update include headers_0225

inc/external/acl/acl_base.h

@@ -174,6 +174,12 @@ typedef enum {
     ACL_ERROR = 3,
 } aclLogLevel;
 
+typedef enum {
+    ACL_MEMTYPE_DEVICE = 0,
+    ACL_MEMTYPE_HOST = 1,
+} aclMemType;
+
+
 /**
  * @ingroup AscendCL
  * @brief Converts data of type aclFloat16 to data of type float
@@ -596,6 +602,18 @@ ACL_FUNC_VISIBILITY aclError aclSetTensorConst(aclTensorDesc *desc, void *dataBu
 
 /**
  * @ingroup AscendCL
+ * @brief Set tensor memory type specified by the tensor description
+ *
+ * @param  desc [OUT]      pointer to the instance of aclTensorDesc
+ * @param  memType [IN]       ACL_MEMTYPE_DEVICE means device, ACL_MEMTYPE_HOST means host
+ *
+ * @retval ACL_SUCCESS     The function is successfully executed.
+ * @retval OtherValues Failure
+ */
+ACL_FUNC_VISIBILITY aclError aclSetTensorPlaceMent(aclTensorDesc *desc, aclMemType memType);
+
+/**
+ * @ingroup AscendCL
  * @brief an interface for users to output  APP logs
  *
  * @param logLevel [IN]    the level of current log

inc/external/acl/acl_mdl.h

@@ -1203,6 +1203,18 @@ ACL_FUNC_VISIBILITY aclError aclmdlDestroyConfigHandle(aclmdlConfigHandle *handl
 ACL_FUNC_VISIBILITY aclError aclmdlSetConfigOpt(aclmdlConfigHandle *handle, aclmdlConfigAttr attr,
     const void *attrValue, size_t valueSize);
 
+/**
+ * @ingroup AscendCL
+ * @brief get real tensor name from modelDesc
+ *
+ * @param modelDesc [IN]  pointer to modelDesc
+ * @param name [IN]       tensor name
+ *
+ * @retval the pointer of real tensor name
+ * @retval Failure return NULL
+ */
+ACL_FUNC_VISIBILITY const char *aclmdlGetTensorRealName(const aclmdlDesc *modelDesc, const char *name);
+
 #ifdef __cplusplus
 }
 #endif

inc/external/acl/acl_rt.h

@@ -25,6 +25,8 @@
 extern "C" {
 #endif
 
+#define ACL_EVENT_TIME_LINE 0x00000008u
+
 typedef enum aclrtRunMode {
     ACL_DEVICE,
     ACL_HOST,
@@ -427,6 +429,18 @@ ACL_FUNC_VISIBILITY aclError aclrtCreateEvent(aclrtEvent *event);
 
 /**
  * @ingroup AscendCL
+ * @brief create event instance with flag
+ *
+ * @param event [OUT]   created event
+ * @param flag [IN]     event flag
+ *
+ * @retval ACL_SUCCESS The function is successfully executed.
+ * @retval OtherValues Failure
+ */
+ACL_FUNC_VISIBILITY aclError aclrtCreateEventWithFlag(aclrtEvent *event, uint32_t flag);
+
+/**
+ * @ingroup AscendCL
  * @brief destroy event instance
  *
  * @par Function

inc/external/hccl/hccl.h

@@ -27,7 +27,7 @@
 
 #ifdef __cplusplus
 extern "C" {
-#endif  // __cplusplus
+#endif // __cplusplus
 
 /**
  * @brief Initialize HCCL.
@@ -66,15 +66,14 @@ extern HcclResult HcclCommInitRootInfo(uint32_t nRanks, const HcclRootInfo *root
  * @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 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 comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
- * @return HcclResult
+ * @return HcclResult 
  */
-extern HcclResult HcclAllReduce(void *sendBuf, void *recvBuf, uint64_t count, HcclDataType dataType, HcclReduceOp op,
-                                HcclComm comm, aclrtStream stream);
+extern HcclResult HcclAllReduce(void *sendBuf, void *recvBuf, uint64_t count, HcclDataType dataType, 
+HcclReduceOp op, HcclComm comm, aclrtStream stream);
 
 /**
  * @brief Broadcast operator.
@@ -85,10 +84,10 @@ extern HcclResult HcclAllReduce(void *sendBuf, void *recvBuf, uint64_t count, Hc
  * @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
+ * @return HcclResult 
  */
-extern HcclResult HcclBroadcast(void *buf, uint64_t count, HcclDataType dataType, uint32_t root, HcclComm comm,
-                                aclrtStream stream);
+extern HcclResult HcclBroadcast(void *buf, uint64_t count, HcclDataType dataType, uint32_t root, HcclComm comm, 
+aclrtStream stream);
 
 /**
  * @brief ReduceScatter operator.
@@ -100,10 +99,10 @@ extern HcclResult HcclBroadcast(void *buf, uint64_t count, HcclDataType dataType
  * @param op The reduction type of the operator, must be one of the following types: sum, min, max, prod.
  * @param comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
- * @return HcclResult
+ * @return HcclResult 
  */
-extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvCount, HcclDataType dataType,
-                                    HcclReduceOp op, HcclComm comm, aclrtStream stream);
+extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvCount, HcclDataType dataType, 
+HcclReduceOp op, HcclComm comm, aclrtStream stream);
 
 /**
  * @brief AllGather operator.
@@ -114,10 +113,10 @@ extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvC
  * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32.
  * @param comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
- * @return HcclResult
+ * @return HcclResult 
  */
-extern HcclResult HcclAllGather(void *sendBuf, void *recvBuf, uint64_t sendCount, HcclDataType dataType, HcclComm comm,
-                                aclrtStream stream);
+extern HcclResult HcclAllGather(void *sendBuf, void *recvBuf, uint64_t sendCount, HcclDataType dataType, 
+HcclComm comm, aclrtStream stream);
 
 /**
  * @brief Destroy HCCL comm
@@ -130,5 +129,5 @@ extern HcclResult HcclCommDestroy(HcclComm comm);
 
 #ifdef __cplusplus
 }
-#endif  // __cplusplus
-#endif  // HCCL_H_
+#endif // __cplusplus
+#endif // HCCL_H_

inc/external/hccl/hccl_types.h

@@ -16,10 +16,10 @@
 
 /**
  * @file hccl_types.h
- * @brief HCCL data type definition
- *
+ * @brief HCCL data type definition 
+ * 
  */
-
+ 
 #ifndef HCCL_TYPES_H_
 #define HCCL_TYPES_H_
 
@@ -27,33 +27,33 @@
 
 #ifdef __cplusplus
 extern "C" {
-#endif  // __cplusplus
+#endif // __cplusplus
 
 /**
  * @brief HCCL functions return value definition
  */
 typedef enum {
-  HCCL_SUCCESS = 0,              /**< success */
-  HCCL_E_PARA = 1,               /**< parameter error */
-  HCCL_E_PTR = 2,                /**< empty pointer */
-  HCCL_E_MEMORY = 3,             /**< memory error */
-  HCCL_E_INTERNAL = 4,           /**< internal error */
-  HCCL_E_NOT_SUPPORT = 5,        /**< not support feature */
-  HCCL_E_NOT_FOUND = 6,          /**< not found specific resource */
-  HCCL_E_UNAVAIL = 7,            /**< resource unavailable */
-  HCCL_E_SYSCALL = 8,            /**< call system interface error */
-  HCCL_E_TIMEOUT = 9,            /**< timeout */
-  HCCL_E_OPEN_FILE_FAILURE = 10, /**< open file fail */
-  HCCL_E_TCP_CONNECT = 11,       /**< tcp connect fail */
-  HCCL_E_ROCE_CONNECT = 12,      /**< roce connect fail */
-  HCCL_E_TCP_TRANSFER = 13,      /**< tcp transfer fail */
-  HCCL_E_ROCE_TRANSFER = 14,     /**< roce transfer fail */
-  HCCL_E_RUNTIME = 15,           /**< call runtime api fail */
-  HCCL_E_DRV = 16,               /**< call driver api fail */
-  HCCL_E_PROFILING = 17,         /**< call profiling api fail */
-  HCCL_E_CCE = 18,               /**< call cce api fail */
-  HCCL_E_NETWORK = 19,           /**< call network api fail */
-  HCCL_E_RESERVED                /**< reserved */
+    HCCL_SUCCESS = 0,               /**< success */
+    HCCL_E_PARA = 1,                /**< parameter error */
+    HCCL_E_PTR = 2,                 /**< empty pointer */
+    HCCL_E_MEMORY = 3,              /**< memory error */
+    HCCL_E_INTERNAL = 4,            /**< internal error */
+    HCCL_E_NOT_SUPPORT = 5,         /**< not support feature */
+    HCCL_E_NOT_FOUND = 6,           /**< not found specific resource */
+    HCCL_E_UNAVAIL = 7,             /**< resource unavailable */
+    HCCL_E_SYSCALL = 8,             /**< call system interface error */
+    HCCL_E_TIMEOUT = 9,             /**< timeout */
+    HCCL_E_OPEN_FILE_FAILURE = 10,  /**< open file fail */
+    HCCL_E_TCP_CONNECT = 11,        /**< tcp connect fail */
+    HCCL_E_ROCE_CONNECT = 12,       /**< roce connect fail */
+    HCCL_E_TCP_TRANSFER = 13,       /**< tcp transfer fail */
+    HCCL_E_ROCE_TRANSFER = 14,      /**< roce transfer fail */
+    HCCL_E_RUNTIME = 15,            /**< call runtime api fail */
+    HCCL_E_DRV = 16,                /**< call driver api fail */
+    HCCL_E_PROFILING = 17,          /**< call profiling api fail */
+    HCCL_E_CCE = 18,                /**< call cce api fail */
+    HCCL_E_NETWORK = 19,            /**< call network api fail */
+    HCCL_E_RESERVED                 /**< reserved */
 } HcclResult;
 
 /**
@@ -65,37 +65,37 @@ typedef void *HcclComm;
  * @brief HCCL Reduction opperation
  */
 typedef enum {
-  HCCL_REDUCE_SUM = 0,  /**< sum */
-  HCCL_REDUCE_PROD = 1, /**< prod */
-  HCCL_REDUCE_MAX = 2,  /**< max */
-  HCCL_REDUCE_MIN = 3,  /**< min */
-  HCCL_REDUCE_RESERVED  /**< reserved */
+    HCCL_REDUCE_SUM = 0,    /**< sum */
+    HCCL_REDUCE_PROD = 1,   /**< prod */
+    HCCL_REDUCE_MAX = 2,    /**< max */
+    HCCL_REDUCE_MIN = 3,    /**< min */
+    HCCL_REDUCE_RESERVED    /**< reserved */
 } HcclReduceOp;
 
 /**
  * @brief HCCL data type
  */
 typedef enum {
-  HCCL_DATA_TYPE_INT8 = 0,   /**< int8 */
-  HCCL_DATA_TYPE_INT16 = 1,  /**< int16 */
-  HCCL_DATA_TYPE_INT32 = 2,  /**< int32 */
-  HCCL_DATA_TYPE_FP16 = 3,   /**< fp16 */
-  HCCL_DATA_TYPE_FP32 = 4,   /**< fp32 */
-  HCCL_DATA_TYPE_INT64 = 5,  /**< int64 */
-  HCCL_DATA_TYPE_UINT64 = 6, /**< uint64 */
-  HCCL_DATA_TYPE_RESERVED    /**< reserved */
+    HCCL_DATA_TYPE_INT8 = 0,    /**< int8 */
+    HCCL_DATA_TYPE_INT16 = 1,   /**< int16 */
+    HCCL_DATA_TYPE_INT32 = 2,   /**< int32 */
+    HCCL_DATA_TYPE_FP16 = 3,    /**< fp16 */
+    HCCL_DATA_TYPE_FP32 = 4,    /**< fp32 */
+    HCCL_DATA_TYPE_INT64 = 5,    /**< int64 */
+    HCCL_DATA_TYPE_UINT64 = 6,    /**< uint64 */
+    HCCL_DATA_TYPE_RESERVED     /**< reserved */
 } HcclDataType;
 
-const uint32_t HCCL_ROOT_INFO_BYTES = 4108;  // 4108: root info length
+const uint32_t HCCL_ROOT_INFO_BYTES =  4108; // 4108: root info length
 
 /**
  * @brief HCCL root info
  */
 typedef struct HcclRootInfoDef {
-  char internal[HCCL_ROOT_INFO_BYTES];
+    char internal[HCCL_ROOT_INFO_BYTES];
 } HcclRootInfo;
 
 #ifdef __cplusplus
 }
-#endif  // __cplusplus
-#endif  // HCCL_TYPES_H_
+#endif // __cplusplus
+#endif // HCCL_TYPES_H_

inc/external/runtime/rt_error_codes.h

@@ -23,80 +23,80 @@
 extern "C" {
 #endif
 
-static const int32_t ACL_RT_SUCCESS = 0;  // success
+static const int32_t ACL_RT_SUCCESS                          = 0; // success
 
-static const int32_t ACL_ERROR_RT_PARAM_INVALID = 107000;             // param invalid
-static const int32_t ACL_ERROR_RT_INVALID_DEVICEID = 107001;          // invalid device id
-static const int32_t ACL_ERROR_RT_CONTEXT_NULL = 107002;              // current context null
-static const int32_t ACL_ERROR_RT_STREAM_CONTEXT = 107003;            // stream not in current context
-static const int32_t ACL_ERROR_RT_MODEL_CONTEXT = 107004;             // model not in current context
-static const int32_t ACL_ERROR_RT_STREAM_MODEL = 107005;              // stream not in model
-static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_INVALID = 107006;   // event timestamp invalid
-static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_REVERSAL = 107007;  // event timestamp reversal
-static const int32_t ACL_ERROR_RT_ADDR_UNALIGNED = 107008;            // memory address unaligned
-static const int32_t ACL_ERROR_RT_FILE_OPEN = 107009;                 // open file failed
-static const int32_t ACL_ERROR_RT_FILE_WRITE = 107010;                // write file failed
-static const int32_t ACL_ERROR_RT_STREAM_SUBSCRIBE = 107011;          // error subscribe stream
-static const int32_t ACL_ERROR_RT_THREAD_SUBSCRIBE = 107012;          // error subscribe thread
-static const int32_t ACL_ERROR_RT_GROUP_NOT_SET = 107013;             // group not set
-static const int32_t ACL_ERROR_RT_GROUP_NOT_CREATE = 107014;          // group not create
-static const int32_t ACL_ERROR_RT_STREAM_NO_CB_REG = 107015;          // callback not register to stream
-static const int32_t ACL_ERROR_RT_INVALID_MEMORY_TYPE = 107016;       // invalid memory type
-static const int32_t ACL_ERROR_RT_INVALID_HANDLE = 107017;            // invalid handle
-static const int32_t ACL_ERROR_RT_INVALID_MALLOC_TYPE = 107018;       // invalid malloc type
+static const int32_t ACL_ERROR_RT_PARAM_INVALID              = 107000; // param invalid
+static const int32_t ACL_ERROR_RT_INVALID_DEVICEID           = 107001; // invalid device id
+static const int32_t ACL_ERROR_RT_CONTEXT_NULL               = 107002; // current context null
+static const int32_t ACL_ERROR_RT_STREAM_CONTEXT             = 107003; // stream not in current context
+static const int32_t ACL_ERROR_RT_MODEL_CONTEXT              = 107004; // model not in current context
+static const int32_t ACL_ERROR_RT_STREAM_MODEL               = 107005; // stream not in model
+static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_INVALID    = 107006; // event timestamp invalid
+static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_REVERSAL   = 107007; // event timestamp reversal
+static const int32_t ACL_ERROR_RT_ADDR_UNALIGNED             = 107008; // memory address unaligned
+static const int32_t ACL_ERROR_RT_FILE_OPEN                  = 107009; // open file failed
+static const int32_t ACL_ERROR_RT_FILE_WRITE                 = 107010; // write file failed
+static const int32_t ACL_ERROR_RT_STREAM_SUBSCRIBE           = 107011; // error subscribe stream
+static const int32_t ACL_ERROR_RT_THREAD_SUBSCRIBE           = 107012; // error subscribe thread
+static const int32_t ACL_ERROR_RT_GROUP_NOT_SET              = 107013; // group not set
+static const int32_t ACL_ERROR_RT_GROUP_NOT_CREATE           = 107014; // group not create
+static const int32_t ACL_ERROR_RT_STREAM_NO_CB_REG           = 107015; // callback not register to stream
+static const int32_t ACL_ERROR_RT_INVALID_MEMORY_TYPE        = 107016; // invalid memory type
+static const int32_t ACL_ERROR_RT_INVALID_HANDLE             = 107017; // invalid handle
+static const int32_t ACL_ERROR_RT_INVALID_MALLOC_TYPE        = 107018; // invalid malloc type
 
-static const int32_t ACL_ERROR_RT_FEATURE_NOT_SUPPORT = 207000;  // feature not support
-static const int32_t ACL_ERROR_RT_MEMORY_ALLOCATION = 207001;    // memory allocation error
-static const int32_t ACL_ERROR_RT_MEMORY_FREE = 207002;          // memory free error
-static const int32_t ACL_ERROR_RT_AICORE_OVER_FLOW = 207003;     // aicore over flow
-static const int32_t ACL_ERROR_RT_NO_DEVICE = 207004;            // no device
-static const int32_t ACL_ERROR_RT_RESOURCE_ALLOC_FAIL = 207005;  // resource alloc fail
-static const int32_t ACL_ERROR_RT_NO_PERMISSION = 207006;        // no permission
-static const int32_t ACL_ERROR_RT_NO_EVENT_RESOURCE = 207007;    // no event resource
-static const int32_t ACL_ERROR_RT_NO_STREAM_RESOURCE = 207008;   // no stream resource
-static const int32_t ACL_ERROR_RT_NO_NOTIFY_RESOURCE = 207009;   // no notify resource
-static const int32_t ACL_ERROR_RT_NO_MODEL_RESOURCE = 207010;    // no model resource
+static const int32_t ACL_ERROR_RT_FEATURE_NOT_SUPPORT        = 207000; // feature not support
+static const int32_t ACL_ERROR_RT_MEMORY_ALLOCATION          = 207001; // memory allocation error
+static const int32_t ACL_ERROR_RT_MEMORY_FREE                = 207002; // memory free error
+static const int32_t ACL_ERROR_RT_AICORE_OVER_FLOW           = 207003; // aicore over flow
+static const int32_t ACL_ERROR_RT_NO_DEVICE                  = 207004; // no device
+static const int32_t ACL_ERROR_RT_RESOURCE_ALLOC_FAIL        = 207005; // resource alloc fail
+static const int32_t ACL_ERROR_RT_NO_PERMISSION              = 207006; // no permission
+static const int32_t ACL_ERROR_RT_NO_EVENT_RESOURCE          = 207007; // no event resource
+static const int32_t ACL_ERROR_RT_NO_STREAM_RESOURCE         = 207008; // no stream resource
+static const int32_t ACL_ERROR_RT_NO_NOTIFY_RESOURCE         = 207009; // no notify resource
+static const int32_t ACL_ERROR_RT_NO_MODEL_RESOURCE          = 207010; // no model resource
 
-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
-static const int32_t ACL_ERROR_RT_STREAM_TASK_FULL = 507002;        // task full in stream
-static const int32_t ACL_ERROR_RT_STREAM_TASK_EMPTY = 507003;       // task empty in stream
-static const int32_t ACL_ERROR_RT_STREAM_NOT_COMPLETE = 507004;     // stream not complete
-static const int32_t ACL_ERROR_RT_END_OF_SEQUENCE = 507005;         // end of sequence
-static const int32_t ACL_ERROR_RT_EVENT_NOT_COMPLETE = 507006;      // event not complete
-static const int32_t ACL_ERROR_RT_CONTEXT_RELEASE_ERROR = 507007;   // context release error
-static const int32_t ACL_ERROR_RT_SOC_VERSION = 507008;             // soc version error
-static const int32_t ACL_ERROR_RT_TASK_TYPE_NOT_SUPPORT = 507009;   // task type not support
-static const int32_t ACL_ERROR_RT_LOST_HEARTBEAT = 507010;          // ts lost heartbeat
-static const int32_t ACL_ERROR_RT_MODEL_EXECUTE = 507011;           // model execute failed
-static const int32_t ACL_ERROR_RT_REPORT_TIMEOUT = 507012;          // report timeout
-static const int32_t ACL_ERROR_RT_SYS_DMA = 507013;                 // sys dma error
-static const int32_t ACL_ERROR_RT_AICORE_TIMEOUT = 507014;          // aicore timeout
-static const int32_t ACL_ERROR_RT_AICORE_EXCEPTION = 507015;        // aicore exception
-static const int32_t ACL_ERROR_RT_AICORE_TRAP_EXCEPTION = 507016;   // aicore trap exception
-static const int32_t ACL_ERROR_RT_AICPU_TIMEOUT = 507017;           // aicpu timeout
-static const int32_t ACL_ERROR_RT_AICPU_EXCEPTION = 507018;         // aicpu exception
-static const int32_t ACL_ERROR_RT_AICPU_DATADUMP_RSP_ERR = 507019;  // aicpu datadump response error
-static const int32_t ACL_ERROR_RT_AICPU_MODEL_RSP_ERR = 507020;     // aicpu model operate response error
-static const int32_t ACL_ERROR_RT_PROFILING_ERROR = 507021;         // profiling error
-static const int32_t ACL_ERROR_RT_IPC_ERROR = 507022;               // ipc error
-static const int32_t ACL_ERROR_RT_MODEL_ABORT_NORMAL = 507023;      // model abort normal
-static const int32_t ACL_ERROR_RT_KERNEL_UNREGISTERING = 507024;    // kernel unregistering
-static const int32_t ACL_ERROR_RT_RINGBUFFER_NOT_INIT = 507025;     // ringbuffer not init
-static const int32_t ACL_ERROR_RT_RINGBUFFER_NO_DATA = 507026;      // ringbuffer no data
-static const int32_t ACL_ERROR_RT_KERNEL_LOOKUP = 507027;           // kernel lookup error
-static const int32_t ACL_ERROR_RT_KERNEL_DUPLICATE = 507028;        // kernel register duplicate
-static const int32_t ACL_ERROR_RT_DEBUG_REGISTER_FAIL = 507029;     // debug register failed
-static const int32_t ACL_ERROR_RT_DEBUG_UNREGISTER_FAIL = 507030;   // debug unregister failed
-static const int32_t ACL_ERROR_RT_LABEL_CONTEXT = 507031;           // label not in current context
-static const int32_t ACL_ERROR_RT_PROGRAM_USE_OUT = 507032;         // program register num use out
-static const int32_t ACL_ERROR_RT_DEV_SETUP_ERROR = 507033;         // device setup error
+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
+static const int32_t ACL_ERROR_RT_STREAM_TASK_FULL           = 507002; // task full in stream
+static const int32_t ACL_ERROR_RT_STREAM_TASK_EMPTY          = 507003; // task empty in stream
+static const int32_t ACL_ERROR_RT_STREAM_NOT_COMPLETE        = 507004; // stream not complete
+static const int32_t ACL_ERROR_RT_END_OF_SEQUENCE            = 507005; // end of sequence
+static const int32_t ACL_ERROR_RT_EVENT_NOT_COMPLETE         = 507006; // event not complete
+static const int32_t ACL_ERROR_RT_CONTEXT_RELEASE_ERROR      = 507007; // context release error
+static const int32_t ACL_ERROR_RT_SOC_VERSION                = 507008; // soc version error
+static const int32_t ACL_ERROR_RT_TASK_TYPE_NOT_SUPPORT      = 507009; // task type not support
+static const int32_t ACL_ERROR_RT_LOST_HEARTBEAT             = 507010; // ts lost heartbeat
+static const int32_t ACL_ERROR_RT_MODEL_EXECUTE              = 507011; // model execute failed
+static const int32_t ACL_ERROR_RT_REPORT_TIMEOUT             = 507012; // report timeout
+static const int32_t ACL_ERROR_RT_SYS_DMA                    = 507013; // sys dma error
+static const int32_t ACL_ERROR_RT_AICORE_TIMEOUT             = 507014; // aicore timeout
+static const int32_t ACL_ERROR_RT_AICORE_EXCEPTION           = 507015; // aicore exception
+static const int32_t ACL_ERROR_RT_AICORE_TRAP_EXCEPTION      = 507016; // aicore trap exception
+static const int32_t ACL_ERROR_RT_AICPU_TIMEOUT              = 507017; // aicpu timeout
+static const int32_t ACL_ERROR_RT_AICPU_EXCEPTION            = 507018; // aicpu exception
+static const int32_t ACL_ERROR_RT_AICPU_DATADUMP_RSP_ERR     = 507019; // aicpu datadump response error
+static const int32_t ACL_ERROR_RT_AICPU_MODEL_RSP_ERR        = 507020; // aicpu model operate response error
+static const int32_t ACL_ERROR_RT_PROFILING_ERROR            = 507021; // profiling error
+static const int32_t ACL_ERROR_RT_IPC_ERROR                  = 507022; // ipc error
+static const int32_t ACL_ERROR_RT_MODEL_ABORT_NORMAL         = 507023; // model abort normal
+static const int32_t ACL_ERROR_RT_KERNEL_UNREGISTERING       = 507024; // kernel unregistering
+static const int32_t ACL_ERROR_RT_RINGBUFFER_NOT_INIT        = 507025; // ringbuffer not init
+static const int32_t ACL_ERROR_RT_RINGBUFFER_NO_DATA         = 507026; // ringbuffer no data
+static const int32_t ACL_ERROR_RT_KERNEL_LOOKUP              = 507027; // kernel lookup error
+static const int32_t ACL_ERROR_RT_KERNEL_DUPLICATE           = 507028; // kernel register duplicate
+static const int32_t ACL_ERROR_RT_DEBUG_REGISTER_FAIL        = 507029; // debug register failed
+static const int32_t ACL_ERROR_RT_DEBUG_UNREGISTER_FAIL      = 507030; // debug unregister failed
+static const int32_t ACL_ERROR_RT_LABEL_CONTEXT              = 507031; // label not in current context
+static const int32_t ACL_ERROR_RT_PROGRAM_USE_OUT            = 507032; // program register num use out
+static const int32_t ACL_ERROR_RT_DEV_SETUP_ERROR            = 507033; // device setup error
 
-static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899;    // drv internal error
-static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR = 507900;  // aicpu internal error
+static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR         = 507899; // drv internal error
+static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR       = 507900; // aicpu internal error
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // __INC_EXTERNEL_RT_ERROR_CODES_H__
+#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__

third_party/fwkacllib/inc/ops/elewise_calculation_ops.h

@@ -3781,6 +3781,32 @@ REG_OP(ArgMaxGradD)
     .REQUIRED_ATTR(dimension, Int)
     .OP_END_FACTORY_REG(ArgMaxGradD)
 
+/**
+*@brief Returns cosine similarity between x1 and x2,computed along dim. \n
+
+*@par Inputs:
+*Two inputs, including:
+* @li input_x1: A tensor. Must be the following types:
+*     float32. \n
+
+*@par Inputs:
+*@li input_x2: A tensor. Must of the following types:
+*     float32. \n
+
+*@par Outputs:
+*@li output_y: A Tensor with the same type of input_x's. \n
+
+*@par Third-party framework compatibility
+*Compatible with the Pytorch operator CosineSimilarity. \n
+*/
+REG_OP(CosineSimilarity)
+    .INPUT(input_x1, TensorType({DT_FLOAT}))  /* "First operand." */
+    .INPUT(input_x2, TensorType({DT_FLOAT}))  /* "Second operand." */
+    .OUTPUT(output_y, TensorType({DT_FLOAT})) /* "Result, has same element type as two inputs" */
+    .ATTR(dim, Int, 1)
+    .ATTR(eps, Float, 1e-8)
+    .OP_END_FACTORY_REG(CosineSimilarity)
+
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_ELEWISE_CALCULATION_OPS_H_

third_party/fwkacllib/inc/ops/math_ops.h

@@ -952,6 +952,36 @@ REG_OP(Angle)
     .ATTR(Tout, Type, DT_FLOAT)
     .OP_END_FACTORY_REG(Angle)
 
+/**
+*@brief Computes the gradient of SoftMarginLossGrad. \n
+
+*@par Inputs:
+*Three inputs, including:
+* @li predict: A tensor. Must be one of the following types:
+*     float16, float32. \n
+* @li label: A tensor with same shape of predict. Must be one of the following types:
+*     float16, float32. \n
+* @li dout: A tensor with same shpae of predcit. Must be one of the following types:
+*     float16, float32. \n
+
+*@par Attributes:
+* @li reduction: Specifies the reduction to apply to the output:
+*     'none' | 'mean' | 'sum'. Default: 'mean'. \n
+
+*@par Outputs:
+* gradient: A Tensor with the same type of predict. \n
+
+*@par Third-party framework compatibility
+*Compatible with the Pytorch operator SoftMarginLoss Backward. \n
+*/
+REG_OP(SoftMarginLossGrad)
+    .INPUT(predict, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(label, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(dout, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .OUTPUT(gradient, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .ATTR(reduction, String, "mean")
+    .OP_END_FACTORY_REG(SoftMarginLossGrad)
+
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_MATH_OPS_H_

third_party/fwkacllib/inc/ops/nn_norm_ops.h

@@ -1206,6 +1206,34 @@ REG_OP(Centralization)
     .OP_END_FACTORY_REG(Centralization)
 
 /**
+ *@brief Calculate the loss. Creates a criterion that optimizes a two-class classification
+ logistic loss between input_x and input_y (containing 1 or -1). \n
+
+ *@par Inputs:
+ *One inputs, including:
+ * @li input_x: A tensor. Must be one of the following types:
+ *     float16, float32. \n
+ * @li input_y: A tensor. Must be one of the following types:
+ *     float16, float32. \n
+
+ *@par Attributes:
+ *@li lambd: An optional string.Defaults to "mean". \n
+
+ *@par Outputs:
+ *output_z: while reduction == "none", A Tensor with the same type and shape of input_x's. \n
+ *          while reduction == "sum" or "mean", A Tensor with the same type of input_x , shape of which is (1,)
+
+ *@par Third-party framework compatibility
+ *Compatible with the Pytorch operator SoftMarginLoss. \n
+ */
+REG_OP(SoftMarginLoss)
+    .INPUT(input_x, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .INPUT(input_y, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .ATTR(reduction, String, "mean")
+    .OUTPUT(output_z, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .OP_END_FACTORY_REG(SoftMarginLoss)
+
+/**
 * @brief Computes gradients of sigmoid_cross_entropy_with_logits_v2.
 
 * @par Inputs:

third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h

@@ -793,6 +793,34 @@ REG_OP(HardShrink)
     .OP_END_FACTORY_REG(HardShrink)
 
 /**
+*@brief Calculate the hard shrink grad function. \n
+*
+* Computes the gradient for the HardShrink: if x > lambda or x < -lambda, x,otherwise 0
+*
+*@par Inputs:
+*Two inputs, including:
+* @li gradients: A tensor. Must be one of the following types:
+*     float16, float32. \n
+* @li features: A tensor. Must be one of the following types:
+*     float16, float32. \n
+*
+*@par Outputs:
+*backprops: A Tensor with the same type and shape of features's. \n
+*
+*@par Attributes:
+*@li lambda: An optional float.Defaults to 0.5. \n
+*
+*@par Third-party framework compatibility
+*Compatible with the Pytorch operator Hardshrink_backward. \n
+*/
+  REG_OP(HardShrinkGrad)
+  .INPUT(gradients, TensorType({DT_FLOAT16, DT_FLOAT}))
+  .INPUT(features, TensorType({DT_FLOAT16, DT_FLOAT}))
+  .OUTPUT(backprops, TensorType({DT_FLOAT16, DT_FLOAT}))
+  .ATTR(lambda, Float, 0.5)
+  .OP_END_FACTORY_REG(HardShrinkGrad)
+
+/**
 * @brief Calculate the hard sigmoid function. \n
 
 * @par Inputs:
@@ -884,6 +912,36 @@ REG_OP(LogSigmoid)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) /* "input:x" */
     .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))  /* "output:y" */
     .OP_END_FACTORY_REG(LogSigmoid)
+
+/**
+*@brief Calculate the backward outputs of the function "hard_sigmoid" \n
+
+*@par Inputs:
+*One inputs, including:
+* @li grads: A tensor. Must be one of the following types:
+*       float16, float32. \n
+* @li input_x: A tensor. Must be one of the following types:
+*       float16, float32. \n
+
+*@par Outputs:
+*One outputs, including:
+* @li y: A tensor with the same type and shape of x's. \n
+
+* @par Attributes:
+* @li alpha: An optional float. Defaults to 0.16666666. \n
+* @li beta: An optional float. Defaults to 0.5. \n
+
+*@par Third-party framework compatibility
+*Compatible with the Pytorch operator LogSigmoidGrad. \n
+*/
+REG_OP(HardSigmoidGrad)
+    .INPUT(grads, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .INPUT(input_x, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .ATTR(alpha, Float, 0.16666666)
+    .ATTR(beta, Float, 0.5)
+    .OP_END_FACTORY_REG(HardSigmoidGrad)
+
 } // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_NONLINEAR_FUC_OPS_H_

third_party/fwkacllib/inc/ops/transformation_ops.h

@@ -737,14 +737,51 @@ where ho/wo is do = (output_d + 2*padding_d - dilation_d*(kernel_d - 1) - 1)//st
 * Compatible with Pytorch col2im/im2col_backward operator.
 */
 REG_OP(Col2im)
-    .INPUT(x, TensorType({DT_FLOAT}))
-    .INPUT(output_size, TensorType({DT_INT32}))
-    .OUTPUT(y, TensorType({DT_FLOAT}))
+    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .INPUT(output_size, TensorType({DT_INT32, DT_INT32}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16}))
     .REQUIRED_ATTR(kernel_size, ListInt)
     .REQUIRED_ATTR(dilation, ListInt)
     .REQUIRED_ATTR(padding, ListInt)
     .REQUIRED_ATTR(stride, ListInt)
     .OP_END_FACTORY_REG(Col2im)
+
+/**
+*@brief Generates a 2D or 3D flow field (sampling grid), given a batch of affine
+matrices theta. \n
+
+*@par Inputs:
+*Input theta must be float16 or float, output_size must be int32 type.Inputs
+include:
+*@li theta: input batch of affine matrices with shape (N,2,3) for 2D or (N,3,4)
+for 3D
+*@li output_size: the target output image size. (N×C×H×W for 2D or N×C×D×H×W for
+3D) Example: torch.Size((32, 3, 24, 24)) . \n
+
+
+*@par Attributes:
+*align_corners: if True, consider -1 and 1 to refer to the centers of the corner
+pixels rather than the image corners.Refer to grid_sample() for a more complete
+description. A grid generated by affine_grid() should be passed to grid_sample()
+with the same setting for this option. Default: False \n
+
+*@par Outputs:
+*@li y: A 2-D integer tensor of shape [M] representing the
+selected indices from the boxes tensor, where M <= max_output_size. \n
+
+*@attention Constraints:
+*Input theta must be float16 or float, output_size must be int32 type . \n
+
+*@par Third-party framework compatibility
+*Compatible with Pytorch affine_grid operator.
+*/
+
+REG_OP(AffineGrid)
+    .INPUT(theta, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(output_size, TensorType({DT_INT32}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .ATTR(align_corners, Bool, false)
+    .OP_END_FACTORY_REG(AffineGrid)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_TRANSFORMATION_OPS_H_

third_party/fwkacllib/inc/runtime/event.h

@@ -30,6 +30,11 @@ extern "C" {
 #define RT_EVENT_DEFAULT (0x00)
 #define RT_EVENT_WITH_FLAG (0x01)
 
+#define RT_EVENT_DDSYNC_NS    0x01U
+#define RT_EVENT_STREAM_MARK  0x02U
+#define RT_EVENT_DDSYNC       0x04U
+#define RT_EVENT_TIME_LINE    0x08U
+
 /**
  * @ingroup dvrt_event
  * @brief create event instance

third_party/fwkacllib/inc/toolchain/slog.h

@@ -141,7 +141,7 @@ enum {
   IDEDD,         /**< IDE daemon device */
   IDEDH,         /**< IDE daemon host */
   HCCL,          /**< HCCL */
-  FMK,           /**< Framework */
+  FMK,           /**< Adapter */
   HIAIENGINE,    /**< Matrix */
   DVPP,          /**< DVPP */
   RUNTIME,       /**< Runtime */
@@ -162,11 +162,11 @@ enum {
   MDCDEFAULT, /**< MDC undefine */
   MDCSC,      /**< MDC spatial cognition */
   MDCPNC,
-  MLL,
+  MLL,      /**< abandon */
   DEVMM,    /**< Dlog memory managent */
   KERNEL,   /**< Kernel */
   LIBMEDIA, /**< Libmedia */
-  CCECPU,   /**< ai cpu */
+  CCECPU,   /**< aicpu shedule */
   ASCENDDK, /**< AscendDK */
   ROS,      /**< ROS */
   HCCP,
@@ -179,7 +179,7 @@ enum {
   TSDUMP,    /**< TSDUMP module */
   AICPU,     /**< AICPU module */
   LP,        /**< LP module */
-  TDT,
+  TDT,       /**< tsdaemon or aicpu shedule */
   FE,
   MD,
   MB,