upgrade Ascend software package 10 Aug 22

inc/external/acl/acl_rt.h

@@ -161,6 +161,17 @@ ACL_FUNC_VISIBILITY uint32_t aclrtGetThreadIdFromExceptionInfo(const aclrtExcept
  */
 ACL_FUNC_VISIBILITY uint32_t aclrtGetDeviceIdFromExceptionInfo(const aclrtExceptionInfo *info);
 
+/**
+ * @ingroup AscendCL
+ * @brief Get error code from exception information
+ *
+ * @param info [IN]   pointer of exception information
+ *
+ * @retval The error code from exception information
+ * @retval 0xFFFFFFFF if info is null
+ */
+ACL_FUNC_VISIBILITY uint32_t aclrtGetErrorCodeFromExceptionInfo(const aclrtExceptionInfo *info);
+
 /**
  * @ingroup AscendCL
  * @brief The thread that handles the callback function on the Stream

inc/external/acl/error_codes/rt_error_codes.h

@@ -65,6 +65,7 @@ static const int32_t ACL_ERROR_RT_QUEUE_FULL = 207014;           // queue is ful
 static const int32_t ACL_ERROR_RT_REPEATED_INIT = 207015;        // repeated init
 static const int32_t ACL_ERROR_RT_AIVEC_OVER_FLOW = 207016;      // aivec over flow
 static const int32_t ACL_ERROR_RT_OVER_FLOW = 207017;            // common over flow
+static const int32_t ACL_ERROR_RT_DEVIDE_OOM = 207018;           // device oom
 
 static const int32_t ACL_ERROR_RT_INTERNAL_ERROR = 507000;                   // runtime internal error
 static const int32_t ACL_ERROR_RT_TS_ERROR = 507001;                         // ts internel error

inc/external/ge/ge_api.h

@@ -191,6 +191,8 @@ class GE_FUNC_VISIBILITY Session {
 
   bool IsGraphNeedRebuild(uint32_t graph_id);
 
+  uint64_t GetSessionId() const;
+
  private:
   uint64_t sessionId_;
 };

inc/external/ge/ge_api_types.h

@@ -326,6 +326,10 @@ const char_t *const OP_COMPILER_CACHE_DIR = "ge.op_compiler_cache_dir";
 // Its value should be "disable", "enable" or "force", default value is "disable"
 const char_t *const OP_COMPILER_CACHE_MODE = "ge.op_compiler_cache_mode";
 
+// Configure build model type. FE need this option to judge inner model or not
+// Its value should be "true" or "false"
+const char_t *const BUILD_INNER_MODEL = "ge.build_inner_model";
+
 // Configure whether to use single stream.
 // Its value should be "true" or "false", default value is "false"
 const char_t *const ENABLE_SINGLE_STREAM = "ge.enableSingleStream";
@@ -375,6 +379,17 @@ const std::string OP_EXECUTE_TIMEOUT = "ge.exec.opExecuteTimeout";
 
 const char_t *const FILE_CONSTANT_PATH = "ge.exec.value_bins";
 
+const char_t *const ENABLE_GRAPH_PARALLEL = "ge.enableGraphParallel";
+
+const char_t *const RESOURCE_CONFIG_PATH = "ge.resourceConfigPath";
+
+const std::string RECOMPUTE = "ge.recompute";
+
+const char_t *const GRAPH_PARALLEL_OPTION_PATH = "ge.graphParallelOptionPath";
+
+// 1: Complete graph resource evaluation(Inclusion graph fusion optimization)
+// 2: Simplified graph resource evaluation(No graph fusion optimization)
+const char_t *const EVALUATE_GRAPH_RESOURCE_MODE = "ge.evaluateGraphResourceMode";
 // Graph run mode
 enum GraphRunMode { PREDICTION = 0, TRAIN };
 
@@ -450,6 +465,7 @@ static const char_t *const OPTYPELIST_FOR_IMPLMODE = ge::OPTYPELIST_FOR_IMPLMODE
 static const char_t *const DEBUG_DIR = ge::DEBUG_DIR;
 static const char_t *const OP_COMPILER_CACHE_DIR = ge::OP_COMPILER_CACHE_DIR;
 static const char_t *const OP_COMPILER_CACHE_MODE = ge::OP_COMPILER_CACHE_MODE;
+static const char_t *const BUILD_INNER_MODEL = ge::BUILD_INNER_MODEL;
 static const char_t *const MDL_BANK_PATH = ge::MDL_BANK_PATH_FLAG.c_str();
 static const char_t *const OP_BANK_PATH = ge::OP_BANK_PATH_FLAG.c_str();
 static const char_t *const OP_BANK_UPDATE = ge::OP_BANK_UPDATE_FLAG.c_str();
@@ -490,7 +506,9 @@ const std::set<std::string> ir_builder_suppported_options = {INPUT_FORMAT,
                                                              PERFORMANCE_MODE,
                                                              SHAPE_GENERALIZED_BUILD_MODE,
                                                              MODIFY_MIXLIST,
-                                                             CUSTOMIZE_DTYPES};
+                                                             CUSTOMIZE_DTYPES,
+                                                             BUILD_INNER_MODEL,
+                                                             EVALUATE_GRAPH_RESOURCE_MODE};
 
 // for interface: aclgrphParse
 const std::set<std::string> ir_parser_suppported_options = {

inc/external/hccl/hccl.h

@@ -81,7 +81,8 @@ extern HcclResult HcclAllReduce(void *sendBuf, void *recvBuf, uint64_t count, Hc
  *
  * @param buf A pointer identifying the data address of the operator.
  * @param count An integer(u64) identifying the number of the data.
- * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32.
+ * @param dataType The data type of the operator, must be one of the following types: int8, int16, int32, int64,
+uint8, uint16, uint32, uint64, float16, float32, float64.
  * @param root An integer(u32) identifying the the root rank in the operator.
  * @param comm A pointer identifying the communication resource based on
  * @param stream A pointer identifying the stream information.
@@ -111,7 +112,8 @@ extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvC
  * @param sendBuf A pointer identifying the input data address of the operator.
  * @param recvBuf A pointer identifying the output data address of the operator.
  * @param sendCount An integer(u64) identifying the number of the input data.
- * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32.
+ * @param dataType The data type of the operator, must be one of the following types: int8, int16, int32, int64,
+uint8, uint16, uint32, uint64, float16, float32, float64.
  * @param comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
  * @return HcclResult
@@ -149,7 +151,8 @@ extern HcclResult HcclBarrier(HcclComm comm, aclrtStream stream);
  *
  * @param sendBuff A pointer identifying the input data address of the operator.
  * @param count An integer(u64) identifying the number of the send data.
- * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32.
+ * @param dataType The data type of the operator, must be one of the following types: int8, int16, int32, int64,
+uint8, uint16, uint32, uint64, float16, float32, float64.
  * @param destRank An integer identifying the destination rank.
  * @param comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
@@ -162,7 +165,8 @@ extern HcclResult HcclSend(void *sendBuf, uint64_t count, HcclDataType dataType,
  *
  * @param recvBuff A pointer identifying the output data address of the operator.
  * @param count An integer(u64) identifying the number of the receive data.
- * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32.
+ * @param dataType The data type of the operator, must be one of the following types: int8, int16, int32, int64,
+uint8, uint16, uint32, uint64, float16, float32, float64.
  * @param srcRank An integer identifying the source rank.
  * @param comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
@@ -177,15 +181,15 @@ extern HcclResult HcclRecv(void *recvBuf, uint64_t count, HcclDataType dataType,
  * @param sendBuff A pointer identifying the input data address of the operator.
  * @param sendCounts Integer array, where entry i specifies the number of elements to send to rank i.
  * @param sdispls Integer array, where entry i specifies the displacement (offset from sendbuf, in units of sendtype)
- * from which to send data to rank i.
- * @param sendType Datatype of send buffer elements, must be one of the following types: int8, int32, int64, uint64,
- * float16, float32.
+from which to send data to rank i.
+ * @param sendType Datatype of send buffer elements, must be one of the following types: int8, int16, int32, int64,
+uint8, uint16, uint32, uint64, float16, float32, float64.
  * @param recvBuf A pointer identifying the output data address of the operator.
  * @param recvCounts Integer array, where entry j specifies the number of elements to receive from rank j.
  * @param rdispls Integer array, where entry j specifies the displacement (offset from recvbuf, in units of recvtype) to
- * which data from rank j should be written.
- * @param recvType Datatype of receive buffer elements, must be one of the following types: int8, int32, int64, uint64,
- * float16, float32.
+which data from rank j should be written.
+ * @param recvType Datatype of receive buffer elements, must be one of the following types: int8, int16, int32, int64,
+uint8, uint16, uint32, uint64, float16, float32, float64.
  * @param comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
  * @return HcclResult
@@ -195,6 +199,23 @@ extern HcclResult HcclAlltoAllV(const void *sendBuf, const void *sendCounts, con
                                 const void *recvBuf, const void *recvCounts, const void *rdispls, HcclDataType recvType,
                                 HcclComm comm, aclrtStream stream);
 
+/**
+ * @brief Reduce operator.
+ *
+ * @param sendBuf A pointer identifying the input data address of the operator.
+ * @param recvBuf A pointer identifying the output data address of the operator.
+ * @param count An integer(u64) identifying the number of the output data.
+ * @param dataType The data type of the operator, must be one of the following types: int8, int16, int32, float16,
+ * float32.
+ * @param op The reduction type of the operator, must be one of the following types: sum, min, max, prod.
+ * @param root An integer(u32) identifying the the root rank in the operator.
+ * @param comm A pointer identifying the communication resource based on.
+ * @param stream A pointer identifying the stream information.
+ * @return HcclResult
+ */
+extern HcclResult HcclReduce(void *sendBuf, void *recvBuf, uint64_t count, HcclDataType dataType, HcclReduceOp op,
+                             uint32_t root, HcclComm comm, aclrtStream stream);
+
 /**
  * @brief Destroy HCCL comm
  *

inc/external/hccl/hccl_types.h

@@ -84,6 +84,10 @@ typedef enum {
   HCCL_DATA_TYPE_FP32 = 4,   /**< fp32 */
   HCCL_DATA_TYPE_INT64 = 5,  /**< int64 */
   HCCL_DATA_TYPE_UINT64 = 6, /**< uint64 */
+  HCCL_DATA_TYPE_UINT8 = 7,  /**< uint8 */
+  HCCL_DATA_TYPE_UINT16 = 8, /**< uint16 */
+  HCCL_DATA_TYPE_UINT32 = 9, /**< uint32 */
+  HCCL_DATA_TYPE_FP64 = 10,  /**< fp64 */
   HCCL_DATA_TYPE_RESERVED    /**< reserved */
 } HcclDataType;
 

inc/external/runtime/rt_error_codes.h

@@ -65,6 +65,7 @@ static const int32_t ACL_ERROR_RT_QUEUE_FULL = 207014;           // queue is ful
 static const int32_t ACL_ERROR_RT_REPEATED_INIT = 207015;        // repeated init
 static const int32_t ACL_ERROR_RT_AIVEC_OVER_FLOW = 207016;      // aivec over flow
 static const int32_t ACL_ERROR_RT_OVER_FLOW = 207017;            // common over flow
+static const int32_t ACL_ERROR_RT_DEVIDE_OOM = 207018;           // device oom
 
 static const int32_t ACL_ERROR_RT_INTERNAL_ERROR = 507000;                   // runtime internal error
 static const int32_t ACL_ERROR_RT_TS_ERROR = 507001;                         // ts internel error

inc/framework/common/debug/ge_log.h

@@ -85,9 +85,10 @@ inline bool IsLogEnable(const int32_t module_name, const int32_t log_level) {
     }                                                                                                      \
   } while (false)
 
-#define GEEVENT(fmt, ...)                                                                                \
-  do {                                                                                                   \
-    dlog_event(GE_MODULE_NAME, "%" PRIu64 " %s:" fmt, GeLog::GetTid(), &__FUNCTION__[0], ##__VA_ARGS__); \
+#define GEEVENT(fmt, ...)                                                                                 \
+  do {                                                                                                    \
+    dlog_event((RUN_LOG_MASK | GE_MODULE_NAME), "%" PRIu64 " %s:" fmt, GeLog::GetTid(), &__FUNCTION__[0], \
+               ##__VA_ARGS__);                                                                            \
   } while (false)
 
 #define GELOGT(VALUE, fmt, ...)                                                                              \

inc/framework/common/fmk_error_codes.h

@@ -46,7 +46,7 @@
 #define DECLARE_ERRORNO_OME(name, value) DECLARE_ERRORNO(SYSID_FWK, MODID_OME, name, value)
 #define DECLARE_ERRORNO_CALIBRATION(name, value) DECLARE_ERRORNO(SYSID_FWK, MODID_CALIBRATION, name, value)
 
-#define DEF_ERRORNO(name, desc) const ErrorNoRegisterar g_##name##_errorno((name), (desc));
+#define DEF_ERRORNO(name, desc) const bool g_##name##_errorno = StatusFactory::Instance()->RegisterErrorNo(name, desc)
 
 // Interface for Obtaining Error Code Description
 #define GET_ERRORNO_STR(value) domi::StatusFactory::Instance()->GetErrDesc(value)
@@ -60,7 +60,7 @@ class GE_FUNC_VISIBILITY StatusFactory {
  public:
   static StatusFactory *Instance();
 
-  void RegisterErrorNo(const uint32_t err, const std::string &desc);
+  bool RegisterErrorNo(const uint32_t err, const std::string &desc);
 
   std::string GetErrDesc(const uint32_t err);
 
@@ -72,14 +72,6 @@ class GE_FUNC_VISIBILITY StatusFactory {
   std::map<uint32_t, std::string> err_desc_;
 };
 
-class GE_FUNC_VISIBILITY ErrorNoRegisterar {
- public:
-  ErrorNoRegisterar(const uint32_t err, const std::string &desc) {
-    StatusFactory::Instance()->RegisterErrorNo(err, desc);
-  }
-  ~ErrorNoRegisterar() {}
-};
-
 // Common errocode
 DECLARE_ERRORNO_COMMON(MEMALLOC_FAILED, 0);  // 50331648
 DECLARE_ERRORNO_COMMON(CCE_FAILED, 2);       // 50331650

inc/framework/common/ge_types.h

@@ -83,6 +83,7 @@ const std::string kAtomicOpType = "DynamicAtomicAddrClean";
 
 const std::string kShapeTypeStatic = "static";
 const std::string kShapeTypeDynamic = "dynamic";
+const std::string kAtomicPrefix = "_atomic";
 
 constexpr uint64_t kInferSessionId = 0U;
 constexpr uint64_t kReleaseFlag = 1U;
@@ -369,7 +370,16 @@ struct DumpConfig {
   std::string dump_status;
   std::string dump_op_switch;
   std::string dump_debug;
+  std::string dump_step;
   std::vector<ModelDumpConfig> dump_list;
 };
+
+struct ModelQueueParam {
+  uint32_t group_total_count{1};
+  uint32_t group_index{0U};
+  uint32_t group_policy{0U};
+  std::vector<uint32_t> input_queues;
+  std::vector<uint32_t> output_queues;
+};
 }  // namespace ge
 #endif  // INC_FRAMEWORK_COMMON_GE_TYPES_H_

inc/framework/common/ge_visibility.h

@@ -0,0 +1,28 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_COMMON_GE_VISIBILITY_H_
+#define AIR_CXX_INC_FRAMEWORK_COMMON_GE_VISIBILITY_H_
+
+#if defined(_MSC_VER)
+#define VISIBILITY_EXPORT _declspec(dllexport)
+#define VISIBILITY_HIDDEN _declspec(dllimport)
+#else
+#define VISIBILITY_EXPORT __attribute__((visibility("default")))
+#define VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
+#endif
+
+#endif  // AIR_CXX_INC_FRAMEWORK_COMMON_GE_VISIBILITY_H_

inc/framework/common/helper/model_helper.h

@@ -68,7 +68,7 @@ class GE_FUNC_VISIBILITY ModelHelper {
   bool IsPartitionedGraph(const GeModelPtr &cur_model) const;
 
   Status GenerateGeModel(const OmFileLoadHelper &om_load_helper, GeModelPtr &cur_model, const size_t mode_index,
-                         const bool is_dyn_root);
+                         const bool is_dyn_root) const;
   Status GenerateGeRootModel(const OmFileLoadHelper &om_load_helper);
 
   Status LoadModelData(const OmFileLoadHelper &om_load_helper, const GeModelPtr &cur_model,

inc/framework/common/op/ge_op_utils.h

@@ -20,7 +20,7 @@
 #include <memory>
 #include <vector>
 
-#include "framework/common/op/attr_value_util.h"
+#include "graph/debug/ge_attr_define.h"
 #include "framework/common/util.h"
 #include "graph/attr_value.h"
 #include "graph/ge_tensor.h"

inc/framework/common/types.h

@@ -87,6 +87,8 @@ REGISTER_OPTYPE_DECLARE(DROPOUTDOMASK, "DropOutDoMask");
 REGISTER_OPTYPE_DECLARE(DROPOUTDOMASKV3, "DropOutDoMaskV3");
 REGISTER_OPTYPE_DECLARE(DROPOUTDOMASKV3D, "DropOutDoMaskV3D");
 REGISTER_OPTYPE_DECLARE(SOFTMAXV2WITHDROPOUTDOMASKV3D, "SoftmaxV2WithDropOutDoMaskV3D");
+REGISTER_OPTYPE_DECLARE(ATTENTIONSCORE, "AttentionScore");
+REGISTER_OPTYPE_DECLARE(ATTENTIONSCOREGRAD, "AttentionScoreGrad");
 REGISTER_OPTYPE_DECLARE(DROPOUTGENMASK, "DropOutGenMask");
 REGISTER_OPTYPE_DECLARE(AXPYWITHSOFTMAXANDDROPOUTDOMASK, "AxpyWithSoftmaxAndDropOutDoMask");
 REGISTER_OPTYPE_DECLARE(CONCAT, "Concat");
@@ -587,9 +589,6 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string OP_CON
 // dim default size value
 constexpr int32_t DIM_DEFAULT_SIZE = 4;
 
-// dim extension default value
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const int32_t DIM_DEFAULT_VALUE;
-
 // default NCHW index
 FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const uint32_t NCHW_DIM_N;
 FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const uint32_t NCHW_DIM_C;

inc/framework/common/util.h

@@ -21,13 +21,11 @@
 #include <sstream>
 #include <string>
 
-#include <google/protobuf/text_format.h>
 #include "external/graph/types.h"
 #include "external/register/register.h"
 #include "framework/common/debug/log.h"
 #include "framework/common/scope_guard.h"
 #include "framework/common/ge_inner_error_codes.h"
-#include "graph/detail/attributes_holder.h"
 
 #define GE_CHECK_POSITIVE_SIZE_RANGE(size)                             \
   do {                                                                 \
@@ -247,67 +245,6 @@ GE_FUNC_VISIBILITY std::string ToString(const std::vector<T> &v) {
   return ss.str();
 }
 
-///
-/// @ingroup domi_common
-/// @brief Converts RepeatedField to String.
-/// @param [in] rpd_field  RepeatedField
-/// @return string
-///
-template <typename T>
-GE_FUNC_VISIBILITY std::string ToString(const google::protobuf::RepeatedField<T> &rpd_field) {
-  std::stringstream ss;
-  ss << "[";
-  for (const T x : rpd_field) {
-    ss << x;
-    ss << ", ";
-  }
-  // Delete the two extra characters at the end of the line.
-  std::string str = ss.str().substr(0U, ss.str().length() - 2U);
-  str += "]";
-  return str;
-}
-
-///
-///  @ingroup ge_ir_utils
-///  @brief RepeatedPtrField->String
-///  @param [in] const rpd_field  RepeatedPtrField
-///  @return String
-///
-template <typename T>
-GE_FUNC_VISIBILITY std::string ToString(const google::protobuf::RepeatedPtrField<T> &rpd_ptr_field) {
-  std::stringstream ss;
-  ss << "[";
-  for (const T &x : rpd_ptr_field) {
-    ss << x;
-    ss << ", ";
-  }
-  std::string str_ret = ss.str().substr(0U, ss.str().length() - 2U);
-  str_ret += "]";
-  return str_ret;
-}
-
-///
-/// @ingroup domi_common
-/// @brief Reads the proto structure from an array.
-/// @param [in] data proto data to be read
-/// @param [in] size proto data size
-/// @param [out] proto Memory for storing the proto file
-/// @return true success
-/// @return false fail
-///
-GE_FUNC_VISIBILITY bool ReadProtoFromArray(const void *const data, const int32_t size,
-                                           google::protobuf::Message *const proto);
-
-///
-/// @ingroup domi_proto
-/// @brief Reads the proto file in the text format.
-/// @param [in] file path of proto file
-/// @param [out] message Memory for storing the proto file
-/// @return true success
-/// @return false fail
-///
-GE_FUNC_VISIBILITY bool ReadProtoFromText(const char_t *const file, google::protobuf::Message *const message);
-
 ///
 /// @ingroup: domi_common
 /// @brief: get length of file
@@ -359,15 +296,6 @@ GE_FUNC_VISIBILITY uint64_t GetCurrentTimestamp();
 ///
 GE_FUNC_VISIBILITY uint32_t GetCurrentSecondTimestap();
 
-///
-/// @ingroup domi_common
-/// @brief Check whether the product of two int64 numbers exceeds the int64 range.
-/// @param [in] a
-/// @param [in] b
-/// @return false: true: The result is within the normal int64 range.
-///
-GE_FUNC_VISIBILITY bool CheckInt64MulOverflow(const int64_t a, const int64_t b);
-
 ///
 /// @ingroup domi_common
 /// @brief Absolute path for obtaining files.

inc/framework/executor/ge_executor.h

@@ -229,15 +229,14 @@ class GE_FUNC_VISIBILITY GeExecutor {
 
   ///
   /// @ingroup ge
-  /// @brief Load task list from ModelData with queue.
+  /// @brief Load task list from GeRootModel with queue and param.
   /// @param [out] model_id: model id allocate from manager.
   /// @param [in] root_model: Instance of GeRootModel.
-  /// @param [in] input_queue_ids: input queue ids create from user.
-  /// @param [in] output_queue_ids: input queue ids create from user.
+  /// @param [in] model_queue_param: params and queue ids and create from user.
   /// @return: 0 for success / others for fail
   ///
   Status LoadModelWithQ(uint32_t &model_id, const std::shared_ptr<GeRootModel> &root_model,
-                        const std::vector<uint32_t> &input_queue_ids, const std::vector<uint32_t> &output_queue_ids);
+                        const ModelQueueParam &model_queue_param);
 
   ///
   /// @ingroup ge
@@ -324,6 +323,8 @@ class GE_FUNC_VISIBILITY GeExecutor {
 
   static Status ReleaseSingleOpResource(void *const stream);
 
+  static Status ClearCustomAicpuSo();
+
   static Status GetDeviceIdByModelId(const uint32_t model_id, uint32_t &device_id);
 
   Status GetBatchInfoSize(const uint32_t model_id, size_t &shape_count);

inc/framework/generator/ge_generator.h

@@ -27,8 +27,8 @@
 #include "graph/ge_tensor.h"
 #include "graph/graph.h"
 #include "graph/op_desc.h"
-#include "graph/detail/attributes_holder.h"
 #include "framework/omg/omg_inner_types.h"
+#include "graph/detail/attributes_holder.h"
 
 namespace ge {
 const std::string kAttrSupportDynamicShape = "support_dynamicshape";

inc/framework/memory/memory_api.h

@@ -64,5 +64,14 @@ GE_FUNC_VISIBILITY Status MallocSharedMemory(const TensorInfo &tensor_info, uint
 /// \param var_size [out] var_size memory_size of host variable.
 /// \return Status result of function
 GE_FUNC_VISIBILITY Status GetVarBaseAddrAndSize(const std::string &var_name, uint64_t &base_addr, uint64_t &var_size);
+
+/*
+ * @brief
+ * @param [in]  session_id
+ * @param [out] var_size:session variables mem size
+ * @param [out] graphs_mem_info: graphs mem info, include key:graph_id; value: {feature_map_size, const_size}
+ */
+GE_FUNC_VISIBILITY Status GetSessionMemInfo(const uint64_t session_id, uint64_t &var_size,
+                                            std::map<uint32_t, std::vector<uint64_t>> &graphs_mem_info);
 }  // namespace ge
 #endif  // INC_FRAMEWORK_MEMORY_MEMORY_API_H_

inc/framework/omg/parser/model_parser.h

@@ -30,8 +30,6 @@
 #include "graph/utils/op_desc_utils.h"
 #include "graph/utils/tensor_utils.h"
 
-using Status = domi::Status;
-
 namespace domi {
 using GetGraphCallback = std::function<std::unique_ptr<google::protobuf::Message>(
     const google::protobuf::Message *root_proto, const std::string &graph)>;

inc/framework/omg/parser/op_parser.h

@@ -24,7 +24,6 @@
 #include "graph/utils/op_desc_utils.h"
 
 using google::protobuf::Message;
-using Status = domi::Status;
 
 namespace ge {
 /**

inc/framework/omg/parser/parser_types.h

@@ -21,6 +21,7 @@
 #include <string>
 
 #include "register/register_types.h"
+#include "graph/types.h"
 
 #if !defined(__ANDROID__) && !defined(ANDROID)
 #ifndef DOMI_DYNAMIC_CAST
@@ -40,427 +41,428 @@
 
 namespace ge {
 namespace parser {
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DATA;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *AIPPDATA;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONVOLUTION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CORRELATION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CORRELATIONV2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DECONVOLUTION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *POOLING;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ELTWISE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RELU;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RELU6;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SIGMOID;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ABSVAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TANH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PRELU;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BATCHNORM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FUSIONBATCHNORM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SCALE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FULL_CONNECTION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SOFTMAX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PLUS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ACTIVATION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FLATTEN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ADD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SUB;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MUL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MATMUL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RSQRT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BIASADD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RESHAPE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REFORMAT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DEPCONVOLUTION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DROPOUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DROPOUTGENMASK;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DROPOUTDOMASK;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONCAT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ROIPOOLING;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PROPOSAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FSRDETECTIONOUTPUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DETECTIONPOSTPROCESS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LRN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TRANSDATA;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PERMUTE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDNORMALIZE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDPRIORBOX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *NETOUTPUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDDETECTIONOUTPUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REFINEDETDETECTIONOUTPUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CHANNELAXPY;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PSROIPOOLING;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *POWER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *POW;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ROIALIGN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PYTHON;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FREESPACEEXTRACT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPATIALTF;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SHAPE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SHAPEN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ARGMAX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *GATHERND;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *GATHER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REALDIV;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PACK;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SLICE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SLICED;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FLOORDIV;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SQUEEZE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *UNSQUEEZE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STRIDEDSLICE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RANGE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RPNPROPOSALS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DECODEBBOX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PADV2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MIRRORPAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TILE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SIZE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CLIPBOXES;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FASTRCNNPREDICTIONS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPLIT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPLITV;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *EXPANDDIMS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *EMPTY;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MEAN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *GREATER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SWITCH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SWITCHN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MERGE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SYMBOLICGRADIENT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REMOTECALL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *_IF;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STATELESSIF;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *IF;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CASE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STATELESSCASE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *_WHILE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *WHILE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STATELESSWHILE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PARTITIONEDCALL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STATEFULPARTITIONEDCALL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FAKEPARAM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TRANSPOSE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TRANSPOSED;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CAST;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REGION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *YOLO;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *YOLODETECTIONOUTPUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FILL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REVERSE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *UNPACK;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *YOLO2REORG;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REDUCESUM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SUM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONSTANT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RESIZEBILINEAR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RESIZEBILINEARGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MAXIMUM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FRAMEWORKOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ARG;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FUSEDBATCHNORMGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LSTM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HIGHWAY;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RNN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ATTENTIONDECODER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LOGICAL_NOT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LOGICAL_AND;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LOGICAL_OR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *EQUAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *NOTEQUAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *INTERP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SHUFFLECHANNEL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *AIPP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MULTISHAPE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RECIPROCAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SELU;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ELU;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ACOSH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASINH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MINIMUM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CLIP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *L2NORMALIZE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CROPANDRESIZE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *UNUSEDCONST;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPARSETODENSE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *NONMAXSUPPRESSION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TOPKV2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *INVERTPERMUTATION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MULTINOMIAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REVERSESEQUENCE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REDUCEPROD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REDUCEMAX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REDUCEMIN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *EXTRACTIMAGEPATCHES;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SQRT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REDUCEALL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RESIZENEARESTNEIGHBOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPACETOBATCHND;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BATCHTOSPACEND;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASSERT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *GREATEREQUAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FLOOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RANDOMUNIFORM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BATCHMATMUL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPACETODEPTH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DEPTHTOSPACE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RINT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ATAN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ATAN2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ATANH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ACOS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASIN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *NEG;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LOG;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TAN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ROUND;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *UPSAMPLE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FLOORMOD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LESS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LESSEQUAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ONEHOT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REFSWITCH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REFMERGE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ENTER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REFENTER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LOOPCOND;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *NEXTITERATION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REFNEXTITERATION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *EXIT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REFEXIT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONTROLTRIGGER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ZEROSLIKE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *EXP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *WHERE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FAKEQUANTWITHMINMAXVARS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SOFTPLUS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SOFTSIGN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *COSH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SINH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SQUAREDDIFFERENCE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DATA;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *AIPPDATA;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONVOLUTION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CORRELATION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CORRELATIONV2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DECONVOLUTION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *POOLING;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ELTWISE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RELU;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RELU6;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SIGMOID;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ABSVAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TANH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PRELU;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BATCHNORM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FUSIONBATCHNORM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SCALE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FULL_CONNECTION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SOFTMAX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PLUS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ACTIVATION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FLATTEN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ADD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SUB;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MUL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MATMUL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RSQRT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BIASADD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RESHAPE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REFORMAT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DEPCONVOLUTION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DROPOUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DROPOUTGENMASK;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DROPOUTDOMASK;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONCAT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ROIPOOLING;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PROPOSAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FSRDETECTIONOUTPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DETECTIONPOSTPROCESS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LRN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TRANSDATA;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PERMUTE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDNORMALIZE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDPRIORBOX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *NETOUTPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDDETECTIONOUTPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REFINEDETDETECTIONOUTPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CHANNELAXPY;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PSROIPOOLING;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *POWER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *POW;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ROIALIGN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PYTHON;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FREESPACEEXTRACT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPATIALTF;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SHAPE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SHAPEN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ARGMAX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *GATHERND;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *GATHER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REALDIV;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PACK;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SLICE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SLICED;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FLOORDIV;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SQUEEZE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *UNSQUEEZE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STRIDEDSLICE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RANGE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RPNPROPOSALS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DECODEBBOX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PADV2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MIRRORPAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TILE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SIZE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CLIPBOXES;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FASTRCNNPREDICTIONS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPLIT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPLITV;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *EXPANDDIMS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *EMPTY;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MEAN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *GREATER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SWITCH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SWITCHN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MERGE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SYMBOLICGRADIENT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REMOTECALL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *_IF;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STATELESSIF;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *IF;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CASE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STATELESSCASE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *_WHILE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *WHILE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STATELESSWHILE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PARTITIONEDCALL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STATEFULPARTITIONEDCALL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FAKEPARAM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TRANSPOSE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TRANSPOSED;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CAST;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REGION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *YOLO;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *YOLODETECTIONOUTPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FILL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REVERSE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *UNPACK;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *YOLO2REORG;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REDUCESUM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SUM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONSTANT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FILECONSTANT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RESIZEBILINEAR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RESIZEBILINEARGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MAXIMUM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FRAMEWORKOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ARG;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FUSEDBATCHNORMGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LSTM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HIGHWAY;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RNN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ATTENTIONDECODER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LOGICAL_NOT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LOGICAL_AND;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LOGICAL_OR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *EQUAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *NOTEQUAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *INTERP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SHUFFLECHANNEL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *AIPP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MULTISHAPE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RECIPROCAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SELU;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ELU;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ACOSH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASINH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MINIMUM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CLIP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *L2NORMALIZE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CROPANDRESIZE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *UNUSEDCONST;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPARSETODENSE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *NONMAXSUPPRESSION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TOPKV2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *INVERTPERMUTATION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MULTINOMIAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REVERSESEQUENCE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REDUCEPROD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REDUCEMAX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REDUCEMIN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *EXTRACTIMAGEPATCHES;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SQRT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REDUCEALL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RESIZENEARESTNEIGHBOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPACETOBATCHND;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BATCHTOSPACEND;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASSERT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *GREATEREQUAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FLOOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RANDOMUNIFORM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BATCHMATMUL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPACETODEPTH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DEPTHTOSPACE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RINT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ATAN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ATAN2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ATANH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ACOS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASIN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *NEG;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LOG;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TAN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ROUND;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *UPSAMPLE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FLOORMOD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LESS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LESSEQUAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ONEHOT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REFSWITCH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REFMERGE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ENTER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REFENTER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LOOPCOND;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *NEXTITERATION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REFNEXTITERATION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *EXIT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REFEXIT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONTROLTRIGGER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ZEROSLIKE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *EXP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *WHERE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FAKEQUANTWITHMINMAXVARS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SOFTPLUS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SOFTSIGN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *COSH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SINH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SQUAREDDIFFERENCE;
 // for retinanet scope fusion
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REQUIREDSPACETOBATCHPADDINGS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDPOSTPROCESSOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RETINANETBOXES;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RETINAMULTIANCHORS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RETINANETCLIPPEDBOXES;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RETINANETFILTEREDDETECTIONS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RETINANETPOSTPROCESSOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RETINANETANCHORS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FASTERRCNNMAP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FASTERRCNNMAP1;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FASTERRCNNSECONDSTAGEPOSTPROCESSOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FASTERRCNNROIINTERPOOLING;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FASTERRCNNFIRSTSTAGEPOSTPROCESSOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FASTERRCNNGRIDANCHORGENERATOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ROIINTERPOOLING;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FASTERRCNNCLIPTOWINDOW;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *EMBEDLOOKUP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HASHLOOKUP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LSH_PROJ;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SVDF;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDANCHORGENERATOR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *IDENTITY;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *IDENTITYN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PLACEHOLDERWITHDEFAULT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SELECT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *GETSPAN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STOPGRADIENT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PREVENTGRADIENT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *GUARANTEECONST;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BROADCASTGRADIENTARGS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BROADCASTARGS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONFUSIONMATRIX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RANK;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PLACEHOLDER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *END;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BASICLSTMCELL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *GETNEXT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *INITDATA;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REFIDENTITY;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BITCAST;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REQUIREDSPACETOBATCHPADDINGS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDPOSTPROCESSOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RETINANETBOXES;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RETINAMULTIANCHORS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RETINANETCLIPPEDBOXES;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RETINANETFILTEREDDETECTIONS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RETINANETPOSTPROCESSOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RETINANETANCHORS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FASTERRCNNMAP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FASTERRCNNMAP1;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FASTERRCNNSECONDSTAGEPOSTPROCESSOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FASTERRCNNROIINTERPOOLING;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FASTERRCNNFIRSTSTAGEPOSTPROCESSOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FASTERRCNNGRIDANCHORGENERATOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ROIINTERPOOLING;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FASTERRCNNCLIPTOWINDOW;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *EMBEDLOOKUP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HASHLOOKUP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LSH_PROJ;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SVDF;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDANCHORGENERATOR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *IDENTITY;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *IDENTITYN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PLACEHOLDERWITHDEFAULT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SELECT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *GETSPAN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STOPGRADIENT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PREVENTGRADIENT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *GUARANTEECONST;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BROADCASTGRADIENTARGS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BROADCASTARGS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONFUSIONMATRIX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RANK;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PLACEHOLDER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *END;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BASICLSTMCELL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *GETNEXT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *INITDATA;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REFIDENTITY;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BITCAST;
 
 /***************Ann special operator*************************/
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_MEAN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_CONVOLUTION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_DEPCONVOLUTION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_FULLCONNECTION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_NETOUTPUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_DATA;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_RESHAPE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_ADD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_MUL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_SUB;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_DIV;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_DEQUANTIZE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_QUANTIZE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_PAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANN_RESIZE_BILINEAR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_MEAN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_CONVOLUTION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_DEPCONVOLUTION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_FULLCONNECTION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_NETOUTPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_DATA;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_RESHAPE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_ADD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_MUL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_SUB;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_DIV;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_DEQUANTIZE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_QUANTIZE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_PAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANN_RESIZE_BILINEAR;
 
 /***************************************************/
 /******************Training operator*************************/
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *GATHERV2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONVGRADFILTER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONV2D;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONV2DBACKPROPINPUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FUSEDBATCHNORM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BIASADDGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ACTIVATIONGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MAXPOOLWITHARGMAX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MAXPOOLGRADWITHARGMAX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPARSESOFTMAXCROSSENTROPYWITHLOGITS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SNAPSHOT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *VAR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MEANGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TRANSLATE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ADDN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *L2LOSS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MULTIPLY;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HUBERLOSSGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HUBERLOSS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *NEGATIVE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDCAST;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPARSESOFTMAXCROSSENTROPY;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SPARSESOFTMAXCROSSENTROPYGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDSQUEEZEFUSION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONCATFOUR2FIVE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONCATFIVE2FOUR;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDREALDIVTILEMUL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SSDSUMMULREALDIVMEAN;
-
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *VARIABLEV2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *VARHANDLEOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TEMPORARYVARIABLE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DESTROYTEMPORARYVARIABLE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *VARIABLE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASSIGN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASSIGNVARIABLEOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASSIGNADD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASSIGNADDVARIABLEOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASSIGNSUB;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASSIGNSUBVARIABLEOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYMOMENTUM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RESOURCEAPPLYMOMENTUM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SGD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *NOOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *READVARIABLEOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *PARALLELCONCATSTART;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONSTANTOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DEPTHWISECONV2DBACKPROPFILTER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DEPTHWISECONV2DBACKPORPINPUT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DEPTHWISECONV2DFORWARDNATIVE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DROPOUTGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYRMSPROPMIXEDPRECISION;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYRMSPROP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RELU6GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *AVGPOOLGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONCATV2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONCATOFFSET;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LAYERNORMGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LAYERNORM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LARS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DYNAMICSTITCH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *GATHERV2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONVGRADFILTER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONV2D;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONV2DBACKPROPINPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FUSEDBATCHNORM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BIASADDGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ACTIVATIONGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MAXPOOLWITHARGMAX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MAXPOOLGRADWITHARGMAX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPARSESOFTMAXCROSSENTROPYWITHLOGITS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SNAPSHOT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *VAR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MEANGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TRANSLATE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ADDN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *L2LOSS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MULTIPLY;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HUBERLOSSGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HUBERLOSS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *NEGATIVE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDCAST;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPARSESOFTMAXCROSSENTROPY;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SPARSESOFTMAXCROSSENTROPYGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDSQUEEZEFUSION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONCATFOUR2FIVE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONCATFIVE2FOUR;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDREALDIVTILEMUL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SSDSUMMULREALDIVMEAN;
+
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *VARIABLEV2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *VARHANDLEOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TEMPORARYVARIABLE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DESTROYTEMPORARYVARIABLE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *VARIABLE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASSIGN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASSIGNVARIABLEOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASSIGNADD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASSIGNADDVARIABLEOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASSIGNSUB;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASSIGNSUBVARIABLEOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYMOMENTUM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RESOURCEAPPLYMOMENTUM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SGD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *NOOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *READVARIABLEOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *PARALLELCONCATSTART;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONSTANTOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DEPTHWISECONV2DBACKPROPFILTER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DEPTHWISECONV2DBACKPORPINPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DEPTHWISECONV2DFORWARDNATIVE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DROPOUTGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYRMSPROPMIXEDPRECISION;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYRMSPROP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RELU6GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *AVGPOOLGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONCATV2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONCATOFFSET;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LAYERNORMGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LAYERNORM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LARS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DYNAMICSTITCH;
 
 /***************************************************/
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SQUARE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMBROADCAST;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMALLGATHER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMALLREDUCE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMREDUCESCATTER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMSEND;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMRECEIVE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMREMOTEREAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMREMOTEREFREAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMREMOTEWRITE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HCOMREMOTESCATTERWRITE;
-
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *VARASSIGN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *VARISINITIALIZEDOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LogTimeStamp;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ISVARIABLEINITIALIZED;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STREAMSWITCH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STREAMSWITCHN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STREAMACTIVE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MEMCPYASYNC;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *MEMCPYADDRASYNC;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *STREAMMERGE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ENDGRAPH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SEND;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *RECV;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ENDOFSEQUENCE;
-
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LABELSET;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LABELGOTO;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LABELGOTOEX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LABELSWITCH;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *LABELSWITCHBYINDEX;
-
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ATOMICADDRCLEAN;
-
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ABS_GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ACCUMULATE_N_V2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ACOS_GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ACOSH_GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ANY;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPROXIMATE_EQUAL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASIN_GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ASINH_GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ATAN_GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BROADCAST_TO;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ELU_GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ADD_V2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DATAFORMATDIMMAP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DATAFORMATVECPERMUTE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BESSELI0E;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *BESSELI1E;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYADADELTA;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYADAGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYADAGRADDA;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYADAM;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYADAMAX;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYADDSIGN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYCENTEREDRMSPROP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYFTRL;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYFTRLV2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYGRADIENTDESCENT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYPOWERSIGN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYPROXIMALADAGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *APPLYPROXIMALGRADIENTDESCENT;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DEQUANTIZE;
-
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FOCAL_LOSS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *FOCAL_LOSS_GRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SMOOTHL1_LOSS;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SMOOTHL1_LOSS_grad;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *REDUCEMEAN;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CONCAT_V2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *ONEHOT_V2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SLICE_V2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TILE_V2;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SUM_V2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SQUARE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMBROADCAST;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMALLGATHER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMALLREDUCE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMREDUCESCATTER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMSEND;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMRECEIVE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMREMOTEREAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMREMOTEREFREAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMREMOTEWRITE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HCOMREMOTESCATTERWRITE;
+
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *VARASSIGN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *VARISINITIALIZEDOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LogTimeStamp;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ISVARIABLEINITIALIZED;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STREAMSWITCH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STREAMSWITCHN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STREAMACTIVE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MEMCPYASYNC;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *MEMCPYADDRASYNC;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *STREAMMERGE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ENDGRAPH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SEND;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *RECV;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ENDOFSEQUENCE;
+
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LABELSET;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LABELGOTO;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LABELGOTOEX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LABELSWITCH;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *LABELSWITCHBYINDEX;
+
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ATOMICADDRCLEAN;
+
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ABS_GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ACCUMULATE_N_V2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ACOS_GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ACOSH_GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ANY;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPROXIMATE_EQUAL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASIN_GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ASINH_GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ATAN_GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BROADCAST_TO;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ELU_GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ADD_V2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DATAFORMATDIMMAP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DATAFORMATVECPERMUTE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BESSELI0E;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *BESSELI1E;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYADADELTA;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYADAGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYADAGRADDA;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYADAM;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYADAMAX;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYADDSIGN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYCENTEREDRMSPROP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYFTRL;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYFTRLV2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYGRADIENTDESCENT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYPOWERSIGN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYPROXIMALADAGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *APPLYPROXIMALGRADIENTDESCENT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DEQUANTIZE;
+
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FOCAL_LOSS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *FOCAL_LOSS_GRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SMOOTHL1_LOSS;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SMOOTHL1_LOSS_grad;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *REDUCEMEAN;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CONCAT_V2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *ONEHOT_V2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SLICE_V2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TILE_V2;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SUM_V2;
 // Common type when the operator has the same name
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DETECTIONOUTPUT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DETECTIONOUTPUT;
 // Custom operator
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CUSTOMOP;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CUSTOMOP_NCHW;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CUSTOMOP_NHWC;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *CUSTOMOP_NC1HWC0;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CUSTOMOP;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CUSTOMOP_NCHW;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CUSTOMOP_NHWC;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *CUSTOMOP_NC1HWC0;
 
 // Depthwise 4d_2_6d,6d_2_4d
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DEPTHWISEWEIGHT4D26D;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *DEPTHWISEWEIGHT6D24D;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DEPTHWISEWEIGHT4D26D;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *DEPTHWISEWEIGHT6D24D;
 
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SQRTGRAD;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *SIGMOIDGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SQRTGRAD;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *SIGMOIDGRAD;
 
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *TRANSSHAPE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *TRANSSHAPE;
 
 // Horovod operator
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HVDCALLBACKALLREDUCE;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HVDCALLBACKALLGATHER;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HVDCALLBACKBROADCAST;
-FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const char *HVDWAIT;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HVDCALLBACKALLREDUCE;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HVDCALLBACKALLGATHER;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HVDCALLBACKBROADCAST;
+FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const ge::char_t *HVDWAIT;
 
 ///
 /// @brief Magic number of model file

inc/framework/pne/pne_model.h

@@ -31,8 +31,10 @@
 namespace ge {
 const std::string PNE_ID_NPU = "NPU";
 const std::string PNE_ID_CPU = "HOST_CPU";
+const std::string PNE_ID_UDF = "UDF";
 
 struct ModelRelation;
+struct ModelDeployResource;
 class PneModel {
  public:
   PneModel() = default;
@@ -100,6 +102,14 @@ class PneModel {
     return model_relation_;
   }
 
+  inline void SetDeployResource(std::shared_ptr<ModelDeployResource> deploy_resource) {
+    deploy_resource_ = std::move(deploy_resource);
+  }
+
+  inline const std::shared_ptr<ModelDeployResource> GetDeployResource() const {
+    return deploy_resource_;
+  }
+
  public:
   virtual Status SerializeModel(ModelBufferData &model_buff) = 0;
 
@@ -113,9 +123,14 @@ class PneModel {
     return model_id_;
   }
 
+  virtual std::string GetLogicDeviceId() const {
+    return "";
+  }
+
  private:
   std::map<std::string, std::shared_ptr<PneModel>> submodels_;
   std::shared_ptr<ModelRelation> model_relation_;
+  std::shared_ptr<ModelDeployResource> deploy_resource_;
   ComputeGraphPtr root_graph_ = nullptr;
   std::string model_name_;
   std::string model_type_;

inc/framework/pne/process_node_engine.h

@@ -29,6 +29,8 @@
 namespace ge {
 class ProcessNodeEngineImpl {
  public:
+  virtual ~ProcessNodeEngineImpl() = default;
+
   virtual Status OptimizeGraph(const std::vector<GeTensor> &inputs, ComputeGraphPtr &compute_graph) = 0;
 
   virtual Status BuildGraph(ComputeGraphPtr &compute_graph, PneModelPtr &model) = 0;
@@ -56,6 +58,22 @@ class ProcessNodeEngine {
 
   virtual void SetImpl(ProcessNodeEngineImplPtr impl) = 0;
 
+  virtual Status AddGraph(const ComputeGraphPtr &compute_graph, const std::map<std::string, std::string> &options) {
+    (void)compute_graph;
+    (void)options;
+    return SUCCESS;
+  }
+
+  virtual Status RemoveGraph(const uint32_t graph_id) {
+    (void)graph_id;
+    return SUCCESS;
+  }
+
+  virtual Status ParallelPartition(const ComputeGraphPtr &compute_graph) {
+    (void)compute_graph;
+    return NOT_CHANGED;
+  }
+
  protected:
   std::string engine_id_;
   ProcessNodeEngineImplPtr impl_ = nullptr;

inc/framework/runtime/exe_graph_executor.h

@@ -0,0 +1,56 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_EXE_GRAPH_EXECUTOR_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_EXE_GRAPH_EXECUTOR_H_
+#include "graph/ge_error_codes.h"
+
+#include "common/ge_visibility.h"
+#include "exe_graph_resource_guard.h"
+#include "subscriber/executor_subscriber_c.h"
+namespace gert {
+class VISIBILITY_EXPORT ExeGraphExecutor {
+ public:
+  ge::graphStatus Load() const {
+    return ge::GRAPH_SUCCESS;
+  }
+  ge::graphStatus UnLoad() const {
+    return ge::GRAPH_SUCCESS;
+  }
+
+  /**
+   * 设置图执行的输入/输出，需要注意的是，使用者需要自己保证inputs/outputs刷新完全！！！
+   */
+  ge::graphStatus SpecifyInputs(void **inputs, size_t start, size_t num);
+  ge::graphStatus SpecifyOutputs(void **outputs, size_t num);
+  ge::graphStatus Execute();
+  ge::graphStatus Execute(ExecutorSubscriber *callback);
+
+  const void *GetExecutionData() const {
+    return execution_data_;
+  }
+
+  ResourceGuard &GetResourceGuard();
+  void *SetExecutionData(std::unique_ptr<uint8_t[]> execution_data);
+
+ private:
+  friend class ModelV2ExecutorTestHelper;
+
+  void *execution_data_{nullptr};
+  ResourceGuard resource_guard_;
+};
+}  // namespace gert
+#endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_EXE_GRAPH_EXECUTOR_H_

inc/framework/runtime/exe_graph_resource_guard.h

@@ -0,0 +1,69 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_EXE_GRAPH_RESOURCE_GUARD_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_EXE_GRAPH_RESOURCE_GUARD_H_
+#include <memory>
+#include <vector>
+#include <cstdint>
+#include "common/ge_visibility.h"
+
+namespace gert {
+class VISIBILITY_EXPORT ResourceGuard {
+ public:
+  void *ResetExecutionData(std::unique_ptr<uint8_t[]> execution_data);
+  void ResetAnyValue(std::unique_ptr<uint8_t[]> any_values, size_t count);
+  void PushNode(void *node);
+  void PushWatcher(void *watcher);
+  void *ResetNodesArray(std::unique_ptr<uint8_t[]> nodes_array);
+  void *ResetStartNodesArray(std::unique_ptr<uint8_t[]> start_nodes_array);
+  void *ResetNodesIndgreeArray(std::unique_ptr<uint8_t[]> nodes_indgree_array);
+  void *ResetNodesWaitIndgreeArray(std::unique_ptr<uint8_t[]> nodes_indgree_array);
+  void *ResetInputsArray(std::unique_ptr<uint8_t[]> inputs_array);
+  void *ResetOutputsArray(std::unique_ptr<uint8_t[]> outputs_array);
+  void *ResetWatchersArray(std::unique_ptr<uint8_t[]> watchers_array);
+  void *ResetReadyQueue(void *ready_queue);
+  void *ResetBuffer(std::unique_ptr<uint8_t[]> buffer);
+  void *ResetComputeNodeInfo(std::unique_ptr<uint8_t[]> compute_node_info);
+  void *ResetKernelExtendInfo(std::unique_ptr<uint8_t[]> kernel_extend_info);
+  void *ResetModelDesc(std::unique_ptr<uint8_t[]> model_desc);
+
+  ~ResourceGuard();
+
+ private:
+  std::unique_ptr<uint8_t[]> execution_data_holder_;
+  size_t any_values_num_;
+  std::unique_ptr<uint8_t[]> any_values_guard_;
+
+  std::vector<std::unique_ptr<void, decltype(&free)>> nodes_guarder_;
+  std::vector<std::unique_ptr<void, decltype(&free)>> watchers_guarder_;
+  std::unique_ptr<uint8_t[]> continuous_buffer_guarder_;
+  std::unique_ptr<uint8_t[]> buffer_guarder_;
+  std::unique_ptr<uint8_t[]> compute_node_info_guarder_;
+  std::unique_ptr<uint8_t[]> kernel_extend_info_guarder_;
+  std::unique_ptr<uint8_t[]> model_desc_guarder_;
+
+  std::unique_ptr<uint8_t[]> nodes_array_guarder_;
+  std::unique_ptr<uint8_t[]> start_nodes_array_guarder_;
+  std::unique_ptr<uint8_t[]> nodes_indgree_array_guarder_;
+  std::unique_ptr<uint8_t[]> nodes_wait_indgree_array_guarder_;
+  std::unique_ptr<uint8_t[]> inputs_array_guarder_;
+  std::unique_ptr<uint8_t[]> outputs_array_guarder_;
+  std::unique_ptr<uint8_t[]> watchers_array_guarder_;
+  std::unique_ptr<void, decltype(&free)> ready_queue_guarder_{nullptr, nullptr};
+};
+}  // namespace gert
+#endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_EXE_GRAPH_RESOURCE_GUARD_H_

inc/framework/runtime/gert_api.h

@@ -18,9 +18,13 @@
 #define AIR_CXX_INC_FRAMEWORK_RUNTIME_GERT_API_H_
 #include "model_v2_executor.h"
 #include "common/ge_types.h"
+#include "common/ge_visibility.h"
 
 namespace gert {
-std::unique_ptr<ModelV2Executor> LoadExecutorFromFile(const char *file_path, ge::graphStatus &error_code);
+VISIBILITY_EXPORT
+std::unique_ptr<ModelV2Executor> LoadExecutorFromFile(const char *model_path, ge::graphStatus &error_code);
+
+VISIBILITY_EXPORT
 std::unique_ptr<ModelV2Executor> LoadExecutorFromModelData(const ge::ModelData &model_data,
                                                            ge::graphStatus &error_code);
 }  // namespace gert

inc/framework/runtime/model_desc.h

@@ -1,94 +1,96 @@
-/**
- * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_MODEL_DESC_H_
-#define AIR_CXX_INC_FRAMEWORK_RUNTIME_MODEL_DESC_H_
-#include "common/ge_types.h"
-#include "exe_graph/runtime/shape.h"
-#include "exe_graph/runtime/continuous_vector.h"
-#include "exe_graph/runtime/storage_format.h"
-#include "exe_graph/runtime/storage_shape.h"
-
-namespace gert {
-class ShapeRange {
- public:
-  const Shape &GetMin() const;
-  const Shape &GetMax() const;
-  Shape &MutableMin();
-  Shape &MutableMax();
-
- private:
-  Shape min_;
-  Shape max_;
-};
-
-class ModelIoDesc {
- public:
-  const char *GetName() const;
-  int32_t GetDataType() const;
-  ge::Format GetStorageFormat() const;
-  ge::Format GetOriginFormat() const;
-  int64_t GetSize() const;
-  const Shape &GetStorageShape() const;
-  const Shape &GetOriginShape() const;
-  const ShapeRange &GetOriginShapeRange() const;
-  const ShapeRange &GetStorageShapeRange() const;
-
-  void SetName(const char *name);
-  void SetDataType(int32_t data_type);
-  void SetStorageFormat(ge::Format format);
-  void SetOriginFormat(ge::Format format);
-  Shape &MutableStorageShape();
-  Shape &MutableOriginShape();
-  ShapeRange &MutableOriginShapeRange();
-  ShapeRange &MutableStorageShapeRange();
-
- private:
-  const char *name_;
-  int32_t data_type_;
-  StorageFormat format_;
-  StorageShape shape_;
-  ShapeRange storage_shape_range_;
-  ShapeRange origin_shape_range_;
-};
-
-class ModelDesc {
- public:
-  static size_t CalcSize(size_t input_num, size_t output_num);
-  const ModelIoDesc *GetInputDesc(size_t index) const;
-  const ModelIoDesc *GetAllInputsDesc(size_t &input_num) const;
-
-  const ModelIoDesc *GetOutputDesc(size_t index) const;
-  const ModelIoDesc *GetAllOutputsDesc(size_t &output_num) const;
-
-  ModelIoDesc *MutableInputDesc(size_t index);
-  ModelIoDesc *MutableOutputDesc(size_t index);
-  ModelIoDesc *AllMutableIoDesc(size_t &input_num, size_t &output_num);
-  void SetInputNum(size_t input_num);
-  void SetOutputNum(size_t output_num);
-
-  ge::graphStatus GetDynamicBatchInfo(std::vector<std::vector<int64_t>> &batch_info, int32_t &dynamic_type) const;
-  ge::graphStatus GetUserDesignateShapeOrder(std::vector<std::string> &user_designate_shape_order) const;
-  ge::graphStatus GetModelAttrs(std::vector<std::string> &attrs) const;
-
- private:
-  size_t input_num_;
-  size_t output_num_;
-  ContinuousVector model_io_descs_;
-};
-}  // namespace gert
-
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_MODEL_DESC_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_MODEL_DESC_H_
+#include "common/ge_types.h"
+#include "common/ge_visibility.h"
+
+#include "exe_graph/runtime/shape.h"
+#include "exe_graph/runtime/continuous_vector.h"
+#include "exe_graph/runtime/storage_format.h"
+#include "exe_graph/runtime/storage_shape.h"
+
+namespace gert {
+class VISIBILITY_EXPORT ShapeRange {
+ public:
+  const Shape &GetMin() const;
+  const Shape &GetMax() const;
+  Shape &MutableMin();
+  Shape &MutableMax();
+
+ private:
+  Shape min_;
+  Shape max_;
+};
+
+class VISIBILITY_EXPORT ModelIoDesc {
+ public:
+  const char *GetName() const;
+  int32_t GetDataType() const;
+  ge::Format GetStorageFormat() const;
+  ge::Format GetOriginFormat() const;
+  int64_t GetSize() const;
+  const Shape &GetStorageShape() const;
+  const Shape &GetOriginShape() const;
+  const ShapeRange &GetOriginShapeRange() const;
+  const ShapeRange &GetStorageShapeRange() const;
+
+  void SetName(const char *name);
+  void SetDataType(int32_t data_type);
+  void SetStorageFormat(ge::Format format);
+  void SetOriginFormat(ge::Format format);
+  Shape &MutableStorageShape();
+  Shape &MutableOriginShape();
+  ShapeRange &MutableOriginShapeRange();
+  ShapeRange &MutableStorageShapeRange();
+
+ private:
+  const char *name_;
+  int32_t data_type_;
+  StorageFormat format_;
+  StorageShape shape_;
+  ShapeRange storage_shape_range_;
+  ShapeRange origin_shape_range_;
+};
+
+class VISIBILITY_EXPORT ModelDesc {
+ public:
+  static size_t CalcSize(size_t input_num, size_t output_num);
+  const ModelIoDesc *GetInputDesc(size_t index) const;
+  const ModelIoDesc *GetAllInputsDesc(size_t &input_num) const;
+
+  const ModelIoDesc *GetOutputDesc(size_t index) const;
+  const ModelIoDesc *GetAllOutputsDesc(size_t &output_num) const;
+
+  ModelIoDesc *MutableInputDesc(size_t index);
+  ModelIoDesc *MutableOutputDesc(size_t index);
+  ModelIoDesc *AllMutableIoDesc(size_t &input_num, size_t &output_num);
+  void SetInputNum(size_t input_num);
+  void SetOutputNum(size_t output_num);
+
+  ge::graphStatus GetDynamicBatchInfo(std::vector<std::vector<int64_t>> &batch_info, int32_t &dynamic_type) const;
+  ge::graphStatus GetUserDesignateShapeOrder(std::vector<std::string> &user_designate_shape_order) const;
+  ge::graphStatus GetModelAttrs(std::vector<std::string> &attrs) const;
+
+ private:
+  size_t input_num_;
+  size_t output_num_;
+  ContinuousVector model_io_descs_;
+};
+}  // namespace gert
+
 #endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_MODEL_DESC_H_

inc/framework/runtime/model_v2_executor.h

@@ -22,94 +22,25 @@
 #include "model_desc.h"
 #include "runtime/stream.h"
 #include "exe_graph/runtime/tensor.h"
+#include "common/ge_visibility.h"
+#include "exe_graph_resource_guard.h"
+#include "exe_graph_executor.h"
+#include "subscriber/executor_subscribers_scheduler.h"
 
 namespace gert {
 enum SubExeGraphType { kInitExeGraph, kMainExeGraph, kDeInitExeGraph, kSubExeGraphTypeEnd };
-static constexpr char *kSubExeGraphTypeStrs[kSubExeGraphTypeEnd] = {(char *)"Init", (char *)"Main", (char *)"DeInit"};
+static constexpr char *kSubExeGraphTypeStrs[kSubExeGraphTypeEnd] = {
+    const_cast<char *>("Init"), const_cast<char *>("Main"), const_cast<char *>("DeInit")};
 inline const char *GetSubExeGraphTypeStr(SubExeGraphType type) {
   return kSubExeGraphTypeStrs[type];
 }
 
-class ResourceGuard {
- public:
-  void *ResetExecutionData(std::unique_ptr<uint8_t[]> execution_data);
-  void ResetAnyValue(std::unique_ptr<uint8_t[]> any_values, size_t count);
-  void PushNode(void *node);
-  void PushWatcher(void *watcher);
-  void *ResetNodesArray(std::unique_ptr<uint8_t[]> nodes_array);
-  void *ResetStartNodesArray(std::unique_ptr<uint8_t[]> start_nodes_array);
-  void *ResetNodesIndgreeArray(std::unique_ptr<uint8_t[]> nodes_indgree_array);
-  void *ResetNodesWaitIndgreeArray(std::unique_ptr<uint8_t[]> nodes_indgree_array);
-  void *ResetInputsArray(std::unique_ptr<uint8_t[]> inputs_array);
-  void *ResetOutputsArray(std::unique_ptr<uint8_t[]> outputs_array);
-  void *ResetWatchersArray(std::unique_ptr<uint8_t[]> watchers_array);
-  void *ResetReadyQueue(void *ready_queue);
-  void *ResetBuffer(std::unique_ptr<uint8_t[]> buffer);
-  void *ResetComputeNodeInfo(std::unique_ptr<uint8_t[]> compute_node_info);
-  void *ResetKernelExtendInfo(std::unique_ptr<uint8_t[]> kernel_extend_info);
-  void *ResetModelDesc(std::unique_ptr<uint8_t[]> model_desc);
-
-  ~ResourceGuard();
-
- private:
-  std::unique_ptr<uint8_t[]> execution_data_holder_;
-  size_t any_values_num_;
-  std::unique_ptr<uint8_t[]> any_values_guard_;
-
-  std::vector<std::unique_ptr<void, decltype(&free)>> nodes_guarder_;
-  std::vector<std::unique_ptr<void, decltype(&free)>> watchers_guarder_;
-  std::unique_ptr<uint8_t[]> continuous_buffer_guarder_;
-  std::unique_ptr<uint8_t[]> buffer_guarder_;
-  std::unique_ptr<uint8_t[]> compute_node_info_guarder_;
-  std::unique_ptr<uint8_t[]> kernel_extend_info_guarder_;
-  std::unique_ptr<uint8_t[]> model_desc_guarder_;
-
-  std::unique_ptr<uint8_t[]> nodes_array_guarder_;
-  std::unique_ptr<uint8_t[]> start_nodes_array_guarder_;
-  std::unique_ptr<uint8_t[]> nodes_indgree_array_guarder_;
-  std::unique_ptr<uint8_t[]> nodes_wait_indgree_array_guarder_;
-  std::unique_ptr<uint8_t[]> inputs_array_guarder_;
-  std::unique_ptr<uint8_t[]> outputs_array_guarder_;
-  std::unique_ptr<uint8_t[]> watchers_array_guarder_;
-  std::unique_ptr<void, decltype(&free)> ready_queue_guarder_{nullptr, nullptr};
-};
-
 struct ModelExecuteArg {
   rtStream_t stream;
 };
 static_assert(std::is_standard_layout<ModelExecuteArg>::value, "The class ModelExecuteArg must be a POD");
 
-class ExeGraphExecutor {
- public:
-  // todo unload时释放anyvalue资源
-  ge::graphStatus Load() {
-    return ge::GRAPH_SUCCESS;
-  }
-  ge::graphStatus UnLoad() {
-    return ge::GRAPH_SUCCESS;
-  }
-
-  /**
-   * 设置图执行的输入/输出，需要注意的是，使用者需要自己保证inputs/outputs刷新完全！！！
-   */
-  ge::graphStatus SpecifyInputs(void **inputs, size_t start, size_t num);
-  ge::graphStatus SpecifyOutputs(void **outputs, size_t num);
-  ge::graphStatus Execute();
-
-  const void *GetExecutionData() const {
-    return execution_data_;
-  }
-
-  ResourceGuard &GetResourceGuard();
-  void *SetExecutionData(std::unique_ptr<uint8_t[]> execution_data);
-
- private:
-  friend class ModelV2ExecutorTestHelper;
-
-  void *execution_data_;
-  ResourceGuard resource_guard_;
-};
-class ModelV2Executor {
+class VISIBILITY_EXPORT ModelV2Executor {
  public:
   static std::unique_ptr<ModelV2Executor> Create(const ge::ComputeGraphPtr &root_graph);
 
@@ -121,6 +52,12 @@ class ModelV2Executor {
 
   const ModelDesc &GetModelDesc() const;
   void SetModelDesc(ModelDesc *model_desc);
+  ExeGraphExecutor &GetMainExeGraphExecutor() {
+    return graphs_[kMainExeGraph];
+  }
+  ExecutorSubscribersScheduler &GetSubscribers();
+  const ExecutorSubscribersScheduler &GetSubscribers() const;
+
   ModelV2Executor(const ModelV2Executor &) = delete;
   ModelV2Executor(ModelV2Executor &&) = delete;
   ModelV2Executor &operator=(const ModelV2Executor &) = delete;
@@ -129,13 +66,14 @@ class ModelV2Executor {
  private:
   friend class ModelV2ExecutorBuilder;
   friend class ModelV2ExecutorTestHelper;
-  ModelV2Executor() = default;
+  ModelV2Executor();
 
  private:
   std::array<ExeGraphExecutor, kSubExeGraphTypeEnd> graphs_;
   ResourceGuard resource_guard_;
   ModelDesc *model_desc_ = nullptr;
   rtStream_t default_stream_ = nullptr;
+  ExecutorSubscribersScheduler subscribers_;
 };
 }  // namespace gert
 

inc/framework/runtime/subscriber/built_in_subscriber_definitions.h

@@ -0,0 +1,71 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_SUBSCRIBER_BUILT_IN_SUBSCRIBER_DEFINITIONS_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_SUBSCRIBER_BUILT_IN_SUBSCRIBER_DEFINITIONS_H_
+#include <type_traits>
+#include <vector>
+#include "framework/common/ge_visibility.h"
+namespace gert {
+constexpr size_t kProfilingDataCap = 10UL * 1024UL * 1024UL;
+constexpr size_t kInitSize = 10UL * 1024UL;
+constexpr size_t kModelStrIdx = 0UL;
+constexpr size_t kExecuteStrIdx = 1UL;
+constexpr size_t kRegStartIdx = 2UL;
+constexpr size_t kDouble = 2UL;
+
+enum class BuiltInSubscriberType { kProfiling, kDumper, kNum };
+
+enum class ProfilingType {
+  kHost,  // 打开Host侧调度的profiling
+  kDevice,
+  kGeHost,       // 打开GE Host侧调度的profiling
+  kSingleOpReg,  // 单算子需要打开此开关开始register node name和kernel type
+  kNum,
+  kAll = kNum
+};
+static_assert(static_cast<size_t>(ProfilingType::kNum) < sizeof(uint64_t) * 8,
+              "The max num of profiling type must less than the width of uint64");
+
+enum class DumpType { kDataDump, kExceptionDump, kExceptionSave, kNum, kAll = kNum };
+static_assert(static_cast<size_t>(DumpType::kNum) < sizeof(uint64_t) * 8,
+              "The max num of dumper type must less than the width of uint64");
+
+class VISIBILITY_EXPORT BuiltInSubscriberUtil {
+ public:
+  template <typename T,
+            typename std::enable_if<(std::is_same<T, ProfilingType>::value) || (std::is_same<T, DumpType>::value),
+                                    int>::type = 0>
+  constexpr static uint64_t EnableBit(T et) {
+    return 1UL << static_cast<size_t>(et);
+  }
+
+  template <typename T,
+            typename std::enable_if<(std::is_same<T, ProfilingType>::value) || (std::is_same<T, DumpType>::value),
+                                    int>::type = 0>
+  static uint64_t BuildEnableFlags(const std::vector<T> &enable_types) {
+    uint64_t flag = 0UL;
+    for (auto et : enable_types) {
+      if (et == T::kAll) {
+        return EnableBit(T::kNum) - 1UL;
+      }
+      flag |= EnableBit(et);
+    }
+    return flag;
+  }
+};
+}  // namespace gert
+#endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_SUBSCRIBER_BUILT_IN_SUBSCRIBER_DEFINITIONS_H_

inc/framework/runtime/subscriber/executor_subscriber_c.h

@@ -0,0 +1,33 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBER_C_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBER_C_H_
+#include "exe_graph/runtime/base_type.h"
+#ifdef __cplusplus
+extern "C" {
+#endif
+typedef enum { kExecuteStart, kExecuteEnd, kModelStart, kModelEnd, kExecuteEventEnd } ExecutorEvent;
+
+typedef void (*SubscriberFunc)(void *arg, ExecutorEvent event, const void *node, KernelStatus result);
+typedef struct {
+  SubscriberFunc callback;
+  void *arg;
+} ExecutorSubscriber;
+#ifdef __cplusplus
+}
+#endif
+#endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBER_C_H_

inc/framework/runtime/subscriber/executor_subscriber_guarder.h

@@ -0,0 +1,76 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBER_GUARDER_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBER_GUARDER_H_
+#include "framework/common/ge_visibility.h"
+#include "common/checker.h"
+#include "executor_subscriber_c.h"
+namespace gert {
+template <typename T>
+void ObjectDeleter(void *obj) {
+  delete static_cast<T *>(obj);
+}
+
+class VISIBILITY_EXPORT ExecutorSubscriberGuarder {
+ public:
+  using ArgDeleter = void (*)(void *);
+
+  ExecutorSubscriberGuarder(::SubscriberFunc func, void *arg, ArgDeleter deleter)
+      : subscriber_({func, arg}), arg_deleter_(deleter) {}
+  ExecutorSubscriberGuarder(ExecutorSubscriberGuarder &&other) noexcept {
+    MoveAssignment(other);
+  }
+  ExecutorSubscriberGuarder &operator=(ExecutorSubscriberGuarder &&other) noexcept {
+    DeleteArg();
+    MoveAssignment(other);
+    return *this;
+  }
+
+  ExecutorSubscriber &GetSubscriber() {
+    return subscriber_;
+  }
+
+  const ExecutorSubscriber &GetSubscriber() const {
+    return subscriber_;
+  }
+
+  ~ExecutorSubscriberGuarder() {
+    DeleteArg();
+  }
+
+  ExecutorSubscriberGuarder(const ExecutorSubscriberGuarder &) = delete;
+  ExecutorSubscriberGuarder &operator=(const ExecutorSubscriberGuarder &) = delete;
+
+ private:
+  void DeleteArg() {
+    if (arg_deleter_ != nullptr) {
+      arg_deleter_(subscriber_.arg);
+    }
+  }
+  void MoveAssignment(ExecutorSubscriberGuarder &other) {
+    subscriber_ = other.subscriber_;
+    arg_deleter_ = other.arg_deleter_;
+    other.subscriber_ = {nullptr, nullptr};
+    other.arg_deleter_ = nullptr;
+  }
+
+ private:
+  ExecutorSubscriber subscriber_{nullptr, nullptr};
+  ArgDeleter arg_deleter_{nullptr};
+};
+}  // namespace gert
+#endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBER_GUARDER_H_

inc/framework/runtime/subscriber/executor_subscribers_scheduler.h

@@ -0,0 +1,149 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBERS_SCHEDULER_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBERS_SCHEDULER_H_
+#include <vector>
+#include <array>
+#include "executor_subscriber_guarder.h"
+#include "built_in_subscriber_definitions.h"
+#include "global_profiling.h"
+#include "framework/common/ge_visibility.h"
+namespace gert {
+namespace {
+constexpr size_t kInitSubscriberSize = 1UL;
+}
+class ModelV2Executor;
+class VISIBILITY_EXPORT ExecutorSubscribersScheduler {
+ public:
+  static void OnExecuteEvent(ExecutorSubscribersScheduler *ins, ExecutorEvent event, const void *node,
+                             KernelStatus result);
+
+  ExecutorSubscribersScheduler()
+      : executor_(nullptr),
+        enabled_(false),
+        built_in_subscribers_ptr_(),
+        subscribers_(),
+        subscriber_wrapper_({reinterpret_cast<::SubscriberFunc>(ExecutorSubscribersScheduler::OnExecuteEvent), this}) {}
+#ifdef ONLY_COMPILE_OPEN_SRC
+  ~ExecutorSubscribersScheduler();
+#endif
+  void Init(ModelV2Executor *executor);
+  ExecutorSubscribersScheduler(const ExecutorSubscribersScheduler &) = delete;
+  ExecutorSubscribersScheduler &operator=(const ExecutorSubscribersScheduler &) = delete;
+  ExecutorSubscriber &GetSubscriber() {
+    if (subscribers_.size() == 1UL) {
+      return subscribers_[0].GetSubscriber();
+    } else {
+      return subscriber_wrapper_;
+    }
+  }
+
+  ModelV2Executor *GetModelV2Executor() {
+    return executor_;
+  }
+  const ModelV2Executor *GetModelV2Executor() const {
+    return executor_;
+  }
+
+  /**
+   * 设置订阅者，订阅者需要实现一个static方法，原型为：
+   * ```c++
+   * static void OnExecuteEvent(T *void_arg, ExecutorEvent event, const void *node, KernelStatus result);
+   * ```
+   *
+   * 默认情况下，subscribers处于disable状态，在添加首个subscriber时，自动将状态切换到enable状态。
+   *
+   * @tparam T 订阅者类型
+   * @tparam Args 订阅者初始化参数类型
+   * @param args 订阅者初始化参数
+   * @return 添加的subscriber指针，注意subscriber所有权归`ExecutorSubscribersScheduler`所有，外部使用者不可以释放此指针
+   */
+  template <typename T, typename... Args>
+  T *AddSubscriber(Args... args) {
+    auto ins = new (std::nothrow) T(args...);
+    if (ins == nullptr) {
+      return nullptr;
+    }
+
+    // profiler exists when ess init
+    if (subscribers_.size() == kInitSubscriberSize) {
+      enabled_ = true;
+    }
+    subscribers_.emplace_back(reinterpret_cast<::SubscriberFunc>(T::OnExecuteEvent), ins, ObjectDeleter<T>);
+    return ins;
+  }
+
+  /**
+   * 添加一个内置的subscriber
+   * 内置subscriber较少，当前没有使用注册机制，后续如果需要扩展，那么可以考虑通过注册机制自动注册。
+   * 为了易用性，在本类提供了获取内置subscriber的指针的接口。而自注册的subscriber将丢失此能力。
+   * @param subscriber_type
+   */
+  void AddBuiltIn(BuiltInSubscriberType subscriber_type, uint64_t enable_flag);
+  void RemoveSubscriber(void *subscriber_ptr) {
+    for (auto iter = subscribers_.begin(); iter != subscribers_.end(); ++iter) {
+      if (iter->GetSubscriber().arg == subscriber_ptr) {
+        subscribers_.erase(iter);
+        break;
+      }
+    }
+    for (auto &built_in_subscriber : built_in_subscribers_ptr_) {
+      if (built_in_subscriber == subscriber_ptr) {
+        built_in_subscriber = nullptr;
+      }
+    }
+    if (subscribers_.empty()) {
+      enabled_ = false;
+    }
+  }
+
+  template <typename T>
+  inline T *MutableBuiltInSubscriber(const BuiltInSubscriberType type) {
+    return static_cast<T *>(built_in_subscribers_ptr_[static_cast<size_t>(type)]);
+  }
+
+  template <typename T>
+  inline const T *GetBuiltInSubscriber(const BuiltInSubscriberType type) {
+    return static_cast<T *>(built_in_subscribers_ptr_[static_cast<size_t>(type)]);
+  }
+
+  bool IsEnable() const {
+    return enabled_ || GlobalProfilingWrapper::GetInstance()->GetEnableFlags();
+  }
+  void SetEnable(bool enable_flag) {
+    enabled_ = enable_flag;
+  }
+  void Clear() {
+    subscribers_.clear();
+    for (auto &built_in_subscriber : built_in_subscribers_ptr_) {
+      built_in_subscriber = nullptr;
+    }
+    enabled_ = false;
+  }
+  size_t GetSize() const {
+    return subscribers_.size();
+  }
+
+ private:
+  ModelV2Executor *executor_{nullptr};
+  bool enabled_{false};
+  std::array<void *, static_cast<size_t>(BuiltInSubscriberType::kNum)> built_in_subscribers_ptr_;
+  std::vector<ExecutorSubscriberGuarder> subscribers_;
+  ExecutorSubscriber subscriber_wrapper_;
+};
+}  // namespace gert
+#endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_EXECUTOR_SUBSCRIBERS_SCHEDULER_H_

inc/framework/runtime/subscriber/global_profiling.h

@@ -0,0 +1,138 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_SUBSCRIBER_GLOBAL_PROFILING_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_SUBSCRIBER_GLOBAL_PROFILING_H_
+
+#include <algorithm>
+#include <memory>
+#include <unordered_map>
+#include "built_in_subscriber_definitions.h"
+#include "common/debug/ge_log.h"
+#include "framework/common/ge_visibility.h"
+#include "runtime/subscriber/executor_subscriber_c.h"
+
+namespace gert {
+struct ProfilingData {
+  uint64_t name_idx;
+  uint64_t type_idx;
+  ExecutorEvent event;
+  std::chrono::time_point<std::chrono::system_clock> timestamp;
+};
+class GlobalProfiler {
+ public:
+  GlobalProfiler() = default;
+  void Record(uint64_t name_idx, uint64_t type_idx, ExecutorEvent event,
+              std::chrono::time_point<std::chrono::system_clock> timestamp) {
+    auto index = count_++;
+    if (index >= kProfilingDataCap) {
+      return;
+    }
+    records_[index] = {name_idx, type_idx, event, timestamp};
+  }
+  void Dump(std::ostream &out_stream, std::vector<std::string> &idx_to_str) const;
+  size_t GetCount() const {
+    return count_;
+  }
+
+ private:
+  std::atomic<size_t> count_{0UL};
+  ProfilingData records_[kProfilingDataCap];
+};
+
+class VISIBILITY_EXPORT GlobalProfilingWrapper {
+ public:
+  static GlobalProfilingWrapper *GetInstance() {
+    static GlobalProfilingWrapper global_prof_wrapper;
+    return &global_prof_wrapper;
+  }
+
+  static void OnGlobalProfilingSwitch(void *ins, uint64_t enable_flags);
+
+  void Init(uint64_t enable_flags);
+
+  void Free() {
+    global_profiler_.reset(nullptr);
+    SetEnableFlags(0UL);
+  }
+
+  GlobalProfiler *GetGlobalProfiler() const {
+    return global_profiler_.get();
+  }
+
+  void SetEnableFlags(uint64_t enable_flags) {
+    enable_flags_ = enable_flags;
+  }
+
+  uint64_t GetRecordCount() {
+    if (global_profiler_ == nullptr) {
+      return 0UL;
+    }
+    return global_profiler_->GetCount();
+  }
+
+  uint64_t GetEnableFlags() const {
+    return enable_flags_;
+  }
+
+  bool IsEnable(ProfilingType profiling_type) const {
+    return enable_flags_ & BuiltInSubscriberUtil::EnableBit<ProfilingType>(profiling_type);
+  }
+
+  void DumpAndFree(std::ostream &out_stream) {
+    Dump(out_stream);
+    Free();
+  }
+  void Dump(std::ostream &out_stream) {
+    if (global_profiler_ != nullptr) {
+      global_profiler_->Dump(out_stream, idx_to_str_);
+    }
+  }
+  void Record(uint64_t name_idx, uint64_t type_idx, ExecutorEvent event,
+              std::chrono::time_point<std::chrono::system_clock> timestamp) {
+    if (global_profiler_ != nullptr) {
+      global_profiler_->Record(name_idx, type_idx, event, timestamp);
+    }
+  }
+
+  uint64_t RegisterString(const char *name) {
+    const std::lock_guard<std::mutex> lk(register_mutex_);
+    std::string str_name = name;
+    const auto iter = std::find(idx_to_str_.begin(), idx_to_str_.end(), str_name);
+    if (iter == idx_to_str_.end()) {
+      idx_to_str_[str_idx_] = str_name;
+      ++str_idx_;
+      if (str_idx_ >= idx_to_str_.size()) {
+        idx_to_str_.resize(idx_to_str_.size() * kDouble);
+      }
+      return str_idx_ - 1UL;
+    } else {
+      return iter - idx_to_str_.begin();
+    }
+  }
+
+ private:
+  GlobalProfilingWrapper();
+
+ private:
+  std::unique_ptr<GlobalProfiler> global_profiler_{nullptr};
+  uint64_t enable_flags_{0UL};
+  uint64_t str_idx_{0UL};
+  std::vector<std::string> idx_to_str_;
+  std::mutex register_mutex_;
+};
+}  // namespace gert
+
+#endif

metadef

@@ -1 +1 @@
-Subproject commit e4d1efc47349f13af1bcdb53ba408118779fc27e
+Subproject commit 17536092c004f6f9e08116939f4f49e1e11a99d3

third_party/fwkacllib/inc/common/type_def.h

@@ -27,12 +27,12 @@ typedef double float64_t;
 
 inline uint64_t PtrToValue(const void *ptr)
 {
-    return static_cast<const uint64_t>(reinterpret_cast<const uintptr_t>(ptr));
+    return static_cast<uint64_t>(reinterpret_cast<uintptr_t>(ptr));
 }
 
 inline void *ValueToPtr(const uint64_t value)
 {
-    return reinterpret_cast<void *>(static_cast<const uintptr_t>(value));
+    return reinterpret_cast<void *>(static_cast<uintptr_t>(value));
 }
 
 template<typename TI, typename TO>

third_party/fwkacllib/inc/external/runtime/rt_error_codes.h

@@ -59,6 +59,7 @@ static const int32_t ACL_ERROR_RT_QUEUE_FULL                 = 207014; // queue
 static const int32_t ACL_ERROR_RT_REPEATED_INIT              = 207015; // repeated init
 static const int32_t ACL_ERROR_RT_AIVEC_OVER_FLOW            = 207016; // aivec over flow
 static const int32_t ACL_ERROR_RT_OVER_FLOW                  = 207017; // common over flow
+static const int32_t ACL_ERROR_RT_DEVIDE_OOM                 = 207018; // device oom
 
 static const int32_t ACL_ERROR_RT_INTERNAL_ERROR             = 507000; // runtime internal error
 static const int32_t ACL_ERROR_RT_TS_ERROR                   = 507001; // ts internel error

third_party/fwkacllib/inc/ops/cluster.h

@@ -30,18 +30,18 @@ namespace ge {
 
 * @par Inputs:
 * Three required inputs and one optional inputs, including:
-* @li x: A 2D tensor of data type float32. 
-* @li y: A 2D tensor of data type float32. 
-* @li sum_square_x: An optional 2D tensor of data type float32. 
+* @li x: A 2D tensor of data type float32.
+* @li y: A 2D tensor of data type float32.
+* @li sum_square_x: An optional 2D tensor of data type float32.
 * @li sum_square_y: A 2D tensor of data type float32. \n
 
 * @par Attributes:
 * use_actual_distance: Indicates whether to calculate the complete distance. \n
 
 * @par Outputs:
-* @li segment_sum: A tensor of data type float32. 
-* @li segment_count: A tensor of data type float32. 
-* @li k_mean_total_sum: A tensor of data type float32. 
+* @li segment_sum: A tensor of data type float32.
+* @li segment_count: A tensor of data type float32.
+* @li k_mean_total_sum: A tensor of data type float32.
 */
 REG_OP(KMeansCentroids)
     .INPUT(x, TensorType({DT_FLOAT}))

third_party/fwkacllib/inc/ops/data_flow_ops.h

@@ -2549,5 +2549,32 @@ REG_OP(OptionalGetValue)
   .REQUIRED_ATTR(output_types, ListType)
   .REQUIRED_ATTR(output_shapes, ListListInt)
   .OP_END_FACTORY_REG(OptionalGetValue)
+
+/**
+* @brief User define function process. \n
+
+* @par Inputs:
+* @li x: A list of input tensor objects. It's a dynamic input. \n
+
+* @par Outputs:
+* @li y: A list of output tensor objects. It's a dynamic output. \n
+
+* @par Attributes:
+* @li bin_path: User's binary path.
+* @li func_name: User defined function name.
+* @li output_types: Types of outputs data.
+* @li output_shapes: Shapes of outputs data.
+* @li _flow_attr_process_node_engine_id: Default process node engine of FlowFunc.
+*/
+REG_OP(FlowFunc)
+    .DYNAMIC_INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, \
+        DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE}))
+    .DYNAMIC_OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, \
+        DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_UINT32, DT_UINT64, DT_BOOL, DT_DOUBLE}))
+    .REQUIRED_ATTR(bin_path, String)
+    .REQUIRED_ATTR(func_name, String)
+    .ATTR(output_shapes, ListListInt, {})
+    .REQUIRED_ATTR(output_types, ListType)
+    .OP_END_FACTORY_REG(FlowFunc)
 } // namespace ge
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_DATA_FLOW_OPS_H_

third_party/fwkacllib/inc/ops/elewise_calculation_ops.h

@@ -48,29 +48,29 @@ REG_OP(AddN)
     .OP_END_FACTORY_REG(AddN)
 
 /**
-*@brief Calculates the reversed outputs of the function "maximum".
+* @brief Calculates the reversed outputs of the function "maximum".
 
-*@par Inputs:
+* @par Inputs:
 * Three inputs, including:
-*@li grads: A mutable Tensor. Must be one of the following types:
+* @li grads: A mutable Tensor. Must be one of the following types:
 * float16, float32, int32.
-*@li x1: A mutable Tensor of the same type as "grads".
-*@li x2: A mutable Tensor of the same type as "grads". \n
+* @li x1: A mutable Tensor of the same type as "grads".
+* @li x2: A mutable Tensor of the same type as "grads". \n
 
-*@par Attributes:
-*@li grad_x: An optional bool. Defaults to "True".
+* @par Attributes:
+* @li grad_x: An optional bool. Defaults to "True".
 * If "True", "y1" will be output.
 * If "False", "y1" will not be output. \n
 
-*@li grad_y: An optional bool. Defaults to "True".
+* @li grad_y: An optional bool. Defaults to "True".
 * If "True", "y2" will be output.
 * If "False", "y2" will not be output. \n
 
-*@par Outputs:
-*@li y1: A mutable Tensor. Has the same type as "grads".
-*@li y2: A mutable Tensor. Has the same type as "grads". \n
+* @par Outputs:
+* @li y1: A mutable Tensor. Has the same type as "grads".
+* @li y2: A mutable Tensor. Has the same type as "grads". \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator MaximumGrad.
 */
 REG_OP(MaximumGrad)
@@ -84,29 +84,29 @@ REG_OP(MaximumGrad)
     .OP_END_FACTORY_REG(MaximumGrad)
 
 /**
-*@brief Calculates the reversed outputs of the function "minimum".
+* @brief Calculates the reversed outputs of the function "minimum".
 
-*@par Inputs:
+* @par Inputs:
 * Three inputs, including:
-*@li grads: A mutable Tensor. Must be one of the following types:
+* @li grads: A mutable Tensor. Must be one of the following types:
 * float16, float32, int32.
-*@li x1: A mutable Tensor of the same type as "grads".
-*@li x2: A mutable Tensor of the same type as "grads". \n
+* @li x1: A mutable Tensor of the same type as "grads".
+* @li x2: A mutable Tensor of the same type as "grads". \n
 
-*@par Attributes:
-*@li grad_x: An optional bool. Defaults to "True".
+* @par Attributes:
+* @li grad_x: An optional bool. Defaults to "True".
 * If "True", "y1" will be output.
 * If "False", "y1" will not be output. \n
 
-*@li grad_y: An optional bool. Defaults to "True".
+* @li grad_y: An optional bool. Defaults to "True".
 * If "True", "y2" will be output.
 * If "False", "y2" will not be output. \n
 
-*@par Outputs:
-*@li y1: A mutable Tensor. Has the same type as "grads".
-*@li y2: A mutable Tensor. Has the same type as "grads". \n
+* @par Outputs:
+* @li y1: A mutable Tensor. Has the same type as "grads".
+* @li y2: A mutable Tensor. Has the same type as "grads". \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator MinimumGrad.
 */
 REG_OP(MinimumGrad)
@@ -552,16 +552,16 @@ REG_OP(Expint)
     .OP_END_FACTORY_REG(Expint)
 
 /**
-*@brief: Computes the reciprocal of "x".
+* @brief: Computes the reciprocal of "x".
 
-*@par Inputs:
-*x: A Tensor. Must be one of the following types: float16, float32,
+* @par Inputs:
+* x: A Tensor. Must be one of the following types: float16, float32,
 * int32, int64, double, complex64, complex128. \n
 
-*@par Outputs:
-*y: A Tensor. Must be one of the following type: float16, float32, int32. \n
+* @par Outputs:
+* y: A Tensor. Must be one of the following type: float16, float32, int32. \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator Inv.
 */
 REG_OP(Inv)
@@ -570,19 +570,19 @@ REG_OP(Inv)
     .OP_END_FACTORY_REG(Inv)
 
 /**
-*@brief: Computes "x" reciprocal grad, dx = -1*dy*y*y, where, "y = 1/x",
+* @brief: Computes "x" reciprocal grad, dx = -1*dy*y*y, where, "y = 1/x",
 * and "dy" is the corresponding input gradient.
 
-*@par Inputs:
+* @par Inputs:
 * Two inputs, including:
-*@li x: A Tensor. Must be one of the following types: float16, float32,
+* @li x: A Tensor. Must be one of the following types: float16, float32,
 * int32, int8.
-*@li grad: A Tensor. Has the same type as "x". \n
+* @li grad: A Tensor. Has the same type as "x". \n
 
-*@par Outputs:
-*y: A Tensor, Has the same type as "x". \n
+* @par Outputs:
+* y: A Tensor, Has the same type as "x". \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator InvGrad.
 */
 REG_OP(InvGrad)
@@ -634,27 +634,27 @@ REG_OP(Log1p)
     .OP_END_FACTORY_REG(Log1p)
 
 /**
-*@brief Returns element-wise remainder of division.
+* @brief Returns element-wise remainder of division.
 
-*@par Inputs:
+* @par Inputs:
 * Two inputs, including:
-*@li x1: A Tensor. Must be one of the following types: float16, float32,
+* @li x1: A Tensor. Must be one of the following types: float16, float32,
 * int32, int64, int8, uint8, double.
-*@li x2: A Tensor of the same type as "x1". \n
+* @li x2: A Tensor of the same type as "x1". \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type as "x1". \n
+* @par Outputs:
+* y: A Tensor. Has the same type as "x1". \n
 
-*@attention Constraints:
-*@li x2: The input data does not support 0.
-*@li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the
+* @attention Constraints:
+* @li x2: The input data does not support 0.
+* @li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the
 * requirement of double thousandths in the mini form.
-*@li Due to different architectures, the calculation results of this operator
+* @li Due to different architectures, the calculation results of this operator
 * on NPU and CPU may be inconsistent.
-*@li If shape is expressed as (D1,D2... ,Dn),
+* @li If shape is expressed as (D1,D2... ,Dn),
 * then D1*D2... *DN<=1000000,n<=8. \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator Mod.
 */
 REG_OP(Mod)
@@ -667,18 +667,18 @@ REG_OP(Mod)
     .OP_END_FACTORY_REG(Mod)
 
 /**
-*@brief Returns the truth value of (x != y) element-wise.
+* @brief Returns the truth value of (x != y) element-wise.
 
-*@par Inputs:
+* @par Inputs:
 * Two inputs, including:
-*@li x1: A Tensor. Must be one of the following types: float16, float32, int32,
+* @li x1: A Tensor. Must be one of the following types: float16, float32, int32,
 * int8, uint8, double, int16, int64, uint16, half, uint32, uint64.
-*@li x2: A Tensor of the same type as "x1". \n
+* @li x2: A Tensor of the same type as "x1". \n
 
-*@par Outputs:
-*y: A Tensor of type bool. \n
+* @par Outputs:
+* y: A Tensor of type bool. \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator NotEqual.
 */
 REG_OP(NotEqual)
@@ -688,17 +688,17 @@ REG_OP(NotEqual)
     .OP_END_FACTORY_REG(NotEqual)
 
 /**
-*@brief Computes ndtri element-wise (y = sqrt(2) * erfinv(2 * x - 1)).
+* @brief Computes ndtri element-wise (y = sqrt(2) * erfinv(2 * x - 1)).
 
-*@par Inputs:
+* @par Inputs:
 * One input, including: \n
-*x: A Tensor. Must be one of the following types: bfloat16, float16,
+* x: A Tensor. Must be one of the following types: bfloat16, float16,
 * float32, double. \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type and format as input "x". \n
+* @par Outputs:
+* y: A Tensor. Has the same type and format as input "x". \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator Ndtri.
 */
 REG_OP(Ndtri)
@@ -726,21 +726,21 @@ REG_OP(Neg)
     .OP_END_FACTORY_REG(Neg)
 
 /**
-*@brief Returns x1/x2 element-wise for integer types.
+* @brief Returns x1/x2 element-wise for integer types.
 
-*@par Inputs:
-*@li x1: A Tensor. Must be one of the following types:
+* @par Inputs:
+* @li x1: A Tensor. Must be one of the following types:
 *     float32, float16, int8, uint8, int32, int16,
 *     uint16, double, int64, complex64, complex128.
-*@li x2: A Tensor of the same data type as "x1". \n
+* @li x2: A Tensor of the same data type as "x1". \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type as "x1".
+* @par Outputs:
+* y: A Tensor. Has the same type as "x1".
 
-*@attention Constraints:
+* @attention Constraints:
 * Broadcasting is supported. \n
 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator TruncateDiv. \n
 
 */
@@ -1427,17 +1427,17 @@ REG_OP(RsqrtGrad)
     .OP_END_FACTORY_REG(RsqrtGrad)
 
 /**
-*@brief Computes hyperbolic sine of "x" element-wise.
+* @brief Computes hyperbolic sine of "x" element-wise.
 
-*@par Inputs:
-*x: An NCHW, NHWC,or ND Tensor of type float, double, complex64,
- * complex128, half. \n
+* @par Inputs:
+* x: An NCHW, NHWC,or ND Tensor of type float, double, complex64,
+* complex128, half. \n
 
-*@par Outputs:
-*y: A NCHW, NHWC,or ND Tensor of type float, double, complex64,
- * complex128, half. \n
+* @par Outputs:
+* y: A NCHW, NHWC,or ND Tensor of type float, double, complex64,
+* complex128, half. \n
 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator Sinh. \n
 
 */
@@ -1513,18 +1513,18 @@ REG_OP(DivNoNan)
     .OP_END_FACTORY_REG(DivNoNan)
 
 /**
-*@brief Reverses specific dimensions of a tensor.
+* @brief Reverses specific dimensions of a tensor.
 
-*@par Inputs:
+* @par Inputs:
 * One input: \n
-*x: A Tensor, Must be one of the following types:
+* x: A Tensor, Must be one of the following types:
 * int32, uint8, int16, int8, int64, int64, uint16, uint32, uint64,
 * and format can be [NCHW,NHWC,ND]. \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type and format as "x". \n
+* @par Outputs:
+* y: A Tensor. Has the same type and format as "x". \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator Invert.
 */
 REG_OP(Invert)
@@ -1772,16 +1772,16 @@ REG_OP(Atan2)
     .OP_END_FACTORY_REG(Atan2)
 
 /**
-*@brief Computes fresnel_cos of x element-wise.
+* @brief Computes fresnel_cos of x element-wise.
 * 
-*@par Inputs:
-*x: A tensor. Must be one of the following types: bfloat16, float16, float32,
+* @par Inputs:
+* x: A tensor. Must be one of the following types: bfloat16, float16, float32,
 * double. \n
 * 
-*@par Outputs:
-*y: A tensor. Has the same type as "x". \n
+* @par Outputs:
+* y: A tensor. Has the same type as "x". \n
 * 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator FresnelCos.
 * 
 */
@@ -1791,17 +1791,17 @@ REG_OP(FresnelCos)
     .OP_END_FACTORY_REG(FresnelCos)
 
 /**
-*@brief Computes fresnel_sin of x element-wise.
+* @brief Computes fresnel_sin of x element-wise.
  
 * 
-*@par Inputs:
-*x: A tensor. Must be one of the following types: bfloat16, float16, float32,
+* @par Inputs:
+* x: A tensor. Must be one of the following types: bfloat16, float16, float32,
 * double. \n
 * 
-*@par Outputs:
-*y: A tensor. Has the same type as "x". \n
+* @par Outputs:
+* y: A tensor. Has the same type as "x". \n
 * 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator FresnelSin.
 * 
 */
@@ -2317,16 +2317,16 @@ REG_OP(Sin)
     .OP_END_FACTORY_REG(Sin)
 
 /**
-*@brief: Computes tan of "x" element-wise.
+* @brief: Computes tan of "x" element-wise.
 
-*@par Inputs:
-*One input:
-*x: A Tensor. Must be one of the following types: float16, float32, double, complex64, complex128, int32, int64
+* @par Inputs:
+* One input:
+* x: A Tensor. Must be one of the following types: float16, float32, double, complex64, complex128, int32, int64
 
-*@par Outputs:
-*y: A Tensor. Has the same type as "x". \n
+* @par Outputs:
+* y: A Tensor. Has the same type as "x". \n
 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with TensorFlow operator Tan.
 */
 REG_OP(Tan)
@@ -2337,27 +2337,27 @@ REG_OP(Tan)
     .OP_END_FACTORY_REG(Tan)
 
 /**
-*@brief Returns element-wise remainder of division.
+* @brief Returns element-wise remainder of division.
 
-*@par Inputs:
-*Two inputs, including:
+* @par Inputs:
+* Two inputs, including:
 * @li x1: A Tensor. Must be one of the following types: float16, float32,
 * double, int32, int64.
 * @li x2: A Tensor of the same type as "x1". \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type as "x1". \n
+* @par Outputs:
+* y: A Tensor. Has the same type as "x1". \n
 
-*@attention Constraints:
-*@li x2: The input data does not support 0
-*@li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the
-*requirement of double thousandths in the mini form
-*@li Due to different architectures, the calculation results of this operator
-*on NPU and CPU may be inconsistent
-*@li If shape is expressed as (D1,D2... ,Dn), then D1*D2... *DN<=1000000,n<=8
+* @attention Constraints:
+* @li x2: The input data does not support 0
+* @li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the
+* requirement of double thousandths in the mini form
+* @li Due to different architectures, the calculation results of this operator
+* on NPU and CPU may be inconsistent
+* @li If shape is expressed as (D1,D2... ,Dn), then D1*D2... *DN<=1000000,n<=8
 
-*@par Third-party framework compatibility
-*Compatible with the TensorFlow operator TruncateMod.
+* @par Third-party framework compatibility
+* Compatible with the TensorFlow operator TruncateMod.
 */
 REG_OP(TruncateMod)
     .INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT64,
@@ -3138,21 +3138,21 @@ REG_OP(SquareSumV2)
     .OP_END_FACTORY_REG(SquareSumV2)
 
 /**
-*@brief Confuse reducesumd and square.
+* @brief Confuse reducesumd and square.
 
-*@par Inputs:
-*x: A Tensor of type float16, float32. \n
+* @par Inputs:
+* x: A Tensor of type float16, float32. \n
 
-*@par Attributes:
+* @par Attributes:
 * Two attributes, including: \n
-*@li axis: A optional listint, specifies the dimensions to reduce.
-*@li keep_dims: A bool, specifying whether to keep dimensions for the output Tensor. Defaults to "false". \n
+* @li axis: A optional listint, specifies the dimensions to reduce.
+* @li keep_dims: A bool, specifying whether to keep dimensions for the output Tensor. Defaults to "false". \n
 
-*@par Outputs:
-y: A Tensor. Has the same type as "x".
+* @par Outputs:
+* y: A Tensor. Has the same type as "x".
 
-*@par Restrictions:
-*Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
+* @par Restrictions:
+* Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
 */
 REG_OP(SquareSumV1)
     .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
@@ -3183,19 +3183,19 @@ REG_OP(SquareSumAll)
     .OP_END_FACTORY_REG(SquareSumAll)
 
 /**
-*@brief Confuse broadcast, addn and mul.
+* @brief Confuse broadcast, addn and mul.
 
-*@par Inputs:
-*Three inputs, including:
-*@li x1: A Tensor. Must be one of the following types:int32, int16,
+* @par Inputs:
+* Three inputs, including:
+* @li x1: A Tensor. Must be one of the following types:int32, int16,
 * float16, float32.
-*@li x2: A Tensor of the same type as "x1".
-*@li x3: A Tensor of the same type as "x1". \n
+* @li x2: A Tensor of the same type as "x1".
+* @li x3: A Tensor of the same type as "x1". \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type as "x1". \n
+* @par Outputs:
+* y: A Tensor. Has the same type as "x1". \n
 
-*@par Restrictions:
+* @par Restrictions:
 * Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
 */
 REG_OP(FusedMulAddN)
@@ -3206,41 +3206,41 @@ REG_OP(FusedMulAddN)
     .OP_END_FACTORY_REG(FusedMulAddN)
 
 /**
-*@brief Add 'bias' to 'x'.
+* @brief Add 'bias' to 'x'.
 
-*@par Inputs:
+* @par Inputs:
 * Two inputs, including:
-*@li x: An ND tensor of type float16 or float32.
-*@li bias: An ND tensor of type float16 or float32. \n
+* @li x: An ND tensor of type float16 or float32.
+* @li bias: An ND tensor of type float16 or float32. \n
 
-*@par Attributes:
-*@li axis: An optional int32 used to compute the shape of bias input from the online bottoms. Defaults to "1".
-*@li num_axes: An optional int32 used to compute the shape of
+* @par Attributes:
+* @li axis: An optional int32 used to compute the shape of bias input from the online bottoms. Defaults to "1".
+* @li num_axes: An optional int32 used to compute the shape of
 * bias input from a Caffe model trained offline. Defaults to "1".
-*@li bias_from_blob: An optional bool. If "true", bias is input from a Caffe model trained offline.
+* @li bias_from_blob: An optional bool. If "true", bias is input from a Caffe model trained offline.
 * If "false", bias is input from online bottoms. Defaults to "true". \n
 
-*@par Outputs:
-*y: An ND tensor of type float16 or float32. \n
+* @par Outputs:
+* y: An ND tensor of type float16 or float32. \n
 
-*@attention Constraints:
+* @attention Constraints:
 * Assume that the shape length of "x" is "n" and that of "bias" is "m".
-*@li "axis" is within the range [-n, n-1]. num_axes >= -1.
-*@li If "bias_from_blob = true", "num_axes = -1", and "axis >= 0",
+* @li "axis" is within the range [-n, n-1]. num_axes >= -1.
+* @li If "bias_from_blob = true", "num_axes = -1", and "axis >= 0",
 * the ith axis of "bias" and the (i+"axis")th axis of "x" must have the same size (0 <= i < n-axis).
 * If "axis < 0", the ith axis of "bias" and the (i+n+"axis")th axis of "x" must have the same size (0 <= i < -axis).
-*@li If "bias_from_blob = true" and "num_axes = 0", "bias" is a scalar with shape length 1 and dimension size 1.
-*@li If "bias_from_blob = true", "num_axes > 0, and "axis >= 0",
+* @li If "bias_from_blob = true" and "num_axes = 0", "bias" is a scalar with shape length 1 and dimension size 1.
+* @li If "bias_from_blob = true", "num_axes > 0, and "axis >= 0",
 * "axis + num_axes" must be less than or equal to "n" and the ith axis of "bias" and
 * the (i+"axis")th axis of "x" must have the same size (0 <= i < num_axes).
 * If "axis < 0", "n + axis + num_axes" must be less than or equal to "n" and
 * the ith axis of "bias" and the (i+n+"axis")th axis of "x" must have the same size (0 <= i < num_axes).
-*@li If "bias_from_blob = false", "bias" is not a scalar, and "axis >= 0",
+* @li If "bias_from_blob = false", "bias" is not a scalar, and "axis >= 0",
 * "axis + m" must be less than or equal to "n" and the ith axis of "bias" and
 * the (i+"axis")th axis of "x" must have the same size (0 <= i < m).
 * If "axis < 0", "n + axis + m" must be less than or equal to "n" and
 * the ith axis of "bias" and the (i+n+"axis")th axis of "x" must have the same size (0 <= i < m). \n
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the Caffe operator Bias.
 */
 
@@ -3383,7 +3383,7 @@ REG_OP(Muls)
 
 *@par Inputs:
 *One input, including:
-*x1: A Tensor. Must be one of the following types:int32,int16, float16, float32.
+*x: A Tensor. Must be one of the following types:float32, float16, int64, int32, int16, bool.
 
 *@par Outputs:
 *y: A Tensor. Has the same type and shape as "x1". \n
@@ -3392,9 +3392,9 @@ REG_OP(Muls)
 * Compatible with the Pytorch operator fills.
 */
 REG_OP(Fills)
-     .INPUT(x, TensorType({DT_FLOAT,DT_INT16,DT_INT32,DT_FLOAT16}))
-     .OUTPUT(y, TensorType({DT_FLOAT,DT_INT16,DT_INT32,DT_FLOAT16}))
-     .REQUIRED_ATTR(value,Float)
+     .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT64, DT_INT32, DT_INT16, DT_BOOL}))
+     .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT64, DT_INT32, DT_INT16, DT_BOOL}))
+     .REQUIRED_ATTR(value, Float)
      .OP_END_FACTORY_REG(Fills)
 
 /**
@@ -3420,19 +3420,19 @@ REG_OP(Fills)
      .OP_END_FACTORY_REG(Adds)
 
 /**
-*@brief Computes the product of x and y and returns 0 if the y is zero,
+* @brief Computes the product of x and y and returns 0 if the y is zero,
 * even if x is NaN or infinite.
 
-*@par Inputs:
+* @par Inputs:
 * Two inputs, including: \n
-*@li x1: A Tensor. Must be one of the following types:float16, float32,
+* @li x1: A Tensor. Must be one of the following types:float16, float32,
 * double, complex64, complex128.
-*@li x2: A Tensor. Has the same type and shape as "x1". \n
+* @li x2: A Tensor. Has the same type and shape as "x1". \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type and shape as "x1". \n
+* @par Outputs:
+* y: A Tensor. Has the same type and shape as "x1". \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator MulNoNan.
 */
  REG_OP(MulNoNan)

third_party/fwkacllib/inc/ops/experiment_ops.h

@@ -0,0 +1,158 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2020-2021. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*!
+ * \file experiment_ops.h
+ * \brief
+ */
+#ifndef OPS_BUILT_IN_OP_PROTO_INC_EXPERIMENT_OPS_H_
+#define OPS_BUILT_IN_OP_PROTO_INC_EXPERIMENT_OPS_H_
+
+#include "graph/operator_reg.h"
+namespace ge {
+/**
+* @brief Updates "var" according to the AdamW algorithm.
+*
+* @attention Constraints:
+*  The input tensors must have the same shape.*
+*
+* @par Inputs:
+* @li var: A mutable Tensor of the type TensorType::NumberType().
+*     Should be from a Variable().
+* @li m: A mutable Tensor of the same type as "var".
+*     Should be from a Variable().
+* @li v: A mutable Tensor of the same type as "var".
+*     Should be from a Variable().
+* @li beta1_power: A scalar of the same type as "var".
+* @li beta2_power: A scalar of the same type as "var".
+* @li lr: learning_rate. A scalar of the same type as "var".
+* @li weight_decay: learning_rate. A scalar of the same type as "var".
+* @li beta1: A scalar of the same type as "var".
+* @li beta2: A scalar of the same type as "var".
+* @li epsilon: A scalar of the same type as "var".
+* @li grad: A Tensor of the same type as "var", for the gradient.
+* @li max_grad_norm: A mutable Tensor of the same type as "var", an optional input.
+*     Should be from a Variable().
+*
+* @par Attributes:
+* @li amsgrad: An optional bool. Defaults to "False".
+*     If "True", max_grad_norm input and output must be entered.
+* @li maximize: An optional bool. Defaults to "False".
+*
+* @par Outputs:
+* @li var: A mutable tensor. Has the same type as input "var".
+* @li m: A mutable tensor. Has the same type as input "m".
+* @li v: A mutable tensor. Has the same type as input "v". \n
+*/
+REG_OP(ApplyAdamW)
+    .INPUT(var, TensorType::NumberType())
+    .INPUT(m, TensorType::NumberType())
+    .INPUT(v, TensorType::NumberType())
+    .INPUT(beta1_power, TensorType::NumberType())
+    .INPUT(beta2_power, TensorType::NumberType())
+    .INPUT(lr, TensorType::NumberType())
+    .INPUT(weight_decay, TensorType::NumberType())
+    .INPUT(beta1, TensorType::NumberType())
+    .INPUT(beta2, TensorType::NumberType())
+    .INPUT(epsilon, TensorType::NumberType())
+    .INPUT(grad, TensorType::NumberType())
+    .OPTIONAL_INPUT(max_grad_norm, TensorType::NumberType())
+    .OUTPUT(var, TensorType::NumberType())
+    .OUTPUT(m, TensorType::NumberType())
+    .OUTPUT(v, TensorType::NumberType())
+    .ATTR(amsgrad, Bool, false)
+    .ATTR(maximize, Bool, false)
+    .OP_END_FACTORY_REG(ApplyAdamW)
+
+/**
+* @brief Multiplies matrix "a" by matrix "b", producing "a * b". \n
+* @par Inputs:
+* Four inputs, including:
+* @li x1: A matrix Tensor. Must be one of the following types: float32,
+* float16, int32, int8, int4, bf16. 3D. Has format ND.
+* @li x2: A matrix Tensor. Must be one of the following types: float32,
+* float16, int32, int8, int4, bf16. 3D. Has format ND.
+* @li bias: A optional Tensor. Must be one of the following types:
+* float32, float16, int32, bf16. 1D. Has format ND.
+* @li offset_w: A optional Tensor. Must be one of the following types:
+* int8, int4. Has format ND. \n
+
+* @par Attributes:
+* Three attributes, including:
+* @li perm_x1: A list int. "x1" is permuted to shape [B, M, K] before multiplication.
+* @li perm_x2: A list int. "x2" is permuted to shape [B, K, N] before multiplication.
+* @li perm_y: A list int. "y" is permuted after multiplication.
+* @li offset_x: An optional integer for quantized TransposeBatchMatMul.
+* The negative offset added to the input "x1" for int8, int4 type. Ensure offset_x
+* within the effective range of input data type. Defaults to "0". \n
+
+* @par Outputs:
+* y: The result matrix Tensor. 3D. Must be one of the following
+* types: float32, float16, int32, bf16. 3D. Has format ND. \n
+*/
+REG_OP(TransposeBatchMatMul)
+    .INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT8, DT_INT4, DT_BF16}))
+    .INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT8, DT_INT4, DT_BF16}))
+    .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_BF16}))
+    .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8, DT_INT4}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_BF16}))
+    .ATTR(perm_x1, ListInt, {})
+    .ATTR(perm_x2, ListInt, {})
+    .ATTR(perm_y, ListInt, {})
+    .ATTR(offset_x, Int, 0)
+    .OP_END_FACTORY_REG(TransposeBatchMatMul)
+
+/**
+* @brief Performs non-maximum suppression (NMS) on the rotated boxes according
+* to their intersection-over-union (IoU). Rotated NMS interatively removes lower
+* scoring rotated boxes which have an IoU greater than iou_threshold with
+* another (higher scoring) rotated box.
+
+* @par Inputs:
+* Three inputs, including:
+* @li boxes: A 2D Tensor of float16 or float32 with shape (N, 5). Rotated boxes to
+* perform NMS on. They are expected to be in (x1, y1, x2, y2, angle_degress) format.
+* @li scores: A 1D Tensor of float16 or float32 with shape (N). Scores for each one of
+* the rotated boxes.
+* @li labels: A 1D Tensor of int32 or int64 with shape (N). Labels for each one of
+* the rotated boxes.
+
+* @par Attributes:
+* iou_threshold: A required float attribute. Discards all overlapping rotated
+* boxes with IoU < iou_threshold.
+
+* @par Outputs:
+* Two outputs, including:
+* @li selected_detections: A 2D Tensor of float16 or float32 with shape (N, 5).
+* The selected boxes that kept by Rotated NMS, sorted in decreasing order of scores.
+* @li keep_indices: A 1D Tensor of int32 or int64 with shape (N). The indices of
+* selected_detections.
+
+* @attention Constraints:
+* Currently, the tensor type of input (boxes, scores) only support float.
+* The tensor type of keep_indices only support int32.
+*/
+REG_OP(RotatedNMS)
+    .INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(labels, TensorType({DT_INT32, DT_INT64}))
+    .OUTPUT(selected_detections, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OUTPUT(keep_indices, TensorType({DT_INT32, DT_INT64}))
+    .REQUIRED_ATTR(iou_threshold, Float)
+    .OP_END_FACTORY_REG(RotatedNMS)
+}  // namespace ge
+
+#endif  // OPS_BUILT_IN_OP_PROTO_INC_EXPERIMENT_OPS_H_

third_party/fwkacllib/inc/ops/functional_ops.h

@@ -362,6 +362,59 @@ REG_OP(ToBool)
     .OUTPUT(output, DT_BOOL)
     .OP_END_FACTORY_REG(ToBool)
 
+/**
+ * @brief Abstract tiling function to an op definition
+ *        The input will be data or shape \n
+
+ * @par Inputs:
+ * @li x: the data of input.  all types are available,
+ * @li outputshape: the shape of previous op output shape . all types are available. \n
+
+ * @par Outputs:
+ * @li tiling_data: tiling data of tiling function. It should be a buffer
+ * @li tiling_key: tiling key of tiling function.
+ * @li block_dim: block dim of tiling function.
+ * @li tiling_cond: tiling condition of tiling function which will be used to determined real execute kernel. \n
+
+ * @par Attributes:
+ * @li tiling_node: A string. real tiling node such as matmul.
+ * @li op_type:  A string. Op type of the original node. \n
+
+ * @par Third-party framework compatibility
+ */
+REG_OP(OpTiling)
+    .DYNAMIC_INPUT(x, TensorType::ALL())
+    .DYNAMIC_INPUT(output_shape, TensorType::ALL())
+    .OUTPUT(tiling_data, TensorType({DT_UINT8}))
+    .OUTPUT(tiling_key, TensorType({DT_UINT64}))
+    .OUTPUT(block_dim, TensorType({DT_INT32}))
+    .OUTPUT(tiling_cond, TensorType({DT_INT32}))
+    .REQUIRED_ATTR(tiling_node, String)
+    .REQUIRED_ATTR(op_type, String)
+    .OP_END_FACTORY_REG(OpTiling)
+
+/**
+ * @brief  Calculate condition value by input tensor which will be used for if input or case input. \n
+
+ * @par Inputs:
+ * @li x: the data or shape of input.  all types are available,
+
+ * @par Outputs:
+ * @li cond: condition value calculated by cond fuction.
+            It will be cond input of if or branch_index input of case. \n
+
+ * @par Attributes:
+ * @li cond_func: A string. real condition function registered to calculate condition value.
+ * @li x_dependency: List of int. It should be the same number of inputs: 0(shape) 1(data). \n
+
+ * @par Third-party framework compatibility
+ */
+REG_OP(ConditionCalc)
+    .DYNAMIC_INPUT(x, TensorType::ALL())
+    .OUTPUT(cond, TensorType({DT_INT32}))
+    .REQUIRED_ATTR(cond_func, String)
+    .REQUIRED_ATTR(x_dependency, ListInt)
+    .OP_END_FACTORY_REG(ConditionCalc)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_FUNCTIONAL_OPS_H_

third_party/fwkacllib/inc/ops/hcom_ops.h

@@ -28,7 +28,7 @@ namespace ge {
  * @brief Outputs a tensor gathering all input tensors.
  * @par Inputs:
  * x: A tensor. Must be one of the following types: int8, int16, int32, float16,
-  float32.
+  float32, uint8, uint16, uint32, float64.
  * @par Attributes:
  * @li rank_size: A required integer identifying the number of ranks
   participating in the op.
@@ -41,8 +41,10 @@ namespace ge {
   as the name of a world group.
  */
 REG_OP(HcomAllGather)
-    .INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
+    .INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
     .REQUIRED_ATTR(rank_size, Int)
     .REQUIRED_ATTR(group, String)
     .OP_END_FACTORY_REG(HcomAllGather)
@@ -99,8 +101,10 @@ REG_OP(HcomAllReduce)
   as the name of a world group.
  */
 REG_OP(HcomBroadcast)
-    .DYNAMIC_INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
-    .DYNAMIC_OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
+    .DYNAMIC_INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
+    .DYNAMIC_OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
     .REQUIRED_ATTR(root_rank, Int)
     .REQUIRED_ATTR(group, String)
     .ATTR(fusion, Int, 0)
@@ -186,7 +190,8 @@ REG_OP(HcomReduceScatter)
  * @see HcomReceive
 */
 REG_OP(HcomSend)
-    .INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
+    .INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
     .REQUIRED_ATTR(group, String)
     .REQUIRED_ATTR(sr_tag, Int)
     .REQUIRED_ATTR(dest_rank, Int)
@@ -217,7 +222,8 @@ REG_OP(HcomSend)
  * @see HcomSend
 */
 REG_OP(HcomReceive)
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
     .REQUIRED_ATTR(group, String)
     .REQUIRED_ATTR(sr_tag, Int)
     .REQUIRED_ATTR(src_rank, Int)
@@ -303,12 +309,14 @@ REG_OP(HcomRemoteScatterWrite)
   using the RDMA.
  */
 REG_OP(HcomAllToAllV)
-    .INPUT(send_data, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
+    .INPUT(send_data, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
     .INPUT(send_counts, TensorType({DT_INT64}))
     .INPUT(send_displacements, TensorType({DT_INT64}))
     .INPUT(recv_counts, TensorType({DT_INT64}))
     .INPUT(recv_displacements, TensorType({DT_INT64}))
-    .OUTPUT(recv_data, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
+    .OUTPUT(recv_data, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
     .REQUIRED_ATTR(group, String)
     .OP_END_FACTORY_REG(HcomAllToAllV)
 
@@ -345,8 +353,10 @@ REG_OP(HcomGatherAllToAllV)
     .INPUT(addrinfo_count_per_rank, TensorType({DT_INT64}))
     .INPUT(recv_counts, TensorType({DT_INT64}))
     .INPUT(recv_displacements, TensorType({DT_INT64}))
-    .OUTPUT(recv_data, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
-    .OUTPUT(gathered, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64}))
+    .OUTPUT(recv_data, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
+    .OUTPUT(gathered, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64,
+                          DT_UINT8, DT_UINT16, DT_UINT32, DT_FLOAT64}))
     .REQUIRED_ATTR(group, String)
     .REQUIRED_ATTR(dtype, Type)
     .REQUIRED_ATTR(addr_length, Int)

third_party/fwkacllib/inc/ops/image_ops.h

@@ -2371,5 +2371,78 @@ REG_OP(ImageProjectiveTransform)
     .ATTR(fill_mode, String, "CONSTANT")
     .OUTPUT(transformed_images, TensorType({DT_UINT8, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
     .OP_END_FACTORY_REG(ImageProjectiveTransform)
+
+/**
+* @brief image to transforms. \n
+
+* @par Inputs:
+* @li images: [batch, height, width, channels], 4-D tensor.
+* @li transforms: [batch, 8] or [1, 8] matrix, 2-D tensor.
+* @li outout_shape: [new_height, new_width], 1-D tensor.
+* @li fill_value: [scalar], 1-D tensor.
+
+* @par Attributes:
+* @li interpolation: Interpolation method, "NEAREST" or "BILINEAR", 0-D tensor.
+* @li fill_mode: Defaults to "CONSTANT". Fill mode, "REFLECT", "WRAP", or "CONSTANT", 0-D tensor.
+
+* @par Outputs
+* transformed_images: has the same type as iamges, 4-D tensor with shape[batch, new_height, new_width, channels]. \n
+
+* @par Third-party framework compatibility.
+* Compatible with tensorflow ImageProjectiveTransformv2 operator.
+*/
+REG_OP(ImageProjectiveTransformV2)
+    .INPUT(images, TensorType({DT_UINT8, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
+    .INPUT(transforms, TensorType({DT_FLOAT}))
+    .INPUT(output_shape, TensorType({DT_INT32}))
+    .OPTIONAL_INPUT(fill_value, TensorType({DT_UINT8, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
+    .REQUIRED_ATTR(interpolation, String)
+    .ATTR(fill_mode, String, "CONSTANT")
+    .OUTPUT(transformed_images, TensorType({DT_UINT8, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
+    .OP_END_FACTORY_REG(ImageProjectiveTransformV2)
+
+/**
+* @brief Extracts a glimpse from the input tensor . \n
+
+* @par Inputs:
+* Input input must be a 4-D tensor. Inputs include:
+* @li input: A 4-D float tensor of shape [batch_size, height, width, channels].
+The format must be NHWC.
+* @li size: A 1-D tensor of 2 elements containing the size of the glimpses to
+extract. The glimpse height must be specified first, following by the glimpse
+width.
+* @li offsets: A 2-D integer tensor of shape [batch_size, 2] containing the y,
+x locations of the center of each window . \n
+
+* @par Attributes:
+* @li centered: indicates if the offset coordinates are centered relative to
+the image, in which case the (0, 0) offset is relative to the center of the
+input images. If false, the (0,0) offset corresponds to the upper left corner
+of the input images.
+* @li normalized: indicates if the offset coordinates are normalized.
+* @li uniform_noise: indicates if the noise should be generated using a
+uniform distribution or a Gaussian distribution.
+* @li noise: indicates if the noise should uniform, gaussian, or zero.
+The default is uniform which means the the noise type will be decided by
+uniform_noise . \n
+
+* @par Outputs:
+* glimpse:A tensor representing the glimpses [batch_size, glimpse_height,
+glimpse_width, channels]. The format must be NHWC. \n
+
+* @par Third-party framework compatibility
+* Compatible with tensorflow ExtractGlimpseV2 operator.
+*/
+
+REG_OP(ExtractGlimpseV2)
+    .INPUT(input, TensorType({DT_FLOAT}))
+    .INPUT(size, TensorType({DT_INT32}))
+    .INPUT(offsets, TensorType({DT_FLOAT}))
+    .OUTPUT(glimpse, TensorType({DT_FLOAT}))
+    .ATTR(centered, Bool, true)
+    .ATTR(normalized, Bool, true)
+    .ATTR(uniform_noise, Bool, true)
+    .ATTR(noise, String, "uniform")
+    .OP_END_FACTORY_REG(ExtractGlimpseV2)
 }  // namespace ge
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_IMAGE_OPS_H_

third_party/fwkacllib/inc/ops/math_ops.h

@@ -230,7 +230,7 @@ REG_OP(Bucketize)
 *input_x: A tensor. Must be one of the following types: float16, float32, int8, uint8, int32. \n
 
 *@par Outputs:
-*output_y: A tensor with the same type and shape of input_x \n
+* output_y: A tensor with the same type and shape of input_x \n
 
 *@par Third-party framework compatibility
 *Compatible with the Pytorch operator Trunc. \n
@@ -1008,6 +1008,36 @@ REG_OP(Complex)
     .ATTR(Tout, Type, DT_COMPLEX64)
     .OP_END_FACTORY_REG(Complex)
 
+/**
+* @brief Counts the number of occurrences of each value in an integer array . \n
+
+* @par Inputs:
+* Five inputs, including:
+* indices: A 2D Tensor of type int64.
+* values: A 1D Tensor of type int32 or int64.
+* dense_shape: A 1D Tensor of type int64.
+* size: A non-negative scalar Tensor.
+* weights: A Tensor of type int32 or int64 or fp32 or fp64 or only 1 \n
+
+* @par Attributes:
+* dtype: An optional bool.Defaults to False. bool . \n
+
+* @par Outputs:
+* y: A Tensor . Has the same type as `input_weights` .\n
+
+* @par Third-party framework compatibility
+* Compatible with the TensorFlow operator SparseBincount.
+*/
+REG_OP(SparseBincount)
+    .INPUT(indices, TensorType({DT_INT64}))
+    .INPUT(values, TensorType({DT_INT32, DT_INT64}))
+    .INPUT(dense_shape, TensorType({DT_INT64}))
+    .INPUT(size, TensorType({DT_INT32, DT_INT64}))
+    .INPUT(weights, TensorType({DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE}))
+    .ATTR(binary_output, Bool, false)
+    .OUTPUT(output, TensorType({DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE}))
+    .OP_END_FACTORY_REG(SparseBincount)
+
 /**
 * @brief  deal complex.
 
@@ -1098,7 +1128,7 @@ REG_OP(Cross)
     .OP_END_FACTORY_REG(Cross)
 
 /**
- *@brief Computes batched the p-norm distance between each pair of
+ * @brief Computes batched the p-norm distance between each pair of
  *the two collections of row vectors. \n
 
  *@par Inputs:
@@ -1445,6 +1475,72 @@ REG_OP(SparseCountSparseOutput)
     .ATTR(maxlength, Int, -1)
     .REQUIRED_ATTR(binary_output, Bool)
     .OP_END_FACTORY_REG(SparseCountSparseOutput)
+
+/**
+* @brief Counts the number of occurrences of each value in an integer array. \n
+
+* @par Inputs:
+* @li splits: A Tensor of type int64. 1D int64 Tensor.
+* @li values: A Tensor. Must be one of the following types: int32, int64. 2D int Tensor.
+* @li size: A Tensor. Must have the same type as values. non-negative int scalar Tensor. 
+* @li weights: A Tensor. Must be one of the following types: float32.
+               is a float32 Tensor with the same shape as input,
+               or a length-0 Tensor, in which case it acts as all weights equal to 1. \n
+
+* @par Outputs:
+* @li output: A Tensor with length "size" for each stride and has the same dtype as weights. \n
+
+* @par Attributes:
+* binary_output: An optional bool. Defaults to False. bool;
+                 Whether the kernel should count the appearance or number of occurrences. \n
+
+* @attention Constraints:
+* The operator will use the interface set_atomic_add(), therefore weights and output should be float32 only. \n
+
+* @par Third-party framework compatibility
+* Compatible with tensorflow RaggedBinCount operator.
+*/
+
+REG_OP(RaggedBinCount)
+    .INPUT(splits, TensorType(DT_INT64))
+    .INPUT(values, TensorType({DT_INT32, DT_INT64}))
+    .INPUT(size, TensorType({DT_INT32, DT_INT64}))
+    .INPUT(weights, TensorType(DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE))
+    .OUTPUT(output, TensorType(DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE))
+    .ATTR(binary_output, Bool, false)
+    .OP_END_FACTORY_REG(RaggedBinCount)
+
+/**
+* @brief Counts the number of occurrences of each value in an integer array. \n
+
+* @par Inputs:
+* @li input: A Tensor of type int32, int64. 1D or 2D int Tensor.
+* @li size: A Tensor. Must have the same type as input. non-negative int scalar Tensor.
+* @li weights: A Tensor. Must be one of the following types: int32, int64, float32, float64.
+               with the same shape as input,
+               or a length-0 Tensor, in which case it acts as all weights equal to 1. \n
+
+* @par Outputs:
+* @li output: A Tensor with length "size" for each stride and has the same dtype as weights. \n
+
+* @par Attributes:
+* binary_output: An optional bool. Defaults to False. bool;
+                 Whether the kernel should count the appearance or number of occurrences. \n
+
+* @attention Constraints:
+* The operator will use the interface set_atomic_add(), therefore weights and output should be float32 only. \n
+
+* @par Third-party framework compatibility
+* Compatible with tensorflow DenseBincount operator.
+*/
+
+REG_OP(DenseBincount)
+    .INPUT(input, TensorType({DT_INT32, DT_INT64}))
+    .INPUT(size, TensorType({DT_INT32, DT_INT64}))
+    .INPUT(weights, TensorType(DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE))
+    .OUTPUT(output, TensorType(DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE))
+    .ATTR(binary_output, Bool, false)
+    .OP_END_FACTORY_REG(DenseBincount)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_MATH_OPS_H_

third_party/fwkacllib/inc/ops/nn_calculation_ops.h

@@ -28,7 +28,7 @@ namespace ge {
 * @brief Computes the gradients of depthwise convolution with respect to
 * the filter. \n
 * @par Inputs:
-* Three inputs include: 
+* Three inputs include:
 * @li input: 4D origin shape of input tensor [N, C, H, W] or [N, H, W, C],
 * support float16.
 * @li filter_size: A 4D tensor of type int32, int64, with shape [H, W, C, K]
@@ -52,8 +52,7 @@ namespace ge {
 
 * @par Outputs:
 * filter_grad: Gradient of the deep convolution relative to the filter with
-* shape [H, W, C, K]. Must be one of the following types: float16, float32,
-* double . \n
+* shape [H, W, C, K]. Must be one of the following types: float16. \n
 
 * @attention Constraints:\n
 * The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
@@ -350,7 +349,7 @@ REG_OP(BiasAddGrad)
 
 /**
 *@brief Computes the gradients of convolution with respect to the input.
-*@par Inputs:
+* @par Inputs:
  * Three inputs:
  * @li input_size: A const Tensor of type int32. Currently does not support
  * data tensor. An integer vector representing the shape of input, where
@@ -454,7 +453,7 @@ REG_OP(Conv2DBackpropInput)
 
 /**
 *@brief Computes the gradients of convolution with respect to the input.
-*@par Inputs:
+* @par Inputs:
  * Two inputs:
  * @li filter: A Tensor. Types is float16.
  * 4-D with shape [filter_height, filter_width, in_channels, out_channels]
@@ -501,7 +500,7 @@ REG_OP(Conv2DBackpropInputD)
 
 /**
 *@brief Computes the Deconvolution with respect to the input.
-*@par Inputs:
+* @par Inputs:
  * Two required inputs:
  * @li x: A Tensor of type float16 or int8. 4D with shape
  * [batch, out_channels, out_height, out_width]. Gradients with respect
@@ -1518,7 +1517,7 @@ REG_OP(Conv2DTranspose)
 
 /**
 *@brief Computes the transpose of convolution 2d with respect to the input.
-*@par Inputs:
+* @par Inputs:
  * Four inputs:
  * @li x: A Tensor of type float16, int8.
  * @li filter: A Tensor of type float16, int8. Must have the same type as "x".
@@ -1566,7 +1565,7 @@ REG_OP(Conv2DTransposeD)
 
 /**
 *@brief Computes the deformed convolution output with the expected input
-*@par Inputs:
+* @par Inputs:
  * Two inputs:
  * @li x: A Tensor of type float16,float32
  * @li offsets: A Tensor of type float16,float32.Deformation offset parameter.
@@ -1599,7 +1598,7 @@ REG_OP(DeformableOffsets)
 
 /**
 *@brief Computes the gradients of DeformableOffsets with respect to input and offsets
-*@par Inputs:
+* @par Inputs:
  * Three inputs:
  * @li grad: A Tensor of type float16,float32. gradients with respect to DeformableOffsets output
  * @li x: A Tensor of type float16,float32.
@@ -1636,7 +1635,7 @@ REG_OP(DeformableOffsetsGrad)
 
 /**
 *@brief Computes the deformed dilation output with the expected input
-*@par Inputs:
+* @par Inputs:
  * One inputs:
  * x: A Tensor of type int8, float16, float32
 *@par Attributes:
@@ -1656,7 +1655,7 @@ REG_OP(Dilation)
 
 /**
 *@brief Computes the post-cube processing output with the expected input
-*@par Inputs:
+* @par Inputs:
  * Ten inputs:
  * x1: A Tensor of type float16, bfloat16, float32, int32
  * x2: A Tensor of type float16, int8, int4

third_party/fwkacllib/inc/ops/nn_pooling_ops.h

@@ -75,37 +75,37 @@ REG_OP(Pooling)
     .OP_END_FACTORY_REG(Pooling)
 
 /**
-*@brief Performs average pooling on the input. \n
-*@par Inputs:
-*x: A tensor of type float16, float32, double. \n
+* @brief Performs average pooling on the input. \n
+* @par Inputs:
+* x: A tensor of type float16, float32, double. \n
 
-*@par Attributes:
-*@li ksize: A required list of 4 ints, specifying the size (N, C, H, and W)
+* @par Attributes:
+* @li ksize: A required list of 4 ints, specifying the size (N, C, H, and W)
 * of the sliding window, where N = C = 1, and H and W are positive integers
 *  within the range [1, 255].
 * @li strides: A required list of 4 ints, specifying the stride of the
 * sliding window. The strides of the N and C dimensions are 1. The strides of
 *  the H and W dimensions are positive integers within the range [1, 63].
-*@li padding: A required string, specifying the padding algorithm,
+* @li padding: A required string, specifying the padding algorithm,
  * either "VALID" or "SAME". With "SAME" means that the outputs will have the
  * same spatial dimensions as its inputs. With "VALID" means no padding.
-*@li data_format: An optional string, specifying the data format of "ksize"
+* @li data_format: An optional string, specifying the data format of "ksize"
 * and "strides", either "NCHW", or "NHWC" (default). \n
 
-*@par Outputs:
+* @par Outputs:
 * y: The average pooled output tensor. Has the same type and format
 * as input "x". \n
 
 * @attention Constraints:
 * @li This operator applies only to a TensorFlow network.
 * @li Only single input and single output are supported.
-*@li Global pooling is supported.
+* @li Global pooling is supported.
 * @li "ksize_H" and "ksize_W" are positive integers within the range [1, 255].
 * ksize_H * ksize_W < 256
-*@li Due to instruction restrictions,
+* @li Due to instruction restrictions,
  * the values of "strides_h" and "strides_w" are positive integers within
  * the range [1, 63].
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator AvgPool.
 */
 REG_OP(AvgPool)
@@ -118,37 +118,44 @@ REG_OP(AvgPool)
     .OP_END_FACTORY_REG(AvgPool)
 
 /**
-*@brief Performs average pooling on the input.
-*@par Inputs:
-*x: A tensor of type float16, float32, double.
+* @brief Performs average pooling on the input.
+* @par Inputs:
+* x: A tensor of type float16, float32, double.
 
-*@par Attributes:
-*@li ksize: A required list of 4 ints, specifying the size (N, C, H, and W) of the sliding window,
- * where N = C = 1, and H and W are positive integers within the range [1, 255].
-*@li strides: A required list of 4 ints, specifying the stride of the sliding window.
- * The strides of the N and C dimensions are 1.
- * The strides of the H and W dimensions are positive integers within the range [1, 63].
-*@li padding_mode: A required string, specifying the padding algorithm,
+* @par Attributes:
+* @li ksize: A required list of 4 ints, specifying the size (N, C, H, and W)
+* of the sliding window, where N = C = 1,
+ * and H and W are positive integers within the range [1, 255].
+* @li strides: A required list of 4 ints, specifying the stride of the
+ * sliding window. The strides of the N and C dimensions are 1.
+ * The strides of the H and W dimensions are positive integers within
+ * the range [1, 63].
+* @li padding_mode: A required string, specifying the padding algorithm,
  * either "VALID", "SAME" and "CALCULATED".
- * With "SAME" means that the outputs will have the same spatial dimensions as its inputs.
- * With "VALID" means no padding.
+ * With "SAME" means that the outputs will have the same spatial dimensions
+ * as its inputs. With "VALID" means no padding.
 * @li pads: Pad value when padding_mode is "CALCULATED".
-* @li data_format: An optional string, specifying the data format of "ksize" and "strides",
- * either "NCHW", or "NHWC" (default).
-* @li global_pooling: Global or not. If true, pads will change to {0,0,0,0} and ksize will change to [input_h, input_w]
-* @li ceil_mode: Use ceil or floor to calculate the output size when padding_mode is "CALCULATED".
+* @li data_format: An optional string, specifying the data format of "ksize"
+ * and "strides", either "NCHW", or "NHWC" (default).
+* @li global_pooling: Global or not. If true, pads will change to {0,0,0,0}
+* and ksize will change to [input_h, input_w].
+* @li ceil_mode: Use ceil or floor to calculate the output size when
+* padding_mode is "CALCULATED".
 * @li exclusive: Ignore padding area or not when calculating average.
 
 * @par Outputs:
-* y: The average pooled output tensor. Has the same type and format as input "x".
+* y: The average pooled output tensor. Has the same type and format as
+* input "x".
 
-*@attention Constraints:
-*@li Only single input and single output are supported.
+* @attention Constraints:
+* @li Only single input and single output are supported.
 * @li Global pooling is supported.
-*@li "ksize_H" and "ksize_W" are positive integers within the range [1, 255]. ksize_H * ksize_W < 256
-*@li Due to instruction restrictions,
- * the values of "strides_h" and "strides_w" are positive integers within the range [1, 63].
-*@par Third-party framework compatibility
+* @li "ksize_H" and "ksize_W" are positive integers within the range [1, 255].
+* ksize_H * ksize_W < 256
+* @li Due to instruction restrictions,
+ * the values of "strides_h" and "strides_w" are positive integers within
+ * the range [1, 63].
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator AvgPoolV2.
 */
 REG_OP(AvgPoolV2)
@@ -173,7 +180,7 @@ REG_OP(AvgPoolV2)
 * @par Attributes:
 * @li ksize: List of ints that has length 1, 3 or 5. The size of the window
 * for each dimension of the input tensor.
-*@li strides:List of ints that has length 1, 3 or 5. The stride of the sliding
+* @li strides:List of ints that has length 1, 3 or 5. The stride of the sliding
 * window for each dimension of the input tensor.
 * @li pads: List of ints, implicit zero paddings on both sides of the input.
 * @li ceil_mode: When true, will use ceil instead of floor in the formula to
@@ -184,13 +191,13 @@ REG_OP(AvgPoolV2)
 * size of the pooling region will be used.
 * @li data_format: A string, format of input data. \n
 
-*@par Outputs:
-*y: The average pooled output tensor. \n
+* @par Outputs:
+* y: The average pooled output tensor. \n
 
-*@attention Constraints:
+* @attention Constraints:
 * @li "ksize" is in the range [1, 255]. "strides" is in the range [1, 63].
 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator AvgPool3D.
 */
 REG_OP(AvgPool3D)
@@ -211,24 +218,24 @@ REG_OP(AvgPool3D)
 * @par Inputs:
 * @li x: A 5-D Tensor of shape [batch, depth, height, width, channels] and type float16, float32, double.
 * @li filter: An optional tensor of type float16, float32, double, fractal_z_3d layout.
-*@li multiplier: An optional tensor of float16, float32, double.
+* @li multiplier: An optional tensor of float16, float32, double.
 
 * @par Attributes:
-*@li ksize: List of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
+* @li ksize: List of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
 * @li strides:List of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
-*@li pads: List of ints, implicit zero paddings on both sides of the input.
-*@li ceil_mode: When true, will use ceil instead of floor in the formula to compute the output shape.
+* @li pads: List of ints, implicit zero paddings on both sides of the input.
+* @li ceil_mode: When true, will use ceil instead of floor in the formula to compute the output shape.
 * @li count_include_pad: When true, will include the zero-padding in the averaging calculation.
-*@li divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used.
-*@li data_format: A string, format of input data . \n
+* @li divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used.
+* @li data_format: A string, format of input data . \n
 
-*@par Outputs:
-*y: The average pooled output tensor . \n
+* @par Outputs:
+* y: The average pooled output tensor . \n
 
-*@attention Constraints:
-*"ksize" is in the range [1, 255]. "strides" is in the range [1, 63]
+* @attention Constraints:
+* "ksize" is in the range [1, 255]. "strides" is in the range [1, 63]
 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator AvgPool3D.
 */
 REG_OP(AvgPool3DD)
@@ -266,7 +273,7 @@ REG_OP(AvgPool3DD)
 * @li data_format: A string, format of input data. \n
 
 * @par Outputs:
-* @li output: A mutable tensor with the same shape and type as "grads".
+* output: A mutable tensor with the same shape and type as "orig_input_shape".
 
 * @attention Constraints:
 * @li "ksize" is in the range [1, 255]. "strides" is in the range [1, 63]. \n
@@ -336,31 +343,31 @@ REG_OP(AvgPool3DGradD)
     .OP_END_FACTORY_REG(AvgPool3DGradD)
 
 /**
-*@brief Performs max_pool_ext2 on the input . \n
+* @brief Performs max_pool_ext2 on the input . \n
 
-*@par Inputs:
+* @par Inputs:
 * One input:
-*x: A Tensor of type float16.
+* x: A Tensor of type float16.
 
 
-*@par Attributes:
-*@li ksize: A required list of int8, int16, int32, or int64 values,
- * specifying the size of the window for each dimension of the input tensor. No default value.
-*@li strides: A required list of int8, int16, int32, or int64 values,
- * specifying the stride of the sliding window for each dimension of the input tensor. No default value.
-*@li padding: A required string. No default value.
-*@li data_format: An optional string . \n
+* @par Attributes:
+* @li ksize: A required list of int8, int16, int32, or int64 values,
+* specifying the size of the window for each dimension of the input tensor. No default value.
+* @li strides: A required list of int8, int16, int32, or int64 values,
+* specifying the stride of the sliding window for each dimension of the input tensor. No default value.
+* @li padding: A required string. No default value.
+* @li data_format: An optional string . \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type and format as input "x" . \n
+* @par Outputs:
+* y: A Tensor. Has the same type and format as input "x" . \n
 
-*@attention Constraints:
-*@li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1, ksize[1] * ksize[2] <= 255.
-*@li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
+* @attention Constraints:
+* @li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1, ksize[1] * ksize[2] <= 255.
+* @li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
  * strides[1] <= 63, strides[0] >= 1, strides[2] <= 63, strides[2] >= 1.
-*@li "padding" is either "SAME" or "VALID" . \n
+* @li "padding" is either "SAME" or "VALID" . \n
 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator MaxPoolV2.
 */
 REG_OP(MaxPoolExt2)
@@ -377,35 +384,35 @@ REG_OP(MaxPoolExt2)
     .OP_END_FACTORY_REG(MaxPoolExt2)
 
 /**
-*@brief Performs max pooling on the input . \n
+* @brief Performs max pooling on the input . \n
 
 * @par Inputs:
 * One input:
 * x: A Tensor. Supported type:float16, float32, double, int8, int16,
 * int32, int64, uint8, uint16, qint8
 
-*@par Attributes:
-*@li ksize: A required list of int8, int16, int32, or int64 values,
+* @par Attributes:
+* @li ksize: A required list of int8, int16, int32, or int64 values,
 * specifying the size of the window for each dimension of the input tensor.
 * No default value.
-*@li strides: A required list of int8, int16, int32, or int64 values,
+* @li strides: A required list of int8, int16, int32, or int64 values,
 * specifying the stride of the sliding window for each dimension of
 * the input tensor. No default value.
-*@li padding: A required string. No default value.
-*@li data_format: An optional string. Defaults to "NHWC" . \n
+* @li padding: A required string. No default value.
+* @li data_format: An optional string. Defaults to "NHWC" . \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type and format as input "x" . \n
+* @par Outputs:
+* y: A Tensor. Has the same type and format as input "x" . \n
 
-*@attention Constraints:
-*@li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1,
+* @attention Constraints:
+* @li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1,
 * ksize[1] * ksize[2] <= 255.
-*@li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
+* @li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
 * strides[1] <= 63, strides[0] >= 1, strides[2] <= 63, strides[2] >= 1.
-*@li "padding" is either "SAME" or "VALID".
+* @li "padding" is either "SAME" or "VALID".
 
 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator MaxPool.
 */
 REG_OP(MaxPool)
@@ -423,33 +430,33 @@ REG_OP(MaxPool)
 /**
 * @brief Performs max 3d pooling on the input . \n
 
-*@par Inputs:
+* @par Inputs:
 * x: A Tensor. Supported type float16, float32, double . \n
 
-*@par Attributes:
-*@li ksize: A required list of int8, int16, int32, or int64 values,
+* @par Attributes:
+* @li ksize: A required list of int8, int16, int32, or int64 values,
 specifying the size of the window for each dimension of the input tensor.
 No default value.
-*@li strides: A required list of int8, int16, int32, or int64 values,
+* @li strides: A required list of int8, int16, int32, or int64 values,
 specifying the stride of the sliding window for each dimension of
 the input tensor. No default value.
-*@li padding: A required string type of float16.
-*@li pads: A list type of int32. Default value {0,0,0,0,0,0}.
-*@li dilation: A list type of int32. Default value {1,1,1,1,1,1}.
-*@li ceil_mode: A ceil mode number of int32 . Default value 0.
-*@li data_format: An optional string. Defaults to "NDHWC" . \n
+* @li padding: A required string type of float16.
+* @li pads: A list type of int32. Default value {0,0,0,0,0,0}.
+* @li dilation: A list type of int32. Default value {1,1,1,1,1,1}.
+* @li ceil_mode: A ceil mode number of int32 . Default value 0.
+* @li data_format: An optional string. Defaults to "NDHWC" . \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type and format as input "x" . \n
+* @par Outputs:
+* y: A Tensor. Has the same type and format as input "x" . \n
 
-*@attention Constraints:
-*@li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1,
+* @attention Constraints:
+* @li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1,
  * ksize[1] * ksize[2] <= 255.
-*@li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
+* @li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
  * strides[1] <= 63, strides[0] >= 1, strides[2] <= 63, strides[2] >= 1.
-*@li "padding" is either "SAME" or "VALID" . \n
+* @li "padding" is either "SAME" or "VALID" . \n
 
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator MaxPool3D.
 */
 REG_OP(MaxPool3D)
@@ -466,7 +473,7 @@ REG_OP(MaxPool3D)
 
 /**
 * @brief Performs max pooling3d on both max values and indices.
-* 
+*
 * @par Inputs:
 *  One input:
 *  x: An 6D tensor. Supported type: float16. Format as NDC1HWC0.
@@ -484,7 +491,7 @@ REG_OP(MaxPool3D)
 *  @li data_format: the format of torch input, default value is "NCDHW".
 *  @li argmax_type: the function of this field is to determine the type of
 *   output argmax, "bitmask" is the default value, the argmax will return
-*   a img2col bitmask. "index_int32" and "index_int64" represent the torch 
+*   a img2col bitmask. "index_int32" and "index_int64" represent the torch
 *   output indices.
 * @par Outputs:
 *  y: An 6D tensor. the maxpool3d output(max value), format as NDoC1HoWoC0.
@@ -505,8 +512,8 @@ REG_OP(MaxPool3DWithArgmax)
     .OP_END_FACTORY_REG(MaxPool3DWithArgmax)
 
 /**
-*@brief Applies a 2D adaptive max pooling over an input signal conposed of several input planes. \n
-* The output is of size H x W, for any input size. 
+* @brief Applies a 2D adaptive max pooling over an input signal conposed of several input planes. \n
+* The output is of size H x W, for any input size.
 
 * @par Inputs:
 * One input, including:
@@ -698,31 +705,31 @@ REG_OP(MaxPoolV2)
 * @brief Performs max pooling on the input and outputs both max values and
  * indices . \n
 
-*@par Inputs:
+* @par Inputs:
 * One input:
 * x: An 4D Tensor. Supported type: float, double, int32,
  * uint8, int16, int8, int64, uint16, half, uint32, uint64.
  * Must set the format, supported format list ["NCHW, NHWC"]. \n
 
-*@par Attributes:
-*@li ksize: A required list of int8, int16, int32, or int64 values,
+* @par Attributes:
+* @li ksize: A required list of int8, int16, int32, or int64 values,
  * specifying the size of the window for each dimension of the input tensor.
  * No default value.
-*@li strides: A required list of int8, int16, int32, or int64 values,
+* @li strides: A required list of int8, int16, int32, or int64 values,
  * specifying the stride of the sliding window for each dimension of
  * the input tensor. No default value.
-*@li padding: A required string. No default value .
-*@li Targmax:An optional int with default value 7 . \n
+* @li padding: A required string. No default value .
+* @li Targmax:An optional int with default value 7 . \n
 
-*@par Outputs:
-*@li y: A Tensor. Has the same type and format as input "x".
-*@li argmax: A Tensor. Has the same type and format as input "x".
+* @par Outputs:
+* @li y: A Tensor. Has the same type and format as input "x".
+* @li argmax: A Tensor. Has the same type and format as input "x".
 * @attention Constraints:
-*@li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1,
+* @li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1,
  * ksize[1] * ksize[2] <= 255.
-*@li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
+* @li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
  * strides[1] <= 63, strides[0] >= 1, strides[2] <= 63, strides[2] >= 1.
-*@li "padding" is either "SAME" or "VALID" .
+* @li "padding" is either "SAME" or "VALID" .
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator MaxPoolWithArgmax.
@@ -760,7 +767,7 @@ REG_OP(MaxPoolWithArgmax)
 * @li padding: A required string. No default value . \n
 
 * @par Outputs:
-*y: A Tensor. Has the same type and format as input "x" . \n
+* y: A Tensor. Has the same type and format as input "x" . \n
 
 * @attention Constraints:
 * @li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1,
@@ -873,7 +880,7 @@ REG_OP(MaxPoolGradGradWithArgmax)
 * @li data_format: An optional string. Defaults to "NHWC". \n
 
 * @par Outputs:
-* out_grad: A mutable tensor with the same shape and type as "input_grad". \n
+* out_grad: A mutable tensor with the same shape and type as "orig_input_shape". \n
 
 * @par Third-party framework compatibility
 * @li Compatible with the TensorFlow operator AvgPoolGrad.
@@ -965,7 +972,7 @@ REG_OP(AvgPoolV2Grad)
 /**
 * @brief Computes gradients of averagev2 pooling function.
 * @par Inputs:
-*input_grad: An NHWC tensor of type float16, float32, or double.
+* input_grad: An NHWC tensor of type float16, float32, or double.
 
 * @par Attributes:
 * @li orig_input_shape: A required tuple or list of type int32.
@@ -983,10 +990,10 @@ REG_OP(AvgPoolV2Grad)
 * @li data_format: An optional string. Defaults to "NHWC".
 
 * @par Outputs:
-*out_grad: A mutable tensor with the same shape and type as "orig_input".
+* out_grad: A mutable tensor with the same shape and type as "orig_input".
 
 * @par Third-party framework compatibility
-*Compatible with the TensorFlow operator AvgPoolGrad.
+* Compatible with the TensorFlow operator AvgPoolGrad.
 */
 REG_OP(AvgPoolV2GradD)
     .INPUT(input_grad, TensorType({DT_FLOAT16}))
@@ -1005,16 +1012,16 @@ REG_OP(AvgPoolV2GradD)
     .OP_END_FACTORY_REG(AvgPoolV2GradD)
 
 /**
-*@brief upsample the layer, similar to the nearest-neighbor difference scaling algorithm.
+* @brief upsample the layer, similar to the nearest-neighbor difference scaling algorithm.
 
-*@par Inputs:
+* @par Inputs:
 * one input, including:
 * x: A tensor of type float16 or float32.
-*@par Attributes:
-*@li  scale: A optional float32, scale factor of x. Defaults to "1.0".
-*@li  stride_h: An optional int32, broadcast the axis of h. Defaults to "2".
-*@li  stride_w: An optional int32, broadcast the axis of w. Defaults to "2".
-*@par Outputs:
+* @par Attributes:
+* @li  scale: A optional float32, scale factor of x. Defaults to "1.0".
+* @li  stride_h: An optional int32, broadcast the axis of h. Defaults to "2".
+* @li  stride_w: An optional int32, broadcast the axis of w. Defaults to "2".
+* @par Outputs:
 *y: A tensor of type float16 or float32.
 */
 REG_OP(Upsample)
@@ -1026,10 +1033,10 @@ REG_OP(Upsample)
    .OP_END_FACTORY_REG(Upsample)
 
 /**
-*@brief Computes gradient of the FractionalMaxPool function . \n
+* @brief Computes gradient of the FractionalMaxPool function . \n
 
-*@par Inputs:
-*Inputs include:
+* @par Inputs:
+* Inputs include:
 * @li orig_input: A Tensor. Must be one of the following types: float32, float64, int32, int64.
 * @li orig_output: A Tensor. Must have the same type as orig_input.
 * @li out_backprop: A Tensor. Must have the same type as orig_input.
@@ -1037,17 +1044,17 @@ REG_OP(Upsample)
 * @li row_pooling_sequence: A Tensor of type int64.
 * @li col_pooling_sequence: A Tensor of type int64 . \n
 
-*@par Attributes:
-*overlapping: An optional bool. Defaults to False . \n
+* @par Attributes:
+* overlapping: An optional bool. Defaults to False . \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type as orig_input . \n
+* @par Outputs:
+* y: A Tensor. Has the same type as orig_input . \n
 
-*@attention Constraints:
-*The implementation for FractionalMaxPoolGrad on Ascend uses AICPU, with bad performance.
+* @attention Constraints:
+* The implementation for FractionalMaxPoolGrad on Ascend uses AICPU, with bad performance.
 
-*@par Third-party framework compatibility
-*@li compatible with tensorflow FractionalMaxPoolGrad operator.
+* @par Third-party framework compatibility
+* @li compatible with tensorflow FractionalMaxPoolGrad operator.
 */
 REG_OP(FractionalMaxPoolGrad)
     .INPUT(orig_input, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
@@ -1330,7 +1337,7 @@ REG_OP(AvgPool1DD)
 
 * @par Inputs:
 * One input:
-* x: An 5hd Tensor of type float16. 
+* x: An 5hd Tensor of type float16.
 * Must set the format, supported format list ["NC1HWC0"].
 * @par Attributes:
 * @li ksize: A required list of int8, int16, int32, or int64 values,
@@ -1376,19 +1383,19 @@ REG_OP(MaxPoolWithArgmaxV2)
 
 * @par Inputs:
 * Three inputs, including:
-* @li x: An 5hd tensor of type float16. 
+* @li x: An 5hd tensor of type float16.
 * Must set the format, supported format list ["NC1HWC0"]
-* @li grad: An 5hd tensor of type float16. 
+* @li grad: An 5hd tensor of type float16.
 * Must set the format, supported format list ["NC1HWC0"]
-* @li argmax: An 5hd tensor of type uint16 or int64. 
+* @li argmax: An 5hd tensor of type uint16 or int64.
 * Must set the format, supported format list ["NC1HWC0"] \n
 
 * @par Attributes:
-* @li ksize: A required list of int8, int16, int32, or int64 values, 
+* @li ksize: A required list of int8, int16, int32, or int64 values,
 * specifying the size of the window for each dimension of the input tensor. No default value.
-* @li strides: A required list of int8, int16, int32, or int64 values, 
+* @li strides: A required list of int8, int16, int32, or int64 values,
 * specifying the stride of the sliding window for each dimension of the input tensor. No default value.
-* @li pads: A required list of int8, int16, int32, or int64 values, 
+* @li pads: A required list of int8, int16, int32, or int64 values,
 * specifying the pad of the input feature map. No default value. \n
 * @li dtype: A optional int. default value is 3.
 * @li dilation: A optional list of int8, int16, int32, or int64 values.
@@ -1398,11 +1405,11 @@ REG_OP(MaxPoolWithArgmaxV2)
 * y: A Tensor. Has the same type and format as input "x". \n
 
 * @attention Constraints:
-* @li ksize: a list that has length 4: 
+* @li ksize: a list that has length 4:
 * ksize[0] = 1, ksize[1] = 1, ksize[2] * ksize[3] <= (ub_size-8)*1024//7//2//16.
-* @li strides: a list that has length 4: 
+* @li strides: a list that has length 4:
 * strides[0] = 1, strides[1] = 1, 1 <= strides[2] <= 2048, 1 <= strides[3] <= 2048.
-* @li pads: a list that has length 4: 
+* @li pads: a list that has length 4:
 * pads[0] = 1, pads[1] = 1, 1 <= pads[2] <= (ksize[2]//2), 1 <= pads[3] <= (ksize[3]//2).
 * @li dilation: a list that has length 4.
 * @li ceil_mode: is a bool, default is false. \n
@@ -1532,7 +1539,7 @@ REG_OP(MaxPoolV3Grad)
 *@brief Performs Dilation2D on the input . \n
 
 *@par Inputs:
-*@li x: A tensor of shape is 4d, format is support NHWC.
+* @li x: A tensor of shape is 4d, format is support NHWC.
 *@li filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x. \n
 
 *@par Attributes:
@@ -1566,12 +1573,12 @@ REG_OP(Dilation2D)
 
 *@par Inputs:
 *@li x: A tensor of shape is 4d, format is support NHWC.
-*@li filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x.
+* @li filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x.
 *@li out_backprop: Has the same type and format as input x and the c dimension is same with x. \n
 
 *@par Attributes
 *@li strides: A required list of 4 ints, specifying the stride of the sliding window. The strides of the N and C dimension are 1.
-*@li rates: A required list of 4 ints, the rates of the N and C dimensions are 1.
+* @li rates: A required list of 4 ints, the rates of the N and C dimensions are 1.
 *@li padding_mode: A optional string. Defaults to "SAME", it support SAME and VALID.
 *@li pads: A optional list of 4 ints.
 *@li ceil_mode: An optional bool. Defaults to "false". Use ceil or floor to calculate the output size when padding_mode is "CALCULATED".
@@ -1605,7 +1612,7 @@ REG_OP(Dilation2DBackpropFilter)
 
 *@par Inputs:
 *@li x: A tensor of shape is 4d, format is support NHWC.
-*@li filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x.
+* @li filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x.
 *@li out_backprop: Has the same type and format as input x and the c dimension is same with x. \n
 
 *@par Attributes
@@ -1640,7 +1647,7 @@ REG_OP(Dilation2DBackpropInput)
     .OP_END_FACTORY_REG(Dilation2DBackpropInput)
 
 /**
-* @brief Applies a 2D adaptive average pooling over  
+* @brief Applies a 2D adaptive average pooling over
 *       an input signal composed of several input planes.  \n
 
 * @par Inputs:
@@ -1696,11 +1703,11 @@ REG_OP(AdaptiveAvgPool2dGrad)
 * @li argmax: A tensor of type uint16 or int64. \n
 
 * @par Attributes:
-* @li ksize: A required list of int8, int16, int32, or int64 values, 
+* @li ksize: A required list of int8, int16, int32, or int64 values,
 * specifying the size of the window for each dimension of the input tensor. No default value.
-* @li strides: A required list of int8, int16, int32, or int64 values, 
+* @li strides: A required list of int8, int16, int32, or int64 values,
 * specifying the stride of the sliding window for each dimension of the input tensor. No default value.
-* @li pads: A required list of int8, int16, int32, or int64 values, 
+* @li pads: A required list of int8, int16, int32, or int64 values,
 * specifying the pad of the input feature map. No default value. \n
 
 * @par Outputs:
@@ -1708,11 +1715,11 @@ REG_OP(AdaptiveAvgPool2dGrad)
 
 * @attention Constraints:
 * @li The MaxPoolGradWithArgmaxV2 operator has the same function, and it is recommended to use the V2 operator.
-* @li ksize: a list that has length 4: 
+* @li ksize: a list that has length 4:
 * ksize[0] = 1, ksize[3] = 1, ksize[1] * ksize[2] <= (ub_size-8)*1024//7//2//16.
-* @li strides: a list that has length 4: 
+* @li strides: a list that has length 4:
 * strides[0] = 1, strides[3] = 1, 1 <= strides[1] <= 2048, 1 <= strides[2] <= 2048.
-* @li pads: a list that has length 4: 
+* @li pads: a list that has length 4:
 * pads[0] = 1, pads[3] = 1, 1 <= pads[2] <= (ksize[1]//2), 1 <= pads[2] <= (ksize[3]//2).
 * @li ceil_mode: defaults to False.\n
 
@@ -1778,7 +1785,7 @@ REG_OP(MaxPoolWithArgmaxV1)
     .OP_END_FACTORY_REG(MaxPoolWithArgmaxV1)
 
 /**
-*@brief Randomly sample a subset of positive and negative examples,and overwrite
+* @brief Randomly sample a subset of positive and negative examples,and overwrite
 the label vector to the ignore value (-1) for all elements that are not
 included in the sample.\n
 
@@ -1790,14 +1797,14 @@ included in the sample.\n
 * @li batch_size_per_images: A require attribute of type int.
 * @li positive_fraction: A require attribute of type float.
 
-*@par Outputs:
-*y: The result of subSample. \n
+* @par Outputs:
+* y: The result of subSample. \n
 
-*@par Third-party framework compatibility
-*Compatible with the Pytorch operator SubSample.
+* @par Third-party framework compatibility
+* Compatible with the Pytorch operator SubSample.
 
-*@attention Constraints:
-*Warning: This operator can be integrated only by MaskRcnn. Please do not use it directly.
+* @attention Constraints:
+* Warning: This operator can be integrated only by MaskRcnn. Please do not use it directly.
 */
 REG_OP(SubSample)
     .INPUT(labels, TensorType({DT_INT32}))
@@ -1807,7 +1814,7 @@ REG_OP(SubSample)
     .OP_END_FACTORY_REG(SubSample)
 
 /**
-*@brief Randomly sample a subset of positive and negative examples,and overwrite
+* @brief Randomly sample a subset of positive and negative examples,and overwrite
 the label vector to the ignore value (-1) for all elements that are not
 included in the sample.\n
 
@@ -1820,14 +1827,14 @@ included in the sample.\n
 * @li batch_size_per_images: A require attribute of type int.
 * @li positive_fraction: A require attribute of type float.
 
-*@par Outputs:
-*y: The result of subSample. \n
+* @par Outputs:
+* y: The result of subSample. \n
 
-*@par Third-party framework compatibility
-*Compatible with the Pytorch operator SubSampleLabels.
+* @par Third-party framework compatibility
+* Compatible with the Pytorch operator SubSampleLabels.
 
-*@attention Constraints:
-*Warning: This operator can be integrated only by MaskRcnn. Please do not use it directly.
+* @attention Constraints:
+* Warning: This operator can be integrated only by MaskRcnn. Please do not use it directly.
 */
 REG_OP(SubSampleLabels)
     .INPUT(labels, TensorType({DT_INT32}))
@@ -1838,22 +1845,22 @@ REG_OP(SubSampleLabels)
     .OP_END_FACTORY_REG(SubSampleLabels)
 
 /**
-*@brief Computes GlobalLpPool, GlobalLpPool consumes an input tensor X and applies lp pool pooling across the
+* @brief Computes GlobalLpPool, GlobalLpPool consumes an input tensor X and applies lp pool pooling across the
 values in the same channel. \n
 
-*@par Inputs:
+* @par Inputs:
 * x: A Tensor of type float16 or float32 . \n
 
-*@par Attributes:
-*@li p: Optional. Must be one of the following types: float32. Defaults to 2.0. \n
+* @par Attributes:
+* @li p: Optional. Must be one of the following types: float32. Defaults to 2.0. \n
 
-*@par Outputs:
+* @par Outputs:
 * y: A Tensor. Has the same type as "x", when shape of x is [N,C,H,W], shape of y is [N,C,1,1].
-*@par Third-party framework compatibility
+* @par Third-party framework compatibility
 * Compatible with the onnx operator GlobalLpPool.
-*@par Restrictions:
-*Warning: THIS FUNCTION IS DEPRECATED.
-*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
+* @par Restrictions:
+* Warning: THIS FUNCTION IS DEPRECATED.
+* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
 */
 
 REG_OP(GlobalLpPool)

third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h

@@ -25,17 +25,17 @@
 
 namespace ge {
 /**
-*@brief The GELU activation function is x*Φ(x),
+* @brief The GELU activation function is x*Φ(x),
 * where Φ(x) the standard Gaussian cumulative distribution function.
 
-*@par Inputs:
-*One input, including: \n
-*x: A Tensor. Must be one of the following types: float16, float32. \n
+* @par Inputs:
+* One input, including: \n
+* x: A Tensor. Must be one of the following types: float16, float32. \n
 
-*@par Outputs:
-*y: A Tensor. Has the same type as "x". \n
+* @par Outputs:
+* y: A Tensor. Has the same type as "x". \n
 
-*@par Third-party framework compatibility:
+* @par Third-party framework compatibility:
 * Compatible with the TensorFlow operator Gelu.
 */
 REG_OP(Gelu)
@@ -125,17 +125,17 @@ REG_OP(SwishGrad)
     .OP_END_FACTORY_REG(SwishGrad)
 
 /**
-*@brief Computes the gradient for the gelu of "x" .
+* @brief Computes the gradient for the gelu of "x" .
 
-*@par Inputs:
+* @par Inputs:
 * Three inputs, including:
-*@li dy: A Tensor. Must be one of the following types: float16, float32.
-*@li x: A Tensor of the same type as "dy".
-*@li y: A Tensor of the same type as "dy" . \n
+* @li dy: A Tensor. Must be one of the following types: float16, float32.
+* @li x: A Tensor of the same type as "dy".
+* @li y: A Tensor of the same type as "dy" . \n
 
-*@par Outputs:
-*z: A Tensor. Has the same type as "dy".
-*@par Third-party framework compatibility
+* @par Outputs:
+* z: A Tensor. Has the same type as "dy".
+* @par Third-party framework compatibility
 * Compatible with the TensorFlow operator GeluGrad.
 */
 REG_OP(GeluGrad)
@@ -799,20 +799,20 @@ REG_OP(LeakyReluGrad)
     .OP_END_FACTORY_REG(LeakyReluGrad)
 
 /**
-*@brief Thresholds grad each element of the input Tensor .
+* @brief Thresholds grad each element of the input Tensor .
 
-*@par Inputs:
+* @par Inputs:
 * @li gradients: A Tensor shape and dtype of input gradients. Support float16, int32.
 * @li features: A Tensor shape and dtype of input features. Support float16, int32 . \n
 
-*@par Attributes:
-*threshold: A float32 scale value to threshold at . \n
+* @par Attributes:
+* threshold: A float32 scale value to threshold at . \n
 
-*@par Outputs:
-*backprops: A Tensor of shape and dtype of output backprops, should be same shape and type as inputs . \n
+* @par Outputs:
+* backprops: A Tensor of shape and dtype of output backprops, should be same shape and type as inputs . \n
 
-*@par Restrictions:
-*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
+* @par Restrictions:
+* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
 */
 REG_OP(ThresholdGradV2D)
     .INPUT(gradients, TensorType({DT_INT32, DT_FLOAT16}))
@@ -822,20 +822,20 @@ REG_OP(ThresholdGradV2D)
     .OP_END_FACTORY_REG(ThresholdGradV2D)
 
 /**
-*@brief Thresholds each element of the input Tensor y = (x > threshold) ? x : value .
+* @brief Thresholds each element of the input Tensor y = (x > threshold) ? x : value .
 
-*@par Inputs:
-*x: A Tensor dtype of real number . \n
+* @par Inputs:
+* x: A Tensor dtype of real number . \n
 
-*@par Attributes:
-*@li threshold: A float32 scale value to threshold at.
-*@li value: A float32 scale value to replace with . \n
+* @par Attributes:
+* @li threshold: A float32 scale value to threshold at.
+* @li value: A float32 scale value to replace with . \n
 
-*@par Outputs:
-*y: A Tensor of shape and dtype of output, should be same shape and type as input . \n
+* @par Outputs:
+* y: A Tensor of shape and dtype of output, should be same shape and type as input . \n
 
-*@par Restrictions:
-*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
+* @par Restrictions:
+* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
 */
 REG_OP(ThresholdV2D)
     .INPUT(x, TensorType::RealNumberType())

third_party/fwkacllib/inc/ops/random_ops.h

@@ -528,33 +528,57 @@ REG_OP(DropOutGenMaskV3)
 
     
 /**
-*@brief Generate stateless random bit mask for dropout . \n
+* @brief Generate stateless random bit mask for dropout . \n
 
-*@par Inputs:
+* @par Inputs:
 include:
-*@li shape:The shape of the output tensor.
-*@li prob:0-D. Number of bit 1 . \n
-*@li seed:If either seed or seed2 are set to be non-zero, the random number
-*generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
-*@li seed2:A second seed to avoid seed collision . \n
+* @li shape:The shape of the output tensor.
+* @li prob:0-D. Number of bit 1 . \n
+* @li seed:Frist seed to avoid seed collision.
+* @li seed1:Second seed to avoid seed collision . \n
+* @li offset:Initial offset of random number . \n
 
-*@par Outputs:
+* @par Outputs:
 *y:Output (1-D) random number using uint data format . \n
 
-*@attention Constraints:
+* @attention Constraints:
 *The output is aligned with 128 bits
 
-*@see StatelessDropOutGenMask()
+* @see StatelessDropOutGenMask()
 */
 REG_OP(StatelessDropOutGenMask)
     .INPUT(shape, TensorType({ DT_INT32, DT_INT64 }))
     .INPUT(prob, TensorType({ DT_FLOAT16, DT_FLOAT }))
     .INPUT(seed, TensorType({ DT_INT32, DT_INT64 }))
     .INPUT(seed1, TensorType({ DT_INT32, DT_INT64 }))
+    .OPTIONAL_INPUT(offset, TensorType({ DT_INT64 }))
     .OUTPUT(y, TensorType({ DT_UINT8 }))
     .OP_END_FACTORY_REG(StatelessDropOutGenMask)
 
 /**
+* @brief Generate bernoulli distribution for tensor input . \n
+
+* @par Inputs:
+include:
+* @li shape:The shape of the output tensor. A Tensor of type int32, int64.
+* @li prob:0-D. Number of bit 1 . \n
+* @li seed:If seed is set to be -1, and offset is set to be 0, the random number
+* generator is seeded by arandom seed. Otherwise, it is seeded by the given seed.
+* @li offset:To avoid seed collision . \n
+
+* @par Outputs:
+* y:A Tensor. A Tensor of type int8, uint8, int16, uint16, 
+*  int32, uint32, int64, uint64, bool, float16, float, double, bf16. \n
+*/
+REG_OP(StatelessBernoulli)
+    .INPUT(shape, TensorType({ DT_INT32, DT_INT64}))
+    .INPUT(prob, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
+    .INPUT(seed, TensorType({ DT_INT64 }))
+    .INPUT(offset, TensorType({ DT_INT64 }))
+    .OUTPUT(y, TensorType({ DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_UINT32,
+        DT_INT64, DT_UINT64, DT_BOOL, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_BF16}))
+    .OP_END_FACTORY_REG(StatelessBernoulli)
+/**
 *@brief Generates values in an interval . \n
 
 *@par Inputs:

third_party/fwkacllib/inc/ops/sparse_ops.h

@@ -1041,4 +1041,4 @@ REG_OP(DeserializeManySparse)
     .OP_END_FACTORY_REG(DeserializeManySparse)
 }  // namespace ge
 
-#endif  // OPS_BUILT_IN_OP_PROTO_INC_SPARSE_OPS_H_
+#endif  // OPS_BUILT_IN_OP_PROTO_INC_SPARSE_OPS_H_

third_party/fwkacllib/inc/ops/spectral_ops.h

@@ -143,6 +143,42 @@ REG_OP(IFFT2D)
     .OUTPUT(y, TensorType({DT_COMPLEX64,DT_COMPLEX128}))
     .OP_END_FACTORY_REG(IFFT2D)
 
+/**
+* @brief Computes the Fourier transform of short overlapping windows of the input. \n
+
+* @par Inputs:
+* @li x: A 1-D or 2-D tensor. 
+* @li window: An optional tensor. The optional window function. Default: None (treated as window of all 1 s) \n
+
+* @par Attributes:
+* @li n_fft: A required int. Size of Fourier transform
+* @li hop_length: An optional int. The distance between neighboring sliding window frames.
+* Default: None (treated as equal to floor(n_fft/4))
+* @li win_length: An optional int. The size of window frame and STFT filter.
+* Default: None (treated as equal to n_fft)
+* @li normalized: An optional bool. Controls whether to return the normalized STFT results Default: False
+* @li onesided: An optional bool. Controls whether to return half of results to avoid redundancy for real inputs. 
+* Default: True for real input and window, False otherwise.
+* @li return_complex: An optional bool. Whether to return a complex tensor, or a real tensor 
+* with an extra last dimension for the real and imaginary components. \n
+
+* @par Outputs:
+* y: A tensor containing the STFT result with shape described above. \n
+
+* @par Third-party framework compatibility
+* Compatible with pytorch STFT operator.
+*/
+REG_OP(STFT)
+    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .OPTIONAL_INPUT(window, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .ATTR(hop_length, Int, 0)
+    .ATTR(win_length, Int, 0)
+    .ATTR(normalized, Bool, false)
+    .ATTR(onesided, Bool, true)
+    .ATTR(return_complex, Bool, true)
+    .REQUIRED_ATTR(n_fft, Int)
+    .OP_END_FACTORY_REG(STFT)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_SPECTRAL_OPS_H_

third_party/fwkacllib/inc/runtime/config.h

@@ -79,7 +79,11 @@ typedef enum tagRtPlatformType {
     PLATFORM_LHISI_SD3403 = 7,
     PLATFORM_MINI_V3 = 8,
     PLATFORM_MINI_5612 = 9,
-    PLATFORM_END = 10,
+    PLATFORM_CLOUD_V2_910B1 = 10,
+    PLATFORM_CLOUD_V2_910B2 = 11,
+    PLATFORM_CLOUD_V2_910B3 = 12,
+    PLATFORM_CLOUD_V2_910B4 = 13,
+    PLATFORM_END = 14,
 } rtPlatformType_t;
 
 typedef enum tagRtCubeFracMKNFp16 {

third_party/fwkacllib/inc/runtime/kernel.h

@@ -166,6 +166,19 @@ typedef enum tagRtDumpKind {
     RT_DATA_DUMP_KIND_RESERVED = 1,
 } rtDumpKind_t;
 
+/**
+ * @ingroup rt_kernel
+ * @brief rt kernel type
+ */
+typedef enum rtKernelType {
+    KERNEL_TYPE_CCE = 0,
+    KERNEL_TYPE_FWK = 1,
+    KERNEL_TYPE_AICPU = 2,
+    KERNEL_TYPE_AICPU_CUSTOM = 4,
+    KERNEL_TYPE_HWTS = 10,
+    KERNEL_TYPE_RESERVED = 99,
+} rtKernelType_t;
+
 /**
  * @ingroup rt_kernel
  * @brief report callback
@@ -523,6 +536,23 @@ RTS_API rtError_t rtAicpuKernelLaunchWithFlag(const rtKernelLaunchNames_t *launc
                                               const rtArgsEx_t *argsInfo, rtSmDesc_t *smDesc, rtStream_t stm,
                                               uint32_t flags);
 
+/**
+ * @ingroup rtAicpuKernelLaunchEx
+ * @brief launch cpu kernel to device  with dump identifier and kernelType
+ * @param [in] kernelType    aicpu kernel type
+ * @param [in] launchNames   names address for kernel launch
+ * @param [in] blockDim      block dimentions
+ * @param [in] argsInfo      argments address for kernel function
+ * @param [in] smDesc        shared memory description
+ * @param [in] stm           associated stream
+ * @param [in] flags         dump flag or others function flag
+ * @return RT_ERROR_NONE for ok
+ * @return RT_ERROR_INVALID_VALUE for error input
+ */
+RTS_API rtError_t rtAicpuKernelLaunchEx(uint32_t kernelType, const rtKernelLaunchNames_t *launchNames,
+                                        uint32_t blockDim, const rtArgsEx_t *argsInfo, rtSmDesc_t *smDesc,
+                                        rtStream_t stm, uint32_t flags);
+
 /**
  * @ingroup rt_kernel
  * @brief L1 fusion dump addr transfered to device

third_party/fwkacllib/inc/runtime/mem.h

@@ -31,6 +31,7 @@ extern "C" {
 #define RT_MEMORY_TS (0x40U)       // Used for Ts memory
 #define RT_MEMORY_TS_4G (0x40U)    // Used for Ts memory(only 1951)
 #define RT_MEMORY_HOST (0x81U)     // Memory on host
+#define RT_MEMORY_SVM (0x90U)      // Memory for SVM
 #define RT_MEMORY_RESERVED (0x100U)
 
 #define RT_MEMORY_L1 (0x1U << 16U)

third_party/fwkacllib/inc/runtime/rt_model.h

@@ -49,6 +49,7 @@ typedef enum tagModelTaskType {
     RT_MODEL_TASK_BARRIER,
     RT_MODEL_TASK_NPU_GET_FLOAT_STATUS,
     RT_MODEL_TASK_NPU_CLEAR_FLOAT_STATUS,
+    RT_MODEL_TASK_DVPP,
 } rtModelTaskType_t;
 
 typedef enum tagModelStreamType {

third_party/fwkacllib/inc/toolchain/prof_callback.h

@@ -1,13 +1,20 @@
-/*
- * Copyright (c) Huawei Technologies Co., Ltd. 2019-2021. All rights reserved.
- * Description: handle perf data
- * Author: xp
- * Create: 2019-10-13
+/**
+ * @file prof_callback.h
+ *
+ * Copyright (c) Huawei Technologies Co., Ltd. 2019-2022. All rights reserved.
+ *
+ * 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.
+ *
  */
 
 #ifndef MSPROFILER_PROF_CALLBACK_H_
 #define MSPROFILER_PROF_CALLBACK_H_
 
+#include <stddef.h>
+#include <stdint.h>
+
 #ifdef __cplusplus
 extern "C" {
 #endif // __cplusplus
@@ -18,9 +25,6 @@ extern "C" {
 #define MSVP_PROF_API __attribute__((visibility("default")))
 #endif
 
-#include "stddef.h"
-#include "stdint.h"
-
 /**
  * @name  MsprofErrorCode
  * @brief error code
@@ -170,7 +174,7 @@ MSVP_PROF_API int32_t MsprofInit(uint32_t moduleId, void *data, uint32_t dataLen
  * @param moduleId  [IN] module Id
  * @param handle    [IN] the pointer of callback
  */
-MSVP_PROF_API int32_t MsprofRegisterCallback(uint32_t moduleId, ProfCommandHandle callback);
+MSVP_PROF_API int32_t MsprofRegisterCallback(uint32_t moduleId, ProfCommandHandle handle);
 /*
  * @name profReportData
  * @brief start reporter/stop reporter/report date

third_party/fwkacllib/inc/toolchain/prof_common.h

@@ -1,23 +1,27 @@
-/*
- * Copyright (c) Huawei Technologies Co., Ltd. 2019-2021. All rights reserved.
- * Description: handle perf data
- * Author: Huawei Technologies Co., Ltd.
- * Create: 2019-10-13
+/**
+ * @file prof_common.h
+ *
+ * Copyright (c) Huawei Technologies Co., Ltd. 2019-2022. All rights reserved.
+ *
+ * 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.
+ *
  */
 #ifndef MSPROFILER_PROF_COMMON_H_
 #define MSPROFILER_PROF_COMMON_H_
 
+#include <stdint.h>
+
 #ifdef __cplusplus
 extern "C" {
 #endif // __cplusplus
 
-#include <stdint.h>
-
 #define MSPROF_DATA_HEAD_MAGIC_NUM  0x5a5a
 
 enum MsprofDataTag {
-    MSPROF_ACL_DATA_TAG = 0,            //acl data tag, range: 0~19
-    MSPROF_GE_DATA_TAG_MODEL_LOAD = 20, //ge data tag, range: 20~39
+    MSPROF_ACL_DATA_TAG = 0,            // acl data tag, range: 0~19
+    MSPROF_GE_DATA_TAG_MODEL_LOAD = 20, // ge data tag, range: 20~39
     MSPROF_GE_DATA_TAG_FUSION = 21,
     MSPROF_GE_DATA_TAG_INFER = 22,
     MSPROF_GE_DATA_TAG_TASK = 23,
@@ -25,14 +29,14 @@ enum MsprofDataTag {
     MSPROF_GE_DATA_TAG_STEP = 25,
     MSPROF_GE_DATA_TAG_ID_MAP = 26,
     MSPROF_GE_DATA_TAG_HOST_SCH = 27,
-    MSPROF_RUNTIME_DATA_TAG_API = 40,   //runtime data tag, range: 40~59
+    MSPROF_RUNTIME_DATA_TAG_API = 40,   // runtime data tag, range: 40~59
     MSPROF_RUNTIME_DATA_TAG_TRACK = 41,
-    MSPROF_AICPU_DATA_TAG = 60,         //aicpu data tag, range: 60~79
+    MSPROF_AICPU_DATA_TAG = 60,         // aicpu data tag, range: 60~79
     MSPROF_AICPU_MODEL_TAG = 61,
-    MSPROF_HCCL_DATA_TAG = 80,          //hccl data tag, range: 80~99
-    MSPROF_DP_DATA_TAG = 100,           //dp data tag, range: 100~119
-    MSPROF_MSPROFTX_DATA_TAG = 120,     //hccl data tag, range: 120~139
-    MSPROF_DATA_TAG_MAX = 65536,        //data tag value type is uint16_t
+    MSPROF_HCCL_DATA_TAG = 80,          // hccl data tag, range: 80~99
+    MSPROF_DP_DATA_TAG = 100,           // dp data tag, range: 100~119
+    MSPROF_MSPROFTX_DATA_TAG = 120,     // hccl data tag, range: 120~139
+    MSPROF_DATA_TAG_MAX = 65536,        // data tag value type is uint16_t
 };
 
 /**
@@ -154,6 +158,8 @@ enum MsprofGeTaskType {
     MSPROF_GE_TASK_TYPE_AI_CORE = 0,
     MSPROF_GE_TASK_TYPE_AI_CPU,
     MSPROF_GE_TASK_TYPE_AIV,
+    MSPROF_GE_TASK_TYPE_WRITE_BACK,
+    MSPROF_GE_TASK_TYPE_INVALID
 };
 enum MsprofGeShapeType {
     MSPROF_GE_SHAPE_TYPE_STATIC = 0,
@@ -368,11 +374,11 @@ struct MsprofHcclProfReduce {
     uint64_t src;
     uint64_t dst;
     uint64_t size;
-    uint32_t op;       // {0: sum, 1: mul, 2: max, 3: min}
-    uint32_t dataType; // data type {0: INT8, 1: INT16, 2: INT32, 3: FP16, 4:FP32, 5:INT64, 6:UINT64}
-    uint32_t linkType; // link type {0: 'OnChip', 1: 'HCCS', 2: 'PCIe', 3: 'RoCE'}
+    uint32_t op;            // {0: sum, 1: mul, 2: max, 3: min}
+    uint32_t dataType;      // data type {0: INT8, 1: INT16, 2: INT32, 3: FP16, 4:FP32, 5:INT64, 6:UINT64}
+    uint32_t linkType;      // link type {0: 'OnChip', 1: 'HCCS', 2: 'PCIe', 3: 'RoCE'}
     uint32_t remoteRank;
-    uint32_t transportType; //  transport type {0: SDMA, 1: RDMA, 2:LOCAL}
+    uint32_t transportType; // transport type {0: SDMA, 1: RDMA, 2:LOCAL}
     uint32_t role;          // role {0: dst, 1:src}
     double durationEstimated;
 };
@@ -383,9 +389,9 @@ struct MsprofHcclProfRDMA {
     uint64_t dst;
     uint64_t size;
     uint64_t notifyID;
-    uint32_t linkType; // link type {0: 'OnChip', 1: 'HCCS', 2: 'PCIe', 3: 'RoCE'}
+    uint32_t linkType;      // link type {0: 'OnChip', 1: 'HCCS', 2: 'PCIe', 3: 'RoCE'}
     uint32_t remoteRank;
-    uint32_t transportType; //  transport type {0: RDMA, 1:SDMA, 2:LOCAL}
+    uint32_t transportType; // transport type {0: RDMA, 1:SDMA, 2:LOCAL}
     uint32_t role;          // role {0: dst, 1:src}
     uint32_t type;          // RDMA type {0: RDMASendNotify, 1:RDMASendPayload}
     double durationEstimated;
@@ -397,7 +403,7 @@ struct MsprofHcclProfMemcpy {
     uint64_t dst;
     uint64_t size;
     uint64_t notifyID;
-    uint32_t linkType; // link type {0: 'OnChip', 1: 'HCCS', 2: 'PCIe', 3: 'RoCE'}
+    uint32_t linkType;      // link type {0: 'OnChip', 1: 'HCCS', 2: 'PCIe', 3: 'RoCE'}
     uint32_t remoteRank;
     uint32_t transportType; // transport type {0: RDMA, 1:SDMA, 2:LOCAL}
     uint32_t role;          // role {0: dst, 1:src}
@@ -448,18 +454,17 @@ struct MsprofStampInfo {
     uint16_t dataTag;
     uint32_t processId;
     uint32_t threadId;
-    uint32_t category;         //marker category
+    uint32_t category;    // marker category
     uint32_t  eventType;
     int32_t payloadType;
-    union PayloadValue         //payload info for marker
-    {
+    union PayloadValue {
         uint64_t ullValue;
         int64_t llValue;
         double dValue;
         uint32_t uiValue[2];
         int32_t iValue[2];
         float fValue[2];
-    } payload;
+    } payload;            // payload info for marker
     uint64_t startTime;
     uint64_t endTime;
     int32_t messageType;

third_party/fwkacllib/inc/toolchain/prof_engine.h

@@ -1,17 +1,12 @@
 /**
- * Copyright 2019-2020 Huawei Technologies Co., Ltd
+ * @file prof_engine.h
  *
- * 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
+ * Copyright (c) Huawei Technologies Co., Ltd. 2019-2022. All rights reserved.
  *
- * http://www.apache.org/licenses/LICENSE-2.0
+ * 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.
  *
- * 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 MSPROF_ENGINE_PROF_ENGINE_H_
@@ -35,7 +30,7 @@ namespace Engine {
  * record config info
  */
 struct ModuleJobConfig {
-  std::map<std::string, std::string> switches; /**< key is the config name, value is the config value(on or off) */
+    std::map<std::string, std::string> switches; /**< key is the config name, value is the config value(on or off) */
 };
 
 /**
@@ -48,59 +43,59 @@ struct ModuleJobConfig {
  * @brief class PluginIntf
  */
 class MSVP_PROF_API PluginIntf {
- public:
-  virtual ~PluginIntf() {}
-
- public:
-  /**
-   * @ingroup PluginIntf
-   * @name  : Init
-   * @brief : API of user plugin, libmsporf call this API to send a Reporter to user plugin
-   * @par description :
-   *  API of user plugin, libmsporf call this API to send a Reporter to user plugin.
-   * @param reporter [IN] const Reporter* the Reporter from libmsprof
-   * @retval PROFILING_SUCCESS 0 (success)
-   * @retval PROFILING_FAILED -1 (failed)
-   *
-   * @par depend:
-   * @li libmsprof
-   * @li prof_engine.h
-   * @since c60
-   * @see UnInit
-   */
-  virtual int Init(const Reporter *reporter) = 0;
-
-  /**
-   * @ingroup PluginIntf
-   * @name  : OnNewConfig
-   * @brief : API of user plugin, libmsprof call this API to send config info to user plugin \n
-              If the user plugin needn't config, no need to redefine this function
-   * @param config [IN] const ModuleJobConfig * the config from libmsprof
-   * @retval PROFILING_SUCCESS 0 (success)
-   * @retval PROFILING_FAILED -1 (failed)
-   *
-   * @par depend:
-   * @li libmsprof
-   * @li prof_engine.h
-   * @since c60
-   * @see Init | UnInit
-   */
-  virtual int OnNewConfig(const ModuleJobConfig *config) { return 0; }
-
-  /**
-   * @ingroup PluginIntf
-   * @name  : UnInit
-   * @brief : API of user plugin, libmsprof call this API to notify plugin stop to send data
-   * @retval PROFILING_SUCCESS 0 (success)
-   * @retval PROFILING_FAILED -1 (failed)
-   *
-   * @par depend:
-   * @li libmsprof
-   * @li prof_engine.h
-   * @since c60
-   * @see Init
-   */
-  virtual int UnInit() = 0;
+public:
+    virtual ~PluginIntf() {}
+
+public:
+/**
+ * @ingroup PluginIntf
+ * @name  : Init
+ * @brief : API of user plugin, libmsporf call this API to send a Reporter to user plugin
+ * @par description :
+ *  API of user plugin, libmsporf call this API to send a Reporter to user plugin.
+ * @param reporter [IN] const Reporter* the Reporter from libmsprof
+ * @retval PROFILING_SUCCESS 0 (success)
+ * @retval PROFILING_FAILED -1 (failed)
+ *
+ * @par depend:
+ * @li libmsprof
+ * @li prof_engine.h
+ * @since c60
+ * @see UnInit
+ */
+    virtual int Init(const Reporter *reporter) = 0;
+
+/**
+ * @ingroup PluginIntf
+ * @name  : OnNewConfig
+ * @brief : API of user plugin, libmsprof call this API to send config info to user plugin \n
+            If the user plugin needn't config, no need to redefine this function
+ * @param config [IN] const ModuleJobConfig * the config from libmsprof
+ * @retval PROFILING_SUCCESS 0 (success)
+ * @retval PROFILING_FAILED -1 (failed)
+ *
+ * @par depend:
+ * @li libmsprof
+ * @li prof_engine.h
+ * @since c60
+ * @see Init | UnInit
+ */
+    virtual int OnNewConfig(const ModuleJobConfig *config) = 0;
+
+/**
+ * @ingroup PluginIntf
+ * @name  : UnInit
+ * @brief : API of user plugin, libmsprof call this API to notify plugin stop to send data
+ * @retval PROFILING_SUCCESS 0 (success)
+ * @retval PROFILING_FAILED -1 (failed)
+ *
+ * @par depend:
+ * @li libmsprof
+ * @li prof_engine.h
+ * @since c60
+ * @see Init
+ */
+    virtual int UnInit() = 0;
 };
 
 /**
@@ -113,39 +108,39 @@ class MSVP_PROF_API PluginIntf {
  *  @brief class EngineIntf
  */
 class MSVP_PROF_API EngineIntf {
- public:
-  virtual ~EngineIntf() {}
-
- public:
-  /**
-   * @ingroup EngineIntf
-   * @name  : CreatePlugin
-   * @brief : API of user engine, libmsporf call this API to get a plugin
-   * @retval PluginIntf * The pointer of the new plugin
-   *
-   * @par depend:
-   * @li libmsprof
-   * @li prof_engine.h
-   * @since c60
-   * @see ReleasePlugin
-   */
-  virtual PluginIntf *CreatePlugin() = 0;
-
-  /**
-   * @ingroup EngineIntf
-   * @name  : ReleasePlugin
-   * @brief : API of user engine, libmsprof call this API to release a plugin
-   * @param plugin [IN] PluginIntf * the plugin to release
-   * @retval PROFILING_SUCCESS 0 (success)
-   * @retval PROFILING_FAILED -1 (failed)
-   *
-   * @par depend:
-   * @li libmsprof
-   * @li prof_engine.h
-   * @since c60
-   * @see CreatePlugin
-   */
-  virtual int ReleasePlugin(PluginIntf *plugin) = 0;
+public:
+    virtual ~EngineIntf() {}
+
+public:
+/**
+ * @ingroup EngineIntf
+ * @name  : CreatePlugin
+ * @brief : API of user engine, libmsporf call this API to get a plugin
+ * @retval PluginIntf * The pointer of the new plugin
+ *
+ * @par depend:
+ * @li libmsprof
+ * @li prof_engine.h
+ * @since c60
+ * @see ReleasePlugin
+ */
+    virtual PluginIntf *CreatePlugin() = 0;
+
+ /**
+ * @ingroup EngineIntf
+ * @name  : ReleasePlugin
+ * @brief : API of user engine, libmsprof call this API to release a plugin
+ * @param plugin [IN] PluginIntf * the plugin to release
+ * @retval PROFILING_SUCCESS 0 (success)
+ * @retval PROFILING_FAILED -1 (failed)
+*
+ * @par depend:
+ * @li libmsprof
+ * @li prof_engine.h
+ * @since c60
+ * @see CreatePlugin
+ */
+    virtual int ReleasePlugin(PluginIntf *plugin) = 0;
 };
 
 /**

third_party/fwkacllib/inc/toolchain/prof_reporter.h

@@ -1,17 +1,8 @@
-/**
- * Copyright 2019-2020 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.
+/*
+ * Copyright (c) Huawei Technologies Co., Ltd. 2019-2021. All rights reserved.
+ * Description: handle perf data
+ * Author: xp
+ * Create: 2019-10-13
  */
 
 #ifndef MSPROF_ENGINE_PROF_REPORTER_H
@@ -81,4 +72,4 @@ public:
 }  // namespace Engine
 }  // namespace Msprof
 
-#endif  // MSPROF_ENGINE_PROF_REPORTER_H_
+#endif  // MSPROF_ENGINE_PROF_REPORTER_H

third_party/fwkacllib/inc/toolchain/slog.h

@@ -207,6 +207,8 @@ enum {
     HSS, /**< helper */
     FFTS,
     OP,
+    UDF,
+    HICAID,
     INVLID_MOUDLE_ID
 };