!1880 code_sync_0626

Merge pull request !1880 from mindspore_ding/code_sync_0626

.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/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/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/common/util.cc

@@ -340,15 +340,24 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY std::string RealPath(const char
   return res;
 }
 
+void PathValidErrReport(const std::string &file_path, const std::string &atc_param, const std::string &reason) {
+  if (!atc_param.empty()) {
+    REPORT_INPUT_ERROR("E10001", std::vector<std::string>({"parameter", "value", "reason"}),
+                       std::vector<std::string>({atc_param, file_path, reason}));
+  } else {
+    REPORT_INNER_ERROR("E19999", "Path[%s] invalid, reason:%s", file_path.c_str(), reason.c_str());
+  }
+}
+
 FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY bool CheckInputPathValid(const std::string &file_path,
                                                                           const std::string &atc_param) {
   // The specified path is empty
   std::map<std::string, std::string> args_map;
   if (file_path.empty()) {
-    if (atc_param != "") {
-      ErrorManager::GetInstance().ATCReportErrMessage("E10004", {"parameter"}, {atc_param});
+    if (!atc_param.empty()) {
+      REPORT_INPUT_ERROR("E10004", std::vector<std::string>({"parameter"}), std::vector<std::string>({atc_param}));
     } else {
-      REPORT_INNER_ERROR("E19999", "Param file_path is empty, check invalid");
+      REPORT_INNER_ERROR("E19999", "Param file_path is empty, check invalid.");
     }
     GELOGW("Input parameter %s is empty.", file_path.c_str());
     return false;
@@ -356,13 +365,8 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY bool CheckInputPathValid(const
   std::string real_path = RealPath(file_path.c_str());
   // Unable to get absolute path (does not exist or does not have permission to access)
   if (real_path.empty()) {
-    if (atc_param != "") {
-      std::string reason = "realpath error, errmsg:" + std::string(strerror(errno));
-      ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
-                                                      {atc_param, file_path, reason});
-    } else {
-      REPORT_INNER_ERROR("E19999", "Path[%s]'s realpath is empty, errmsg[%s]", file_path.c_str(), strerror(errno));
-    }
+    std::string reason = "realpath error, errmsg:" + std::string(strerror(errno));
+    PathValidErrReport(file_path, atc_param, reason);
     GELOGW("Path[%s]'s realpath is empty, errmsg[%s]", file_path.c_str(), strerror(errno));
     return false;
   }
@@ -378,23 +382,12 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY bool CheckInputPathValid(const
 
   GE_CHK_BOOL_TRUE_EXEC_WITH_LOG(
     !ValidateStr(real_path, mode),
-    if (atc_param != "") {
-      ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
-                                                      {atc_param, real_path, kPathValidReason});
-    } else {
-      REPORT_INNER_ERROR("E19999", "Path[%s] has invalid char, %s", file_path.c_str(), kPathValidReason);
-    }
+    PathValidErrReport(file_path, atc_param, kPathValidReason);
     return false, "Invalid value for %s[%s], %s.", atc_param.c_str(), real_path.c_str(), kPathValidReason);
 
   // The absolute path points to a file that is not readable
   if (mmAccess2(real_path.c_str(), M_R_OK) != EN_OK) {
-    if (atc_param != "") {
-      std::string reason = "cat not access, errmsg:" + std::string(strerror(errno));
-      ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
-                                                      {atc_param, file_path, reason});
-    } else {
-      REPORT_INNER_ERROR("E19999", "Path[%s] can't acccess, errmsg:%s", file_path.c_str(), strerror(errno));
-    }
+    PathValidErrReport(file_path, atc_param, "cat not access, errmsg:" + std::string(strerror(errno)));
     GELOGW("Read file[%s] failed, errmsg[%s]", file_path.c_str(), strerror(errno));
     return false;
   }
@@ -406,10 +399,10 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY bool CheckOutputPathValid(const
                                                                            const std::string &atc_param) {
   // The specified path is empty
   if (file_path.empty()) {
-    if (atc_param != "") {
-      ErrorManager::GetInstance().ATCReportErrMessage("E10004", {"parameter"}, {atc_param});
+    if (!atc_param.empty()) {
+      REPORT_INPUT_ERROR("E10004", std::vector<std::string>({"parameter"}), std::vector<std::string>({atc_param}));
     } else {
-      REPORT_INNER_ERROR("E19999", "Param file_path is empty, check invalid");
+      REPORT_INNER_ERROR("E19999", "Param file_path is empty, check invalid.");
     }
     ErrorManager::GetInstance().ATCReportErrMessage("E10004", {"parameter"}, {atc_param});
     GELOGW("Input parameter's value is empty.");
@@ -417,17 +410,10 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY bool CheckOutputPathValid(const
   }
 
   GE_CHK_BOOL_TRUE_EXEC_WITH_LOG(strlen(file_path.c_str()) >= MMPA_MAX_PATH,
-                                 if (atc_param != "") {
-                                   std::string reason = "len is too long, it must be less than " +
-                                                        std::to_string(MMPA_MAX_PATH);
-                                   ErrorManager::GetInstance().ATCReportErrMessage(
-                                     "E10001", {"parameter", "value", "reason"},
-                                     {atc_param, file_path, reason});
-                                 } else {
-                                   REPORT_INNER_ERROR("E19999", "Path[%s] len is too long, it must be less than %d",
-                                                      file_path.c_str(), MMPA_MAX_PATH);
-                                 }
-                                 return "", "Path[%s] len is too long, it must be less than %d", file_path.c_str(),
+                                 std::string reason = "len is too long, it must be less than " +
+                                                      std::to_string(MMPA_MAX_PATH);
+                                 PathValidErrReport(file_path, atc_param, reason);
+                                 return false, "Path[%s] len is too long, it must be less than %d", file_path.c_str(),
                                         MMPA_MAX_PATH);
 
   // A regular matching expression to verify the validity of the input file path
@@ -441,12 +427,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY bool CheckOutputPathValid(const
 
   GE_CHK_BOOL_TRUE_EXEC_WITH_LOG(
     !ValidateStr(file_path, mode),
-    if (atc_param != "") {
-      ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
-                                                      {atc_param, file_path, kPathValidReason});
-    } else {
-      REPORT_INNER_ERROR("E19999", "Path[%s] has invalid char, %s", file_path.c_str(), kPathValidReason);
-    }
+    PathValidErrReport(file_path, atc_param, kPathValidReason);
     return false, "Invalid value for %s[%s], %s.", atc_param.c_str(), file_path.c_str(), kPathValidReason);
 
   std::string real_path = RealPath(file_path.c_str());
@@ -454,13 +435,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY bool CheckOutputPathValid(const
   if (!real_path.empty()) {
     // File is not readable or writable
     if (mmAccess2(real_path.c_str(), M_W_OK | M_F_OK) != EN_OK) {
-      if (atc_param != "") {
-        std::string reason = "cat not access, errmsg:" + std::string(strerror(errno));
-        ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
-                                                        {atc_param, file_path, reason});
-      } else {
-        REPORT_INNER_ERROR("E19999", "Path[%s] can't acccess, errmsg:%s", file_path.c_str(), strerror(errno));
-      }
+      PathValidErrReport(file_path, atc_param, "cat not access, errmsg:" + std::string(strerror(errno)));
       GELOGW("Write file[%s] failed, errmsg[%s]", real_path.c_str(), strerror(errno));
       return false;
     }
@@ -479,12 +454,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY bool CheckOutputPathValid(const
       std::string prefix_path = std::string(file_path).substr(0, static_cast<size_t>(path_split_pos));
       // Determine whether the specified path is valid by creating the path
       if (CreateDirectory(prefix_path) != 0) {
-        if (atc_param != "") {
-          ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
-                                                          {atc_param, file_path, "Can not create directory"});
-        } else {
-          REPORT_INNER_ERROR("E19999", "Path[%s] Can not create directory", file_path.c_str());
-        }
+        PathValidErrReport(file_path, atc_param, "Can not create directory");
         GELOGW("Can not create directory[%s].", file_path.c_str());
         return false;
       }

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/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 +1,31 @@
+/**
+ * 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;
+  static Status SetOptInfo();
+};
+}  // namespace ge
+
+#endif  // GE_OPT_INFO_GE_OPT_INFO_H_

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;
 }
 

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/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());

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);

ge/graph/common/omg_util.cc

@@ -274,21 +274,6 @@ bool IsUnknownShapeTensor(const GeTensorDesc &tensor_desc) {
   return false;
 }
 
-///
-/// @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

ge/graph/common/omg_util.h

@@ -125,15 +125,6 @@ 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

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;
@@ -3463,11 +3466,11 @@ bool DavinciModel::CheckUserAndModelSize(const int64_t &size, const int64_t &op_
   }
   // The input and model input size can not be exactly equal because user input is not definite.
   if ((size + kDataMemAlignSizeCompare) < op_size) {
-    REPORT_INNER_ERROR("E19999", "%s size:%ld from user add align:%u < input_op_size:%ld in model, model_id:%u, "
+    REPORT_INNER_ERROR("E19999", "%s size:%ld from user add align:%u < op_size:%ld in model, model_id:%u, "
                        "check invalid",
                        input_or_output.c_str(), size, kDataMemAlignSizeCompare, op_size, model_id_);
     GELOGE(ACL_ERROR_GE_PARAM_INVALID,
-           "[Check][Param] %s size:%ld from user add align:%u < input_op_size:%ld in model, model_id:%u",
+           "[Check][Param] %s size:%ld from user add align:%u < op_size:%ld in model, model_id:%u",
            input_or_output.c_str(), size, kDataMemAlignSizeCompare, op_size, model_id_);
     return false;
   }
@@ -3700,6 +3703,7 @@ 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 +3712,61 @@ 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 +3774,115 @@ 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();

ge/graph/load/model_manager/davinci_model.h

@@ -771,6 +771,12 @@ 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();

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/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"
@@ -949,7 +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;
@@ -1001,6 +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.

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

@@ -364,6 +364,7 @@ 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();
@@ -373,17 +374,32 @@ void DynamicShapePartitioner::MergeClustersControlFlow() {
     }
 
     const auto &cluster = *rit;
+    if (all_merged_clusters.count(cluster) > 0) {
+      continue;
+    }
+
+    bool is_unknown_cluster = cluster->IsUnknownShape();
     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;
         }
       }
     }
+
+    if (!is_unknown_cluster && cluster->IsUnknownShape()) {
+      GELOGD("Add to ordered cluster: %s", cluster->DebugString().c_str());
+      ordered_cluster_.push_back(cluster);
+    }
   }
 }
 
@@ -703,7 +719,12 @@ 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

@@ -161,7 +161,7 @@ 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

@@ -132,39 +132,17 @@ 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);
-  };
-
-  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)) {
+  for (auto it = switch_groups.begin(); it != switch_groups.end(); ++it) {
+    const auto &op_node = it->first;
+    const auto &op_desc = op_node->GetOpDesc();
+    if (op_desc->HasAttr(ATTR_NAME_CONTROL_FLOW_GROUP)) {
       continue;
     }
 
-    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 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;
-        }
-
-        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);
-          }
-        }
-      }
+    int64_t group_index = op_desc->GetId();
+    SetControlFlowGroup(op_node, group_index);
+    for (const auto &n : it->second) {
+      SetControlFlowGroup(n, group_index);
     }
   }
 }

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

@@ -284,13 +284,21 @@ 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);
+      } else {
+        // For: Switch -> Cast -> Exit
+        for (const auto &n : node->GetOutDataNodes()) {
+          (void)GetOriginalType(n, node_type);
+          if (kExitOpTypes.count(node_type) > 0) {
+            SetControlFlowGroup(n, group_index);
+          }
+        }
       }
     }
   }

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 +395,8 @@ 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);
+  (void)AttrUtils::GetInt(switch_node->GetOpDesc(), ATTR_NAME_CONTROL_FLOW_GROUP, group_index);
+  SetControlFlowGroup(stream_switch, group_index);
   return stream_switch;
 }
 
@@ -491,8 +491,8 @@ 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 +524,13 @@ 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 +559,7 @@ 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());

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/ge_aipp_op.cc

@@ -114,7 +114,7 @@ Status GetDataDimN(const ge::NodePtr &data_node, ge::Format format, int64_t &bat
                            std::vector<std::string>({
                                data_node->GetName() + " format",
                                TypeUtils::FormatToSerialString(format),
-                               "only format " + TypeUtils::FormatToSerialString(FORMAT_NCHW) + " and "+
+                               "only format " + TypeUtils::FormatToSerialString(FORMAT_NCHW) + " and " +
                                TypeUtils::FormatToSerialString(FORMAT_NHWC) +
                                " supported which dynamic aipp is linked"}));
         GELOGE(PARAM_INVALID, "[Check][Param] Not support data format:%s, node:%s",

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/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 +325,54 @@ std::shared_ptr<TaskContext> NodeState::GetTaskContext() {
   return task_context_;
 }
 
+void NodeState::SavePersistTensor(int input_idx, const TensorValue &tensor) {
+  if (node_item_->root_data_.count(input_idx) > 0) {
+    GELOGD("[%s] Save Root input tensor: %d", GetName().c_str(), input_idx);
+    root_tensor_values_[input_idx] = tensor;
+  }
+
+  if (node_item_->enter_data_.count(input_idx) > 0) {
+    GELOGD("[%s] Save Enter input tensor: %d", GetName().c_str(), input_idx);
+    root_tensor_values_[input_idx] = tensor;
+  }
+}
+
+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());
+  for (auto item : node_item_->root_data_) {
+    UpdatePersistTensor(item.first);
+  }
+
+  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());
+    for (auto item : node_item_->enter_data_) {
+      UpdatePersistTensor(item.first);
+    }
   }
 
   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 +131,8 @@ struct NodeState {
   void RunStreamActive();
   void RunNextIteration();
 
+  void SavePersistTensor(int input_idx, const TensorValue &tensor);
+
   Status NodeScheduled(const std::function<void(const NodeItem *)> &ready) const;
 
   void SetScheduleFuture(std::future<Status> &&future);
@@ -150,18 +154,10 @@ 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 +177,17 @@ 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 +200,7 @@ 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 +208,7 @@ 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 +103,14 @@ 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());
+      output_desc->SetDataType(tensor_desc->GetDataType());
+      node_state->SetSkipInferShape(true);
     }
   }
 
@@ -175,16 +183,12 @@ 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 +275,12 @@ 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 +480,15 @@ 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/shape_inference_engine.cc

@@ -68,8 +68,9 @@ Status ShapeInferenceEngine::InferShape(NodeState &node_state) {
   }
 
   // Do shape inference
+  // Skipping infer shape of input node.
   GELOGD("[%s] Start to invoke InferShapeAndType", node_item.NodeName().c_str());
-  {
+  if (!node_state.MaySkipShapeInference()) {
     RECORD_SHAPE_INFERENCE_EVENT(execution_context_, node_item.NodeName().c_str(), "[InferShapeAndType] Start");
     GE_CHK_STATUS_RET(ShapeRefiner::InferShapeAndTypeForRunning(node_item.node, true),
         "[Invoke][InferShapeAndType] for %s failed.", node_item.NodeName().c_str());

ge/hybrid/model/hybrid_model_builder.cc

@@ -1227,6 +1227,28 @@ Status HybridModelBuilder::LoadGeModel(ComputeGraph &sub_graph, const GeModelPtr
     hybrid_model_.known_shape_sub_models_.emplace(parent_node, ge_model);
   }
 
+  GE_CHK_STATUS_RET_NOLOG(InitHcclExecutorOnDemand(ge_model));
+  return SUCCESS;
+}
+
+Status HybridModelBuilder::InitHcclExecutorOnDemand(const GeModelPtr &ge_model) {
+  if (NodeExecutorManager::GetInstance().IsExecutorInitialized(NodeExecutorManager::ExecutorType::HCCL)) {
+    return SUCCESS;
+  }
+
+  // HCCL tasks in known-shaped subgraph which resides in a dynamic root graph
+  // still depends on the initialization of the HcclExecutor
+  auto tasks = ge_model->GetModelTaskDefPtr()->task();
+  for (int i = 0; i < tasks.size(); ++i) {
+    const domi::TaskDef &task_def = tasks[i];
+    auto task_type = static_cast<rtModelTaskType_t>(task_def.type());
+    if (task_type == RT_MODEL_TASK_HCCL) {
+      const NodeExecutor *unused = nullptr;
+      GE_CHK_STATUS_RET_NOLOG(NodeExecutorManager::GetInstance()
+                                  .GetOrCreateExecutor(NodeExecutorManager::ExecutorType::HCCL, &unused));
+      return SUCCESS;
+    }
+  }
   return SUCCESS;
 }
 

ge/hybrid/model/hybrid_model_builder.h

@@ -57,6 +57,7 @@ class HybridModelBuilder {
   Status ValidateParams();
   Status LoadGraph();
   Status LoadGeModel(ComputeGraph &graph, const GeModelPtr &ge_model);
+  static Status InitHcclExecutorOnDemand(const GeModelPtr &ge_model);
   Status LoadTask(NodeItem &node_item);
   Status LoadTasks();
   Status IdentifyVariableOutputs(NodeItem &node_item, const ComputeGraphPtr &subgraph);

ge/hybrid/model/node_item.cc

@@ -398,12 +398,11 @@ void NodeItem::SetDataSend(NodeItem *node_item, int anchor_index) {
   data_send_.emplace(node_item);
   node_item->data_recv_[this] = anchor_index;
   if (is_root_node_) {
-    node_item->root_data_.emplace(this);
+    node_item->root_data_[anchor_index] = this;
   }
   // If Enter feed Not Merge, take as root Node.
   if (IsEnterOp() && (node_item->node_type != STREAMMERGE)) {
-    node_item->enter_data_.emplace(this);
-    node_item->enter_inside_.emplace(anchor_index);
+    node_item->enter_data_[anchor_index] = this;
   }
   GELOGI("Node[%s] will control node[%s]", NodeName().c_str(), node_item->NodeName().c_str());
 }

ge/hybrid/model/node_item.h

@@ -148,15 +148,14 @@ struct NodeItem {
   int64_t frame_index_ = -1;
   int64_t parent_frame_ = -1;
   std::set<const NodeItem *> root_ctrl_;  // Recv ctrl from root node
-  std::set<const NodeItem *> root_data_;  // Recv data from root node
+  std::map<int, const NodeItem *> root_data_;  // Recv data from root node
   std::set<const NodeItem *> enter_ctrl_; // Recv ctrl from Enter node
-  std::set<const NodeItem *> enter_data_; // Recv data from Enter node
+  std::map<int, const NodeItem *> enter_data_; // Recv data from Enter node
   std::set<const NodeItem *> data_send_;  // Send data notify to
   std::map<const NodeItem *, int> data_recv_;  // Recv data notify from
   std::set<const NodeItem *> ctrl_send_;  // Send ctrl notify to
   std::set<const NodeItem *> ctrl_recv_;  // Recv ctrl notify from
   std::vector<std::vector<const NodeItem *>> switch_groups_;  // Send ctrl notify to
-  std::set<int> enter_inside_;  // Enter feed loop inside Node, Not cross Merge.
 
   std::shared_ptr<NodeTask> kernel_task;
   std::unique_ptr<FusedSubgraph> fused_subgraph;

ge/hybrid/node_executor/aicpu/aicpu_node_executor.cc

@@ -64,10 +64,6 @@ Status AicpuNodeTaskBase::InitExtInfo(const std::string &kernel_ext_info, int64_
   GE_CHK_STATUS_RET(aicpu_ext_handle_.UpdateSessionInfoSessionId(session_id),
                     "[Update][SessionInfoSessionId] failed, session_id:%ld.", session_id);
 
-  bool execute_mode = !aicpu_ext_handle_.IsNeedRefreshIOAddr() && !node_item_->is_dynamic;
-  GE_CHK_STATUS_RET(aicpu_ext_handle_.UpdateExecuteMode(execute_mode),
-                    "[Update][ExecuteMode] failed, node:%s.", node_name_.c_str());
-
   // copy task args buf
   GE_CHK_STATUS_RET(AllocTensorBuffer(aicpu_ext_handle_.GetExtInfoLen(), ext_info_addr_dev_),
                     "[Invoke][AllocTensorBuffer]Node[%s] alloc kernel_ext_info buf failed, size=%zu",

ge/hybrid/node_executor/hccl/hccl_node_executor.cc

@@ -24,6 +24,7 @@
 #include "graph/types.h"
 #include "hybrid/executor/hybrid_execution_context.h"
 #include "hccl/hcom.h"
+#include "runtime/event.h"
 
 namespace ge {
 namespace {
@@ -325,7 +326,7 @@ Status RdmaNodeTask::ExecuteAsync(TaskContext &context, std::function<void()> do
 
   rtEvent_t evt = nullptr;
   if (context.GetExecutionContext()->hccl_stream != nullptr) {
-    GE_CHK_RT_RET(rtEventCreateWithFlag(&evt, 0x01));
+    GE_CHK_RT_RET(rtEventCreateWithFlag(&evt, RT_EVENT_WITH_FLAG));
     GE_CHK_RT_RET(rtStreamWaitEvent(context.GetExecutionContext()->hccl_stream, evt));
   }
   TaskContext *p_ctx = &context;

ge/hybrid/node_executor/node_executor.cc

@@ -58,8 +58,8 @@ Status NodeExecutor::CompileTask(const HybridModel &model, const NodePtr &node,
 }
 
 Status NodeExecutorManager::EnsureInitialized() {
-  GE_CHK_STATUS_RET(InitializeExecutors());
   std::lock_guard<std::mutex> lk(mu_);
+  ++ref_count_;
   if (initialized_) {
     return SUCCESS;
   }
@@ -115,17 +115,14 @@ NodeExecutorManager::ExecutorType NodeExecutorManager::ResolveExecutorType(Node
   return it->second;
 }
 
-Status NodeExecutorManager::GetExecutor(Node &node, const NodeExecutor **executor) const {
+Status NodeExecutorManager::GetExecutor(Node &node, const NodeExecutor **executor) {
   auto executor_type = ResolveExecutorType(node);
+  GELOGD("[%s] Set node executor by type: %d.", node.GetName().c_str(), static_cast<int>(executor_type));
   const auto it = executors_.find(executor_type);
   if (it == executors_.end()) {
-    REPORT_INNER_ERROR("E19999", "Failed to get executor by type: %d.", static_cast<int>(executor_type));
-    GELOGE(INTERNAL_ERROR, "[Check][ExecutorType]Failed to get executor by type: %d.",
-           static_cast<int>(executor_type));
-    return INTERNAL_ERROR;
+    return GetOrCreateExecutor(executor_type, executor);
   }
 
-  GELOGD("[%s] Set node executor by type: %d.", node.GetName().c_str(), static_cast<int>(executor_type));
   *executor = it->second.get();
   return SUCCESS;
 }
@@ -178,51 +175,55 @@ Status NodeExecutorManager::CalcOpRunningParam(Node &node) const {
   return OpsKernelBuilderManager::Instance().CalcOpRunningParam(node);
 }
 
-Status NodeExecutorManager::InitializeExecutors() {
+bool NodeExecutorManager::IsExecutorInitialized(NodeExecutorManager::ExecutorType executor_type) {
+  std::lock_guard<std::mutex> lk(mu_);
+  return executors_.find(executor_type) != executors_.end();
+}
+
+Status NodeExecutorManager::GetOrCreateExecutor(ExecutorType executor_type, const NodeExecutor **out_executor) {
   std::lock_guard<std::mutex> lk(mu_);
-  if (executor_initialized_) {
-    ++ref_count_;
-    GELOGI("Executor is already initialized. add ref count to [%d]", ref_count_);
+  const auto executor_it = executors_.find(executor_type);
+  if (executor_it != executors_.end()) {
+    *out_executor = executor_it->second.get();
     return SUCCESS;
   }
 
-  GELOGI("Start to Initialize NodeExecutors");
-  for (auto &it : builders_) {
-    auto engine_type = it.first;
-    auto build_fn = it.second;
-    GE_CHECK_NOTNULL(build_fn);
-    auto executor = std::unique_ptr<NodeExecutor>(build_fn());
-    if (executor == nullptr) {
-      REPORT_CALL_ERROR("E19999", "Create NodeExecutor failed for engine type = %d",
-                        static_cast<int>(engine_type));
-      GELOGE(INTERNAL_ERROR, "[Create][NodeExecutor] failed for engine type = %d", static_cast<int>(engine_type));
-      return INTERNAL_ERROR;
-    }
+  GELOGI("Start to Initialize NodeExecutor, type = %d", static_cast<int>(executor_type));
+  auto it = builders_.find(executor_type);
+  if (it == builders_.end()) {
+    REPORT_CALL_ERROR("E19999", "Create NodeExecutor failed for executor type = %d",
+                      static_cast<int>(executor_type));
+    GELOGE(INTERNAL_ERROR, "[Create][NodeExecutor] failed for executor type = %d", static_cast<int>(executor_type));
+    return INTERNAL_ERROR;
+  }
 
-    GELOGD("Executor of engine type = %d was created successfully", static_cast<int>(engine_type));
-    auto ret = executor->Initialize();
-    if (ret != SUCCESS) {
-      REPORT_CALL_ERROR("E19999", "Initialize NodeExecutor failed for type = %d", static_cast<int>(engine_type));
-      GELOGE(ret, "[Initialize][NodeExecutor] failed for type = %d", static_cast<int>(engine_type));
-      for (auto &executor_it : executors_) {
-        executor_it.second->Finalize();
-      }
-      executors_.clear();
-      return ret;
-    }
+  auto build_fn = it->second;
+  GE_CHECK_NOTNULL(build_fn);
+  auto executor = std::unique_ptr<NodeExecutor>(build_fn());
+  if (executor == nullptr) {
+    REPORT_CALL_ERROR("E19999", "Create NodeExecutor failed for executor type = %d",
+                      static_cast<int>(executor_type));
+    GELOGE(INTERNAL_ERROR, "[Create][NodeExecutor] failed for engine type = %d", static_cast<int>(executor_type));
+    return INTERNAL_ERROR;
+  }
 
-    executors_.emplace(engine_type, std::move(executor));
+  GELOGD("Executor of engine type = %d was created successfully", static_cast<int>(executor_type));
+  auto ret = executor->Initialize();
+  if (ret != SUCCESS) {
+    REPORT_CALL_ERROR("E19999", "Initialize NodeExecutor failed for type = %d", static_cast<int>(executor_type));
+    GELOGE(ret, "[Initialize][NodeExecutor] failed for type = %d", static_cast<int>(executor_type));
+    return ret;
   }
 
-  ++ref_count_;
-  executor_initialized_ = true;
-  GELOGI("Initializing NodeExecutors successfully.");
+  *out_executor = executor.get();
+  executors_.emplace(executor_type, std::move(executor));
+  GELOGI("Initializing NodeExecutor successfully, type = %d", static_cast<int>(executor_type));
   return SUCCESS;
 }
 
 void NodeExecutorManager::FinalizeExecutors() {
   std::lock_guard<std::mutex> lk(mu_);
-  if (!executor_initialized_) {
+  if (ref_count_ <= 0) {
     GELOGD("No need for finalizing for not initialized.");
     return;
   }
@@ -237,7 +238,6 @@ void NodeExecutorManager::FinalizeExecutors() {
     it.second->Finalize();
   }
   executors_.clear();
-  executor_initialized_ = false;
   GELOGD("Done invoking Finalize successfully.");
 }
 

ge/hybrid/node_executor/node_executor.h

@@ -179,8 +179,6 @@ class NodeExecutorManager {
    */
   Status EnsureInitialized();
 
-  Status InitializeExecutors();
-
   void FinalizeExecutors();
 
   /**
@@ -196,7 +194,7 @@ class NodeExecutorManager {
    * @param executor        executor
    * @return SUCCESS on success, error code otherwise
    */
-  Status GetExecutor(Node &node, const NodeExecutor **executor) const;
+  Status GetExecutor(Node &node, const NodeExecutor **executor);
 
   /**
    * Resolve executor type by node
@@ -205,13 +203,16 @@ class NodeExecutorManager {
    */
   ExecutorType ResolveExecutorType(Node &node) const;
 
+  Status GetOrCreateExecutor(ExecutorType executor_type, const NodeExecutor **executor);
+
+  bool IsExecutorInitialized(ExecutorType executor_type);
+
  private:
   std::map<ExecutorType, std::unique_ptr<NodeExecutor>> executors_;
   std::map<ExecutorType, std::function<NodeExecutor *()>> builders_;
   std::map<std::string, NodeExecutorManager::ExecutorType> engine_mapping_;
   std::mutex mu_;
   bool initialized_ = false;
-  bool executor_initialized_ = false;
   int ref_count_ = 0;
 };
 

ge/hybrid/node_executor/task_context.cc

@@ -52,9 +52,7 @@ void TaskContext::ReleaseWorkspace() {
   }
 }
 
-std::unique_ptr<TaskContext> TaskContext::Create(NodeState *node_state,
-                                                 GraphExecutionContext *execution_context,
-                                                 SubgraphContext *subgraph_context) {
+std::unique_ptr<TaskContext> TaskContext::Create(NodeState *node_state, SubgraphContext *subgraph_context) {
   const NodeItem &node_item = *node_state->GetNodeItem();
   GELOGI("[%s] To create task context, input start = %d, num_inputs = %d, output start = %d, num_outputs = %d.",
          node_item.NodeName().c_str(),
@@ -75,7 +73,7 @@ std::unique_ptr<TaskContext> TaskContext::Create(NodeState *node_state,
   }
 
   auto task_context = std::unique_ptr<TaskContext>(
-      new(std::nothrow)TaskContext(execution_context, node_state, subgraph_context));
+      new(std::nothrow)TaskContext(subgraph_context->execution_context_, node_state, subgraph_context));
   if (task_context == nullptr) {
     REPORT_CALL_ERROR("E19999", "Create TaskContext failed for [%s].", node_item.NodeName().c_str());
     GELOGE(MEMALLOC_FAILED, "[Create][TaskContext] failed for [%s].", node_item.NodeName().c_str());
@@ -85,7 +83,7 @@ std::unique_ptr<TaskContext> TaskContext::Create(NodeState *node_state,
   task_context->node_item_ = &node_item;
   task_context->inputs_start_ = subgraph_context->all_inputs_.data() + node_item.input_start;
   task_context->outputs_start_ = subgraph_context->all_outputs_.data() + node_item.output_start;
-  task_context->iteration_ = execution_context->iteration;
+  task_context->iteration_ = subgraph_context->execution_context_->iteration;
   return task_context;
 }
 
@@ -460,6 +458,10 @@ Status TaskContext::PropagateOutputs() {
         subgraph_context_->all_inputs_[input_offset].SetName(
             node_item_->NodeName() + "_in_" + std::to_string(dst_input_idx));
       }
+
+      auto dst_node_state = subgraph_context_->GetOrCreateNodeState(dst_node_item);
+      GE_CHECK_NOTNULL(dst_node_state);
+      dst_node_state->SavePersistTensor(dst_input_idx, *tensor);
     }
   }
   (void)guard;
@@ -489,11 +491,6 @@ void TaskContext::ReleaseInputsAndOutputs() {
 }
 
 void TaskContext::ReleaseInput(int index) {
-  if (node_item_->enter_inside_.count(index) > 0) {
-    GELOGD("[%s] Tensor of input[%d] is enter, keep it", GetNodeName(), index);
-    return;
-  }
-
   auto input_tensor = MutableInput(index);
   if (input_tensor != nullptr) {
     input_tensor->Destroy();

ge/hybrid/node_executor/task_context.h

@@ -36,9 +36,7 @@ class SubgraphContext;
 
 class TaskContext {
  public:
-  static std::unique_ptr<TaskContext> Create(NodeState *node_state,
-                                             GraphExecutionContext *execution_context,
-                                             SubgraphContext *subgraph_context);
+  static std::unique_ptr<TaskContext> Create(NodeState *node_state, SubgraphContext *subgraph_context);
 
   ~TaskContext();
 

ge/ir_build/ge_ir_build.cc

@@ -263,6 +263,7 @@ class Impl {
     omg_context_.user_attr_index_valid = false;
   };
   ~Impl() { (void)generator_.Finalize(); };
+  graphStatus CheckBuildModeAndBuildStep();
   graphStatus GetSupportedOptions(const std::map<std::string, std::string> &in,
                                   std::map<std::string, std::string> &out);
   graphStatus CheckOptions(const std::map<std::string, std::string> &options);
@@ -451,6 +452,37 @@ graphStatus Impl::UpdateDataOpAttr(const Graph &graph) {
   return GRAPH_SUCCESS;
 }
 
+graphStatus Impl::CheckBuildModeAndBuildStep() {
+  std::string build_mode;
+  auto it = options_.find(BUILD_MODE);
+  if (it != options_.end() && !(it->second.empty())) {
+    if (build_mode_options.find(it->second) == build_mode_options.end()) {
+      REPORT_INPUT_ERROR("E10001", std::vector<std::string>({"parameter", "value", "reason"}),
+          std::vector<std::string>({BUILD_MODE, it->second, "value is unsupported. Please check!"}));
+      GELOGE(GRAPH_PARAM_INVALID, "[Check][BuildMode]:%s is unsupported. Please check!", it->second.c_str());
+      return GRAPH_PARAM_INVALID;
+    }
+    build_mode = it->second;
+  }
+  it = options_.find(BUILD_STEP);
+  if (it != options_.end() && !(it->second.empty())) {
+    if (build_step_options.find(it->second) == build_step_options.end()) {
+      REPORT_INPUT_ERROR("E10001", std::vector<std::string>({"parameter", "value", "reason"}),
+          std::vector<std::string>({BUILD_STEP, it->second, "value is unsupported. Please check!"}));
+      GELOGE(GRAPH_PARAM_INVALID, "[Check][BuildStep]:%s is unsupported. Please check!", it->second.c_str());
+      return GRAPH_PARAM_INVALID;
+    }
+  } else {
+    if (build_mode == BUILD_MODE_TUNING) {
+      REPORT_INPUT_ERROR("E10001", std::vector<std::string>({"parameter", "value", "reason"}),
+          std::vector<std::string>({BUILD_MODE, it->second, "tuning must specify build step. Please check!"}));
+      GELOGE(GRAPH_PARAM_INVALID, "[Check][BuildMode] tuning must specify build step. Please check!");
+      return GRAPH_PARAM_INVALID;
+    }
+  }
+  return GRAPH_SUCCESS;
+}
+
 graphStatus Impl::GetSupportedOptions(const std::map<std::string, std::string> &in,
                                       std::map<std::string, std::string> &out) {
   for (auto &ele : in) {
@@ -475,29 +507,12 @@ graphStatus Impl::CheckOptions(const std::map<std::string, std::string> &options
   }
 
   // Check options build_mode and build_step.
-  std::string build_mode;
-  auto it = options_.find(BUILD_MODE);
-  if (it != options_.end() && !(it->second.empty())) {
-    if (build_mode_options.find(it->second) == build_mode_options.end()) {
-      GELOGE(GRAPH_PARAM_INVALID, "[Check][BuildMode]:%s is unsupported. Please check!", it->second.c_str());
-      return GRAPH_PARAM_INVALID;
-    }
-    build_mode = it->second;
-  }
-  it = options_.find(BUILD_STEP);
-  if (it != options_.end() && !(it->second.empty())) {
-    if (build_step_options.find(it->second) == build_step_options.end()) {
-      GELOGE(GRAPH_PARAM_INVALID, "[Check][BuildStep]:%s is unsupported. Please check!", it->second.c_str());
-      return GRAPH_PARAM_INVALID;
-    }
-  } else {
-    if (build_mode == BUILD_MODE_TUNING) {
-      GELOGE(GRAPH_PARAM_INVALID, "[Check][BuildMode] tuning must specify build step. Please check!");
-      return GRAPH_PARAM_INVALID;
-    }
+  ret = CheckBuildModeAndBuildStep();
+  if (ret != GRAPH_SUCCESS) {
+    return ret;
   }
   // Check option EXEC_DISABLE_REUSED_MEMORY
-  it = options_.find(ge::ir_option::EXEC_DISABLE_REUSED_MEMORY);
+  auto it = options_.find(ge::ir_option::EXEC_DISABLE_REUSED_MEMORY);
   if (it != options_.end() && (CheckDisableReuseMemoryParamValid(it->second) != GRAPH_SUCCESS)) {
     return GRAPH_PARAM_INVALID;
   }
@@ -505,6 +520,18 @@ graphStatus Impl::CheckOptions(const std::map<std::string, std::string> &options
   if (ge::CheckModifyMixlistParamValid(options_) != GRAPH_SUCCESS) {
     return GRAPH_PARAM_INVALID;
   }
+  // Check option OP_PRECISION_MODE
+  it = options_.find(ge::ir_option::OP_PRECISION_MODE);
+  if (it != options_.end() && !it->second.empty() && !ge::CheckInputPathValid(it->second)) {
+    REPORT_INPUT_ERROR("E10001", std::vector<std::string>({"parameter", "value", "reason"}),
+        std::vector<std::string>({ge::ir_option::OP_PRECISION_MODE, it->second, "path is not found"}));
+    GELOGE(GRAPH_PARAM_INVALID, "[Check][OP_PRECISION_MODE] %s not found", it->second.c_str());
+    return GRAPH_PARAM_INVALID;
+  }
+  if (it != options_.end()) {
+    GELOGI("Option set successfully, option_key=%s, option_value=%s",
+           ge::ir_option::OP_PRECISION_MODE, it->second.c_str());
+  }
   // Check Input Format
   if (options_.find(kInputFormat) != options_.end()) {
     return CheckInputFormat(options_[kInputFormat]);
@@ -559,8 +586,8 @@ graphStatus Impl::Init(const Graph &graph, const std::map<std::string, std::stri
   std::string output_type = GetParam(ge::ir_option::OUTPUT_TYPE);
   GE_CHK_BOOL_EXEC(ge::CheckOutputTypeParamValid(output_type) == ge::SUCCESS,
       return ge::GRAPH_PARAM_INVALID, "[Check][OutputType] failed!");
-  // check insert_op_conf
 
+  // check insert_op_conf
   std::string insert_op_conf = GetParam(ge::ir_option::INSERT_OP_FILE);
   GE_CHK_BOOL_EXEC(ge::CheckInsertOpConfParamValid(std::string(insert_op_conf)) == ge::SUCCESS,
       return ge::GRAPH_PARAM_INVALID, "[Check][InsertOpConf] failed!");

ge/ir_build/option_utils.cc

@@ -204,7 +204,7 @@ bool CheckDynamicImagesizeInputShapeValid(map<string, vector<int64_t>> shape_map
   if (!input_format.empty() && !ge::TypeUtils::IsFormatValid(input_format.c_str())) {
     GELOGE(ge::PARAM_INVALID,
         "[Check][DynamicImagesizeInputShape] input_format [%s] invalid, can not support now.", input_format.c_str());
-    REPORT_INPUT_ERROR("E10003", std::vector<std::string>({"parameter","value","reason"}),
+    REPORT_INPUT_ERROR("E10003", std::vector<std::string>({"parameter", "value", "reason"}),
                        std::vector<std::string>({"input_format", input_format, "this format is not support"}));
     return false;
   }

ge/offline/main.cc

@@ -106,10 +106,14 @@ DEFINE_string(out_nodes, "",
               "Optional; output nodes designated by users."
               "Format: \"node_name1:0;node_name1:1;node_name2:0\"");
 
+DEFINE_string(op_precision_mode, "", "Optional; operator precision mode configuration file path");
+
 DEFINE_string(precision_mode, "force_fp16",
               "Optional; precision mode."
               "Support force_fp16, force_fp32, allow_mix_precision, allow_fp32_to_fp16, must_keep_origin_dtype.");
 
+DEFINE_string(modify_mixlist, "", "Optional; operator mixed precision configuration file path");
+
 DEFINE_string(keep_dtype, "",
               "Optional; config file to specify the precision used by the operator during compilation.");
 
@@ -192,8 +196,11 @@ DEFINE_string(log, "null", "Optional; generate atc log. Support debug, info, war
 
 DEFINE_string(dump_mode, "0", "Optional; generate infershape json,only support 1 , 0.");
 
-DEFINE_int32(op_debug_level, 0, "Optional; configure debug level of compiler. 0(default): close debug;"
-             "1: open TBE compiler, export ccec file and TBE instruction mapping file; 2: open ccec compiler");
+DEFINE_int32(op_debug_level, 0, "Optional; configure debug level of compiler. 0(default): close debug; "
+             "1: open TBE compiler, export ccec file and TBE instruction mapping file; 2: open ccec compiler; "
+             "3: disable debug, and keep generating kernel file (.o and .json); 4: disable debug, "
+             "keep generation kernel file (.o and .json) and generate the operator CCE file (.cce) "
+             "and the UB fusion computing description file (.json)");
 DEFINE_string(enable_scope_fusion_passes, "", "Optional; validate the non-general scope fusion pass,"
               "multiple names can be set and separated by ','.");
 DEFINE_string(debug_dir, "", "Optional; the path to save the intermediate files of operator compilation");
@@ -210,8 +217,6 @@ DEFINE_string(display_model_info, "0", "Optional; display model info");
 
 DEFINE_string(device_id, "0", "Optional; user device id");
 
-DEFINE_string(modify_mixlist, "", "Optional; operator mixed precision configuration file path");
-
 class GFlagUtils {
  public:
   /**
@@ -298,8 +303,10 @@ class GFlagUtils {
         "\"l1_optimize\", \"off_optimize\"\n"
 	"  --mdl_bank_path           Set the path of the custom repository generated after model tuning.\n"
         "\n[Operator Tuning]\n"
+        "  --op_precision_mode     Set the path of operator precision mode configuration file (.ini)\n"
         "  --precision_mode        precision mode, support force_fp16(default), force_fp32, allow_mix_precision, "
         "allow_fp32_to_fp16, must_keep_origin_dtype.\n"
+        "  --modify_mixlist        Set the path of operator mixed precision configuration file.\n"
         "  --keep_dtype            Retains the precision of certain operators in inference "
         "scenarios by using a configuration file.\n"
         "  --auto_tune_mode        Set tune mode. E.g.: \"GA,RL\", support configure multiple, spit by ,\n"
@@ -315,7 +322,8 @@ class GFlagUtils {
         "                          2: Enable TBE pipe_all, generate the operator CCE file and Python-CCE mapping file "
         "(.json), and enable the CCE compiler -O0-g.\n"
         "                          3: Disable debug, and keep generating kernel file (.o and .json)\n"
-        "  --modify_mixlist        Set the path of operator mixed precision configuration file.\n"
+        "                          4: Disable debug, keep generation kernel file (.o and .json) and generate the "
+        "operator CCE file (.cce) and the UB fusion computing description file (.json)"
         "\n[Debug]\n"
         "  --save_original_model   Control whether to output original model. E.g.: true: output original model\n"
         "  --log                   Generate log with level. Support debug, info, warning, error, null\n"
@@ -365,6 +373,14 @@ class GFlagUtils {
                                     FLAGS_op_select_implmode) != ge::SUCCESS,
         ret = ge::FAILED, "[Check][ImplMode]check optypelist_for_implmode and op_select_implmode failed!");
 
+    if (!FLAGS_op_precision_mode.empty() && !ge::CheckInputPathValid(FLAGS_op_precision_mode, "--op_precision_mode")) {
+      ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
+                                                      {"op_precision_mode", FLAGS_op_precision_mode.c_str(),
+                                                      "path is not found"});
+      GELOGE(ge::FAILED, "[Check][op_precision_mode] %s not found", FLAGS_op_precision_mode.c_str());
+      ret = ge::FAILED;
+    }
+
     if (ge::CheckModifyMixlistParamValid(FLAGS_precision_mode, FLAGS_modify_mixlist) != ge::SUCCESS) {
       ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
                                                       {"modify_mixlist", FLAGS_modify_mixlist.c_str(),
@@ -847,6 +863,7 @@ domi::Status GenerateInfershapeJson() {
   ge::Graph graph;
   std::map<string, string> atc_params;
   atc_params.insert(std::pair<string, string>("input_format", FLAGS_input_format));
+  atc_params.insert(std::pair<string, string>("check_report", FLAGS_check_report));
   ret = ParseGraph(graph, atc_params, FLAGS_om.c_str(), FLAGS_weight.c_str(), (domi::FrameworkType) FLAGS_framework,
                    "", FLAGS_target.c_str(), (ge::RunMode) FLAGS_mode, false);
   if (ret != ge::SUCCESS) {
@@ -953,8 +970,7 @@ domi::Status GenerateModel(std::map<string, string> &options, std::string output
     ge::Model load_model = ge::Model("loadmodel", "version2");
     auto ret1 = load_model.LoadFromFile(FLAGS_model);
     if (ret1 != ge::GRAPH_SUCCESS) {
-      REPORT_INPUT_ERROR("E10041", std::vector<std::string>({"file"}), std::vector<std::string>({FLAGS_model}));
-      REPORT_CALL_ERROR("E19999", "load from model file:%s failed", FLAGS_model.c_str());
+      REPORT_INPUT_ERROR("E10041", std::vector<std::string>({"parameter"}), std::vector<std::string>({FLAGS_model}));
       DOMI_LOGE("Load model from %s failed, please check model file or "
           "input parameter[--framework] is correct", FLAGS_model.c_str());
       (void)ge_generator.Finalize();
@@ -1050,6 +1066,7 @@ static void SetEnvForSingleOp(std::map<string, string> &options) {
   options.emplace(ge::RUN_FLAG, flag_off);
   options.emplace(ge::OPTION_GRAPH_RUN_MODE, flag_off);
   options.emplace(ge::SINGLE_OP_FLAG, flag_on);
+  options.emplace(ge::OP_PRECISION_MODE, FLAGS_op_precision_mode);
   options.emplace(ge::PRECISION_MODE, FLAGS_precision_mode);
   options.emplace(ge::SOC_VERSION, FLAGS_soc_version);
   options.emplace(ge::CORE_TYPE, FLAGS_core_type);
@@ -1077,6 +1094,14 @@ domi::Status GenerateSingleOp(const std::string& json_file_path) {
       ge::CheckImplmodeParamValid(FLAGS_optypelist_for_implmode, FLAGS_op_select_implmode) != ge::SUCCESS,
       return ge::FAILED, "[Check][ImplmodeParam] fail for input optypelist_for_implmode and op_select_implmode.");
 
+  if (!FLAGS_op_precision_mode.empty() && !ge::CheckInputPathValid(FLAGS_op_precision_mode, "--op_precision_mode")) {
+    ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
+                                                    {"op_precision_mode", FLAGS_op_precision_mode.c_str(),
+                                                    "path is not found"});
+    GELOGE(ge::FAILED, "[Check][op_precision_mode] %s not found", FLAGS_op_precision_mode.c_str());
+    return ge::FAILED;
+  }
+
   if (ge::CheckModifyMixlistParamValid(FLAGS_precision_mode, FLAGS_modify_mixlist) != ge::SUCCESS) {
     ErrorManager::GetInstance().ATCReportErrMessage("E10001", {"parameter", "value", "reason"},
                                                     {"modify_mixlist", FLAGS_modify_mixlist.c_str(),
@@ -1160,6 +1185,7 @@ domi::Status GenerateOmModel() {
   options.insert(std::pair<string, string>(string(ge::CALIBRATION_CONF_FILE), FLAGS_cal_conf));
   options.insert(std::pair<string, string>(string(ge::OUTPUT_NODE_NAME), FLAGS_out_nodes));
   options.insert(std::pair<string, string>(string(ge::INSERT_OP_FILE), FLAGS_insert_op_conf));
+  options.insert(std::pair<string, string>(string(ge::OP_PRECISION_MODE), FLAGS_op_precision_mode));
   options.insert(std::pair<string, string>(string(ge::PRECISION_MODE), FLAGS_precision_mode));
   options.insert(std::pair<string, string>(string(ge::TUNE_DEVICE_IDS), FLAGS_device_id));
 

ge/offline/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/offline/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/offline/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/offline/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/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/proto/fusion_model.proto

@@ -1,21 +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";
-
-import "om.proto";
-
-package domi;
-
-message FusionModelDef {
-    string version = 1;
-    repeated OpDef fusion_op = 2;
-}

ge/proto/fwk_adapter.proto

@@ -1,37 +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 aicpu.FWKAdapter;
-option cc_enable_arenas = true;
-
-
-// Defines an struct for input and output.
-message TensorDataInfo {
-
-  // value DataType
-  uint32 dtype = 1;
-
-  // shape dim
-  repeated int64 dim = 2;
-
-  // data point addr
-  int64 data_addr = 3;
-}
-
-message KernelRunParam {
-  // input
-  repeated TensorDataInfo input = 1;
-  // output
-  repeated TensorDataInfo output = 2;
-}
-

ge/proto/ge_api.proto

@@ -1,88 +0,0 @@
-syntax = "proto3";
-package ge.api_pb;
-
-import "ge_ir.proto";
-
-// GE initialize
-message GEInitialize {
-    map<string, string> options = 1;
-};
-
-// initialize response
-message GEInitializeResponse {
-    uint32 status = 1;
-    uint32 clientId = 2;
-};
-
-// GE finalize
-message GEFinalize {
-    bool final = 1;
-    uint32 clientId = 2;
-};
-
-message GEFinalizeResponse {
-    uint32 status = 1;
-};
-
-// GE Session
-message CreateSession{
-    map<string, string> options = 1;
-};
-
-message CreateSessionResponse {
-    uint32 status = 1;
-    uint64 sessionId = 2;
-};
-
-//GE AddGraph
-//model serialize :: serializegraph
-message SessionAddGraph{
-    uint32 graphId = 1;
-    uint64 sessionId = 2;
-    ge.proto.GraphDef graph = 3;
-};
-
-message SessionAddGraphResponse {
-    uint32 status = 1; 
-};
-
-//GE SessionRemoveGraph
-message SessionRemoveGraph{
-    uint32 graphId = 1;
-    uint64 sessionId = 2;
-};
-
-message SessionRemoveGraphResponse {
-    uint32 status = 1;
-};
-
-message SessionRunGraph{
-    uint32 graphId = 1;
-    uint64 sessionId = 2;
-    repeated ge.proto.TensorDef tensor = 3;
-};
-
-message SessionBuildGraph{
-    uint32 graphId = 1;
-    uint64 sessionId = 2;
-    repeated ge.proto.TensorDef tensor = 3;
-    string savePath = 4;
-};
-
-message SessionRunGraphResponse {
-    uint32 status = 1;
-    repeated ge.proto.TensorDef tensor = 2;
-};
-
-message SessionBuildGraphResponse {
-    uint32 status = 1;
-};
-
-message DestroySession{
-    bool final = 1;
-    uint64 sessionId = 2;
-};
-
-message DestroySessionResponse {
-    uint32 status = 1;
-};