DealAsLoopSwitch(in_node, dst_span, search_queue)) {
+          continue;
+        }
+
         if (dst_span > 0) {
           search_queue.push({in_node, dst_span - 1});
         } else {
-          const auto &all_in_nodes = in_node->GetInDataNodes();
-          if (std::any_of(all_in_nodes.begin(), all_in_nodes.end(), IsSwitchInLoop)) {
-            GELOGW("Travel node: %s, %s node: %s, Skip LoopCond switch", dst_node->GetName().c_str(), node_type.c_str(),
-                   in_node->GetName().c_str());
-          } else {
-            switch_group.emplace_back(in_node);
-          }
+          switch_group.emplace_back(in_node);
         }
       } else if (kMergeOpTypes.count(node_type) > 0) { // Merge input node.
         search_queue.push({in_node, dst_span + 1});
@@ -132,39 +145,63 @@ void MarkForceUnknownForCondPass::MarkUnknownForSwitch(const NodePtr &node, std:
 /// @return
 ///
 void MarkForceUnknownForCondPass::MarkUnknownForSwitch(const std::map<NodePtr, std::vector<NodePtr>> &switch_groups) {
-  std::function<bool(const NodePtr &)> callback = [](const NodePtr &n) {
-    return n->GetOpDesc()->HasAttr(ATTR_NAME_CONTROL_FLOW_GROUP);
-  };
+  // Step 0: no group assigned. such as:
+  // Merge1{id=0, group=} => {Switch1{id=1, group=}, Switch2{id=2, group=}}
+  // Merge2{id=3, group=} => {Switch1{id=1, group=}, Switch3{id=4, group=}}
+  // Merge3{id=5, group=} => {Switch4{id=6, group=}, Switch5{id=7, group=}}
+  // Merge4{id=8, group=} => {Switch1{id=1, group=}, Switch5{id=7, group=}}
+  std::map<int64_t, int64_t> unique_groups;
+  const auto get_group_index = [&unique_groups](const NodePtr &merge, const std::vector<NodePtr> &switch_group) {
+    int64_t group_index = merge->GetOpDesc()->GetId();
+    std::set<int64_t> group_ids{group_index};
+    for (const auto &node : switch_group) {
+      if (AttrUtils::GetInt(node->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index)) {
+        GELOGI("[%s] Get group from [%s], index[%ld]", merge->GetName().c_str(), node->GetName().c_str(), group_index);
+        group_ids.insert(group_index);
+      }
+    }
 
-  for (auto it1 = switch_groups.begin(); it1 != switch_groups.end(); ++it1) {
-    const auto &op_node1 = it1->first;
-    const auto &op_desc1 = op_node1->GetOpDesc();
-    if (op_desc1->HasAttr(ATTR_NAME_CONTROL_FLOW_GROUP)) {
-      continue;
+    const auto it = unique_groups.find(group_index);
+    if (it != unique_groups.end()) {
+      group_index = it->second;
     }
 
-    if (IsUnknownShapeTensor(op_desc1->GetOutputDesc(0))) {
-      int64_t group_index = op_desc1->GetId();
-      GELOGI("Mark %s as unknown shape control flow, group index: %ld", op_desc1->GetName().c_str(), group_index);
-      MarkForceUnknownShape(op_node1, true, group_index);
-      for (const auto &n : it1->second) {
-        MarkForceUnknownShape(n, true, group_index);
-      }
+    for (auto id : group_ids) {
+      unique_groups[id] = group_index;
+    }
 
-      for (auto it2 = switch_groups.begin(); it2 != switch_groups.end(); ++it2) {
-        const auto &op_node2 = it2->first;
-        const auto &op_desc2 = op_node2->GetOpDesc();
-        if (op_desc2->HasAttr(ATTR_NAME_CONTROL_FLOW_GROUP)) {
-          continue;
-        }
+    return group_index;
+  };
 
-        if (std::any_of(it2->second.begin(), it2->second.end(), callback)) {
-          MarkForceUnknownShape(op_node2, true, group_index);
-          for (const auto &n : it2->second) {
-            MarkForceUnknownShape(n, true, group_index);
-          }
-        }
-      }
+  const auto set_group_index = [](const NodePtr &merge, const std::vector<NodePtr> &switch_group, int64_t group_index) {
+    SetControlFlowGroup(merge, group_index);
+    for (const auto &node : switch_group) {
+      SetControlFlowGroup(node, group_index);
+    }
+  };
+
+  // Step 1: Set group index to merge, if switch already has group, use assigned group.
+  // Merge1{id=0, group=0} => {Switch1{id=1, group=0}, Switch2{id=2, group=0}}
+  // Merge2{id=3, group=0} => {Switch1{id=1, group=0}, Switch3{id=4, group=0}}
+  // Merge3{id=5, group=5} => {Switch4{id=6, group=5}, Switch5{id=7, group=5}}
+  // Merge4{id=8, group=0} => {Switch1{id=1, group=0}, Switch5{id=7, group=0}}
+  for (const auto group : switch_groups) {
+    int64_t group_index = get_group_index(group.first, group.second);
+    set_group_index(group.first, group.second, group_index);
+  }
+
+  // Step 2: Adjust crossed merge group for unique group.
+  // Merge1{id=0, group=0} => {Switch1{id=1, group=0}, Switch2{id=2, group=0}}
+  // Merge2{id=3, group=0} => {Switch1{id=1, group=0}, Switch3{id=4, group=0}}
+  // Merge3{id=5, group=0} => {Switch4{id=6, group=0}, Switch5{id=7, group=0}}
+  // Merge4{id=8, group=0} => {Switch1{id=1, group=0}, Switch5{id=7, group=0}}
+  for (const auto group : switch_groups) {
+    int64_t group_index = -1;
+    (void)AttrUtils::GetInt(group.first->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index);
+
+    const auto it = unique_groups.find(group_index);
+    if (it != unique_groups.end() && it->first != it->second) {
+      set_group_index(group.first, group.second, it->second);
     }
   }
 }

ge/graph/passes/mark_force_unknown_for_cond_pass.h

@@ -19,6 +19,8 @@
 
 #include "inc/graph_pass.h"
 
+#include <queue>
+
 namespace ge {
 class MarkForceUnknownForCondPass : public GraphPass {
  public:
@@ -26,6 +28,15 @@ class MarkForceUnknownForCondPass : public GraphPass {
 
  private:
   ///
+  /// @brief Deal with Switch node for LoopCond
+  /// @param [in] Switch node
+  /// @param [in] dest span
+  /// @param [out] Search queue
+  /// @return true: Switch In while loop / false: Not in while Loop.
+  ///
+  bool DealAsLoopSwitch(const NodePtr &node, uint32_t dst_span, std::queue<std::pair<NodePtr, uint32_t>> &search_queue);
+
+  ///
   /// @brief Mark force unknown shape for Switch node
   /// @param [in] merge node
   /// @param [out] switch group

ge/graph/passes/mark_graph_unknown_status_pass.cc

@@ -40,6 +40,12 @@ Status MarkGraphUnknownStatusPass::Run(ComputeGraphPtr graph) {
     }
   }
 
+  const auto &node = graph->GetParentNode();
+  if (!is_unknown_shape && node != nullptr && node->GetType() == PARTITIONEDCALL) {
+    GE_CHK_GRAPH_STATUS_RET(NodeUtils::GetNodeUnknownShapeStatus(*node, is_unknown_shape),
+                            "[Get][ShapeStatus] of node[%s] failed!", node->GetName().c_str());
+  }
+
   for (const auto &node : graph->GetDirectNode()) {
     GELOGD("Set OwnerGraphIsUnknown attr to node[%s]", node->GetName().c_str());
     (void)AttrUtils::SetBool(node->GetOpDesc(), kOwnerGraphIsUnknown, is_unknown_shape);

ge/graph/passes/merge_to_stream_merge_pass.cc

@@ -89,8 +89,7 @@ Status MergeToStreamMergePass::AddActiveNodes(const ComputeGraphPtr &graph, cons
                    REPORT_INNER_ERROR("E19999", "Param node is nullptr, check invalid");
                    return FAILED, "[Check][Param] Param of pre node is nullptr.");
   int64_t group_index = -1;
-  bool force_unknown = AttrUtils::GetInt(node->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index);
-  MarkForceUnknownShape(node, force_unknown, group_index);
+  (void)AttrUtils::GetInt(node->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index);
   for (const InDataAnchorPtr &in_data_anchor : node->GetAllInDataAnchors()) {
     OutDataAnchorPtr peer_out_anchor = in_data_anchor->GetPeerOutAnchor();
     GE_IF_BOOL_EXEC(peer_out_anchor == nullptr, continue);
@@ -109,7 +108,7 @@ Status MergeToStreamMergePass::AddActiveNodes(const ComputeGraphPtr &graph, cons
       GELOGE(FAILED, "[Set][ActiveLabelList] for node %s failed.", active_node->GetName().c_str());
       return FAILED;
     }
-    MarkForceUnknownShape(active_node, force_unknown, group_index);
+    SetControlFlowGroup(active_node, group_index);
   }
 
   return SUCCESS;

ge/graph/passes/next_iteration_pass.cc

@@ -24,7 +24,9 @@ using std::string;
 
 namespace ge {
 namespace {
-const int64_t kLoopType = 1;
+constexpr int64_t kLoopType = 1;
+constexpr uint8_t kMaxTransOp = 3;
+constexpr uint8_t kTransOpIoSize = 1;
 }
 
 Status NextIterationPass::Run(ComputeGraphPtr graph) {
@@ -284,13 +286,28 @@ Status NextIterationPass::HandleWhileGroup(ComputeGraphPtr &graph) {
 /// @return void
 ///
 void NextIterationPass::HandleSwitchExitNodes(const LoopCondGroup &loop_group, int64_t group_index) {
+  std::string node_type;
   for (const auto &switch_node : loop_group.switch_nodes) {
     SetControlFlowGroup(switch_node, group_index);
-    for (const auto &node : switch_node->GetOutDataNodes()) {
-      std::string node_type;
-      (void)GetOriginalType(node, node_type);
-      if (kExitOpTypes.count(node_type) > 0) {
-        SetControlFlowGroup(node, group_index);
+    for (auto node : switch_node->GetOutDataNodes()) {
+      // Switch --> Exit
+      // Switch --> Cast --> Exit
+      // Switch --> TransData --> Cast --> Exit
+      for (uint8_t i  = 0; i < kMaxTransOp; ++i) {
+        if (node->GetInDataNodes().size() != kTransOpIoSize || node->GetAllOutDataAnchorsSize() != kTransOpIoSize) {
+          break;
+        }
+
+        if (kExitOpTypes.count(NodeUtils::GetNodeType(node)) > 0) {
+          SetControlFlowGroup(node, group_index);
+          break;
+        }
+
+        const auto &all_nodes = node->GetOutAllNodes();
+        if (all_nodes.size() != kTransOpIoSize) {
+          break;
+        }
+        node = all_nodes.at(0);
       }
     }
   }

ge/graph/passes/parallel_group_pass.cc

@@ -15,7 +15,7 @@
  */
 
 #include "graph/passes/parallel_group_pass.h"
-
+#include <queue>
 #include "framework/common/debug/ge_log.h"
 #include "common/ge/ge_util.h"
 #include "framework/common/ge_inner_error_codes.h"
@@ -299,24 +299,19 @@ Status ParallelGroupPass::ReplaceWithSwitchAndMerge(NodePtr pre_node, NodePtr cu
       for (const auto &switch_node : cur_itr->second.first) {
         int64_t pre_id = pre_node->GetOpDesc()->GetId();
         int64_t switch_id = switch_node->GetOpDesc()->GetId();
-        // avoid ring
-        if (pre_id > switch_id) {
-          auto merge_node = cur_itr->second.second;
-          if (AddCtrlEdge(merge_node, pre_node) != SUCCESS) {
-            GELOGE(FAILED, "[AddEdge][Node]Add edge for nodes: %s->%s failed.",
-                   pre_node->GetName().c_str(), switch_node->GetName().c_str());
-            REPORT_CALL_ERROR("E19999", "[AddEdge][Node]Add edge for nodes: %s->%s failed.",
-                              pre_node->GetName().c_str(), switch_node->GetName().c_str());
-            return FAILED;
-          }
-        } else {
-          if (AddCtrlEdge(pre_node, switch_node) != SUCCESS) {
-            GELOGE(FAILED, "[AddEdge][Node]Add edge for nodes: %s->%s failed.",
-                   pre_node->GetName().c_str(), switch_node->GetName().c_str());
-            REPORT_CALL_ERROR("E19999", "[AddEdge][Node]Add edge for nodes: %s->%s failed.",
-                              pre_node->GetName().c_str(), switch_node->GetName().c_str());
-            return FAILED;
-          }
+        NodePtr first_node = pre_node;
+        NodePtr second_node = switch_node;
+        if (pre_id > switch_id && IsIndirectConnect(switch_node, pre_node)) {
+          // avoid ring, merge->pre_node
+          first_node = cur_itr->second.second;
+          second_node = pre_node;
+        }
+        if (AddCtrlEdge(first_node, second_node) != SUCCESS) {
+          GELOGE(FAILED, "[AddEdge][Node]Add edge for nodes: %s->%s failed.",
+                 first_node->GetName().c_str(), second_node->GetName().c_str());
+          REPORT_CALL_ERROR("E19999", "[AddEdge][Node]Add edge for nodes: %s->%s failed.",
+                            first_node->GetName().c_str(), second_node->GetName().c_str());
+          return FAILED;
         }
       }
     } else {
@@ -345,4 +340,29 @@ bool ParallelGroupPass::IsWhileStreamSwitch(OpDescPtr switch_op_desc) {
   return (AttrUtils::GetInt(switch_op_desc, ATTR_NAME_STREAM_SWITCH_TYPE, stream_switch_type) &&
     stream_switch_type == kLoopType);
 }
+
+bool ParallelGroupPass::IsIndirectConnect(const NodePtr &node_a, const NodePtr &node_b) {
+  if (node_a == nullptr || node_b == nullptr) {
+    GELOGW("node_a or node_b is nullptr.");
+    return false;
+  }
+  int64_t end_id = node_b->GetOpDesc()->GetId();
+  std::queue<NodePtr> nodes;
+  nodes.push(node_a);
+  while (!nodes.empty()) {
+    NodePtr tmp_node = nodes.front();
+    nodes.pop();
+    if (tmp_node == nullptr || tmp_node->GetOpDesc() == nullptr ||
+        tmp_node->GetOpDesc()->GetId() > end_id) {
+      continue;
+    }
+    if (tmp_node == node_b) {
+      return true;
+    }
+    for (const auto &out_node : tmp_node->GetOutAllNodes()) {
+      nodes.push(out_node);
+    }
+  }
+  return false;
+}
 } // namespace ge

ge/graph/passes/parallel_group_pass.h

@@ -48,6 +48,7 @@ class ParallelGroupPass : public GraphPass {
 
   bool IsBigSmallLoopStreamSwitch(OpDescPtr switch_op_desc);
   bool IsWhileStreamSwitch(OpDescPtr switch_op_desc);
+  bool IsIndirectConnect(const NodePtr &node_a, const NodePtr &node_b);
 };
 }  // namespace ge
 #endif // GE_GRAPH_PASSES_PARALLEL_GROUP_PASS_H

ge/graph/passes/replace_with_empty_const_pass.cc

@@ -21,7 +21,23 @@
 #include "framework/common/debug/ge_log.h"
 #include "framework/common/ge_inner_error_codes.h"
 #include "graph/utils/graph_utils.h"
+#include "graph/utils/node_utils.h"
 
+namespace {
+const std::unordered_set<std::string> kControlFlowOps = {
+  ge::SWITCH,
+  ge::REFSWITCH,
+  ge::MERGE,
+  ge::REFMERGE,
+  ge::ENTER,
+  ge::REFENTER,
+  ge::NEXTITERATION,
+  ge::REFNEXTITERATION,
+  ge::EXIT,
+  ge::REFEXIT,
+  ge::LOOPCOND
+};
+}
 namespace ge {
 Status ReplaceWithEmptyConstPass::Run(NodePtr &node) {
   GELOGD("ReplaceWithEmptyConstPass in.");
@@ -39,6 +55,10 @@ Status ReplaceWithEmptyConstPass::Run(NodePtr &node) {
     GELOGI("Node %s is const. Ignore current pass.", node->GetName().c_str());
     return SUCCESS;
   }
+  if (kControlFlowOps.count(NodeUtils::GetNodeType(node)) != 0) {
+    GELOGI("Node %s is control flow op. Ignore current pass.", node->GetName().c_str());
+    return SUCCESS;
+  }
   // Node like no op, it has no output
   if (node->GetOpDesc()->GetAllOutputsDescPtr().empty()) {
     GELOGI("Node %s has no output desc. Ignore current pass.", node->GetName().c_str());

ge/graph/passes/switch_to_stream_switch_pass.cc

@@ -395,8 +395,9 @@ NodePtr SwitchToStreamSwitchPass::CreateStreamSwitchNode(const ComputeGraphPtr &
                 peer_cond_anchor->GetOwnerNode()->GetName().c_str(), stream_switch->GetName().c_str());
 
   int64_t group_index = -1;
-  bool force_unknown = AttrUtils::GetInt(switch_node->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index);
-  MarkForceUnknownShape(stream_switch, force_unknown, group_index);
+  if (AttrUtils::GetInt(switch_node->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index)) {
+    SetControlFlowGroup(stream_switch, group_index);
+  }
   return stream_switch;
 }
 
@@ -491,8 +492,8 @@ int64_t SwitchToStreamSwitchPass::GetGroupId(const NodePtr &node) {
 Status SwitchToStreamSwitchPass::CombineSwitchNode(const ComputeGraphPtr &graph) {
   for (auto iter = cond_node_map_.begin(); iter != cond_node_map_.end(); ++iter) {
     for (auto group_iter = iter->second.begin(); group_iter != iter->second.end(); ++group_iter) {
-      std::list<NodePtr> false_switch_list = group_iter->second[SWITCH_FALSE_OUTPUT];
-      std::list<NodePtr> true_switch_list = group_iter->second[SWITCH_TRUE_OUTPUT];
+      const std::list<NodePtr> &false_switch_list = group_iter->second[SWITCH_FALSE_OUTPUT];
+      const std::list<NodePtr> &true_switch_list = group_iter->second[SWITCH_TRUE_OUTPUT];
       std::set<NodePtr> same_cond_switch;
       same_cond_switch.insert(false_switch_list.begin(), false_switch_list.end());
       same_cond_switch.insert(true_switch_list.begin(), true_switch_list.end());
@@ -524,13 +525,13 @@ Status SwitchToStreamSwitchPass::CombineSwitchNode(const ComputeGraphPtr &graph)
       std::function<bool(const NodePtr &)> callback = [&group_index](const NodePtr &n) {
         return AttrUtils::GetInt(n->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index);
       };
-      bool is_unknown_shape = std::any_of(same_cond_switch.begin(), same_cond_switch.end(), callback);
-      MarkForceUnknownShape(active_node, is_unknown_shape, group_index);
+      (void)std::any_of(same_cond_switch.begin(), same_cond_switch.end(), callback);
+      SetControlFlowGroup(active_node, group_index);
 
       const std::string &cond_group = cond_node->GetName();
       for (uint32_t i = 0; i < SWITCH_OUTPUT_NUM; ++i) {
         bool true_branch_flag = (i == SWITCH_TRUE_OUTPUT);
-        std::list<NodePtr> &switch_list = (true_branch_flag ? true_switch_list : false_switch_list);
+        const std::list<NodePtr> &switch_list = (true_branch_flag ? true_switch_list : false_switch_list);
         GE_IF_BOOL_EXEC(switch_list.empty(), continue);
 
         // select first stream_switch
@@ -559,7 +560,7 @@ Status SwitchToStreamSwitchPass::CombineSwitchNode(const ComputeGraphPtr &graph)
                       "[Add][Edge] between %s and %s failed.",
                       cast_node->GetName().c_str(), stream_switch->GetName().c_str());
 
-        MarkForceUnknownShape(stream_switch, is_unknown_shape, group_index);
+        SetControlFlowGroup(stream_switch, group_index);
         for (const NodePtr &node : switch_list) {
           GE_IF_BOOL_EXEC(node != stream_switch, {
             GE_CHK_STATUS(GraphUtils::RemoveEdge(peer_cond_anchor, node->GetInDataAnchor(0)),

ge/graph/preprocess/graph_preprocess.cc

@@ -1420,9 +1420,10 @@ Status GraphPrepare::AdjustDataOpOutput(const NodePtr &node) {
   return SUCCESS;
 }
 
-Status GraphPrepare::CheckInternalFormat(const NodePtr &input_node, const GeTensorDesc &desc, bool tune_flag) {
+Status GraphPrepare::CheckInternalFormat(const NodePtr &input_node, const GeTensorDesc &desc) {
   auto format = desc.GetFormat();
   auto origin_format = desc.GetOriginFormat();
+  auto tune_flag = (options_.build_mode == BUILD_MODE_TUNING) && (options_.build_step == BUILD_STEP_AFTER_BUILDER);
   bool need_check_internal_format = (!IsTansDataOpData(input_node)) && (!options_.is_single_op) && (!tune_flag);
   if (need_check_internal_format) {
     bool is_internal = TypeUtils::IsInternalFormat(format) || TypeUtils::IsInternalFormat(origin_format);
@@ -1439,6 +1440,63 @@ Status GraphPrepare::CheckInternalFormat(const NodePtr &input_node, const GeTens
   return SUCCESS;
 }
 
+Status GraphPrepare::UpdateDataInputOutputDesc(GeAttrValue::INT index, OpDescPtr &op, GeTensorDesc &desc) {
+  auto data_type = desc.GetDataType();
+  uint32_t length = 1;
+  bool type_ret = TypeUtils::GetDataTypeLength(data_type, length);
+  if (!type_ret) {
+    std::string reason = "Input datatype[" + TypeUtils::DataTypeToSerialString(data_type) + "] of index:" +
+                         std::to_string(index) + " input tensor is not support";
+    REPORT_INPUT_ERROR("E19025", std::vector<std::string>({"reason"}), std::vector<std::string>({reason}));
+    GELOGE(PARAM_INVALID, "[Check][Param] Input datatype %s is not support.",
+           TypeUtils::DataTypeToSerialString(data_type).c_str());
+    return FAILED;
+  }
+  int64_t desc_shape = desc.GetShape().GetShapeSize();
+  FMK_INT64_UINT32_MULCHECK(desc_shape, length);
+  int64_t shape_size = desc_shape * length;
+  GE_IF_BOOL_EXEC(shape_size == 0 && desc.GetShape().GetDimNum() == 0, shape_size = static_cast<int64_t>(length));
+  int64_t size = 0;
+  GE_IF_BOOL_EXEC(ge::TensorUtils::GetSize(desc, size) != GRAPH_SUCCESS,
+                  REPORT_CALL_ERROR("E19999", "Get size of user input tensor failed, index:%ld", index);
+                  GELOGE(INTERNAL_ERROR, "[Get][Size] of user input tensor failed, index:%ld", index); return FAILED);
+  bool size_check = (size != 0 && shape_size != size);
+  if (size_check) {
+    std::string reason = "input tensor[index:" + std::to_string(index) + "]'s data size[" + std::to_string(size) +
+                         "] != shape_size[" + std::to_string(size) + "], check invalid";
+    REPORT_INPUT_ERROR("E19025", std::vector<std::string>({"reason"}), std::vector<std::string>({reason}));
+    GELOGE(PARAM_INVALID, "[Check][Param] input data size = %ld, shape_size = %ld.", size, shape_size);
+    return FAILED;
+  }
+  ge::TensorUtils::SetSize(desc, shape_size);
+
+  auto tune_flag = (options_.build_mode == BUILD_MODE_TUNING) && (options_.build_step == BUILD_STEP_AFTER_BUILDER);
+  if (!tune_flag) {
+    graphStatus graph_ret = op->UpdateInputDesc(0, desc);
+    if (graph_ret != GRAPH_SUCCESS) {
+      REPORT_CALL_ERROR("E19999", "Update input desc of op:%s(%s) failed, index:0",
+                        op->GetName().c_str(), op->GetType().c_str());
+      GELOGE(graph_ret, "[Update][InputDesc] of op:%s(%s) failed, index:0",
+             op->GetName().c_str(), op->GetType().c_str());
+      return graph_ret;
+    }
+    // Size will be recalculated in the build stage
+    ge::TensorUtils::SetSize(desc, 0);
+    graph_ret = op->UpdateOutputDesc(0, desc);
+    if (graph_ret != GRAPH_SUCCESS) {
+      REPORT_CALL_ERROR("E19999", "Update output desc of op:%s(%s) failed, index:0",
+                        op->GetName().c_str(), op->GetType().c_str());
+      GELOGE(graph_ret, "[Update][OutputDesc] of op:%s(%s) failed, index:0",
+             op->GetName().c_str(), op->GetType().c_str());
+      return graph_ret;
+    }
+  } else {
+    GELOGI("data %s skip update info in tune mode", op->GetName().c_str());
+  }
+
+  return SUCCESS;
+}
+
 Status GraphPrepare::UpdateInput(const std::vector<GeTensor> &user_input,
                                  const std::map<string, string> &graph_option) {
   // Get shape range of input in dynamic_execute mode
@@ -1471,63 +1529,18 @@ Status GraphPrepare::UpdateInput(const std::vector<GeTensor> &user_input,
       }
       GeTensorDesc desc(user_input[index].GetTensorDesc());
       // data maybe internal format [FRACTAL_NZ] at singleop process such as GEMM.
-      auto tune_flag = (options_.build_mode == BUILD_MODE_TUNING) && (options_.build_step == BUILD_STEP_AFTER_BUILDER);
-      ret = CheckInternalFormat(input_node, desc, tune_flag);
+      ret = CheckInternalFormat(input_node, desc);
       if (ret != SUCCESS) {
         GELOGE(INTERNAL_ERROR, "[Check][InternalFormat] on %s failed", op->GetName().c_str());
         return ret;
       }
-      auto data_type = desc.GetDataType();
-      uint32_t length = 1;
-      bool type_ret = TypeUtils::GetDataTypeLength(data_type, length);
-      if (!type_ret) {
-        std::string reason = "Input datatype[" + TypeUtils::DataTypeToSerialString(data_type) + "] of index:" +
-                             std::to_string(index) + " input tensor is not support";
-        REPORT_INPUT_ERROR("E19025", std::vector<std::string>({"reason"}), std::vector<std::string>({reason}));
-        GELOGE(PARAM_INVALID, "[Check][Param] Input datatype %s is not support.",
-               TypeUtils::DataTypeToSerialString(data_type).c_str());
-        return FAILED;
-      }
-      int64_t desc_shape = desc.GetShape().GetShapeSize();
-      FMK_INT64_UINT32_MULCHECK(desc_shape, length);
-      int64_t shape_size = desc_shape * length;
-      GE_IF_BOOL_EXEC(shape_size == 0 && desc.GetShape().GetDimNum() == 0, shape_size = static_cast<int64_t>(length));
-      int64_t size = 0;
-      GE_IF_BOOL_EXEC(ge::TensorUtils::GetSize(desc, size) != GRAPH_SUCCESS,
-                      REPORT_CALL_ERROR("E19999", "Get size of user input tensor failed, index:%ld", index);
-                      GELOGE(INTERNAL_ERROR, "[Get][Size] of user input tensor failed, index:%ld", index);
-                      return FAILED);
-      bool size_check = (size != 0 && shape_size != size);
-      if (size_check) {
-        std::string reason = "input tensor[index:" + std::to_string(index) + "]'s data size[" + std::to_string(size) +
-            "] != shape_size[" + std::to_string(size) + "], check invalid";
-        REPORT_INPUT_ERROR("E19025", std::vector<std::string>({"reason"}), std::vector<std::string>({reason}));
-        GELOGE(PARAM_INVALID, "[Check][Param] input data size = %ld, shape_size = %ld.", size, shape_size);
-        return FAILED;
-      }
-      ge::TensorUtils::SetSize(desc, shape_size);
-      if (!tune_flag) {
-        graphStatus graph_ret = op->UpdateInputDesc(0, desc);
-        if (graph_ret != GRAPH_SUCCESS) {
-          REPORT_CALL_ERROR("E19999", "Update input desc of op:%s(%s) failed, index:0",
-                            op->GetName().c_str(), op->GetType().c_str());
-          GELOGE(graph_ret, "[Update][InputDesc] of op:%s(%s) failed, index:0",
-                 op->GetName().c_str(), op->GetType().c_str());
-          return graph_ret;
-        }
-        // Size will be recalculated in the build stage
-        ge::TensorUtils::SetSize(desc, 0);
-        graph_ret = op->UpdateOutputDesc(0, desc);
-        if (graph_ret != GRAPH_SUCCESS) {
-          REPORT_CALL_ERROR("E19999", "Update output desc of op:%s(%s) failed, index:0",
-                            op->GetName().c_str(), op->GetType().c_str());
-          GELOGE(graph_ret, "[Update][OutputDesc] of op:%s(%s) failed, index:0",
-                 op->GetName().c_str(), op->GetType().c_str());
-          return graph_ret;
-        }
-      } else {
-        GELOGI("data %s skip update info in tune mode", op->GetName().c_str());
+
+      ret = UpdateDataInputOutputDesc(index, op, desc);
+      if (ret != SUCCESS) {
+        GELOGE(FAILED, "[Update][DataInputOutputDesc] on %s failed", op->GetName().c_str());
+        return ret;
       }
+
       if (!dynamic_shape_range_vec.empty()) {
         ret = UpdateDynamicInputShapeRange(index, dynamic_shape_range_vec, op, desc);
         GE_CHK_STATUS_RET(ret, "[Update][DynamicInputShapeRange] on %s failed.", op->GetName().c_str());
@@ -1742,8 +1755,8 @@ Status GraphPrepare::CtrlFlowPreProcess() {
   PassManager graph_pass;
 
   // After InferShape Mark v1 control flow for unknown shape.
-  auto mark_force_unknown_pass = new (std::nothrow) MarkForceUnknownForCondPass;
-  GE_CHK_STATUS_RET(graph_pass.AddPass("PreRun::MarkForceUnknownForCondPass", mark_force_unknown_pass));
+  GE_CHK_STATUS_RET(graph_pass.AddPass("PreRun::MarkForceUnknownForCondPass",
+                                       new (std::nothrow) MarkForceUnknownForCondPass));
 
   GE_CHK_STATUS_RET(graph_pass.Run(compute_graph_));
   return SUCCESS;

ge/graph/preprocess/graph_preprocess.h

@@ -63,7 +63,8 @@ class GraphPrepare {
   Status CheckRefOp();
   Status SetRtContext(rtContext_t rt_context, rtCtxMode_t mode);
   Status AdjustDataOpOutput(const NodePtr &node);
-  Status CheckInternalFormat(const NodePtr &input_node, const GeTensorDesc &desc, bool tune_flag);
+  Status CheckInternalFormat(const NodePtr &input_node, const GeTensorDesc &desc);
+  Status UpdateDataInputOutputDesc(GeAttrValue::INT index, OpDescPtr &op, GeTensorDesc &desc);
   Status UpdateInput(const std::vector<GeTensor> &user_input, const std::map<string, string> &graph_option);
   Status CheckAndUpdateInput(const std::vector<GeTensor> &user_input, const std::map<string, string> &graph_option);
   Status CheckConstOp();

ge/graph/preprocess/insert_op/util_insert_aipp_op.cc

@@ -568,6 +568,7 @@ Status InsertNewOpUtil::GetDataRelatedNode(NodePtr &node, std::map<NodePtr, std:
   }
 
   std::unique_ptr<domi::AippOpParams> aipp_params(new (std::nothrow) domi::AippOpParams());
+  GE_CHECK_NOTNULL(aipp_params);
   ge::GeAttrValue::NAMED_ATTRS aipp_attr;
   GE_CHK_BOOL_RET_STATUS(AttrUtils::GetNamedAttrs(data_op, ATTR_NAME_AIPP, aipp_attr), ACL_ERROR_GE_AIPP_NOT_EXIST,
                          "[Get][Attr] %s from op:%s failed", ATTR_NAME_AIPP.c_str(), data_op->GetName().c_str());

ge/graph/preprocess/multi_batch_copy_graph.cc

@@ -1206,7 +1206,7 @@ Status MultiBatchGraphCopyer::CheckCopyResult(const std::vector<NodePtr> &start_
     auto dims = NodeUtils::GetOutputDesc(*node, kDataOutIndex).GetShape().GetDims();
     if (!IsAllDimsPositive(dims)) {
       REPORT_CALL_ERROR("E19999", "Failed to copy multi batch graph, the node %s still has unknown shape %s",
-             node->GetName().c_str(), formats::ShapeToString(dims).c_str());
+                        node->GetName().c_str(), formats::ShapeToString(dims).c_str());
       GELOGE(INTERNAL_ERROR, "[Check][Param] Failed to copy multi batch graph, the node %s still has unknown shape %s",
              node->GetName().c_str(), formats::ShapeToString(dims).c_str());
       return INTERNAL_ERROR;

ge/host_kernels/fill_kernel.cc

@@ -45,6 +45,7 @@ Status FillKernel::Compute(const ge::OpDescPtr op_desc_ptr, const std::vector<ge
     GELOGE(PARAM_INVALID, "Parameter's invalid, Input opDescPtr is nullptr.");
     return PARAM_INVALID;
   }
+  GELOGD("FillKernel in, name: %s.", op_desc_ptr->GetName().c_str());
 
   GE_CHECK_NOTNULL(input.at(kFillDimsInputIndex));
   GE_CHECK_NOTNULL(input.at(kFillDataInputIndex));
@@ -57,6 +58,13 @@ Status FillKernel::Compute(const ge::OpDescPtr op_desc_ptr, const std::vector<ge
     return NOT_CHANGED;
   }
 
+  auto output_desc = op_desc_ptr->GetOutputDescPtr(0);
+  GE_CHECK_NOTNULL(output_desc);
+  if (output_desc->GetShape().IsUnknownShape()) {
+    GELOGD("Output is unknown shape, [%s] skip FillKernel.", op_desc_ptr->GetName().c_str());
+    return NOT_CHANGED;
+  }
+
   GeTensorPtr output_ptr;
   output_ptr = MakeShared<GeTensor>(op_desc_ptr->GetOutputDesc(0));
   if (output_ptr == nullptr) {

ge/hybrid/executor/hybrid_model_async_executor.cc

@@ -297,13 +297,15 @@ Status HybridModelAsyncExecutor::PrepareInputs(const InputData &current_data, Hy
         }
       }
       tensor_desc->SetShape(shape);
-      args.input_desc[input_index] = tensor_desc;
-      GELOGD("Update shape of input[%zu] to [%s]", input_index, tensor_desc->MutableShape().ToString().c_str());
+      GELOGD("Update shape[%s] of input[%zu] to [%s]",
+             shape.ToString().c_str(), input_index, tensor_desc->MutableShape().ToString().c_str());
       GE_CHK_GRAPH_STATUS_RET(TensorUtils::GetTensorMemorySizeInBytes(*tensor_desc, tensor_size),
                               "[Invoke][GetTensorMemorySizeInBytes]Failed to calc tensor size,"
                               "index = %zu, shape = [%s], model_id = %u.",
                               input_index, tensor_desc->GetShape().ToString().c_str(), model_id_);
-      GELOGD("Input tensor[%zu] size = %zu", input_index, tensor_size);
+      GELOGD("Input tensor[%zu] size = %ld", input_index, tensor_size);
+      TensorUtils::SetSize(*tensor_desc, tensor_size);
+      args.input_desc[input_index] = tensor_desc;
     }
 
     GE_CHECK_GE(tensor_size, 0);

ge/hybrid/executor/hybrid_model_executor.cc

@@ -41,6 +41,8 @@ HybridModelExecutor::~HybridModelExecutor() {
 Status HybridModelExecutor::Init() {
   GELOGD("Start to init HybridGraphEngine.");
   GE_CHK_STATUS_RET_NOLOG(InitExecutionContext());
+  root_graph_executor_.reset(new (std::nothrow) SubgraphExecutor(model_->GetRootGraphItem(), &context_));
+  GE_CHECK_NOTNULL(root_graph_executor_);
   GELOGD("HybridGraphEngine initialized successfully.");
   return SUCCESS;
 }
@@ -60,8 +62,7 @@ Status HybridModelExecutor::Execute(HybridModelExecutor::ExecuteArgs &args) {
     GE_CHK_RT_RET(rtMemcpyAsync(context_.global_step, sizeof(uint64_t), &context_.iteration,
                                 sizeof(uint64_t), RT_MEMCPY_HOST_TO_DEVICE_EX, context_.stream));
   }
-  SubgraphExecutor executor(model_->GetRootGraphItem(), &context_);
-  auto ret = ExecuteGraphInternal(executor, args);
+  auto ret = ExecuteGraphInternal(args);
   Cleanup();
   RECORD_MODEL_EXECUTION_EVENT(&context_, "[Cleanup] End");
   GELOGD("Model executed successfully.");
@@ -69,6 +70,7 @@ Status HybridModelExecutor::Execute(HybridModelExecutor::ExecuteArgs &args) {
     context_.profiler->Dump(std::cout);
     context_.profiler->Reset();
   }
+  root_graph_executor_->ReleaseContext();
 
   context_.iteration += 1;
   if (ret == END_OF_SEQUENCE) {
@@ -79,8 +81,7 @@ Status HybridModelExecutor::Execute(HybridModelExecutor::ExecuteArgs &args) {
   return SUCCESS;
 }
 
-Status HybridModelExecutor::ExecuteGraphInternal(SubgraphExecutor &executor,
-                                                 HybridModelExecutor::ExecuteArgs &args) {
+Status HybridModelExecutor::ExecuteGraphInternal(HybridModelExecutor::ExecuteArgs &args) {
   RECORD_MODEL_EXECUTION_EVENT(&context_, "[InitContext] Start");
   GE_CHK_STATUS_RET_NOLOG(ResetExecutionContext(context_));
   RECORD_MODEL_EXECUTION_EVENT(&context_, "[InitContext] End");
@@ -94,7 +95,7 @@ Status HybridModelExecutor::ExecuteGraphInternal(SubgraphExecutor &executor,
     GE_CHK_STATUS_RET_NOLOG(prof_mgr.ProfileStepInfo(index_id, model_id, 0, stream_, device_id));
   }
 
-  HYBRID_CHK_STATUS_RET(executor.ExecuteAsync(args.inputs, args.input_desc, args.outputs),
+  HYBRID_CHK_STATUS_RET(root_graph_executor_->ExecuteAsync(args.inputs, args.input_desc, args.outputs),
                         "Failed to execute partitioned call.");
   RECORD_MODEL_EXECUTION_EVENT(&context_, "[ExecuteAsync] End");
 
@@ -103,7 +104,7 @@ Status HybridModelExecutor::ExecuteGraphInternal(SubgraphExecutor &executor,
   }
 
   if (!model_->IsSingleOp()) {
-    Status ret = executor.Synchronize();
+    Status ret = root_graph_executor_->Synchronize();
     if (ret != ge::SUCCESS) {
       auto model_manager = ModelManager::GetInstance();
       GE_CHECK_NOTNULL(model_manager);
@@ -123,7 +124,7 @@ Status HybridModelExecutor::ExecuteGraphInternal(SubgraphExecutor &executor,
   }
 
   args.outputs.clear();
-  HYBRID_CHK_STATUS_RET(executor.GetOutputs(args.outputs, args.output_desc), "Failed to get outputs");
+  HYBRID_CHK_STATUS_RET(root_graph_executor_->GetOutputs(args.outputs, args.output_desc), "Failed to get outputs");
   RECORD_MODEL_EXECUTION_EVENT(&context_, "[GetOutput] End");
   return SUCCESS;
 }

ge/hybrid/executor/hybrid_model_executor.h

@@ -48,7 +48,7 @@ class HybridModelExecutor {
   Status Execute(ExecuteArgs &args);
 
  private:
-  Status ExecuteGraphInternal(SubgraphExecutor &executor, ExecuteArgs &args);
+  Status ExecuteGraphInternal(ExecuteArgs &args);
   Status Cleanup();
   Status InitExecutionContext();
   static Status ResetExecutionContext(GraphExecutionContext &context);
@@ -58,6 +58,7 @@ class HybridModelExecutor {
   uint32_t device_id_;
   rtStream_t stream_;
   GraphExecutionContext context_;
+  std::unique_ptr<SubgraphExecutor> root_graph_executor_;
 };
 }  // namespace hybrid
 }  // namespace ge

ge/hybrid/executor/hybrid_model_pipeline_executor.cc

@@ -172,6 +172,8 @@ HybridModelPipelineExecutor::HybridModelPipelineExecutor(HybridModel *model, uin
   config_.num_executors = kNumExecutors;
   config_.num_stages = model_->GetRootGraphItem()->NumGroups();
   config_.device_id = device_id_;
+  config_.iteration_end = 0;
+  config_.rt_context = nullptr;
 }
 
 Status StageExecutor::InitExecutionContext() {

ge/hybrid/executor/node_state.cc

@@ -19,8 +19,9 @@
 #include "framework/common/debug/log.h"
 #include "graph/compute_graph.h"
 #include "graph/utils/tensor_utils.h"
-#include "hybrid_execution_context.h"
-#include "subgraph_context.h"
+#include "hybrid/executor/hybrid_execution_context.h"
+#include "hybrid/executor/subgraph_context.h"
+#include "hybrid/node_executor/task_context.h"
 
 #define INC_ITERATION_COUNT(iteration) \
 do {                                   \
@@ -260,6 +261,16 @@ NodeState::NodeState(const NodeItem &node_item, SubgraphContext *subgraph_contex
   this->op_desc_ = node_item.node->GetOpDesc();
 }
 
+Status NodeState::Init(int group, const shared_ptr<FrameState> &frame_state) {
+  GE_CHECK_NOTNULL(frame_state);
+  group_ = group;
+  frame_state_ = frame_state;
+  auto unique_task_context = TaskContext::Create(this, subgraph_context_);
+  GE_CHECK_NOTNULL(unique_task_context);
+  task_context_ = std::shared_ptr<TaskContext>(unique_task_context.release());
+  return SUCCESS;
+}
+
 Status NodeState::AwaitInputTensors(GraphExecutionContext &context) const {
   if (node_item_->IsMergeOp()) {
     GELOGD("[%s] merge index %d, input nodes: %zu", GetName().c_str(), merge_index_, node_item_->data_recv_.size());
@@ -314,15 +325,75 @@ std::shared_ptr<TaskContext> NodeState::GetTaskContext() {
   return task_context_;
 }
 
+void NodeState::SavePersistTensor(int input_idx, const TensorValue &tensor) {
+  const auto is_persist_tensor = [](const std::map<const NodeItem *, std::set<int>> &items, int idx) {
+    const auto is_exist = [&idx](const std::pair<const NodeItem *, std::set<int>> &items) {
+      return items.second.count(idx) > 0;
+    };
+    return std::any_of(items.begin(), items.end(), is_exist);
+  };
+
+  if (root_tensor_values_.count(input_idx) > 0) {
+    return;
+  }
+
+  if (is_persist_tensor(node_item_->root_data_, input_idx)) {
+    GELOGD("[%s] Save Root input tensor: %d", GetName().c_str(), input_idx);
+    root_tensor_values_[input_idx] = tensor;
+  } else if (is_persist_tensor(node_item_->enter_data_, input_idx)) {
+    GELOGD("[%s] Save Enter input tensor: %d", GetName().c_str(), input_idx);
+    root_tensor_values_[input_idx] = tensor;
+  }
+}
+
+void NodeState::UpdatePersistTensor() {
+  const auto update_tensor = [&](const std::map<const NodeItem *, std::set<int>> &items) {
+    for (const auto &item : items) {
+      for (const auto idx : item.second) {
+        UpdatePersistTensor(idx);
+      }
+    }
+  };
+
+  if (root_tensor_values_.empty()) {
+    return;
+  }
+
+  update_tensor(node_item_->root_data_);
+  if (iteration_count_ > 0) {
+    update_tensor(node_item_->enter_data_);
+  }
+}
+
+void NodeState::UpdatePersistTensor(int input_idx) {
+  const auto it = root_tensor_values_.find(input_idx);
+  if (it == root_tensor_values_.end()) {
+    GELOGW("[%s] Not found saved tensor: %d", GetName().c_str(), input_idx);
+    return;
+  }
+
+  auto tensor = task_context_->MutableInput(input_idx);
+  if (tensor == nullptr) {
+    GELOGW("[%s] Not found input tensor: %d", GetName().c_str(), input_idx);
+    return;
+  }
+
+  *tensor = it->second;
+  GELOGD("[%s] Update input tensor: %d", GetName().c_str(), input_idx);
+}
+
 void NodeState::ResetContext(uint64_t iteration) {
   switch_index_ = -1;
   subgraph_context_->ResetContext(node_item_->node);
-  if (iteration == 0) {
-    data_scheduled_ = static_cast<uint32_t>(node_item_->root_data_.size());
-    ctrl_scheduled_ = static_cast<uint32_t>(node_item_->root_ctrl_.size());
-  } else {
-    data_scheduled_ = static_cast<uint32_t>(node_item_->root_data_.size() + node_item_->enter_data_.size());
-    ctrl_scheduled_ = static_cast<uint32_t>(node_item_->root_ctrl_.size() + node_item_->enter_ctrl_.size());
+  auto unique_task_context = TaskContext::Create(this, subgraph_context_);
+  GE_CHECK_NOTNULL_JUST_RETURN(unique_task_context);
+  task_context_ = std::shared_ptr<TaskContext>(unique_task_context.release());
+
+  data_scheduled_ = static_cast<uint32_t>(node_item_->root_data_.size());
+  ctrl_scheduled_ = static_cast<uint32_t>(node_item_->root_ctrl_.size());
+  if (iteration > 0) {
+    data_scheduled_ += static_cast<uint32_t>(node_item_->enter_data_.size());
+    ctrl_scheduled_ += static_cast<uint32_t>(node_item_->enter_ctrl_.size());
   }
 
   iteration_count_ = iteration;

ge/hybrid/executor/node_state.h

@@ -100,6 +100,8 @@ struct NodeState {
   NodeState(const NodeItem &node_item, SubgraphContext *subgraph_context);
   ~NodeState() = default;
 
+  Status Init(int group, const shared_ptr<FrameState> &frame_state);
+
   OpDesc *GetOpDesc() const {
     return op_desc_.get();
   }
@@ -129,6 +131,9 @@ struct NodeState {
   void RunStreamActive();
   void RunNextIteration();
 
+  void SavePersistTensor(int input_idx, const TensorValue &tensor);
+  void UpdatePersistTensor();
+
   Status NodeScheduled(const std::function<void(const NodeItem *)> &ready) const;
 
   void SetScheduleFuture(std::future<Status> &&future);
@@ -150,18 +155,10 @@ struct NodeState {
     return merge_index_;
   }
 
-  void SetGroup(int group) {
-    group_ = group;
-  }
-
   int GetGroup() const {
     return group_;
   }
 
-  void SetFrameState(const shared_ptr<FrameState> &frame_state) {
-    frame_state_ = frame_state;
-  }
-
   const shared_ptr<NodeTask> &GetKernelTask() const {
     return kernel_task_;
   }
@@ -181,12 +178,17 @@ struct NodeState {
   void SetTaskContext(std::shared_ptr<TaskContext> &task_context);
   std::shared_ptr<TaskContext> GetTaskContext();
 
+  void SetSkipInferShape(bool skip_infershape) { skip_infershape_ = skip_infershape; }
+
+  bool MaySkipShapeInference() const { return skip_infershape_; }
+
  private:
   bool IsScheduleReady() const;
   void SetDataSchedule(const NodeState &node_state, const std::function<void(const NodeItem *)> &ready);
   void SetCtrlSchedule(const NodeState &node_state, const std::function<void(const NodeItem *)> &ready);
   void ResetContext(uint64_t iteration);
   void ScheduleContext(const NodeState &node_state);
+  void UpdatePersistTensor(int input_idx);
 
   const NodeItem *node_item_ = nullptr;
   std::shared_ptr<NodeTask> kernel_task_ = nullptr;
@@ -199,6 +201,7 @@ struct NodeState {
 
   std::future<Status> schedule_future_;
   std::shared_ptr<FrameState> frame_state_;
+  std::map<int, TensorValue> root_tensor_values_;
   uint64_t active_count_ = 0;
   uint64_t iteration_count_ = 0;
   uint32_t ctrl_scheduled_ = 0;
@@ -206,6 +209,7 @@ struct NodeState {
   int merge_index_ = -1; // Use for Execute (Reset after Executed).
   int switch_index_ = -1; // Use for Schedule (Reset after Prepared).
   int group_ = -1;
+  bool skip_infershape_ = false;
 };
 }  // namespace hybrid
 }  // namespace ge

ge/hybrid/executor/subgraph_context.cc

@@ -19,7 +19,7 @@
 
 namespace ge {
 namespace hybrid {
-SubgraphContext::SubgraphContext(const GraphItem *graph_item, const GraphExecutionContext *execution_context)
+SubgraphContext::SubgraphContext(const GraphItem *graph_item, GraphExecutionContext *execution_context)
     : graph_item_(graph_item), execution_context_(execution_context) {
 }
 
@@ -79,20 +79,31 @@ NodeStatePtr SubgraphContext::GetOrCreateNodeState(const NodeItem *node_item) {
     return nullptr;
   }
 
+  return CreateNodeState(node_item);
+}
+
+NodeStatePtr SubgraphContext::CreateNodeState(const NodeItem *node_item) {
   GELOGD("[%s] lock for write", node_item->NodeName().c_str());
   if (mmRWLockWRLock(&rw_lock_) != EN_OK) {
     REPORT_CALL_ERROR("E19999", "[Node:%s] Lock for write failed", node_item->NodeName().c_str());
     GELOGE(INTERNAL_ERROR, "[RWLock][Lock][Node:%s] Lock for write failed", node_item->NodeName().c_str());
     return nullptr;
   }
+
   auto &node_state = node_states_[node_item];
-  if (node_state == nullptr) {
-    const auto &guard = node_item->MutexGuard("GetOrCreateNodeState");
-    node_state.reset(new(std::nothrow)NodeState(*node_item, this));
-    node_state->SetFrameState(GetOrCreateFrameState(*node_item));
-    node_state->SetGroup(group_);
-    (void)guard;
-  }
+  do {
+    if (node_state == nullptr) {
+      const auto &guard = node_item->MutexGuard("GetOrCreateNodeState");
+      node_state.reset(new(std::nothrow)NodeState(*node_item, this));
+      if (node_state == nullptr || node_state->Init(group_, GetOrCreateFrameState(*node_item)) != SUCCESS) {
+        GELOGE(INTERNAL_ERROR, "[Create][NodeState] failed for[%s].", node_item->NodeName().c_str());
+        REPORT_CALL_ERROR("E19999", "Create NodeState failed for %s.", node_item->NodeName().c_str());
+        break;
+      }
+      (void)guard;
+    }
+  } while (0);
+
   GELOGD("[%s] unlock for write", node_item->NodeName().c_str());
   if (mmWRLockUnLock(&rw_lock_) != EN_OK) {
     REPORT_CALL_ERROR("E19999", "[Node:%s] Unlock for write failed", node_item->NodeName().c_str());

ge/hybrid/executor/subgraph_context.h

@@ -30,7 +30,7 @@ namespace ge {
 namespace hybrid {
 class SubgraphContext {
  public:
-  explicit SubgraphContext(const GraphItem *graph_item, const GraphExecutionContext *execution_context);
+  explicit SubgraphContext(const GraphItem *graph_item, GraphExecutionContext *execution_context);
   ~SubgraphContext();
 
   Status Init();
@@ -51,10 +51,11 @@ class SubgraphContext {
   void NodeDone(const NodePtr &node);
 
  private:
+  NodeStatePtr CreateNodeState(const NodeItem *node_item);
   FrameStatePtr GetOrCreateFrameState(const NodeItem &node_item); // no lock
   friend class TaskContext;
   const GraphItem *graph_item_;
-  const GraphExecutionContext *execution_context_;
+  GraphExecutionContext *execution_context_;
   mmRWLock_t rw_lock_;
   std::vector<TensorValue> all_inputs_;
   std::vector<TensorValue> all_outputs_;

ge/hybrid/executor/subgraph_executor.cc

@@ -103,6 +103,13 @@ Status SubgraphExecutor::InitInputsForUnknownShape(const std::vector<TensorValue
       auto node_state = subgraph_context_->GetOrCreateNodeState(input_node);
       GE_CHECK_NOTNULL(node_state);
       node_state->GetShapeInferenceState().UpdateInputShape(0, *tensor_desc);
+      auto op_desc = input_node->GetOpDesc();
+      GE_CHECK_NOTNULL(op_desc);
+      auto output_desc = op_desc->MutableOutputDesc(kDataInputIndex);
+      GE_CHECK_NOTNULL(output_desc);
+      output_desc->SetShape(tensor_desc->GetShape());
+      output_desc->SetOriginShape(tensor_desc->GetOriginShape());
+      node_state->SetSkipInferShape(true);
     }
   }
 
@@ -175,16 +182,12 @@ Status SubgraphExecutor::ExecuteAsyncForKnownShape(const std::vector<TensorValue
   GE_CHECK_NOTNULL(node_state);
   node_state->SetKernelTask(node_item->kernel_task);
 
-  known_shape_task_context_ = TaskContext::Create(node_state.get(), context_, subgraph_context_.get());
-  GE_CHECK_NOTNULL(known_shape_task_context_);
-  node_state->SetTaskContext(known_shape_task_context_);
-
   std::function<void()> callback;
   GE_CHK_STATUS_RET_NOLOG(InitCallback(node_state.get(), callback));
-  HYBRID_CHK_STATUS_RET(ExecutionEngine::ExecuteAsync(*node_state, known_shape_task_context_, *context_, callback),
+  HYBRID_CHK_STATUS_RET(ExecutionEngine::ExecuteAsync(*node_state, node_state->GetTaskContext(), *context_, callback),
                         "[%s] Failed to execute node [%s] for known subgraph.",
                         graph_item_->GetName().c_str(),
-                        known_shape_task_context_->GetNodeName());
+                        node_state->GetName().c_str());
 
   GELOGD("[%s] Done execute non-dynamic subgraph successfully.", graph_item_->GetName().c_str());
   return SUCCESS;
@@ -271,16 +274,12 @@ Status SubgraphExecutor::PrepareNode(const NodeItem &node_item, int group) {
     } else {
       node_state->SetKernelTask(node_item.kernel_task);
     }
-    auto unique_task_context = TaskContext::Create(node_state.get(), context_, subgraph_context_.get());
-    GE_CHECK_NOTNULL(unique_task_context);
     const auto &task = node_state->GetKernelTask();
     if (task == nullptr) {
       GELOGE(INTERNAL_ERROR, "[Get][KernelTask] failed for[%s], NodeTask is null.", node_state->GetName().c_str());
       REPORT_CALL_ERROR("E19999", "GetKernelTask failed for %s, nodetask is null.", node_state->GetName().c_str());
       return INTERNAL_ERROR;
     }
-    auto shared_task_context = std::shared_ptr<TaskContext>(unique_task_context.release());
-    node_state->SetTaskContext(shared_task_context);
     GE_CHK_STATUS_RET_NOLOG(NodeEnqueue(p_node_state));
     return AfterPrepared(p_node_state);
   }
@@ -480,19 +479,15 @@ Status SubgraphExecutor::PrepareForExecution(GraphExecutionContext *ctx, NodeSta
   } else {
     node_state.SetKernelTask(node_item.kernel_task);
   }
-  auto unique_task_context = TaskContext::Create(&node_state, context_, subgraph_context_.get());
-  GE_CHECK_NOTNULL(unique_task_context);
   const auto &task = node_state.GetKernelTask();
   if (task == nullptr) {
     GELOGE(INTERNAL_ERROR, "[Invoke][GetKernelTask] failed for[%s], NodeTask is null.", node_state.GetName().c_str());
     REPORT_CALL_ERROR("E19999", "invoke GetKernelTask failed for %s, NodeTask is null.", node_state.GetName().c_str());
     return INTERNAL_ERROR;
   }
-  auto shared_task_context = std::shared_ptr<TaskContext>(unique_task_context.release());
-  node_state.SetTaskContext(shared_task_context);
   GE_CHK_RT_RET(rtCtxSetCurrent(ctx->rt_context));
   RECORD_COMPILE_EVENT(ctx, node_item.NodeName().c_str(), "[UpdateTilingData] start");
-  GE_CHK_STATUS_RET_NOLOG(task->UpdateTilingData(*shared_task_context)); // update op_desc before alloc ws
+  GE_CHK_STATUS_RET_NOLOG(task->UpdateTilingData(*node_state.GetTaskContext())); // update op_desc before alloc ws
   RECORD_COMPILE_EVENT(ctx, node_item.NodeName().c_str(), "[UpdateTilingData] end");
   return SUCCESS;
 }

ge/hybrid/executor/subgraph_executor.h

@@ -41,6 +41,8 @@ class SubgraphExecutor {
 
   Status PartialExecuteAsync(int task_group);
 
+  void ReleaseContext() { subgraph_context_.reset(nullptr); }
+
   /**
    * Execute subgraph async, output tensor address(not data) and output tensor descriptions are
    * valid after this method returned
@@ -125,7 +127,6 @@ class SubgraphExecutor {
   ThreadPool pre_run_pool_;
   BlockingQueue<NodeState *> ready_queue_;
   std::unique_ptr<ShapeInferenceEngine> shape_inference_engine_;
-  std::shared_ptr<TaskContext> known_shape_task_context_;
 
   std::mutex mu_; // Guard for prepare_queues_.
   std::map<int, BlockingQueue<const NodeItem *>> prepare_queues_;

ge/hybrid/executor/worker/execution_engine.cc

@@ -373,6 +373,7 @@ Status ExecutionEngine::DoExecuteAsync(NodeState &node_state,
   auto executor = node_item.node_executor;
   GE_CHECK_NOTNULL(executor);
   RECORD_EXECUTION_EVENT(&context, task_context.GetNodeName(), "[PrepareTask] Start");
+  node_state.UpdatePersistTensor();
   GE_CHK_STATUS_RET(executor->PrepareTask(*task, task_context), "[Prepare][Task] for [%s] failed.",
                     node_state.GetName().c_str());
   RECORD_EXECUTION_EVENT(&context, task_context.GetNodeName(), "[PrepareTask] End");
@@ -427,7 +428,7 @@ Status ExecutionEngine::ValidateInputTensors(const NodeState &node_state, const
       continue;
     }
 
-    int64_t expected_size;
+    int64_t expected_size = 0;
     (void)TensorUtils::GetSize(*tensor_desc, expected_size);
     GELOGD("[%s] Input[%d] expects [%ld] bytes.", task_context.GetNodeName(), i, expected_size);
     auto size_diff = expected_size - static_cast<int64_t>(input_tensor->GetSize());