!1882 code_sync_0626_inc

Merge pull request !1882 from mindspore_ding/code_sync_0626

inc/external/acl/acl.h

@@ -26,9 +26,9 @@ extern "C" {
 #endif
 
 // Current version is 1.0.0
-#define ACL_MAJOR_VERSION 1
-#define ACL_MINOR_VERSION 0
-#define ACL_PATCH_VERSION 0
+#define ACL_MAJOR_VERSION    1
+#define ACL_MINOR_VERSION    0
+#define ACL_PATCH_VERSION    0
 
 /**
  * @ingroup AscendCL
@@ -72,11 +72,11 @@ ACL_FUNC_VISIBILITY aclError aclrtGetVersion(int32_t *majorVersion, int32_t *min
  *
  * @retval null for failed
  * @retval OtherValues success
- */
+*/
 ACL_FUNC_VISIBILITY const char *aclGetRecentErrMsg();
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_H_
+#endif // INC_EXTERNAL_ACL_ACL_H_

inc/external/acl/acl_base.h

@@ -136,49 +136,50 @@ static const int ACL_ERROR_PROFILING_FAILURE = 500005;
 #define ACL_UNKNOWN_RANK 0xFFFFFFFFFFFFFFFE
 
 typedef enum {
-  ACL_DT_UNDEFINED = -1,
-  ACL_FLOAT = 0,
-  ACL_FLOAT16 = 1,
-  ACL_INT8 = 2,
-  ACL_INT32 = 3,
-  ACL_UINT8 = 4,
-  ACL_INT16 = 6,
-  ACL_UINT16 = 7,
-  ACL_UINT32 = 8,
-  ACL_INT64 = 9,
-  ACL_UINT64 = 10,
-  ACL_DOUBLE = 11,
-  ACL_BOOL = 12,
-  ACL_STRING = 13,
+    ACL_DT_UNDEFINED = -1,
+    ACL_FLOAT = 0,
+    ACL_FLOAT16 = 1,
+    ACL_INT8 = 2,
+    ACL_INT32 = 3,
+    ACL_UINT8 = 4,
+    ACL_INT16 = 6,
+    ACL_UINT16 = 7,
+    ACL_UINT32 = 8,
+    ACL_INT64 = 9,
+    ACL_UINT64 = 10,
+    ACL_DOUBLE = 11,
+    ACL_BOOL = 12,
+    ACL_STRING = 13,
 } aclDataType;
 
 typedef enum {
-  ACL_FORMAT_UNDEFINED = -1,
-  ACL_FORMAT_NCHW = 0,
-  ACL_FORMAT_NHWC = 1,
-  ACL_FORMAT_ND = 2,
-  ACL_FORMAT_NC1HWC0 = 3,
-  ACL_FORMAT_FRACTAL_Z = 4,
-  ACL_FORMAT_NC1HWC0_C04 = 12,
-  ACL_FORMAT_NDHWC = 27,
-  ACL_FORMAT_FRACTAL_NZ = 29,
-  ACL_FORMAT_NCDHW = 30,
-  ACL_FORMAT_NDC1HWC0 = 32,
-  ACL_FRACTAL_Z_3D = 33
+    ACL_FORMAT_UNDEFINED = -1,
+    ACL_FORMAT_NCHW = 0,
+    ACL_FORMAT_NHWC = 1,
+    ACL_FORMAT_ND = 2,
+    ACL_FORMAT_NC1HWC0 = 3,
+    ACL_FORMAT_FRACTAL_Z = 4,
+    ACL_FORMAT_NC1HWC0_C04 = 12,
+    ACL_FORMAT_NDHWC = 27,
+    ACL_FORMAT_FRACTAL_NZ = 29,
+    ACL_FORMAT_NCDHW = 30,
+    ACL_FORMAT_NDC1HWC0 = 32,
+    ACL_FRACTAL_Z_3D = 33
 } aclFormat;
 
 typedef enum {
-  ACL_DEBUG = 0,
-  ACL_INFO = 1,
-  ACL_WARNING = 2,
-  ACL_ERROR = 3,
+    ACL_DEBUG = 0,
+    ACL_INFO = 1,
+    ACL_WARNING = 2,
+    ACL_ERROR = 3,
 } aclLogLevel;
 
 typedef enum {
-  ACL_MEMTYPE_DEVICE = 0,
-  ACL_MEMTYPE_HOST = 1,
+    ACL_MEMTYPE_DEVICE = 0,
+    ACL_MEMTYPE_HOST = 1,
 } aclMemType;
 
+
 /**
  * @ingroup AscendCL
  * @brief Converts data of type aclFloat16 to data of type float
@@ -311,7 +312,9 @@ ACL_FUNC_VISIBILITY size_t aclDataTypeSize(aclDataType dataType);
  * @retval aclTensorDesc pointer.
  * @retval nullptr if param is invalid or run out of memory
  */
-ACL_FUNC_VISIBILITY aclTensorDesc *aclCreateTensorDesc(aclDataType dataType, int numDims, const int64_t *dims,
+ACL_FUNC_VISIBILITY aclTensorDesc *aclCreateTensorDesc(aclDataType dataType,
+                                                       int numDims,
+                                                       const int64_t *dims,
                                                        aclFormat format);
 
 /**
@@ -333,7 +336,8 @@ ACL_FUNC_VISIBILITY void aclDestroyTensorDesc(const aclTensorDesc *desc);
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclSetTensorShapeRange(aclTensorDesc *desc, size_t dimsCount,
+ACL_FUNC_VISIBILITY aclError aclSetTensorShapeRange(aclTensorDesc* desc,
+                                                    size_t dimsCount,
                                                     int64_t dimsRange[][ACL_TENSOR_SHAPE_RANGE_NUM]);
 
 /**
@@ -430,7 +434,9 @@ ACL_FUNC_VISIBILITY aclError aclGetTensorDescDimV2(const aclTensorDesc *desc, si
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclGetTensorDescDimRange(const aclTensorDesc *desc, size_t index, size_t dimRangeNum,
+ACL_FUNC_VISIBILITY aclError aclGetTensorDescDimRange(const aclTensorDesc *desc,
+                                                      size_t index,
+                                                      size_t dimRangeNum,
                                                       int64_t *dimRange);
 
 /**
@@ -467,7 +473,7 @@ ACL_FUNC_VISIBILITY const char *aclGetTensorDescName(aclTensorDesc *desc);
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclTransTensorDescFormat(const aclTensorDesc *srcDesc, aclFormat dstFormat,
-                                                      aclTensorDesc **dstDesc);
+    aclTensorDesc **dstDesc);
 
 /**
  * @ingroup AscendCL
@@ -555,7 +561,7 @@ ACL_FUNC_VISIBILITY aclError aclSetTensorOriginShape(aclTensorDesc *desc, int nu
  *
  * @retval null for failed.
  * @retval OtherValues success.
- */
+*/
 ACL_FUNC_VISIBILITY aclTensorDesc *aclGetTensorDescByIndex(aclTensorDesc *desc, size_t index);
 
 /**
@@ -566,7 +572,7 @@ ACL_FUNC_VISIBILITY aclTensorDesc *aclGetTensorDescByIndex(aclTensorDesc *desc,
  *
  * @retval null for failed
  * @retval OtherValues success
- */
+*/
 ACL_FUNC_VISIBILITY void *aclGetTensorDescAddress(const aclTensorDesc *desc);
 
 /**
@@ -618,7 +624,7 @@ ACL_FUNC_VISIBILITY aclError aclSetTensorPlaceMent(aclTensorDesc *desc, aclMemTy
  * @param ... [IN]         the value of current log
  */
 ACL_FUNC_VISIBILITY void aclAppLog(aclLogLevel logLevel, const char *func, const char *file, uint32_t line,
-                                   const char *fmt, ...);
+    const char *fmt, ...);
 
 /**
  * @ingroup AscendCL
@@ -626,13 +632,14 @@ ACL_FUNC_VISIBILITY void aclAppLog(aclLogLevel logLevel, const char *func, const
  *
  * @retval null for failed
  * @retval OtherValues success
- */
+*/
 ACL_FUNC_VISIBILITY const char *aclrtGetSocName();
 
-#define ACL_APP_LOG(level, fmt, ...) aclAppLog(level, __FUNCTION__, __FILE__, __LINE__, fmt, ##__VA_ARGS__)
+#define ACL_APP_LOG(level, fmt, ...) \
+    aclAppLog(level, __FUNCTION__, __FILE__, __LINE__, fmt, ##__VA_ARGS__)
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_BASE_H_
+#endif // INC_EXTERNAL_ACL_ACL_BASE_H_

inc/external/acl/acl_mdl.h

@@ -27,19 +27,19 @@
 extern "C" {
 #endif
 
-#define ACL_MAX_DIM_CNT 128
-#define ACL_MAX_TENSOR_NAME_LEN 128
-#define ACL_MAX_BATCH_NUM 128
-#define ACL_MAX_HW_NUM 128
-#define ACL_MAX_SHAPE_COUNT 128
-#define ACL_INVALID_NODE_INDEX 0xFFFFFFFF
-
-#define ACL_MDL_LOAD_FROM_FILE 1
-#define ACL_MDL_LOAD_FROM_FILE_WITH_MEM 2
-#define ACL_MDL_LOAD_FROM_MEM 3
-#define ACL_MDL_LOAD_FROM_MEM_WITH_MEM 4
-#define ACL_MDL_LOAD_FROM_FILE_WITH_Q 5
-#define ACL_MDL_LOAD_FROM_MEM_WITH_Q 6
+#define ACL_MAX_DIM_CNT          128
+#define ACL_MAX_TENSOR_NAME_LEN  128
+#define ACL_MAX_BATCH_NUM        128
+#define ACL_MAX_HW_NUM           128
+#define ACL_MAX_SHAPE_COUNT      128
+#define ACL_INVALID_NODE_INDEX   0xFFFFFFFF
+
+#define ACL_MDL_LOAD_FROM_FILE            1
+#define ACL_MDL_LOAD_FROM_FILE_WITH_MEM   2
+#define ACL_MDL_LOAD_FROM_MEM             3
+#define ACL_MDL_LOAD_FROM_MEM_WITH_MEM    4
+#define ACL_MDL_LOAD_FROM_FILE_WITH_Q     5
+#define ACL_MDL_LOAD_FROM_MEM_WITH_Q      6
 
 #define ACL_DYNAMIC_TENSOR_NAME "ascend_mbatch_shape_data"
 #define ACL_DYNAMIC_AIPP_NAME "ascend_dynamic_aipp_data"
@@ -52,123 +52,123 @@ typedef struct aclAippExtendInfo aclAippExtendInfo;
 typedef struct aclmdlConfigHandle aclmdlConfigHandle;
 
 typedef enum {
-  ACL_YUV420SP_U8 = 1,
-  ACL_XRGB8888_U8,
-  ACL_RGB888_U8,
-  ACL_YUV400_U8,
-  ACL_NC1HWC0DI_FP16,
-  ACL_NC1HWC0DI_S8,
-  ACL_ARGB8888_U8,
-  ACL_YUYV_U8,
-  ACL_YUV422SP_U8,
-  ACL_AYUV444_U8,
-  ACL_RAW10,
-  ACL_RAW12,
-  ACL_RAW16,
-  ACL_RAW24,
-  ACL_AIPP_RESERVED = 0xffff,
+    ACL_YUV420SP_U8 = 1,
+    ACL_XRGB8888_U8,
+    ACL_RGB888_U8,
+    ACL_YUV400_U8,
+    ACL_NC1HWC0DI_FP16,
+    ACL_NC1HWC0DI_S8,
+    ACL_ARGB8888_U8,
+    ACL_YUYV_U8,
+    ACL_YUV422SP_U8,
+    ACL_AYUV444_U8,
+    ACL_RAW10,
+    ACL_RAW12,
+    ACL_RAW16,
+    ACL_RAW24,
+    ACL_AIPP_RESERVED = 0xffff,
 } aclAippInputFormat;
 
 typedef enum {
-  ACL_MDL_PRIORITY_INT32 = 0,
-  ACL_MDL_LOAD_TYPE_SIZET,
-  ACL_MDL_PATH_PTR,     /**< pointer to model load path with deep copy */
-  ACL_MDL_MEM_ADDR_PTR, /**< pointer to model memory with shallow copy */
-  ACL_MDL_MEM_SIZET,
-  ACL_MDL_WEIGHT_ADDR_PTR, /**< pointer to weight memory of model with shallow copy */
-  ACL_MDL_WEIGHT_SIZET,
-  ACL_MDL_WORKSPACE_ADDR_PTR, /**< pointer to worksapce memory of model with shallow copy */
-  ACL_MDL_WORKSPACE_SIZET,
-  ACL_MDL_INPUTQ_NUM_SIZET,
-  ACL_MDL_INPUTQ_ADDR_PTR, /**< pointer to inputQ with shallow copy */
-  ACL_MDL_OUTPUTQ_NUM_SIZET,
-  ACL_MDL_OUTPUTQ_ADDR_PTR /**< pointer to outputQ with shallow copy */
+    ACL_MDL_PRIORITY_INT32 = 0,
+    ACL_MDL_LOAD_TYPE_SIZET,
+    ACL_MDL_PATH_PTR, /**< pointer to model load path with deep copy */
+    ACL_MDL_MEM_ADDR_PTR, /**< pointer to model memory with shallow copy */
+    ACL_MDL_MEM_SIZET,
+    ACL_MDL_WEIGHT_ADDR_PTR, /**< pointer to weight memory of model with shallow copy */
+    ACL_MDL_WEIGHT_SIZET,
+    ACL_MDL_WORKSPACE_ADDR_PTR, /**< pointer to worksapce memory of model with shallow copy */
+    ACL_MDL_WORKSPACE_SIZET,
+    ACL_MDL_INPUTQ_NUM_SIZET,
+    ACL_MDL_INPUTQ_ADDR_PTR, /**< pointer to inputQ with shallow copy */
+    ACL_MDL_OUTPUTQ_NUM_SIZET,
+    ACL_MDL_OUTPUTQ_ADDR_PTR /**< pointer to outputQ with shallow copy */
 } aclmdlConfigAttr;
 
 typedef enum {
-  ACL_DATA_WITHOUT_AIPP = 0,
-  ACL_DATA_WITH_STATIC_AIPP,
-  ACL_DATA_WITH_DYNAMIC_AIPP,
-  ACL_DYNAMIC_AIPP_NODE
+    ACL_DATA_WITHOUT_AIPP = 0,
+    ACL_DATA_WITH_STATIC_AIPP,
+    ACL_DATA_WITH_DYNAMIC_AIPP,
+    ACL_DYNAMIC_AIPP_NODE
 } aclmdlInputAippType;
 
 typedef struct aclmdlIODims {
-  char name[ACL_MAX_TENSOR_NAME_LEN]; /**< tensor name */
-  size_t dimCount;                    /**< dim array count */
-  int64_t dims[ACL_MAX_DIM_CNT];      /**< dim data array */
+    char name[ACL_MAX_TENSOR_NAME_LEN]; /**< tensor name */
+    size_t dimCount;  /**< dim array count */
+    int64_t dims[ACL_MAX_DIM_CNT]; /**< dim data array */
 } aclmdlIODims;
 
 typedef struct aclAippDims {
-  aclmdlIODims srcDims;     /**< input dims before model transform */
-  size_t srcSize;           /**< input size before model transform */
-  aclmdlIODims aippOutdims; /**< aipp output dims */
-  size_t aippOutSize;       /**< aipp output size */
+    aclmdlIODims srcDims; /**< input dims before model transform */
+    size_t srcSize; /**< input size before model transform */
+    aclmdlIODims aippOutdims; /**< aipp output dims */
+    size_t aippOutSize; /**< aipp output size */
 } aclAippDims;
 
 typedef struct aclmdlBatch {
-  size_t batchCount;                 /**< batch array count */
-  uint64_t batch[ACL_MAX_BATCH_NUM]; /**< batch data array */
+    size_t batchCount; /**< batch array count */
+    uint64_t batch[ACL_MAX_BATCH_NUM]; /**< batch data array */
 } aclmdlBatch;
 
 typedef struct aclmdlHW {
-  size_t hwCount;                 /**< height&width array count */
-  uint64_t hw[ACL_MAX_HW_NUM][2]; /**< height&width data array */
+    size_t hwCount; /**< height&width array count */
+    uint64_t hw[ACL_MAX_HW_NUM][2]; /**< height&width data array */
 } aclmdlHW;
 
 typedef struct aclAippInfo {
-  aclAippInputFormat inputFormat;
-  int32_t srcImageSizeW;
-  int32_t srcImageSizeH;
-  int8_t cropSwitch;
-  int32_t loadStartPosW;
-  int32_t loadStartPosH;
-  int32_t cropSizeW;
-  int32_t cropSizeH;
-  int8_t resizeSwitch;
-  int32_t resizeOutputW;
-  int32_t resizeOutputH;
-  int8_t paddingSwitch;
-  int32_t leftPaddingSize;
-  int32_t rightPaddingSize;
-  int32_t topPaddingSize;
-  int32_t bottomPaddingSize;
-  int8_t cscSwitch;
-  int8_t rbuvSwapSwitch;
-  int8_t axSwapSwitch;
-  int8_t singleLineMode;
-  int32_t matrixR0C0;
-  int32_t matrixR0C1;
-  int32_t matrixR0C2;
-  int32_t matrixR1C0;
-  int32_t matrixR1C1;
-  int32_t matrixR1C2;
-  int32_t matrixR2C0;
-  int32_t matrixR2C1;
-  int32_t matrixR2C2;
-  int32_t outputBias0;
-  int32_t outputBias1;
-  int32_t outputBias2;
-  int32_t inputBias0;
-  int32_t inputBias1;
-  int32_t inputBias2;
-  int32_t meanChn0;
-  int32_t meanChn1;
-  int32_t meanChn2;
-  int32_t meanChn3;
-  float minChn0;
-  float minChn1;
-  float minChn2;
-  float minChn3;
-  float varReciChn0;
-  float varReciChn1;
-  float varReciChn2;
-  float varReciChn3;
-  aclFormat srcFormat;
-  aclDataType srcDatatype;
-  size_t srcDimNum;
-  size_t shapeCount;
-  aclAippDims outDims[ACL_MAX_SHAPE_COUNT];
-  aclAippExtendInfo *aippExtend; /**< reserved parameters, current version needs to be null */
+    aclAippInputFormat inputFormat;
+    int32_t srcImageSizeW;
+    int32_t srcImageSizeH;
+    int8_t cropSwitch;
+    int32_t loadStartPosW;
+    int32_t loadStartPosH;
+    int32_t cropSizeW;
+    int32_t cropSizeH;
+    int8_t resizeSwitch;
+    int32_t resizeOutputW;
+    int32_t resizeOutputH;
+    int8_t paddingSwitch;
+    int32_t leftPaddingSize;
+    int32_t rightPaddingSize;
+    int32_t topPaddingSize;
+    int32_t bottomPaddingSize;
+    int8_t cscSwitch;
+    int8_t rbuvSwapSwitch;
+    int8_t axSwapSwitch;
+    int8_t singleLineMode;
+    int32_t matrixR0C0;
+    int32_t matrixR0C1;
+    int32_t matrixR0C2;
+    int32_t matrixR1C0;
+    int32_t matrixR1C1;
+    int32_t matrixR1C2;
+    int32_t matrixR2C0;
+    int32_t matrixR2C1;
+    int32_t matrixR2C2;
+    int32_t outputBias0;
+    int32_t outputBias1;
+    int32_t outputBias2;
+    int32_t inputBias0;
+    int32_t inputBias1;
+    int32_t inputBias2;
+    int32_t meanChn0;
+    int32_t meanChn1;
+    int32_t meanChn2;
+    int32_t meanChn3;
+    float minChn0;
+    float minChn1;
+    float minChn2;
+    float minChn3;
+    float varReciChn0;
+    float varReciChn1;
+    float varReciChn2;
+    float varReciChn3;
+    aclFormat srcFormat;
+    aclDataType srcDatatype;
+    size_t srcDimNum;
+    size_t shapeCount;
+    aclAippDims outDims[ACL_MAX_SHAPE_COUNT];
+    aclAippExtendInfo *aippExtend; /**< reserved parameters, current version needs to be null */
 } aclAippInfo;
 
 /**
@@ -292,7 +292,8 @@ ACL_FUNC_VISIBILITY aclError aclmdlAddDatasetBuffer(aclmdlDataset *dataset, aclD
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclmdlSetDatasetTensorDesc(aclmdlDataset *dataset, aclTensorDesc *tensorDesc,
+ACL_FUNC_VISIBILITY aclError aclmdlSetDatasetTensorDesc(aclmdlDataset *dataset,
+                                                        aclTensorDesc *tensorDesc,
                                                         size_t index);
 
 /**
@@ -354,7 +355,8 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFile(const char *modelPath, uint32_t
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMem(const void *model, size_t modelSize, uint32_t *modelId);
+ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMem(const void *model,  size_t modelSize,
+                                               uint32_t *modelId);
 
 /**
  * @ingroup AscendCL
@@ -376,8 +378,9 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMem(const void *model, size_t modelSi
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFileWithMem(const char *modelPath, uint32_t *modelId, void *workPtr,
-                                                       size_t workSize, void *weightPtr, size_t weightSize);
+ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFileWithMem(const char *modelPath,
+                                                       uint32_t *modelId, void *workPtr, size_t workSize,
+                                                       void *weightPtr, size_t weightSize);
 
 /**
  * @ingroup AscendCL
@@ -400,9 +403,9 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFileWithMem(const char *modelPath, ui
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMemWithMem(const void *model, size_t modelSize, uint32_t *modelId,
-                                                      void *workPtr, size_t workSize, void *weightPtr,
-                                                      size_t weightSize);
+ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMemWithMem(const void *model, size_t modelSize,
+                                                      uint32_t *modelId, void *workPtr, size_t workSize,
+                                                      void *weightPtr, size_t weightSize);
 
 /**
  * @ingroup AscendCL
@@ -437,8 +440,8 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFileWithQ(const char *modelPath, uint
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMemWithQ(const void *model, size_t modelSize, uint32_t *modelId,
-                                                    const uint32_t *inputQ, size_t inputQNum, const uint32_t *outputQ,
-                                                    size_t outputQNum);
+                                                    const uint32_t *inputQ, size_t inputQNum,
+                                                    const uint32_t *outputQ, size_t outputQNum);
 
 /**
  * @ingroup AscendCL
@@ -468,8 +471,8 @@ ACL_FUNC_VISIBILITY aclError aclmdlExecute(uint32_t modelId, const aclmdlDataset
  * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem |
  * aclmdlLoadFromMemWithMem
  */
-ACL_FUNC_VISIBILITY aclError aclmdlExecuteAsync(uint32_t modelId, const aclmdlDataset *input, aclmdlDataset *output,
-                                                aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclmdlExecuteAsync(uint32_t modelId, const aclmdlDataset *input,
+                                                aclmdlDataset *output, aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -644,7 +647,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlGetCurOutputDims(const aclmdlDesc *modelDesc,
  * @param modelDesc [IN]   model description
  * @param opName [IN]      op name
  * @param attr [IN]        attr name
- *
+ * 
  * @retval the attr value
  */
 ACL_FUNC_VISIBILITY const char *aclmdlGetOpAttr(aclmdlDesc *modelDesc, const char *opName, const char *attr);
@@ -856,11 +859,11 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPInputFormat(aclmdlAIPP *aippParmsSet,
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCscParams(aclmdlAIPP *aippParmsSet, int8_t csc_switch, int16_t cscMatrixR0C0,
-                                                    int16_t cscMatrixR0C1, int16_t cscMatrixR0C2, int16_t cscMatrixR1C0,
-                                                    int16_t cscMatrixR1C1, int16_t cscMatrixR1C2, int16_t cscMatrixR2C0,
-                                                    int16_t cscMatrixR2C1, int16_t cscMatrixR2C2,
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCscParams(aclmdlAIPP *aippParmsSet, int8_t csc_switch,
+                                                    int16_t cscMatrixR0C0, int16_t cscMatrixR0C1, int16_t cscMatrixR0C2,
+                                                    int16_t cscMatrixR1C0, int16_t cscMatrixR1C1, int16_t cscMatrixR1C2,
+                                                    int16_t cscMatrixR2C0, int16_t cscMatrixR2C1, int16_t cscMatrixR2C2,
                                                     uint8_t cscOutputBiasR0, uint8_t cscOutputBiasR1,
                                                     uint8_t cscOutputBiasR2, uint8_t cscInputBiasR0,
                                                     uint8_t cscInputBiasR1, uint8_t cscInputBiasR2);
@@ -876,7 +879,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCscParams(aclmdlAIPP *aippParmsSet, in
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPRbuvSwapSwitch(aclmdlAIPP *aippParmsSet, int8_t rbuvSwapSwitch);
 
 /**
@@ -890,7 +893,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPRbuvSwapSwitch(aclmdlAIPP *aippParmsSe
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPAxSwapSwitch(aclmdlAIPP *aippParmsSet, int8_t axSwapSwitch);
 
 /**
@@ -905,7 +908,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPAxSwapSwitch(aclmdlAIPP *aippParmsSet,
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPSrcImageSize(aclmdlAIPP *aippParmsSet, int32_t srcImageSizeW,
                                                        int32_t srcImageSizeH);
 
@@ -925,10 +928,14 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPSrcImageSize(aclmdlAIPP *aippParmsSet,
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPScfParams(aclmdlAIPP *aippParmsSet, int8_t scfSwitch, int32_t scfInputSizeW,
-                                                    int32_t scfInputSizeH, int32_t scfOutputSizeW,
-                                                    int32_t scfOutputSizeH, uint64_t batchIndex);
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPScfParams(aclmdlAIPP *aippParmsSet,
+                                                    int8_t scfSwitch,
+                                                    int32_t scfInputSizeW,
+                                                    int32_t scfInputSizeH,
+                                                    int32_t scfOutputSizeW,
+                                                    int32_t scfOutputSizeH,
+                                                    uint64_t batchIndex);
 
 /**
  * @ingroup AscendCL
@@ -946,9 +953,13 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPScfParams(aclmdlAIPP *aippParmsSet, in
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCropParams(aclmdlAIPP *aippParmsSet, int8_t cropSwitch, int32_t cropStartPosW,
-                                                     int32_t cropStartPosH, int32_t cropSizeW, int32_t cropSizeH,
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCropParams(aclmdlAIPP *aippParmsSet,
+                                                     int8_t cropSwitch,
+                                                     int32_t cropStartPosW,
+                                                     int32_t cropStartPosH,
+                                                     int32_t cropSizeW,
+                                                     int32_t cropSizeH,
                                                      uint64_t batchIndex);
 
 /**
@@ -967,7 +978,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCropParams(aclmdlAIPP *aippParmsSet, i
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPaddingParams(aclmdlAIPP *aippParmsSet, int8_t paddingSwitch,
                                                         int32_t paddingSizeTop, int32_t paddingSizeBottom,
                                                         int32_t paddingSizeLeft, int32_t paddingSizeRight,
@@ -988,10 +999,13 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPaddingParams(aclmdlAIPP *aippParmsSet
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMean(aclmdlAIPP *aippParmsSet, int16_t dtcPixelMeanChn0,
-                                                       int16_t dtcPixelMeanChn1, int16_t dtcPixelMeanChn2,
-                                                       int16_t dtcPixelMeanChn3, uint64_t batchIndex);
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMean(aclmdlAIPP *aippParmsSet,
+                                                       int16_t dtcPixelMeanChn0,
+                                                       int16_t dtcPixelMeanChn1,
+                                                       int16_t dtcPixelMeanChn2,
+                                                       int16_t dtcPixelMeanChn3,
+                                                       uint64_t batchIndex);
 
 /**
  * @ingroup AscendCL
@@ -1008,10 +1022,13 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMean(aclmdlAIPP *aippParmsSet,
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMin(aclmdlAIPP *aippParmsSet, float dtcPixelMinChn0,
-                                                      float dtcPixelMinChn1, float dtcPixelMinChn2,
-                                                      float dtcPixelMinChn3, uint64_t batchIndex);
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMin(aclmdlAIPP *aippParmsSet,
+                                                      float dtcPixelMinChn0,
+                                                      float dtcPixelMinChn1,
+                                                      float dtcPixelMinChn2,
+                                                      float dtcPixelMinChn3,
+                                                      uint64_t batchIndex);
 
 /**
  * @ingroup AscendCL
@@ -1028,10 +1045,13 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMin(aclmdlAIPP *aippParmsSet,
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPixelVarReci(aclmdlAIPP *aippParmsSet, float dtcPixelVarReciChn0,
-                                                       float dtcPixelVarReciChn1, float dtcPixelVarReciChn2,
-                                                       float dtcPixelVarReciChn3, uint64_t batchIndex);
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPixelVarReci(aclmdlAIPP *aippParmsSet,
+                                                       float dtcPixelVarReciChn0,
+                                                       float dtcPixelVarReciChn1,
+                                                       float dtcPixelVarReciChn2,
+                                                       float dtcPixelVarReciChn3,
+                                                       uint64_t batchIndex);
 
 /**
  * @ingroup AscendCL
@@ -1047,8 +1067,10 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPixelVarReci(aclmdlAIPP *aippParmsSet,
  *
  * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem |
  * aclmdlLoadFromMemWithMem | aclmdlGetInputIndexByName | aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlSetInputAIPP(uint32_t modelId, aclmdlDataset *dataset, size_t index,
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlSetInputAIPP(uint32_t modelId,
+                                                aclmdlDataset *dataset,
+                                                size_t index,
                                                 const aclmdlAIPP *aippParmsSet);
 
 /**
@@ -1065,8 +1087,10 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetInputAIPP(uint32_t modelId, aclmdlDataset
  *
  * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem |
  * aclmdlLoadFromMemWithMem | aclmdlGetInputIndexByName | aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPByInputIndex(uint32_t modelId, aclmdlDataset *dataset, size_t index,
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPByInputIndex(uint32_t modelId,
+                                                       aclmdlDataset *dataset,
+                                                       size_t index,
                                                        const aclmdlAIPP *aippParmsSet);
 
 /**
@@ -1084,8 +1108,10 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPByInputIndex(uint32_t modelId, aclmdlD
  *
  * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem |
  * aclmdlLoadFromMemWithMem | aclmdlGetInputIndexByName | aclmdlCreateAIPP
- */
-ACL_FUNC_VISIBILITY aclError aclmdlGetAippType(uint32_t modelId, size_t index, aclmdlInputAippType *type,
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlGetAippType(uint32_t modelId,
+                                               size_t index,
+                                               aclmdlInputAippType *type,
                                                size_t *dynamicAttachedDataIndex);
 
 /**
@@ -1102,7 +1128,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlGetAippType(uint32_t modelId, size_t index, a
  *
  * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem |
  * aclmdlLoadFromMemWithMem | aclmdlGetInputIndexByName
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlGetFirstAippInfo(uint32_t modelId, size_t index, aclAippInfo *aippinfo);
 
 /**
@@ -1121,11 +1147,10 @@ ACL_FUNC_VISIBILITY aclError aclmdlGetFirstAippInfo(uint32_t modelId, size_t ind
  *
  * @retval ACL_SUCCESS The function is successfully executed
  * @retval OtherValues Failure
- */
-ACL_FUNC_VISIBILITY aclError aclmdlCreateAndGetOpDesc(uint32_t deviceId, uint32_t streamId, uint32_t taskId,
-                                                      char *opName, size_t opNameLen, aclTensorDesc **inputDesc,
-                                                      size_t *numInputs, aclTensorDesc **outputDesc,
-                                                      size_t *numOutputs);
+*/
+ACL_FUNC_VISIBILITY aclError aclmdlCreateAndGetOpDesc(uint32_t deviceId, uint32_t streamId,
+    uint32_t taskId, char *opName, size_t opNameLen, aclTensorDesc **inputDesc, size_t *numInputs,
+    aclTensorDesc **outputDesc, size_t *numOutputs);
 
 /**
  * @ingroup AscendCL
@@ -1133,7 +1158,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlCreateAndGetOpDesc(uint32_t deviceId, uint32_
  *
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlInitDump();
 
 /**
@@ -1144,7 +1169,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlInitDump();
  *
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlSetDump(const char *dumpCfgPath);
 
 /**
@@ -1153,7 +1178,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetDump(const char *dumpCfgPath);
  *
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlFinalizeDump();
 
 /**
@@ -1165,7 +1190,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlFinalizeDump();
  *
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlLoadWithConfig(const aclmdlConfigHandle *handle, uint32_t *modelId);
 
 /**
@@ -1175,7 +1200,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadWithConfig(const aclmdlConfigHandle *hand
  * @retval the aclmdlConfigHandle pointer
  *
  * @see aclmdlDestroyConfigHandle
- */
+*/
 ACL_FUNC_VISIBILITY aclmdlConfigHandle *aclmdlCreateConfigHandle();
 
 /**
@@ -1204,7 +1229,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlDestroyConfigHandle(aclmdlConfigHandle *handl
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclmdlSetConfigOpt(aclmdlConfigHandle *handle, aclmdlConfigAttr attr,
-                                                const void *attrValue, size_t valueSize);
+    const void *attrValue, size_t valueSize);
 
 /**
  * @ingroup AscendCL
@@ -1222,4 +1247,4 @@ ACL_FUNC_VISIBILITY const char *aclmdlGetTensorRealName(const aclmdlDesc *modelD
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_MODEL_H_
+#endif // INC_EXTERNAL_ACL_ACL_MODEL_H_

inc/external/acl/acl_op.h

@@ -33,9 +33,9 @@ typedef void (*aclDataDeallocator)(void *data, size_t length);
 static const int ACL_COMPILE_FLAG_BIN_SELECTOR = 1;
 
 typedef enum aclEngineType {
-  ACL_ENGINE_SYS,
-  ACL_ENGINE_AICORE,
-  ACL_ENGINE_VECTOR,
+    ACL_ENGINE_SYS,
+    ACL_ENGINE_AICORE,
+    ACL_ENGINE_VECTOR,
 } aclopEngineType;
 
 /**
@@ -148,7 +148,7 @@ ACL_FUNC_VISIBILITY aclError aclopSetAttrString(aclopAttr *attr, const char *att
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclopSetAttrListBool(aclopAttr *attr, const char *attrName, int numValues,
-                                                  const uint8_t *values);
+    const uint8_t *values);
 
 /**
  * @ingroup AscendCL
@@ -163,7 +163,7 @@ ACL_FUNC_VISIBILITY aclError aclopSetAttrListBool(aclopAttr *attr, const char *a
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclopSetAttrListInt(aclopAttr *attr, const char *attrName, int numValues,
-                                                 const int64_t *values);
+    const int64_t *values);
 
 /**
  * @ingroup AscendCL
@@ -178,7 +178,7 @@ ACL_FUNC_VISIBILITY aclError aclopSetAttrListInt(aclopAttr *attr, const char *at
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclopSetAttrListFloat(aclopAttr *attr, const char *attrName, int numValues,
-                                                   const float *values);
+    const float *values);
 
 /**
  * @ingroup AscendCL
@@ -193,7 +193,7 @@ ACL_FUNC_VISIBILITY aclError aclopSetAttrListFloat(aclopAttr *attr, const char *
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclopSetAttrListString(aclopAttr *attr, const char *attrName, int numValues,
-                                                    const char **values);
+    const char **values);
 
 /**
  * @ingroup AscendCL
@@ -208,8 +208,11 @@ ACL_FUNC_VISIBILITY aclError aclopSetAttrListString(aclopAttr *attr, const char
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopSetAttrListListInt(aclopAttr *attr, const char *attrName, int numLists,
-                                                     const int *numValues, const int64_t *const values[]);
+ACL_FUNC_VISIBILITY aclError aclopSetAttrListListInt(aclopAttr *attr,
+                                                     const char *attrName,
+                                                     int numLists,
+                                                     const int *numValues,
+                                                     const int64_t *const values[]);
 
 /**
  * @ingroup AscendCL
@@ -239,10 +242,15 @@ ACL_FUNC_VISIBILITY aclError aclopSetAttrListListInt(aclopAttr *attr, const char
  * @retval OtherValues Failure
  */
 ACL_DEPRECATED_MESSAGE("aclopExecute is deprecated, use aclopExecuteV2 instead")
-ACL_FUNC_VISIBILITY aclError aclopExecute(const char *opType, int numInputs, const aclTensorDesc *const inputDesc[],
-                                          const aclDataBuffer *const inputs[], int numOutputs,
-                                          const aclTensorDesc *const outputDesc[], aclDataBuffer *const outputs[],
-                                          const aclopAttr *attr, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclopExecute(const char *opType,
+                                          int numInputs,
+                                          const aclTensorDesc *const inputDesc[],
+                                          const aclDataBuffer *const inputs[],
+                                          int numOutputs,
+                                          const aclTensorDesc *const outputDesc[],
+                                          aclDataBuffer *const outputs[],
+                                          const aclopAttr *attr,
+                                          aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -272,9 +280,15 @@ ACL_FUNC_VISIBILITY aclError aclopExecute(const char *opType, int numInputs, con
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopExecuteV2(const char *opType, int numInputs, aclTensorDesc *inputDesc[],
-                                            aclDataBuffer *inputs[], int numOutputs, aclTensorDesc *outputDesc[],
-                                            aclDataBuffer *outputs[], aclopAttr *attr, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclopExecuteV2(const char *opType,
+                                            int numInputs,
+                                            aclTensorDesc *inputDesc[],
+                                            aclDataBuffer *inputs[],
+                                            int numOutputs,
+                                            aclTensorDesc *outputDesc[],
+                                            aclDataBuffer *outputs[],
+                                            aclopAttr *attr,
+                                            aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -292,9 +306,12 @@ ACL_FUNC_VISIBILITY aclError aclopExecuteV2(const char *opType, int numInputs, a
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopCreateHandle(const char *opType, int numInputs,
-                                               const aclTensorDesc *const inputDesc[], int numOutputs,
-                                               const aclTensorDesc *const outputDesc[], const aclopAttr *opAttr,
+ACL_FUNC_VISIBILITY aclError aclopCreateHandle(const char *opType,
+                                               int numInputs,
+                                               const aclTensorDesc *const inputDesc[],
+                                               int numOutputs,
+                                               const aclTensorDesc *const outputDesc[],
+                                               const aclopAttr *opAttr,
                                                aclopHandle **handle);
 
 /**
@@ -326,9 +343,12 @@ ACL_FUNC_VISIBILITY void aclopDestroyHandle(aclopHandle *handle);
  *
  * @see aclopCreateHandle | aclCreateDataBuffer
  */
-ACL_FUNC_VISIBILITY aclError aclopExecWithHandle(aclopHandle *handle, int numInputs,
-                                                 const aclDataBuffer *const inputs[], int numOutputs,
-                                                 aclDataBuffer *const outputs[], aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclopExecWithHandle(aclopHandle *handle,
+                                                 int numInputs,
+                                                 const aclDataBuffer *const inputs[],
+                                                 int numOutputs,
+                                                 aclDataBuffer *const outputs[],
+                                                 aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -344,8 +364,11 @@ ACL_FUNC_VISIBILITY aclError aclopExecWithHandle(aclopHandle *handle, int numInp
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopCast(const aclTensorDesc *srcDesc, const aclDataBuffer *srcBuffer,
-                                       const aclTensorDesc *dstDesc, aclDataBuffer *dstBuffer, uint8_t truncate,
+ACL_FUNC_VISIBILITY aclError aclopCast(const aclTensorDesc *srcDesc,
+                                       const aclDataBuffer *srcBuffer,
+                                       const aclTensorDesc *dstDesc,
+                                       aclDataBuffer *dstBuffer,
+                                       uint8_t truncate,
                                        aclrtStream stream);
 
 /**
@@ -360,9 +383,12 @@ ACL_FUNC_VISIBILITY aclError aclopCast(const aclTensorDesc *srcDesc, const aclDa
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopCreateHandleForCast(aclTensorDesc *srcDesc, aclTensorDesc *dstDesc, uint8_t truncate,
+ACL_FUNC_VISIBILITY aclError aclopCreateHandleForCast(aclTensorDesc *srcDesc,
+                                                      aclTensorDesc *dstDesc,
+                                                      uint8_t truncate,
                                                       aclopHandle **handle);
 
+
 /**
  * @ingroup AscendCL
  * @brief create kernel
@@ -381,10 +407,15 @@ ACL_FUNC_VISIBILITY aclError aclopCreateHandleForCast(aclTensorDesc *srcDesc, ac
  *
  * @see aclopCompile
  */
-ACL_FUNC_VISIBILITY aclError aclopCreateKernel(const char *opType, const char *kernelId, const char *kernelName,
-                                               void *binData, int binSize, aclopEngineType enginetype,
+ACL_FUNC_VISIBILITY aclError aclopCreateKernel(const char *opType,
+                                               const char *kernelId,
+                                               const char *kernelName,
+                                               void *binData,
+                                               int binSize,
+                                               aclopEngineType enginetype,
                                                aclDataDeallocator deallocator);
 
+
 /**
  * @ingroup AscendCL
  * @brief create kernel
@@ -399,8 +430,11 @@ ACL_FUNC_VISIBILITY aclError aclopCreateKernel(const char *opType, const char *k
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-typedef aclError (*aclopCompileFunc)(int numInputs, const aclTensorDesc *const inputDesc[], int numOutputs,
-                                     const aclTensorDesc *const outputDesc[], const aclopAttr *opAttr,
+typedef aclError (*aclopCompileFunc)(int numInputs,
+                                     const aclTensorDesc *const inputDesc[],
+                                     int numOutputs,
+                                     const aclTensorDesc *const outputDesc[],
+                                     const aclopAttr *opAttr,
                                      aclopKernelDesc *aclopKernelDesc);
 
 /**
@@ -441,8 +475,11 @@ ACL_FUNC_VISIBILITY aclError aclopUnregisterCompileFunc(const char *opType);
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopSetKernelArgs(aclopKernelDesc *kernelDesc, const char *kernelId, uint32_t blockDim,
-                                                const void *args, uint32_t argSize);
+ACL_FUNC_VISIBILITY aclError aclopSetKernelArgs(aclopKernelDesc *kernelDesc,
+                                                const char *kernelId,
+                                                uint32_t blockDim,
+                                                const void *args,
+                                                uint32_t argSize);
 
 /**
  * @ingroup AscendCL
@@ -473,9 +510,12 @@ ACL_FUNC_VISIBILITY aclError aclopSetKernelWorkspaceSizes(aclopKernelDesc *kerne
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopUpdateParams(const char *opType, int numInputs,
-                                               const aclTensorDesc *const inputDesc[], int numOutputs,
-                                               const aclTensorDesc *const outputDesc[], const aclopAttr *attr);
+ACL_FUNC_VISIBILITY aclError aclopUpdateParams(const char *opType,
+                                               int numInputs,
+                                               const aclTensorDesc *const inputDesc[],
+                                               int numOutputs,
+                                               const aclTensorDesc *const outputDesc[],
+                                               const aclopAttr *attr);
 
 /**
  * @ingroup AscendCL
@@ -493,12 +533,17 @@ ACL_FUNC_VISIBILITY aclError aclopUpdateParams(const char *opType, int numInputs
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopInferShape(const char *opType, int numInputs, aclTensorDesc *inputDesc[],
-                                             aclDataBuffer *inputs[], int numOutputs, aclTensorDesc *outputDesc[],
+ACL_FUNC_VISIBILITY aclError aclopInferShape(const char *opType,
+                                             int numInputs,
+                                             aclTensorDesc *inputDesc[],
+                                             aclDataBuffer *inputs[],
+                                             int numOutputs,
+                                             aclTensorDesc *outputDesc[],
                                              aclopAttr *attr);
 
+
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_OP_H_
+#endif // INC_EXTERNAL_ACL_ACL_OP_H_

inc/external/acl/acl_op_compiler.h

@@ -24,22 +24,28 @@
 extern "C" {
 #endif
 
-typedef enum aclCompileType { ACL_COMPILE_SYS, ACL_COMPILE_UNREGISTERED } aclopCompileType;
+typedef enum aclCompileType {
+    ACL_COMPILE_SYS,
+    ACL_COMPILE_UNREGISTERED
+} aclopCompileType;
 
 typedef enum {
-  ACL_PRECISION_MODE,
-  ACL_AICORE_NUM,
-  ACL_AUTO_TUNE_MODE,
-  ACL_OP_SELECT_IMPL_MODE,
-  ACL_OPTYPELIST_FOR_IMPLMODE,
-  ACL_OP_DEBUG_LEVEL,
-  ACL_DEBUG_DIR,
-  ACL_OP_COMPILER_CACHE_MODE,
-  ACL_OP_COMPILER_CACHE_DIR,
-  ACL_OP_PERFORMANCE_MODE
+    ACL_PRECISION_MODE,
+    ACL_AICORE_NUM,
+    ACL_AUTO_TUNE_MODE,
+    ACL_OP_SELECT_IMPL_MODE,
+    ACL_OPTYPELIST_FOR_IMPLMODE,
+    ACL_OP_DEBUG_LEVEL,
+    ACL_DEBUG_DIR,
+    ACL_OP_COMPILER_CACHE_MODE,
+    ACL_OP_COMPILER_CACHE_DIR,
+    ACL_OP_PERFORMANCE_MODE
 } aclCompileOpt;
 
-typedef enum aclCompileFlag { ACL_OP_COMPILE_DEFAULT, ACL_OP_COMPILE_FUZZ } aclOpCompileFlag;
+typedef enum aclCompileFlag {
+    ACL_OP_COMPILE_DEFAULT,
+    ACL_OP_COMPILE_FUZZ
+} aclOpCompileFlag;
 
 /**
  * @ingroup AscendCL
@@ -59,10 +65,15 @@ typedef enum aclCompileFlag { ACL_OP_COMPILE_DEFAULT, ACL_OP_COMPILE_FUZZ } aclO
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopCompile(const char *opType, int numInputs, const aclTensorDesc *const inputDesc[],
-                                          int numOutputs, const aclTensorDesc *const outputDesc[],
-                                          const aclopAttr *attr, aclopEngineType engineType,
-                                          aclopCompileType compileFlag, const char *opPath);
+ACL_FUNC_VISIBILITY aclError aclopCompile(const char *opType,
+                                          int numInputs,
+                                          const aclTensorDesc *const inputDesc[],
+                                          int numOutputs,
+                                          const aclTensorDesc *const outputDesc[],
+                                          const aclopAttr *attr,
+                                          aclopEngineType engineType,
+                                          aclopCompileType compileFlag,
+                                          const char *opPath);
 
 /**
  * @ingroup AscendCL
@@ -85,10 +96,11 @@ ACL_FUNC_VISIBILITY aclError aclopCompile(const char *opType, int numInputs, con
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclopCompileAndExecute(
-  const char *opType, int numInputs, const aclTensorDesc *const inputDesc[], const aclDataBuffer *const inputs[],
-  int numOutputs, const aclTensorDesc *const outputDesc[], aclDataBuffer *const outputs[], const aclopAttr *attr,
-  aclopEngineType engineType, aclopCompileType compileFlag, const char *opPath, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclopCompileAndExecute(const char *opType,
+    int numInputs, const aclTensorDesc *const inputDesc[], const aclDataBuffer *const inputs[],
+    int numOutputs, const aclTensorDesc *const outputDesc[], aclDataBuffer *const outputs[],
+    const aclopAttr *attr, aclopEngineType engineType, aclopCompileType compileFlag,
+    const char *opPath, aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -118,4 +130,4 @@ ACL_FUNC_VISIBILITY aclError aclopSetCompileFlag(aclOpCompileFlag flag);
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_
+#endif // INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_

inc/external/acl/acl_prof.h

@@ -23,30 +23,37 @@
 extern "C" {
 #endif
 
-#define ACL_PROF_ACL_API 0x0001
-#define ACL_PROF_TASK_TIME 0x0002
-#define ACL_PROF_AICORE_METRICS 0x0004
-#define ACL_PROF_AICPU 0x0008
+#define ACL_PROF_ACL_API                0x0001
+#define ACL_PROF_TASK_TIME              0x0002
+#define ACL_PROF_AICORE_METRICS         0x0004
+#define ACL_PROF_AICPU                  0x0008
 
 /**
  * @deprecated please use aclprofGetOpTypeLen and aclprofGetOpTNameLen instead
  */
-#define ACL_PROF_MAX_OP_NAME_LEN 257
-#define ACL_PROF_MAX_OP_TYPE_LEN 65
+#define ACL_PROF_MAX_OP_NAME_LEN        257
+#define ACL_PROF_MAX_OP_TYPE_LEN        65
 
 typedef enum {
-  ACL_AICORE_ARITHMETIC_UTILIZATION = 0,
-  ACL_AICORE_PIPE_UTILIZATION = 1,
-  ACL_AICORE_MEMORY_BANDWIDTH = 2,
-  ACL_AICORE_L0B_AND_WIDTH = 3,
-  ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4,
-  ACL_AICORE_NONE = 0xFF
+    ACL_AICORE_ARITHMETIC_UTILIZATION = 0,
+    ACL_AICORE_PIPE_UTILIZATION = 1,
+    ACL_AICORE_MEMORY_BANDWIDTH = 2,
+    ACL_AICORE_L0B_AND_WIDTH = 3,
+    ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4,
+    ACL_AICORE_NONE = 0xFF
 } aclprofAicoreMetrics;
 
+typedef enum {
+    ACL_STEP_START = 0, // step  start
+    ACL_STEP_END = 1   // step  end
+} aclprofStepTag;
+
+
 typedef struct aclprofConfig aclprofConfig;
 typedef struct aclprofStopConfig aclprofStopConfig;
 typedef struct aclprofAicoreEvents aclprofAicoreEvents;
 typedef struct aclprofSubscribeConfig aclprofSubscribeConfig;
+typedef struct aclprofStepInfo aclprofStepInfo;
 
 /**
  * @ingroup AscendCL
@@ -101,8 +108,7 @@ ACL_FUNC_VISIBILITY aclError aclprofStart(const aclprofConfig *profilerConfig);
  * @see aclprofDestroyConfig
  */
 ACL_FUNC_VISIBILITY aclprofConfig *aclprofCreateConfig(uint32_t *deviceIdList, uint32_t deviceNums,
-                                                       aclprofAicoreMetrics aicoreMetrics,
-                                                       aclprofAicoreEvents *aicoreEvents, uint64_t dataTypeConfig);
+    aclprofAicoreMetrics aicoreMetrics, aclprofAicoreEvents *aicoreEvents, uint64_t dataTypeConfig);
 
 /**
  * @ingroup AscendCL
@@ -142,7 +148,8 @@ ACL_FUNC_VISIBILITY aclError aclprofStop(const aclprofConfig *profilerConfig);
  *
  * @see aclprofModelUnSubscribe
  */
-ACL_FUNC_VISIBILITY aclError aclprofModelSubscribe(uint32_t modelId, const aclprofSubscribeConfig *profSubscribeConfig);
+ACL_FUNC_VISIBILITY aclError aclprofModelSubscribe(uint32_t modelId,
+    const aclprofSubscribeConfig *profSubscribeConfig);
 
 /**
  * @ingroup AscendCL
@@ -170,7 +177,7 @@ ACL_FUNC_VISIBILITY aclError aclprofModelUnSubscribe(uint32_t modelId);
  * @see aclprofDestroySubscribeConfig
  */
 ACL_FUNC_VISIBILITY aclprofSubscribeConfig *aclprofCreateSubscribeConfig(int8_t timeInfoSwitch,
-                                                                         aclprofAicoreMetrics aicoreMetrics, void *fd);
+    aclprofAicoreMetrics aicoreMetrics, void *fd);
 
 /**
  * @ingroup AscendCL
@@ -222,7 +229,7 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpNum(const void *opInfo, size_t opInfoLe
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclprofGetOpTypeLen(const void *opInfo, size_t opInfoLen, uint32_t index,
-                                                 size_t *opTypeLen);
+    size_t *opTypeLen);
 
 /**
  * @ingroup AscendCL
@@ -237,8 +244,8 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpTypeLen(const void *opInfo, size_t opIn
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclprofGetOpType(const void *opInfo, size_t opInfoLen, uint32_t index, char *opType,
-                                              size_t opTypeLen);
+ACL_FUNC_VISIBILITY aclError aclprofGetOpType(const void *opInfo, size_t opInfoLen, uint32_t index,
+    char *opType, size_t opTypeLen);
 
 /**
  * @ingroup AscendCL
@@ -253,7 +260,7 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpType(const void *opInfo, size_t opInfoL
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError aclprofGetOpNameLen(const void *opInfo, size_t opInfoLen, uint32_t index,
-                                                 size_t *opNameLen);
+    size_t *opNameLen);
 
 /**
  * @ingroup AscendCL
@@ -268,8 +275,8 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpNameLen(const void *opInfo, size_t opIn
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclprofGetOpName(const void *opInfo, size_t opInfoLen, uint32_t index, char *opName,
-                                              size_t opNameLen);
+ACL_FUNC_VISIBILITY aclError aclprofGetOpName(const void *opInfo, size_t opInfoLen, uint32_t index,
+    char *opName, size_t opNameLen);
 
 /**
  * @ingroup AscendCL
@@ -322,8 +329,38 @@ ACL_FUNC_VISIBILITY uint64_t aclprofGetOpDuration(const void *opInfo, size_t opI
  */
 ACL_FUNC_VISIBILITY size_t aclprofGetModelId(const void *opInfo, size_t opInfoLen, uint32_t index);
 
+/**
+ * @ingroup AscendCL
+ * @brief
+ *
+ * @param  stepInfo [IN]     pointer to stepInfo data
+ * @param  aclprofstepTag [IN] start or end flag
+ * @param  stream [IN] steam info
+ *
+ * @retval 0 for failed
+ */
+ACL_FUNC_VISIBILITY aclError aclprofGetStepTimestamp(aclprofStepInfo* stepInfo, aclprofStepTag tag, aclrtStream stream);
+
+ /**
+ * @ingroup AscendCL
+ * @brief create pointer to aclprofStepInfo data
+ *
+ *
+ * @retval aclprofStepInfo pointer
+ */
+ACL_FUNC_VISIBILITY aclprofStepInfo* aclprofCreateStepInfo();
+
+ /**
+ * @ingroup AscendCL
+ * @brief destroy aclprofStepInfo pointer
+ *
+ *
+ * @retval void
+ */
+ACL_FUNC_VISIBILITY void aclprofDestroyStepInfo(aclprofStepInfo* stepinfo);
+
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_PROF_H_
+#endif // INC_EXTERNAL_ACL_PROF_H_

inc/external/acl/acl_rt.h

@@ -28,63 +28,63 @@ extern "C" {
 #define ACL_EVENT_TIME_LINE 0x00000008u
 
 typedef enum aclrtRunMode {
-  ACL_DEVICE,
-  ACL_HOST,
+    ACL_DEVICE,
+    ACL_HOST,
 } aclrtRunMode;
 
 typedef enum aclrtTsId {
-  ACL_TS_ID_AICORE = 0,
-  ACL_TS_ID_AIVECTOR = 1,
-  ACL_TS_ID_RESERVED = 2,
+    ACL_TS_ID_AICORE   = 0,
+    ACL_TS_ID_AIVECTOR = 1,
+    ACL_TS_ID_RESERVED = 2,
 } aclrtTsId;
 
 typedef enum aclrtEventStatus {
-  ACL_EVENT_STATUS_COMPLETE = 0,
-  ACL_EVENT_STATUS_NOT_READY = 1,
-  ACL_EVENT_STATUS_RESERVED = 2,
+    ACL_EVENT_STATUS_COMPLETE  = 0,
+    ACL_EVENT_STATUS_NOT_READY = 1,
+    ACL_EVENT_STATUS_RESERVED  = 2,
 } aclrtEventStatus;
 
 typedef enum aclrtCallbackBlockType {
-  ACL_CALLBACK_NO_BLOCK,
-  ACL_CALLBACK_BLOCK,
+    ACL_CALLBACK_NO_BLOCK,
+    ACL_CALLBACK_BLOCK,
 } aclrtCallbackBlockType;
 
 typedef enum aclrtMemcpyKind {
-  ACL_MEMCPY_HOST_TO_HOST,
-  ACL_MEMCPY_HOST_TO_DEVICE,
-  ACL_MEMCPY_DEVICE_TO_HOST,
-  ACL_MEMCPY_DEVICE_TO_DEVICE,
+    ACL_MEMCPY_HOST_TO_HOST,
+    ACL_MEMCPY_HOST_TO_DEVICE,
+    ACL_MEMCPY_DEVICE_TO_HOST,
+    ACL_MEMCPY_DEVICE_TO_DEVICE,
 } aclrtMemcpyKind;
 
 typedef enum aclrtMemMallocPolicy {
-  ACL_MEM_MALLOC_HUGE_FIRST,
-  ACL_MEM_MALLOC_HUGE_ONLY,
-  ACL_MEM_MALLOC_NORMAL_ONLY,
-  ACL_MEM_MALLOC_HUGE_FIRST_P2P,
-  ACL_MEM_MALLOC_HUGE_ONLY_P2P,
-  ACL_MEM_MALLOC_NORMAL_ONLY_P2P,
+    ACL_MEM_MALLOC_HUGE_FIRST,
+    ACL_MEM_MALLOC_HUGE_ONLY,
+    ACL_MEM_MALLOC_NORMAL_ONLY,
+    ACL_MEM_MALLOC_HUGE_FIRST_P2P,
+    ACL_MEM_MALLOC_HUGE_ONLY_P2P,
+    ACL_MEM_MALLOC_NORMAL_ONLY_P2P,
 } aclrtMemMallocPolicy;
 
 typedef enum aclrtMemAttr {
-  ACL_DDR_MEM,
-  ACL_HBM_MEM,
-  ACL_DDR_MEM_HUGE,
-  ACL_DDR_MEM_NORMAL,
-  ACL_HBM_MEM_HUGE,
-  ACL_HBM_MEM_NORMAL,
-  ACL_DDR_MEM_P2P_HUGE,
-  ACL_DDR_MEM_P2P_NORMAL,
-  ACL_HBM_MEM_P2P_HUGE,
-  ACL_HBM_MEM_P2P_NORMAL,
+    ACL_DDR_MEM,
+    ACL_HBM_MEM,
+    ACL_DDR_MEM_HUGE,
+    ACL_DDR_MEM_NORMAL,
+    ACL_HBM_MEM_HUGE,
+    ACL_HBM_MEM_NORMAL,
+    ACL_DDR_MEM_P2P_HUGE,
+    ACL_DDR_MEM_P2P_NORMAL,
+    ACL_HBM_MEM_P2P_HUGE,
+    ACL_HBM_MEM_P2P_NORMAL,
 } aclrtMemAttr;
 
 typedef enum aclrtGroupAttr {
-  ACL_GROUP_AICORE_INT,
-  ACL_GROUP_AIV_INT,
-  ACL_GROUP_AIC_INT,
-  ACL_GROUP_SDMANUM_INT,
-  ACL_GROUP_ASQNUM_INT,
-  ACL_GROUP_GROUPID_INT
+    ACL_GROUP_AICORE_INT,
+    ACL_GROUP_AIV_INT,
+    ACL_GROUP_AIC_INT,
+    ACL_GROUP_SDMANUM_INT,
+    ACL_GROUP_ASQNUM_INT,
+    ACL_GROUP_GROUPID_INT
 } aclrtGroupAttr;
 
 typedef struct tagRtGroupInfo aclrtGroupInfo;
@@ -487,7 +487,7 @@ ACL_FUNC_VISIBILITY aclError aclrtRecordEvent(aclrtEvent event, aclrtStream stre
  */
 ACL_FUNC_VISIBILITY aclError aclrtResetEvent(aclrtEvent event, aclrtStream stream);
 
-/**
+ /**
  * @ingroup AscendCL
  * @brief Queries an event's status
  *
@@ -549,7 +549,9 @@ ACL_FUNC_VISIBILITY aclError aclrtEventElapsedTime(float *ms, aclrtEvent start,
  *
  * @see aclrtFree | acldvppMalloc | aclrtMallocCached
  */
-ACL_FUNC_VISIBILITY aclError aclrtMalloc(void **devPtr, size_t size, aclrtMemMallocPolicy policy);
+ACL_FUNC_VISIBILITY aclError aclrtMalloc(void **devPtr,
+                                         size_t size,
+                                         aclrtMemMallocPolicy policy);
 
 /**
  * @ingroup AscendCL
@@ -572,7 +574,9 @@ ACL_FUNC_VISIBILITY aclError aclrtMalloc(void **devPtr, size_t size, aclrtMemMal
  *
  * @see aclrtFree | aclrtMalloc
  */
-ACL_FUNC_VISIBILITY aclError aclrtMallocCached(void **devPtr, size_t size, aclrtMemMallocPolicy policy);
+ACL_FUNC_VISIBILITY aclError aclrtMallocCached(void **devPtr,
+                                               size_t size,
+                                               aclrtMemMallocPolicy policy);
 
 /**
  * @ingroup AscendCL
@@ -663,7 +667,10 @@ ACL_FUNC_VISIBILITY aclError aclrtFreeHost(void *hostPtr);
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclrtMemcpy(void *dst, size_t destMax, const void *src, size_t count,
+ACL_FUNC_VISIBILITY aclError aclrtMemcpy(void *dst,
+                                         size_t destMax,
+                                         const void *src,
+                                         size_t count,
                                          aclrtMemcpyKind kind);
 
 /**
@@ -710,31 +717,38 @@ ACL_FUNC_VISIBILITY aclError aclrtMemset(void *devPtr, size_t maxCount, int32_t
  *
  * @see aclrtSynchronizeStream
  */
-ACL_FUNC_VISIBILITY aclError aclrtMemcpyAsync(void *dst, size_t destMax, const void *src, size_t count,
-                                              aclrtMemcpyKind kind, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclrtMemcpyAsync(void *dst,
+                                              size_t destMax,
+                                              const void *src,
+                                              size_t count,
+                                              aclrtMemcpyKind kind,
+                                              aclrtStream stream);
 
 /**
- * @ingroup AscendCL
- * @brief Asynchronous initialize memory
- * and set contents of memory to specified value async
- *
- * @par Function
+* @ingroup AscendCL
+* @brief Asynchronous initialize memory
+* and set contents of memory to specified value async
+*
+* @par Function
  *  The memory to be initialized is on the Host or device side,
  *  and the system determines whether
  *  it is host or device according to the address
  *
- * @param devPtr [IN]      destination address pointer
- * @param maxCount [IN]    Max length of destination address memory
- * @param value [IN]       set value
- * @param count [IN]       the number of byte to set
- * @param stream [IN]      asynchronized task stream
- *
- * @retval ACL_SUCCESS The function is successfully executed.
- * @retval OtherValues Failure
- *
- * @see aclrtSynchronizeStream
- */
-ACL_FUNC_VISIBILITY aclError aclrtMemsetAsync(void *devPtr, size_t maxCount, int32_t value, size_t count,
+* @param devPtr [IN]      destination address pointer
+* @param maxCount [IN]    Max length of destination address memory
+* @param value [IN]       set value
+* @param count [IN]       the number of byte to set
+* @param stream [IN]      asynchronized task stream
+*
+* @retval ACL_SUCCESS The function is successfully executed.
+* @retval OtherValues Failure
+*
+* @see aclrtSynchronizeStream
+*/
+ACL_FUNC_VISIBILITY aclError aclrtMemsetAsync(void *devPtr,
+                                              size_t maxCount,
+                                              int32_t value,
+                                              size_t count,
                                               aclrtStream stream);
 
 /**
@@ -880,8 +894,11 @@ ACL_FUNC_VISIBILITY aclError aclrtGetAllGroupInfo(aclrtGroupInfo *groupInfo);
  *
  * @see aclrtGetGroupCount | aclrtGetAllGroupInfo
  */
-ACL_FUNC_VISIBILITY aclError aclrtGetGroupInfoDetail(const aclrtGroupInfo *groupInfo, int32_t groupIndex,
-                                                     aclrtGroupAttr attr, void *attrValue, size_t valueLen,
+ACL_FUNC_VISIBILITY aclError aclrtGetGroupInfoDetail(const aclrtGroupInfo *groupInfo,
+                                                     int32_t groupIndex,
+                                                     aclrtGroupAttr attr,
+                                                     void *attrValue,
+                                                     size_t valueLen,
                                                      size_t *paramRetSize);
 
 /**
@@ -955,4 +972,5 @@ ACL_FUNC_VISIBILITY aclError aclrtSetOpWaitTimeout(uint32_t timeout);
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_RT_H_
+#endif // INC_EXTERNAL_ACL_ACL_RT_H_
+

inc/external/acl/acl_tdt.h

@@ -24,10 +24,10 @@ extern "C" {
 #endif
 
 enum acltdtTensorType {
-  ACL_TENSOR_DATA_UNDEFINED = -1,
-  ACL_TENSOR_DATA_TENSOR,
-  ACL_TENSOR_DATA_END_OF_SEQUENCE,
-  ACL_TENSOR_DATA_ABNORMAL
+    ACL_TENSOR_DATA_UNDEFINED = -1,
+    ACL_TENSOR_DATA_TENSOR,
+    ACL_TENSOR_DATA_END_OF_SEQUENCE,
+    ACL_TENSOR_DATA_ABNORMAL
 };
 
 typedef struct acltdtDataItem acltdtDataItem;
@@ -64,7 +64,7 @@ ACL_FUNC_VISIBILITY aclDataType acltdtGetDataTypeFromItem(const acltdtDataItem *
  *
  * @retval null for failed
  * @retval OtherValues success
- */
+*/
 ACL_FUNC_VISIBILITY void *acltdtGetDataAddrFromItem(const acltdtDataItem *dataItem);
 
 /**
@@ -75,7 +75,7 @@ ACL_FUNC_VISIBILITY void *acltdtGetDataAddrFromItem(const acltdtDataItem *dataIt
  *
  * @retval 0 for failed
  * @retval OtherValues success
- */
+*/
 ACL_FUNC_VISIBILITY size_t acltdtGetDataSizeFromItem(const acltdtDataItem *dataItem);
 
 /**
@@ -86,7 +86,7 @@ ACL_FUNC_VISIBILITY size_t acltdtGetDataSizeFromItem(const acltdtDataItem *dataI
  *
  * @retval 0 for failed
  * @retval OtherValues success
- */
+*/
 ACL_FUNC_VISIBILITY size_t acltdtGetDimNumFromItem(const acltdtDataItem *dataItem);
 
 /**
@@ -118,8 +118,12 @@ ACL_FUNC_VISIBILITY aclError acltdtGetDimsFromItem(const acltdtDataItem *dataIte
  *
  * @see acltdtDestroyDataItem
  */
-ACL_FUNC_VISIBILITY acltdtDataItem *acltdtCreateDataItem(acltdtTensorType tdtType, const int64_t *dims, size_t dimNum,
-                                                         aclDataType dataType, void *data, size_t size);
+ACL_FUNC_VISIBILITY acltdtDataItem *acltdtCreateDataItem(acltdtTensorType tdtType,
+                                                         const int64_t *dims,
+                                                         size_t dimNum,
+                                                         aclDataType dataType,
+                                                         void *data,
+                                                         size_t size);
 
 /**
  * @ingroup AscendCL
@@ -250,7 +254,8 @@ ACL_FUNC_VISIBILITY aclError acltdtDestroyChannel(acltdtChannelHandle *handle);
  *
  * @see acltdtReceiveTensor
  */
-ACL_FUNC_VISIBILITY aclError acltdtSendTensor(const acltdtChannelHandle *handle, const acltdtDataset *dataset,
+ACL_FUNC_VISIBILITY aclError acltdtSendTensor(const acltdtChannelHandle *handle,
+                                              const acltdtDataset *dataset,
                                               int32_t timeout);
 
 /**
@@ -266,11 +271,13 @@ ACL_FUNC_VISIBILITY aclError acltdtSendTensor(const acltdtChannelHandle *handle,
  *
  * @see acltdtSendTensor
  */
-ACL_FUNC_VISIBILITY aclError acltdtReceiveTensor(const acltdtChannelHandle *handle, acltdtDataset *dataset,
+ACL_FUNC_VISIBILITY aclError acltdtReceiveTensor(const acltdtChannelHandle *handle,
+                                                 acltdtDataset *dataset,
                                                  int32_t timeout);
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_TDT_H_
+#endif //INC_EXTERNAL_ACL_ACL_TDT_H_
+

inc/external/acl/error_codes/rt_error_codes.h

@@ -23,87 +23,87 @@
 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_WAIT_TIMEOUT = 107019;              // wait timeout
+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_WAIT_TIMEOUT               = 107019; // wait timeout
 
-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_NO_CDQ_RESOURCE = 207011;      // no cdq 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_NO_CDQ_RESOURCE            = 207011; // no cdq 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_VECTOR_CORE_TIMEOUT = 507034;         // vector core timeout
-static const int32_t ACL_ERROR_RT_VECTOR_CORE_EXCEPTION = 507035;       // vector core exception
-static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_EXCEPTION = 507036;  // vector core trap exception
-static const int32_t ACL_ERROR_RT_CDQ_BATCH_ABNORMAL = 507037;          // cdq alloc batch abnormal
+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_VECTOR_CORE_TIMEOUT        = 507034; // vector core timeout
+static const int32_t ACL_ERROR_RT_VECTOR_CORE_EXCEPTION      = 507035; // vector core exception
+static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_EXCEPTION = 507036; // vector core trap exception
+static const int32_t ACL_ERROR_RT_CDQ_BATCH_ABNORMAL         = 507037; // cdq alloc batch abnormal
 
-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_SOCKET_CLOSE = 507901;          // hdc disconnect
+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_SOCKET_CLOSE               = 507901; // hdc disconnect
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // __INC_EXTERNEL_RT_ERROR_CODES_H__
+#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__

inc/external/acl/ops/acl_cblas.h

@@ -23,9 +23,17 @@
 extern "C" {
 #endif
 
-typedef enum aclTransType { ACL_TRANS_N, ACL_TRANS_T, ACL_TRANS_NZ, ACL_TRANS_NZ_T } aclTransType;
+typedef enum aclTransType {
+    ACL_TRANS_N,
+    ACL_TRANS_T,
+    ACL_TRANS_NZ,
+    ACL_TRANS_NZ_T
+} aclTransType;
 
-typedef enum aclComputeType { ACL_COMPUTE_HIGH_PRECISION, ACL_COMPUTE_LOW_PRECISION } aclComputeType;
+typedef enum aclComputeType {
+    ACL_COMPUTE_HIGH_PRECISION,
+    ACL_COMPUTE_LOW_PRECISION
+} aclComputeType;
 
 /**
  * @ingroup AscendCL
@@ -53,11 +61,12 @@ typedef enum aclComputeType { ACL_COMPUTE_HIGH_PRECISION, ACL_COMPUTE_LOW_PRECIS
  *
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
- */
-ACL_FUNC_VISIBILITY aclError aclblasGemvEx(aclTransType transA, int m, int n, const void *alpha, const void *a, int lda,
-                                           aclDataType dataTypeA, const void *x, int incx, aclDataType dataTypeX,
-                                           const void *beta, void *y, int incy, aclDataType dataTypeY,
-                                           aclComputeType type, aclrtStream stream);
+*/
+ACL_FUNC_VISIBILITY aclError aclblasGemvEx(aclTransType transA, int m, int n,
+    const void *alpha, const void *a, int lda, aclDataType dataTypeA,
+    const void *x, int incx, aclDataType dataTypeX,
+    const void *beta, void *y, int incy, aclDataType dataTypeY,
+    aclComputeType type, aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -74,10 +83,15 @@ ACL_FUNC_VISIBILITY aclError aclblasGemvEx(aclTransType transA, int m, int n, co
  *
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
- */
-ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForGemvEx(aclTransType transA, int m, int n, aclDataType dataTypeA,
-                                                          aclDataType dataTypeX, aclDataType dataTypeY,
-                                                          aclComputeType type, aclopHandle **handle);
+*/
+ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForGemvEx(aclTransType transA,
+                                                          int m,
+                                                          int n,
+                                                          aclDataType dataTypeA,
+                                                          aclDataType dataTypeX,
+                                                          aclDataType dataTypeY,
+                                                          aclComputeType type,
+                                                          aclopHandle **handle);
 
 /**
  * @ingroup AscendCL
@@ -101,9 +115,18 @@ ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForGemvEx(aclTransType transA, i
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasHgemv(aclTransType transA, int m, int n, const aclFloat16 *alpha,
-                                          const aclFloat16 *a, int lda, const aclFloat16 *x, int incx,
-                                          const aclFloat16 *beta, aclFloat16 *y, int incy, aclComputeType type,
+ACL_FUNC_VISIBILITY aclError aclblasHgemv(aclTransType transA,
+                                          int m,
+                                          int n,
+                                          const aclFloat16 *alpha,
+                                          const aclFloat16 *a,
+                                          int lda,
+                                          const aclFloat16 *x,
+                                          int incx,
+                                          const aclFloat16 *beta,
+                                          aclFloat16 *y,
+                                          int incy,
+                                          aclComputeType type,
                                           aclrtStream stream);
 
 /**
@@ -119,7 +142,10 @@ ACL_FUNC_VISIBILITY aclError aclblasHgemv(aclTransType transA, int m, int n, con
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForHgemv(aclTransType transA, int m, int n, aclComputeType type,
+ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForHgemv(aclTransType transA,
+                                                         int m,
+                                                         int n,
+                                                         aclComputeType type,
                                                          aclopHandle **handle);
 
 /**
@@ -145,9 +171,19 @@ ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForHgemv(aclTransType transA, in
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasS8gemv(aclTransType transA, int m, int n, const int32_t *alpha, const int8_t *a,
-                                           int lda, const int8_t *x, int incx, const int32_t *beta, int32_t *y,
-                                           int incy, aclComputeType type, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclblasS8gemv(aclTransType transA,
+                                           int m,
+                                           int n,
+                                           const int32_t *alpha,
+                                           const int8_t *a,
+                                           int lda,
+                                           const int8_t *x,
+                                           int incx,
+                                           const int32_t *beta,
+                                           int32_t *y,
+                                           int incy,
+                                           aclComputeType type,
+                                           aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -162,7 +198,10 @@ ACL_FUNC_VISIBILITY aclError aclblasS8gemv(aclTransType transA, int m, int n, co
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForS8gemv(aclTransType transA, int m, int n, aclComputeType type,
+ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForS8gemv(aclTransType transA,
+                                                          int m,
+                                                          int n,
+                                                          aclComputeType type,
                                                           aclopHandle **handle);
 
 /**
@@ -194,11 +233,26 @@ ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForS8gemv(aclTransType transA, i
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasGemmEx(aclTransType transA, aclTransType transB, aclTransType transC, int m, int n,
-                                           int k, const void *alpha, const void *matrixA, int lda,
-                                           aclDataType dataTypeA, const void *matrixB, int ldb, aclDataType dataTypeB,
-                                           const void *beta, void *matrixC, int ldc, aclDataType dataTypeC,
-                                           aclComputeType type, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclblasGemmEx(aclTransType transA,
+                                           aclTransType transB,
+                                           aclTransType transC,
+                                           int m,
+                                           int n,
+                                           int k,
+                                           const void *alpha,
+                                           const void *matrixA,
+                                           int lda,
+                                           aclDataType dataTypeA,
+                                           const void *matrixB,
+                                           int ldb,
+                                           aclDataType dataTypeB,
+                                           const void *beta,
+                                           void *matrixC,
+                                           int ldc,
+                                           aclDataType dataTypeC,
+                                           aclComputeType type,
+                                           aclrtStream stream);
+
 
 /**
  * @ingroup AscendCL
@@ -220,10 +274,18 @@ ACL_FUNC_VISIBILITY aclError aclblasGemmEx(aclTransType transA, aclTransType tra
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForGemmEx(aclTransType transA, aclTransType transB, aclTransType transC,
-                                                          int m, int n, int k, aclDataType dataTypeA,
-                                                          aclDataType dataTypeB, aclDataType dataTypeC,
-                                                          aclComputeType type, aclopHandle **handle);
+ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForGemmEx(aclTransType transA,
+                                                          aclTransType transB,
+                                                          aclTransType transC,
+                                                          int m,
+                                                          int n,
+                                                          int k,
+                                                          aclDataType dataTypeA,
+                                                          aclDataType dataTypeB,
+                                                          aclDataType dataTypeC,
+                                                          aclComputeType type,
+                                                          aclopHandle **handle);
+
 
 /**
  * @ingroup AscendCL
@@ -251,10 +313,22 @@ ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForGemmEx(aclTransType transA, a
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasHgemm(aclTransType transA, aclTransType transB, aclTransType transC, int m, int n,
-                                          int k, const aclFloat16 *alpha, const aclFloat16 *matrixA, int lda,
-                                          const aclFloat16 *matrixB, int ldb, const aclFloat16 *beta,
-                                          aclFloat16 *matrixC, int ldc, aclComputeType type, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclblasHgemm(aclTransType transA,
+                                          aclTransType transB,
+                                          aclTransType transC,
+                                          int m,
+                                          int n,
+                                          int k,
+                                          const aclFloat16 *alpha,
+                                          const aclFloat16 *matrixA,
+                                          int lda,
+                                          const aclFloat16 *matrixB,
+                                          int ldb,
+                                          const aclFloat16 *beta,
+                                          aclFloat16 *matrixC,
+                                          int ldc,
+                                          aclComputeType type,
+                                          aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -272,8 +346,13 @@ ACL_FUNC_VISIBILITY aclError aclblasHgemm(aclTransType transA, aclTransType tran
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForHgemm(aclTransType transA, aclTransType transB, aclTransType transC,
-                                                         int m, int n, int k, aclComputeType type,
+ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForHgemm(aclTransType transA,
+                                                         aclTransType transB,
+                                                         aclTransType transC,
+                                                         int m,
+                                                         int n,
+                                                         int k,
+                                                         aclComputeType type,
                                                          aclopHandle **handle);
 
 /**
@@ -302,10 +381,23 @@ ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForHgemm(aclTransType transA, ac
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasS8gemm(aclTransType transA, aclTransType transB, aclTransType transC, int m, int n,
-                                           int k, const int32_t *alpha, const int8_t *matrixA, int lda,
-                                           const int8_t *matrixB, int ldb, const int32_t *beta, int32_t *matrixC,
-                                           int ldc, aclComputeType type, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError aclblasS8gemm(aclTransType transA,
+                                           aclTransType transB,
+                                           aclTransType transC,
+                                           int m,
+                                           int n,
+                                           int k,
+                                           const int32_t *alpha,
+                                           const int8_t *matrixA,
+                                           int lda,
+                                           const int8_t *matrixB,
+                                           int ldb,
+                                           const int32_t *beta,
+                                           int32_t *matrixC,
+                                           int ldc,
+                                           aclComputeType type,
+                                           aclrtStream stream);
+
 
 /**
  * @ingroup AscendCL
@@ -323,12 +415,17 @@ ACL_FUNC_VISIBILITY aclError aclblasS8gemm(aclTransType transA, aclTransType tra
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForS8gemm(aclTransType transA, aclTransType transB, aclTransType transC,
-                                                          int m, int n, int k, aclComputeType type,
+ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForS8gemm(aclTransType transA,
+                                                          aclTransType transB,
+                                                          aclTransType transC,
+                                                          int m,
+                                                          int n,
+                                                          int k,
+                                                          aclComputeType type,
                                                           aclopHandle **handle);
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_OPS_ACL_CBLAS_H_
+#endif // INC_EXTERNAL_ACL_OPS_ACL_CBLAS_H_

inc/external/acl/ops/acl_dvpp.h

@@ -53,109 +53,123 @@ typedef void (*aclvencCallback)(acldvppPicDesc *input, acldvppStreamDesc *output
 
 // Supported Pixel Format
 enum acldvppPixelFormat {
-  PIXEL_FORMAT_YUV_400 = 0,                      // 0
-  PIXEL_FORMAT_YUV_SEMIPLANAR_420 = 1,           // 1
-  PIXEL_FORMAT_YVU_SEMIPLANAR_420 = 2,           // 2
-  PIXEL_FORMAT_YUV_SEMIPLANAR_422 = 3,           // 3
-  PIXEL_FORMAT_YVU_SEMIPLANAR_422 = 4,           // 4
-  PIXEL_FORMAT_YUV_SEMIPLANAR_444 = 5,           // 5
-  PIXEL_FORMAT_YVU_SEMIPLANAR_444 = 6,           // 6
-  PIXEL_FORMAT_YUYV_PACKED_422 = 7,              // 7
-  PIXEL_FORMAT_UYVY_PACKED_422 = 8,              // 8
-  PIXEL_FORMAT_YVYU_PACKED_422 = 9,              // 9
-  PIXEL_FORMAT_VYUY_PACKED_422 = 10,             // 10
-  PIXEL_FORMAT_YUV_PACKED_444 = 11,              // 11
-  PIXEL_FORMAT_RGB_888 = 12,                     // 12
-  PIXEL_FORMAT_BGR_888 = 13,                     // 13
-  PIXEL_FORMAT_ARGB_8888 = 14,                   // 14
-  PIXEL_FORMAT_ABGR_8888 = 15,                   // 15
-  PIXEL_FORMAT_RGBA_8888 = 16,                   // 16
-  PIXEL_FORMAT_BGRA_8888 = 17,                   // 17
-  PIXEL_FORMAT_YUV_SEMI_PLANNER_420_10BIT = 18,  // 18
-  PIXEL_FORMAT_YVU_SEMI_PLANNER_420_10BIT = 19,  // 19
-  PIXEL_FORMAT_YVU_PLANAR_420 = 20,              // 20
-  PIXEL_FORMAT_YVU_PLANAR_422,
-  PIXEL_FORMAT_YVU_PLANAR_444,
-  PIXEL_FORMAT_RGB_444 = 23,
-  PIXEL_FORMAT_BGR_444,
-  PIXEL_FORMAT_ARGB_4444,
-  PIXEL_FORMAT_ABGR_4444,
-  PIXEL_FORMAT_RGBA_4444,
-  PIXEL_FORMAT_BGRA_4444,
-  PIXEL_FORMAT_RGB_555,
-  PIXEL_FORMAT_BGR_555,
-  PIXEL_FORMAT_RGB_565,
-  PIXEL_FORMAT_BGR_565,
-  PIXEL_FORMAT_ARGB_1555,
-  PIXEL_FORMAT_ABGR_1555,
-  PIXEL_FORMAT_RGBA_1555,
-  PIXEL_FORMAT_BGRA_1555,
-  PIXEL_FORMAT_ARGB_8565,
-  PIXEL_FORMAT_ABGR_8565,
-  PIXEL_FORMAT_RGBA_8565,
-  PIXEL_FORMAT_BGRA_8565,
-  PIXEL_FORMAT_RGB_BAYER_8BPP = 50,
-  PIXEL_FORMAT_RGB_BAYER_10BPP,
-  PIXEL_FORMAT_RGB_BAYER_12BPP,
-  PIXEL_FORMAT_RGB_BAYER_14BPP,
-  PIXEL_FORMAT_RGB_BAYER_16BPP,
-  PIXEL_FORMAT_BGR_888_PLANAR = 70,
-  PIXEL_FORMAT_HSV_888_PACKAGE,
-  PIXEL_FORMAT_HSV_888_PLANAR,
-  PIXEL_FORMAT_LAB_888_PACKAGE,
-  PIXEL_FORMAT_LAB_888_PLANAR,
-  PIXEL_FORMAT_S8C1,
-  PIXEL_FORMAT_S8C2_PACKAGE,
-  PIXEL_FORMAT_S8C2_PLANAR,
-  PIXEL_FORMAT_S16C1,
-  PIXEL_FORMAT_U8C1,
-  PIXEL_FORMAT_U16C1,
-  PIXEL_FORMAT_S32C1,
-  PIXEL_FORMAT_U32C1,
-  PIXEL_FORMAT_U64C1,
-  PIXEL_FORMAT_S64C1,
-  PIXEL_FORMAT_YUV_SEMIPLANAR_440 = 1000,
-  PIXEL_FORMAT_YVU_SEMIPLANAR_440,
-  PIXEL_FORMAT_FLOAT32,
-  PIXEL_FORMAT_BUTT,
-  PIXEL_FORMAT_UNKNOWN = 10000
+    PIXEL_FORMAT_YUV_400 = 0, // 0
+    PIXEL_FORMAT_YUV_SEMIPLANAR_420 = 1, // 1
+    PIXEL_FORMAT_YVU_SEMIPLANAR_420 = 2, // 2
+    PIXEL_FORMAT_YUV_SEMIPLANAR_422 = 3, // 3
+    PIXEL_FORMAT_YVU_SEMIPLANAR_422 = 4, // 4
+    PIXEL_FORMAT_YUV_SEMIPLANAR_444 = 5, // 5
+    PIXEL_FORMAT_YVU_SEMIPLANAR_444 = 6, // 6
+    PIXEL_FORMAT_YUYV_PACKED_422 = 7, // 7
+    PIXEL_FORMAT_UYVY_PACKED_422 = 8, // 8
+    PIXEL_FORMAT_YVYU_PACKED_422 = 9, // 9
+    PIXEL_FORMAT_VYUY_PACKED_422 = 10, // 10
+    PIXEL_FORMAT_YUV_PACKED_444 = 11, // 11
+    PIXEL_FORMAT_RGB_888 = 12, // 12
+    PIXEL_FORMAT_BGR_888 = 13, // 13
+    PIXEL_FORMAT_ARGB_8888 = 14, // 14
+    PIXEL_FORMAT_ABGR_8888 = 15, // 15
+    PIXEL_FORMAT_RGBA_8888 = 16, // 16
+    PIXEL_FORMAT_BGRA_8888 = 17, // 17
+    PIXEL_FORMAT_YUV_SEMI_PLANNER_420_10BIT = 18, // 18
+    PIXEL_FORMAT_YVU_SEMI_PLANNER_420_10BIT = 19, // 19
+    PIXEL_FORMAT_YVU_PLANAR_420 = 20, // 20
+    PIXEL_FORMAT_YVU_PLANAR_422,
+    PIXEL_FORMAT_YVU_PLANAR_444,
+    PIXEL_FORMAT_RGB_444 = 23,
+    PIXEL_FORMAT_BGR_444,
+    PIXEL_FORMAT_ARGB_4444,
+    PIXEL_FORMAT_ABGR_4444,
+    PIXEL_FORMAT_RGBA_4444,
+    PIXEL_FORMAT_BGRA_4444,
+    PIXEL_FORMAT_RGB_555,
+    PIXEL_FORMAT_BGR_555,
+    PIXEL_FORMAT_RGB_565,
+    PIXEL_FORMAT_BGR_565,
+    PIXEL_FORMAT_ARGB_1555,
+    PIXEL_FORMAT_ABGR_1555,
+    PIXEL_FORMAT_RGBA_1555,
+    PIXEL_FORMAT_BGRA_1555,
+    PIXEL_FORMAT_ARGB_8565,
+    PIXEL_FORMAT_ABGR_8565,
+    PIXEL_FORMAT_RGBA_8565,
+    PIXEL_FORMAT_BGRA_8565,
+    PIXEL_FORMAT_RGB_BAYER_8BPP = 50,
+    PIXEL_FORMAT_RGB_BAYER_10BPP,
+    PIXEL_FORMAT_RGB_BAYER_12BPP,
+    PIXEL_FORMAT_RGB_BAYER_14BPP,
+    PIXEL_FORMAT_RGB_BAYER_16BPP,
+    PIXEL_FORMAT_BGR_888_PLANAR = 70,
+    PIXEL_FORMAT_HSV_888_PACKAGE,
+    PIXEL_FORMAT_HSV_888_PLANAR,
+    PIXEL_FORMAT_LAB_888_PACKAGE,
+    PIXEL_FORMAT_LAB_888_PLANAR,
+    PIXEL_FORMAT_S8C1,
+    PIXEL_FORMAT_S8C2_PACKAGE,
+    PIXEL_FORMAT_S8C2_PLANAR,
+    PIXEL_FORMAT_S16C1,
+    PIXEL_FORMAT_U8C1,
+    PIXEL_FORMAT_U16C1,
+    PIXEL_FORMAT_S32C1,
+    PIXEL_FORMAT_U32C1,
+    PIXEL_FORMAT_U64C1,
+    PIXEL_FORMAT_S64C1,
+    PIXEL_FORMAT_YUV_SEMIPLANAR_440 = 1000,
+    PIXEL_FORMAT_YVU_SEMIPLANAR_440,
+    PIXEL_FORMAT_FLOAT32,
+    PIXEL_FORMAT_BUTT,
+    PIXEL_FORMAT_UNKNOWN = 10000
 };
 
 // Stream Format
-enum acldvppStreamFormat { H265_MAIN_LEVEL = 0, H264_BASELINE_LEVEL, H264_MAIN_LEVEL, H264_HIGH_LEVEL };
+enum acldvppStreamFormat {
+    H265_MAIN_LEVEL = 0,
+    H264_BASELINE_LEVEL,
+    H264_MAIN_LEVEL,
+    H264_HIGH_LEVEL
+};
 
 // Supported Channel Mode
-enum acldvppChannelMode { DVPP_CHNMODE_VPC = 1, DVPP_CHNMODE_JPEGD = 2, DVPP_CHNMODE_JPEGE = 4 };
+enum acldvppChannelMode {
+    DVPP_CHNMODE_VPC = 1,
+    DVPP_CHNMODE_JPEGD = 2,
+    DVPP_CHNMODE_JPEGE = 4
+};
 
 // Supported Border Type
-enum acldvppBorderType { BORDER_CONSTANT = 0, BORDER_REPLICATE, BORDER_REFLECT, BORDER_REFLECT_101 };
+enum acldvppBorderType {
+    BORDER_CONSTANT = 0,
+    BORDER_REPLICATE,
+    BORDER_REFLECT,
+    BORDER_REFLECT_101
+};
 
 // Venc parameter type
 enum aclvencChannelDescParamType {
-  ACL_VENC_THREAD_ID_UINT64 = 0,
-  ACL_VENC_CALLBACK_PTR,
-  ACL_VENC_PIXEL_FORMAT_UINT32,
-  ACL_VENC_ENCODE_TYPE_UINT32,
-  ACL_VENC_PIC_WIDTH_UINT32,
-  ACL_VENC_PIC_HEIGHT_UINT32,
-  ACL_VENC_KEY_FRAME_INTERVAL_UINT32,
-  ACL_VENC_BUF_ADDR_PTR,
-  ACL_VENC_BUF_SIZE_UINT32,
-  ACL_VENC_RC_MODE_UINT32,
-  ACL_VENC_SRC_RATE_UINT32,
-  ACL_VENC_MAX_BITRATE_UINT32,
-  ACL_VENC_MAX_IP_PROP_UINT32
+    ACL_VENC_THREAD_ID_UINT64 = 0,
+    ACL_VENC_CALLBACK_PTR,
+    ACL_VENC_PIXEL_FORMAT_UINT32,
+    ACL_VENC_ENCODE_TYPE_UINT32,
+    ACL_VENC_PIC_WIDTH_UINT32,
+    ACL_VENC_PIC_HEIGHT_UINT32,
+    ACL_VENC_KEY_FRAME_INTERVAL_UINT32,
+    ACL_VENC_BUF_ADDR_PTR,
+    ACL_VENC_BUF_SIZE_UINT32,
+    ACL_VENC_RC_MODE_UINT32,
+    ACL_VENC_SRC_RATE_UINT32,
+    ACL_VENC_MAX_BITRATE_UINT32,
+    ACL_VENC_MAX_IP_PROP_UINT32
 };
 
 // Jpeg picture format
 enum acldvppJpegFormat {
-  ACL_JPEG_CSS_444 = 0,
-  ACL_JPEG_CSS_422,
-  ACL_JPEG_CSS_420,
-  ACL_JPEG_CSS_GRAY,
-  ACL_JPEG_CSS_440,
-  ACL_JPEG_CSS_411,
-  ACL_JPEG_CSS_UNKNOWN = 1000
+    ACL_JPEG_CSS_444 = 0,
+    ACL_JPEG_CSS_422,
+    ACL_JPEG_CSS_420,
+    ACL_JPEG_CSS_GRAY,
+    ACL_JPEG_CSS_440,
+    ACL_JPEG_CSS_411,
+    ACL_JPEG_CSS_UNKNOWN = 1000
 };
 
 /**
@@ -509,7 +523,9 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetPicDescRetCode(const acldvppPicDesc *picD
  * @retval null for failed.
  * @retval other success
  */
-ACL_FUNC_VISIBILITY acldvppRoiConfig *acldvppCreateRoiConfig(uint32_t left, uint32_t right, uint32_t top,
+ACL_FUNC_VISIBILITY acldvppRoiConfig *acldvppCreateRoiConfig(uint32_t left,
+                                                             uint32_t right,
+                                                             uint32_t top,
                                                              uint32_t bottom);
 
 /**
@@ -588,7 +604,10 @@ ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfigBottom(acldvppRoiConfig *config,
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfig(acldvppRoiConfig *config, uint32_t left, uint32_t right, uint32_t top,
+ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfig(acldvppRoiConfig *config,
+                                                 uint32_t left,
+                                                 uint32_t right,
+                                                 uint32_t top,
                                                  uint32_t bottom);
 
 /**
@@ -1077,8 +1096,7 @@ ACL_FUNC_VISIBILITY aclError aclvencSetChannelDescMaxBitRate(aclvencChannelDesc
  * @retval ACL_SUCCESS for success, other for failure
  */
 ACL_FUNC_VISIBILITY aclError aclvencSetChannelDescParam(aclvencChannelDesc *channelDesc,
-                                                        aclvencChannelDescParamType paramType, size_t length,
-                                                        const void *param);
+    aclvencChannelDescParamType paramType, size_t length, const void *param);
 
 /**
  * @ingroup AscendCL
@@ -1227,8 +1245,7 @@ ACL_FUNC_VISIBILITY uint32_t aclvencGetChannelDescMaxBitRate(const aclvencChanne
  * @retval ACL_SUCCESS for success, other for failure
  */
 ACL_FUNC_VISIBILITY aclError aclvencGetChannelDescParam(const aclvencChannelDesc *channelDesc,
-                                                        aclvencChannelDescParamType paramType, size_t length,
-                                                        size_t *paramRetSize, void *param);
+    aclvencChannelDescParamType paramType, size_t length, size_t *paramRetSize, void *param);
 
 /**
  * @ingroup AscendCL
@@ -1528,7 +1545,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecDestroyFrameConfig(aclvdecFrameConfig *vdecF
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data, uint32_t size, uint32_t *width, uint32_t *height,
+ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data,
+                                                     uint32_t size,
+                                                     uint32_t *width,
+                                                     uint32_t *height,
                                                      int32_t *components);
 
 /**
@@ -1545,8 +1565,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data, uint32_t
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data, uint32_t size, uint32_t *width,
-                                                       uint32_t *height, int32_t *components,
+ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data,
+                                                       uint32_t size,
+                                                       uint32_t *width,
+                                                       uint32_t *height,
+                                                       int32_t *components,
                                                        acldvppJpegFormat *format);
 
 /**
@@ -1561,7 +1584,8 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data, uint32_
  * @retval OtherValues Failure
  */
 ACL_FUNC_VISIBILITY aclError acldvppJpegPredictEncSize(const acldvppPicDesc *inputDesc,
-                                                       const acldvppJpegeConfig *config, uint32_t *size);
+                                                       const acldvppJpegeConfig *config,
+                                                       uint32_t *size);
 
 /**
  * @ingroup AscendCL
@@ -1575,8 +1599,10 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegPredictEncSize(const acldvppPicDesc *inp
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data, uint32_t dataSize,
-                                                       acldvppPixelFormat outputPixelFormat, uint32_t *decSize);
+ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data,
+                                                       uint32_t dataSize,
+                                                       acldvppPixelFormat outputPixelFormat,
+                                                       uint32_t *decSize);
 
 /**
  * @ingroup AscendCL
@@ -1591,8 +1617,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data, uint32_
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data, uint32_t dataSize, uint32_t *width,
-                                                    uint32_t *height, int32_t *components);
+ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data,
+                                                    uint32_t dataSize,
+                                                    uint32_t *width,
+                                                    uint32_t *height,
+                                                    int32_t *components);
 
 /**
  * @ingroup AscendCL
@@ -1606,8 +1635,10 @@ ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data, uint32_t d
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppPngPredictDecSize(const void *data, uint32_t dataSize,
-                                                      acldvppPixelFormat outputPixelFormat, uint32_t *decSize);
+ACL_FUNC_VISIBILITY aclError acldvppPngPredictDecSize(const void *data,
+                                                      uint32_t dataSize,
+                                                      acldvppPixelFormat outputPixelFormat,
+                                                      uint32_t *decSize);
 
 /**
  * @ingroup AscendCL
@@ -1671,8 +1702,10 @@ ACL_FUNC_VISIBILITY aclError acldvppDestroyChannel(acldvppChannelDesc *channelDe
  * @see acldvppCreateChannel | acldvppCreatePicDesc
  * | acldvppCreateResizeConfig
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
-                                                   acldvppPicDesc *outputDesc, acldvppResizeConfig *resizeConfig,
+ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDesc,
+                                                   acldvppPicDesc *inputDesc,
+                                                   acldvppPicDesc *outputDesc,
+                                                   acldvppResizeConfig *resizeConfig,
                                                    aclrtStream stream);
 
 /**
@@ -1708,8 +1741,10 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDe
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
-                                                 acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea,
+ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc,
+                                                 acldvppPicDesc *inputDesc,
+                                                 acldvppPicDesc *outputDesc,
+                                                 acldvppRoiConfig *cropArea,
                                                  aclrtStream stream);
 
 /**
@@ -1746,9 +1781,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
-                                                       acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea,
-                                                       acldvppResizeConfig *resizeConfig, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *channelDesc,
+                                                       acldvppPicDesc *inputDesc,
+                                                       acldvppPicDesc *outputDesc,
+                                                       acldvppRoiConfig *cropArea,
+                                                       acldvppResizeConfig *resizeConfig,
+                                                       aclrtStream stream);
+
 
 /**
  * @ingroup AscendCL
@@ -1772,9 +1811,12 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *chann
  * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig
  */
 ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAsync(acldvppChannelDesc *channelDesc,
-                                                      acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums,
-                                                      uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs,
-                                                      acldvppRoiConfig *cropAreas[], aclrtStream stream);
+                                                      acldvppBatchPicDesc *srcBatchPicDescs,
+                                                      uint32_t *roiNums,
+                                                      uint32_t size,
+                                                      acldvppBatchPicDesc *dstBatchPicDescs,
+                                                      acldvppRoiConfig *cropAreas[],
+                                                      aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -1799,10 +1841,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAsync(acldvppChannelDesc *channe
  * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateDvppConfig
  */
 ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeAsync(acldvppChannelDesc *channelDesc,
-                                                            acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums,
-                                                            uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs,
+                                                            acldvppBatchPicDesc *srcBatchPicDescs,
+                                                            uint32_t *roiNums,
+                                                            uint32_t size,
+                                                            acldvppBatchPicDesc *dstBatchPicDescs,
                                                             acldvppRoiConfig *cropAreas[],
-                                                            acldvppResizeConfig *resizeConfig, aclrtStream stream);
+                                                            acldvppResizeConfig *resizeConfig,
+                                                            aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -1825,9 +1870,12 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeAsync(acldvppChannelDesc *
  *
  * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateRoiConfig
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
-                                                         acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea,
-                                                         acldvppRoiConfig *pasteArea, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *channelDesc,
+                                                         acldvppPicDesc *inputDesc,
+                                                         acldvppPicDesc *outputDesc,
+                                                         acldvppRoiConfig *cropArea,
+                                                         acldvppRoiConfig *pasteArea,
+                                                         aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -1851,10 +1899,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *cha
  *
  * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
-                                                            acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea,
+ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc *channelDesc,
+                                                            acldvppPicDesc *inputDesc,
+                                                            acldvppPicDesc *outputDesc,
+                                                            acldvppRoiConfig *cropArea,
                                                             acldvppRoiConfig *pasteArea,
-                                                            acldvppResizeConfig *resizeConfig, aclrtStream stream);
+                                                            acldvppResizeConfig *resizeConfig,
+                                                            aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -1879,11 +1930,14 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc *
  *
  * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc *channelDesc,
-                                                              acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums,
-                                                              uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs,
-                                                              acldvppRoiConfig *cropAreas[],
-                                                              acldvppRoiConfig *pasteAreas[], aclrtStream stream);
+ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc *channelDesc,
+                                                               acldvppBatchPicDesc *srcBatchPicDescs,
+                                                               uint32_t *roiNums,
+                                                               uint32_t size,
+                                                               acldvppBatchPicDesc *dstBatchPicDescs,
+                                                               acldvppRoiConfig *cropAreas[],
+                                                               acldvppRoiConfig *pasteAreas[],
+                                                               aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -1909,10 +1963,16 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc
  *
  * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync(
-  acldvppChannelDesc *channelDesc, acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, uint32_t size,
-  acldvppBatchPicDesc *dstBatchPicDescs, acldvppRoiConfig *cropAreas[], acldvppRoiConfig *pasteAreas[],
-  acldvppResizeConfig *resizeConfig, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync(acldvppChannelDesc *channelDesc,
+                                                                 acldvppBatchPicDesc *srcBatchPicDescs,
+                                                                 uint32_t *roiNums,
+                                                                 uint32_t size,
+                                                                 acldvppBatchPicDesc *dstBatchPicDescs,
+                                                                 acldvppRoiConfig *cropAreas[],
+                                                                 acldvppRoiConfig *pasteAreas[],
+                                                                 acldvppResizeConfig *resizeConfig,
+                                                                 aclrtStream stream);
+
 
 /**
  * @ingroup AscendCL
@@ -1940,8 +2000,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync(
  *
  * @see acldvppCreateChannel | acldvppCreatePicDesc
  */
-ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelDesc, const void *data, uint32_t size,
-                                                    acldvppPicDesc *outputDesc, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelDesc,
+                                                    const void *data,
+                                                    uint32_t size,
+                                                    acldvppPicDesc *outputDesc,
+                                                    aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -1959,8 +2022,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelD
  *
  * @see acldvppCreateChannel | acldvppCreateJpegeConfig
  */
-ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
-                                                    const void *data, uint32_t *size, acldvppJpegeConfig *config,
+ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelDesc,
+                                                    acldvppPicDesc *inputDesc,
+                                                    const void *data,
+                                                    uint32_t *size,
+                                                    acldvppJpegeConfig *config,
                                                     aclrtStream stream);
 
 /**
@@ -1978,8 +2044,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelD
  *
  * @see acldvppCreateChannel | acldvppCreatePicDesc
  */
-ACL_FUNC_VISIBILITY aclError acldvppPngDecodeAsync(acldvppChannelDesc *channelDesc, const void *data, uint32_t size,
-                                                   acldvppPicDesc *outputDesc, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppPngDecodeAsync(acldvppChannelDesc *channelDesc,
+                                                   const void *data,
+                                                   uint32_t size,
+                                                   acldvppPicDesc *outputDesc,
+                                                   aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -2034,8 +2103,11 @@ ACL_FUNC_VISIBILITY aclError aclvdecDestroyChannel(aclvdecChannelDesc *channelDe
  *
  * @see aclvdecCreateChannel | acldvppCreateStreamDesc | acldvppCreatePicDesc
  */
-ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc, acldvppStreamDesc *input,
-                                              acldvppPicDesc *output, aclvdecFrameConfig *config, void *userData);
+ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc,
+                                              acldvppStreamDesc *input,
+                                              acldvppPicDesc *output,
+                                              aclvdecFrameConfig *config,
+                                              void *userData);
 
 /**
  * @ingroup AscendCL
@@ -2054,8 +2126,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc, a
  *
  * @see aclvdecCreateChannel | acldvppCreateStreamDesc | acldvppCreatePicDesc | aclvdecSendFrame
  */
-ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channelDesc, acldvppStreamDesc *input,
-                                                     aclvdecFrameConfig *config, void *userData);
+ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channelDesc,
+                                                     acldvppStreamDesc *input,
+                                                     aclvdecFrameConfig *config,
+                                                     void *userData);
 
 /**
  * @ingroup AscendCL
@@ -2076,8 +2150,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channel
  *
  * @see acldvppCreateChannel | acldvppCreatePicDesc
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
-                                                         acldvppPicDesc *outputDesc, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *channelDesc,
+                                                         acldvppPicDesc *inputDesc,
+                                                         acldvppPicDesc *outputDesc,
+                                                         aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -2099,8 +2175,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *cha
  *
  * @see acldvppCreateChannel | acldvppCreatePicDesc
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
-                                                    acldvppPicDesc *outputDesc, void *reserve, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelDesc,
+                                                    acldvppPicDesc *inputDesc,
+                                                    acldvppPicDesc *outputDesc,
+                                                    void *reserve,
+                                                    aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -2112,7 +2191,8 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelD
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppSetChannelDescMode(acldvppChannelDesc *channelDesc, uint32_t mode);
+ACL_FUNC_VISIBILITY aclError acldvppSetChannelDescMode(acldvppChannelDesc *channelDesc,
+                                                       uint32_t mode);
 
 /**
  * @ingroup AscendCL
@@ -2147,7 +2227,8 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetResizeConfigInterpolation(const acldvppRe
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError aclvdecSetChannelDescOutMode(aclvdecChannelDesc *channelDesc, uint32_t outMode);
+ACL_FUNC_VISIBILITY aclError aclvdecSetChannelDescOutMode(aclvdecChannelDesc *channelDesc,
+                                                          uint32_t outMode);
 
 /**
  * @ingroup AscendCL
@@ -2244,7 +2325,9 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetLutMapDims(const acldvppLutMap *lutMap);
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap, uint32_t dim, uint8_t **data,
+ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap,
+                                                  uint32_t dim,
+                                                  uint8_t **data,
                                                   uint32_t *len);
 /**
  * @ingroup AscendCL
@@ -2262,8 +2345,10 @@ ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap, u
  * @see acldvppCreateChannel|acldvppCreatePicDesc|acldvppCreateLutMap
  */
 ACL_FUNC_VISIBILITY aclError acldvppVpcEqualizeHistAsync(const acldvppChannelDesc *channelDesc,
-                                                         const acldvppPicDesc *inputDesc, acldvppPicDesc *outputDesc,
-                                                         const acldvppLutMap *lutMap, aclrtStream stream);
+                                                         const acldvppPicDesc *inputDesc,
+                                                         acldvppPicDesc *outputDesc,
+                                                         const acldvppLutMap *lutMap,
+                                                         aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -2284,7 +2369,8 @@ ACL_FUNC_VISIBILITY acldvppBorderConfig *acldvppCreateBorderConfig();
  *
  * @retval ACL_SUCCESS for success, other for failure
  */
-ACL_FUNC_VISIBILITY aclError acldvppSetBorderConfigValue(acldvppBorderConfig *borderConfig, uint32_t index,
+ACL_FUNC_VISIBILITY aclError acldvppSetBorderConfigValue(acldvppBorderConfig *borderConfig,
+                                                         uint32_t index,
                                                          double value);
 
 /**
@@ -2429,8 +2515,10 @@ ACL_FUNC_VISIBILITY aclError acldvppDestroyBorderConfig(acldvppBorderConfig *bor
  * @see acldvppCreateChannel|acldvppCreatePicDesc|acldvppCreateBorderConfig
  */
 ACL_FUNC_VISIBILITY aclError acldvppVpcMakeBorderAsync(const acldvppChannelDesc *channelDesc,
-                                                       const acldvppPicDesc *inputDesc, acldvppPicDesc *outputDesc,
-                                                       const acldvppBorderConfig *borderConfig, aclrtStream stream);
+                                                       const acldvppPicDesc *inputDesc,
+                                                       acldvppPicDesc *outputDesc,
+                                                       const acldvppBorderConfig *borderConfig,
+                                                       aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -2447,8 +2535,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcMakeBorderAsync(const acldvppChannelDesc
  *
  * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateHist
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *srcPicDesc,
-                                                     acldvppHist *hist, void *reserve, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channelDesc,
+                                                     acldvppPicDesc *srcPicDesc,
+                                                     acldvppHist *hist,
+                                                     void *reserve,
+                                                     aclrtStream stream);
 
 /**
  * @ingroup AscendCL
@@ -2457,7 +2548,7 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channel
  * @retval null for failed.
  * @retval OtherValues success.
  */
-ACL_FUNC_VISIBILITY acldvppHist *acldvppCreateHist();
+ACL_FUNC_VISIBILITY acldvppHist* acldvppCreateHist();
 
 /**
  * @ingroup AscendCL
@@ -2514,7 +2605,7 @@ ACL_FUNC_VISIBILITY aclError acldvppGetHistData(acldvppHist *hist, uint32_t dim,
  *
  * @see acldvppCreateHist | acldvppVpcCalcHistAsync
  */
-ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist *hist);
+ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist* hist);
 
 /**
  * @ingroup AscendCL
@@ -2533,6 +2624,7 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist *hist);
  */
 ACL_FUNC_VISIBILITY aclError acldvppClearHist(acldvppHist *hist);
 
+
 /**
  * @ingroup AscendCL
  * @brief dvpp vpc batch crop, resize config and make border.
@@ -2556,13 +2648,18 @@ ACL_FUNC_VISIBILITY aclError acldvppClearHist(acldvppHist *hist);
  *
  * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig
  */
-ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeMakeBorderAsync(
-  acldvppChannelDesc *channelDesc, acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, uint32_t size,
-  acldvppBatchPicDesc *dstBatchPicDescs, acldvppRoiConfig *cropAreas[], acldvppBorderConfig *borderCfgs[],
-  acldvppResizeConfig *resizeConfig, aclrtStream stream);
+ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeMakeBorderAsync(acldvppChannelDesc *channelDesc,
+                                                                      acldvppBatchPicDesc *srcBatchPicDescs,
+                                                                      uint32_t *roiNums,
+                                                                      uint32_t size,
+                                                                      acldvppBatchPicDesc *dstBatchPicDescs,
+                                                                      acldvppRoiConfig *cropAreas[],
+                                                                      acldvppBorderConfig *borderCfgs[],
+                                                                      acldvppResizeConfig *resizeConfig,
+                                                                      aclrtStream stream);
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_OPS_ACL_DVPP_H_
+#endif // INC_EXTERNAL_ACL_OPS_ACL_DVPP_H_

inc/external/acl/ops/acl_fv.h

@@ -32,8 +32,8 @@ typedef struct aclfvSearchResult aclfvSearchResult;
 
 // search operation type
 enum aclfvSearchType {
-  SEARCH_1_N,  // 1:N operation type
-  SEARCH_N_M   // N:M operation type
+    SEARCH_1_N, // 1:N operation type
+    SEARCH_N_M  // N:M operation type
 };
 
 /**
@@ -104,8 +104,7 @@ ACL_FUNC_VISIBILITY aclError aclfvSetNMTopNum(aclfvInitPara *initPara, uint32_t
  * @retval OtherValues success.
  */
 ACL_FUNC_VISIBILITY aclfvFeatureInfo *aclfvCreateFeatureInfo(uint32_t id0, uint32_t id1, uint32_t offset,
-                                                             uint32_t featureLen, uint32_t featureCount,
-                                                             uint8_t *featureData, uint32_t featureDataLen);
+    uint32_t featureLen, uint32_t featureCount, uint8_t *featureData, uint32_t featureDataLen);
 
 /**
  * @ingroup AscendCL
@@ -234,9 +233,8 @@ ACL_FUNC_VISIBILITY aclError aclfvDestroySearchInput(aclfvSearchInput *searchInp
  * @retval null for failed. OtherValues success
  */
 ACL_FUNC_VISIBILITY aclfvSearchResult *aclfvCreateSearchResult(uint32_t queryCnt, uint32_t *resultNum,
-                                                               uint32_t resultNumDataLen, uint32_t *id0, uint32_t *id1,
-                                                               uint32_t *resultOffset, float *resultDistance,
-                                                               uint32_t dataLen);
+    uint32_t resultNumDataLen, uint32_t *id0, uint32_t *id1, uint32_t *resultOffset, float *resultDistance,
+    uint32_t dataLen);
 
 /**
  * @ingroup AscendCL
@@ -345,4 +343,4 @@ ACL_FUNC_VISIBILITY aclError aclfvSearch(aclfvSearchType type, aclfvSearchInput
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_OPS_ACL_RETR_H_
+#endif // INC_EXTERNAL_ACL_OPS_ACL_RETR_H_

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.
@@ -114,16 +113,16 @@ 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 Get the rank size of this comm.
  *
  * @param comm A pointer identifying the communication resource based on.
  * @param rankSize  A pointer identifying the rank size.
- * @return HcclResult
+ * @return HcclResult 
  */
 extern HcclResult HcclGetRankSize(HcclComm comm, uint32_t *rankSize);
 
@@ -132,7 +131,7 @@ extern HcclResult HcclGetRankSize(HcclComm comm, uint32_t *rankSize);
  *
  * @param comm A pointer identifying the communication resource based on.
  * @param rankSize  A pointer identifying the rank id.
- * @return HcclResult
+ * @return HcclResult 
  */
 extern HcclResult HcclGetRankId(HcclComm comm, uint32_t *rank);
 /**
@@ -140,7 +139,7 @@ extern HcclResult HcclGetRankId(HcclComm comm, uint32_t *rank);
  *
  * @param comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
- * @return HcclResult
+ * @return HcclResult 
  */
 extern HcclResult HcclBarrier(HcclComm comm, aclrtStream stream);
 
@@ -155,5 +154,5 @@ 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

@@ -23,87 +23,87 @@
 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_WAIT_TIMEOUT = 107019;              // wait timeout
+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_WAIT_TIMEOUT               = 107019; // wait timeout
 
-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_NO_CDQ_RESOURCE = 207011;      // no cdq 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_NO_CDQ_RESOURCE            = 207011; // no cdq 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_VECTOR_CORE_TIMEOUT = 507034;         // vector core timeout
-static const int32_t ACL_ERROR_RT_VECTOR_CORE_EXCEPTION = 507035;       // vector core exception
-static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_EXCEPTION = 507036;  // vector core trap exception
-static const int32_t ACL_ERROR_RT_CDQ_BATCH_ABNORMAL = 507037;          // cdq alloc batch abnormal
+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_VECTOR_CORE_TIMEOUT        = 507034; // vector core timeout
+static const int32_t ACL_ERROR_RT_VECTOR_CORE_EXCEPTION      = 507035; // vector core exception
+static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_EXCEPTION = 507036; // vector core trap exception
+static const int32_t ACL_ERROR_RT_CDQ_BATCH_ABNORMAL         = 507037; // cdq alloc batch abnormal
 
-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_SOCKET_CLOSE = 507901;          // hdc disconnect
+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_SOCKET_CLOSE               = 507901; // hdc disconnect
 
 #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

@@ -3154,13 +3154,13 @@ REG_OP(FusedMulAddNL2loss)
 *@brief Tests whether the input exceeds a threshold. \n
 
 *@par Inputs:
-*@li x: A Tensor with any format. Must be one of the following types: float16, float32. \n
+* x: A Tensor with any format. Must be one of the following types: float16, float32. \n
 
 *@par Attributes:
-*@li threshold: A required float32. Defaults to "0.0". "x" is compared with "threshold", outputs "1" for inputs above threshold; "0" otherwise. \n
+* threshold: A required float32. Defaults to "0.0". "x" is compared with "threshold", outputs "1" for inputs above threshold; "0" otherwise. \n
 
 *@par Outputs:
-*@li y: A Tensor with any format. Has the same type as the input. Must be one of the following types: float16, float32.
+* y: A Tensor with any format. Has the same type as the input. Must be one of the following types: float16, float32.
 *@par Third-party framework compatibility
 * Compatible with the Caffe operator Threshold.
 */
@@ -3203,12 +3203,11 @@ REG_OP(ArgMaxWithK)
 *@brief Multiply tensor with scale. \n
 
 *@par Inputs:
-*Five inputs, including:
-* @li x1: A Tensor. Must be one of the following types:int32,int16, float16, float32.
-* @li x2: A scale. Must be float. \n
+*One input, including:
+*x: A Tensor. Must be one of the following types:int32,int16, float16, float32.
 
 *@par Outputs:
-*@li y: A Tensor. Has the same type and shape as "x1". \n
+*y: A Tensor. Has the same type and shape as "x1". \n
 
 *@par Third-party framework compatibility:
 * Compatible with the Pytorch operator muls.
@@ -3223,12 +3222,11 @@ REG_OP(Muls)
 *@brief Fill tensor with scale. \n
 
 *@par Inputs:
-*Five inputs, including:
-* @li x1: A Tensor. Must be one of the following types:int32,int16, float16, float32.
-* @li x2: A scale. Must be float. \n
+*One input, including:
+*x1: A Tensor. Must be one of the following types:int32,int16, float16, float32.
 
 *@par Outputs:
-*@li y: A Tensor. Has the same type and shape as "x1". \n
+*y: A Tensor. Has the same type and shape as "x1". \n
 
 *@par Third-party framework compatibility:
 * Compatible with the Pytorch operator fills.
@@ -3378,7 +3376,7 @@ REG_OP(TensorMove)
 
 *@par Inputs:
 *One inputs, including:
-* @li x: A Tensor. Must be one of the following types: float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64. \n
+*x: A Tensor. Must be one of the following types: float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64. \n
 
 *@par Outputs:
 *output_x: A Tensor. Has the same type as "x". \n

third_party/fwkacllib/inc/ops/image_ops.h

@@ -585,9 +585,11 @@ REG_OP(ResizeNearestNeighborV2GradD)
 channels], The image tensor that was resized . \n
 
 *@par Attributes:
-*align_corners: An optional bool. Defaults to False. If true, the centers of
+*@li align_corners: An optional bool. Defaults to False. If true, the centers of
 the 4 corner pixels of the input and grad tensors are aligned. Defaults to
-false . \n
+false .
+*@li half_pixel_centers: indicates if the offset coordinates are normalized. Defaults
+to false . \n
 
 *@par Outputs:
 *y: A Tensor. Has the same type as original_image . \n
@@ -787,9 +789,10 @@ REG_OP(SampleDistortedBoundingBoxExt2)
 The new size for the images . \n
 
 *@par Attributes:
-*align_corners: If true, the centers of the 4 corner pixels of the input and
+*@li align_corners: If true, the centers of the 4 corner pixels of the input and
 output tensors are aligned, preserving the values at the corner pixels.
 Defaults to false . \n
+*@li half_pixel_centers: An optional bool. Defaults to False . \n
 
 *@par Outputs:
 *y: 4-D with shape [batch, new_height, new_width, channels] . \n
@@ -1253,6 +1256,7 @@ REG_OP(KeepRatioResizeBilinear)
 No default value.
 *@li align_corners: An optional bool. If "true", the centers of the corner
 pixels of the input and output tensors are aligned. Defaults to "false" . \n
+*@li half_pixel_centers: An optional bool. Defaults to False . \n
 
 *@par Outputs:
 *y: A Tensor with the same type and format as input "images" . \n

third_party/fwkacllib/inc/ops/math_ops.h

@@ -670,7 +670,8 @@ REG_OP(Conj)
 *@li weight: A Tensor dtype of float32 . \n
 
 *@par Attributes:
-*reduction: An optional attribute. Defaults to "mean" . \n
+*@li reduction: An optional attribute. Defaults to "mean" .
+*@li ignore_index:An optional attribute.Defaults to -100 . \n
 
 *@par Outputs:
 *@li y: A Tensor dtype of float32.
@@ -700,7 +701,8 @@ REG_OP(NLLLoss)
 *@li total_weight:A Tensor dtype of float32 . \n
 
 *@par Attributes:
-*reduction: An optional attribute. Defaults to "mean" . \n
+*@li reduction: An optional attribute. Defaults to "mean" .
+*@li ignore_index:An optional attribute.Defaults to -100 . \n
 
 *@par Outputs:
 *x_grad: A Tensor. Must be the following type: float32 . \n
@@ -720,24 +722,24 @@ REG_OP(NLLLossGrad)
     .OP_END_FACTORY_REG(NLLLossGrad)
 
 /**
-*@brief The ifmr . \n
+*@brief IFMR(Input Feature Map Reconstruction). \n
 
 *@par Inputs:
-*@li data:A Tensor of feature map
-*@li data_min:A Tensor of min value of feature map.
-*@li data_max:A Tensor of max value of feature map.
-*@li cumsum:A Tensor of cumsum bin of data . \n
+*@li data: A Tensor of feature map.
+*@li data_min: A Tensor of min value of feature map.
+*@li data_max: A Tensor of max value of feature map.
+*@li cumsum: A Tensor of cumsum bin of data . \n
 
 *@par Attributes:
-*min_percentile: min init percentile.
-*max_percentile: max init percentile.
-*search_range: search range.
-*search_step: step size of searching.
-*with_offset: whether using offset . \n
+*@li min_percentile: min init percentile.
+*@li max_percentile: max init percentile.
+*@li search_range: search range.
+*@li search_step: step size of searching.
+*@li with_offset: whether using offset . \n
 
 *@par Outputs:
-*scale: optimal scale.
-*offset: optimal offset . \n
+*@li scale: optimal scale.
+*@li offset: optimal offset . \n
 
 *@par Third-party framework compatibility
 *Compatible with mindspore
@@ -758,16 +760,16 @@ REG_OP(IFMR)
   .OP_END_FACTORY_REG(IFMR)
 
 /**
-*@brief weights adaptive range quantization. \n
+*@brief Weights Adaptive Range Quantization. \n
 
 *@par Inputs:
-*@li w:A Tensor of weights. \n
-*@li w_min:A Tensor of weights reduce_min. \n
-*@li w_max:A Tensor of weights reduce_max. \n
+*@li w: A Tensor of weights. \n
+*@li w_min: A Tensor of weights reduce_min. \n
+*@li w_max: A Tensor of weights reduce_max. \n
 
 *@par Attributes:
-*num_bits: the bits num used for quantize.
-*offset_flag: whether using offset. \n
+*@li num_bits: the bits num used for quantize.
+*@li offset_flag: whether using offset. \n
 
 *@par Outputs:
 *y: fake quantized weights. \n
@@ -789,22 +791,22 @@ REG_OP(WtsARQ)
   .OP_END_FACTORY_REG(WtsARQ)
 
 /**
-*@brief The acts_ulq. \n
+*@brief Activations Universal Linear Quantization. \n
 
 *@par Inputs:
-*@li x:A Tensor of feature map
-*@li clamp _min:A Tensor of min clamp value of feature map.
-*@li clamp _max:A Tensor of max clamp value of feature map.
+*@li x: A Tensor of feature map.
+*@li clamp _min: A Tensor of min clamp value of feature map.
+*@li clamp _max: A Tensor of max clamp value of feature map.
 
 *@par Attributes:
-*fixed_min: fix min to zero.
-*num_bits: quant bits. \n
+*@li fixed_min: fix min to zero.
+*@li num_bits: quant bits. \n
 
 *@par Outputs:
-*y: output fake quant feature map.
-*clamp_min_mask: where x > clamp_min
-*clamp_min_mask: where x < clamp_max
-*x_clamped_loss: clamp loss. \n
+*@li y: output fake quant feature map.
+*@li clamp_min_mask: where x > clamp_min.
+*@li clamp_min_mask: where x < clamp_max.
+*@li x_clamped_loss: clamp loss. \n
 
 *@par Third-party framework compatibility
 *Compatible with mindspore
@@ -826,12 +828,12 @@ REG_OP(ActsULQ)
   .OP_END_FACTORY_REG(ActsULQ)
 
 /**
-*@brief The acts_ulq_input_grad. \n
+*@brief The gradient of Activations Universal Linear Quantization. \n
 
 *@par Inputs:
-*@li y_grad: A Tensor of gradient
-*@li clamp_min_mask: A Tensor of boolean mask indicating whether an additional one is needed'
-*@li clamp_max_mask: A Tensor of boolean mask indicating whether an additional one is needed'
+*@li y_grad: A Tensor of gradient.
+*@li clamp_min_mask: A Tensor of boolean mask indicating whether an additional one is needed'.
+*@li clamp_max_mask: A Tensor of boolean mask indicating whether an additional one is needed'.
 
 *@par Outputs:
 *x_grapd: The gradient of inpust. \n
@@ -851,10 +853,10 @@ REG_OP(ActsULQInputGrad)
   .OP_END_FACTORY_REG(ActsULQInputGrad)
 
 /**
-*@brief The act_ulq_clamp_max_grad. \n
+*@brief The gradient of Activations Universal Linear Quantization clamp max. \n
 
 *@par Inputs:
-*@li y_grad: A Tensor of gradient
+*@li y_grad: A Tensor of gradient.
 *@li clamp_max_mask: A Tensor of boolean mask indicating whether an additional one is needed.
 *@li x_clamped_loss: A Tensor of gradient. \n
 
@@ -876,10 +878,10 @@ REG_OP(ActULQClampMaxGrad)
   .OP_END_FACTORY_REG(ActULQClampMaxGrad)
 
 /**
-*@brief The act_ulq_clamp_min_grad. \n
+*@brief The gradient of Activations Universal Linear Quantization clamp min. \n
 
 *@par Inputs:
-*@li y_grad: A Tensor of gradient
+*@li y_grad: A Tensor of gradient.
 *@li clamp_min_mask: A Tensor of boolean mask indicating whether an additional one is needed.
 *@li x_clamped_loss: A Tensor of gradient. \n
 

third_party/fwkacllib/inc/ops/matrix_calculation_ops.h

@@ -67,11 +67,15 @@ REG_OP(MatMul)
 * @li x2: A matrix Tensor. 2D. Must be one of the following types: float16,
 * float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
 * @li bias: A 1D Tensor. Must be one of the following types: float16,
-* float32, int32. Has format [ND, NHWC] . \n
+* float32, int32. Has format [ND, NHWC]
+* @li offset_w: A Optional 1D Tensor for quantized interference. Type is int8. Reserved. \n
 
 *@par Attributes:
 *@li transpose_x1: A bool. If True, changes the shape of "x1" from [M, K] to [K, M].
-*@li transpose_x2: A bool. If True, changes the shape of "x2" from [M, K] to [K, M] . \n
+*@li transpose_x2: A bool. If True, changes the shape of "x2" from [M, K] to [K, M] .
+*@li offset_x: An optional integer for quantized deconvolution.
+*The negative offset added to the input image for int8 type. Ensure offset_x within the
+*effective range of int8 [-128, 127]. Defaults to "0". \n
 
 *@par Outputs:
 *y: The result matrix Tensor. 2D. Must be one of the following types: float16,
@@ -488,13 +492,13 @@ REG_OP(ScatterElements)
 
 *@par Inputs:
 * Three inputs, including:
-*@li var: An ND Tensor . \n
+*@li var: An ND Tensor .
 
 *Must be one of the following types: float16, float32, int32, int8, uint8
 *@li indices: An ND Tensor of type int32 or int64
 
 
-*@li updates: An Tensor. format:NCHW, NHWC . \n
+*@li updates: An Tensor. format:NCHW, NHWC .
 
 *Must be one of the following types: float16, float32, int32, int8, uint8
 
@@ -925,13 +929,13 @@ REG_OP(ScatterMin)
 
 *@par Inputs:
 * Three inputs, including:
-*@li var: An ND Tensor . \n
+*@li var: An ND Tensor .
 
 *Must be one of the following types: float16, float, int32, int8, uint8
 *@li indices: An NCHW, NHWC, or ND Tensor . \n
 
 *Must be one of the following types: int32 or int64
-*@li updates: An NCHW, NHWC, or ND Tensor . \n
+*@li updates: An NCHW, NHWC, or ND Tensor .
 
 *Must be one of the following types: float16, float, int32, int8, uint8
 
@@ -958,13 +962,13 @@ REG_OP(ScatterMax)
 
 *@par Inputs:
 * Three inputs, including:
-*@li var: An ND Tensor . \n
+*@li var: An ND Tensor .
 
 *Must be one of the following types: float16, float, int32, int8, uint8
 *@li indices: An ND Tensor . \n
 
 *Must be one of the following types: int32 or int64
-*@li updates: An ND Tensor . \n
+*@li updates: An ND Tensor .
 
 *Must be one of the following types: float16, float, int32, int8, uint8
 

third_party/fwkacllib/inc/ops/nn_calculation_ops.h

@@ -367,19 +367,19 @@ REG_OP(BiasAddGrad)
  * Gradients with respect to the output of the convolution.
  *\n
  *\n
- * The following are the supported data types and data formats:
-*@verbatim
-    | Tensor    | out_bckprop | filter  | y
-    ------------|-------------|---------|--------
-    | Data Type | float16     | float16 | float16
-    |           |-------------|---------|--------
-    |           | float32     | float32 | float32
-    |           |-------------|---------|--------
-    |           | float64     | float64 | float64
-    ------------|-------------|---------|--------
-    | Format    | NCHW        | NCHW    | NCHW
-    |           | NHWC        | HWCN    | NHWC
-@endverbatim
+ * The following are the supported data types and data formats:\n
+ *\n
+    | Tensor    | out_bckprop | filter  | y\n
+    ------------|-------------|---------|--------\n
+    | Data Type | float16     | float16 | float16\n
+    |           |-------------|---------|--------\n
+    |           | float32     | float32 | float32\n
+    |           |-------------|---------|--------\n
+    |           | float64     | float64 | float64\n
+    ------------|-------------|---------|--------\n
+    | Format    | NCHW        | NCHW    | NCHW\n
+    |           | NHWC        | HWCN    | NHWC\n
+ *\n
  * For float32 and float64 type, the actual calculation on the chip is based on
  * float16.
  *\n
@@ -398,36 +398,37 @@ REG_OP(BiasAddGrad)
  * "NHWC". Specify the data format of the input and output data.
  *\n
  *\n
- * The following value range restrictions must be met:
-*@verbatim
-    | Name             | Field    | Scope
-    -------------------|----------|--------------
-    | input_size       | H        | [1, 4096]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | Filter           | H        | [1, 255]
-    |                  | W        | [1, 255]
-    -------------------|----------|--------------
-    | out_backprop     | H*strideH| [1, 4096]
-    |                  | W*strideW| [1, 4096]
-    -------------------|----------|--------------
-    | y(fmap)          | H        | [1, 4096]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | Stride           | H        | [1, 63]
-    |                  | W        | [1, 63]
-    -------------------|----------|--------------
-    | Padding          | Top      | [0, 255]
-    |                  | Bottom   | [0, 255]
-    |                  | Left     | [0, 255]
-    |                  | Right    | [0, 255]
-    -------------------|----------|--------------
-    | Dilation         | H        | [1, 255]
-    |                  | W        | [1, 255]
+ * The following value range restrictions must be met:\n
+ *\n
+    | Name             | Field    | Scope\n
+    -------------------|----------|--------------\n
+    | input_size       | H        | [1, 200000]\n
+    |                  | W        | [1, 4096]\n
+    -------------------|----------|--------------\n
+    | Filter           | H        | [1, 255]\n
+    |                  | W        | [1, 255]\n
+    -------------------|----------|--------------\n
+    | out_backprop     | H*strideH| [1, 200000]\n
+    |                  | W*strideW| [1, 4096]\n
+    -------------------|----------|--------------\n
+    | y(fmap)          | H        | [1, 200000]\n
+    |                  | W        | [1, 4096]\n
+    -------------------|----------|--------------\n
+    | Stride           | H        | [1, 63]\n
+    |                  | W        | [1, 63]\n
+    -------------------|----------|--------------\n
+    | Padding          | Top      | [0, 255]\n
+    |                  | Bottom   | [0, 255]\n
+    |                  | Left     | [0, 255]\n
+    |                  | Right    | [0, 255]\n
+    -------------------|----------|--------------\n
+    | Dilation         | H        | [1, 255]\n
+    |                  | W        | [1, 255]\n
+ *\n
 
-@endverbatim
  * In Ascend910, fmap or out_backprop's H and W not support 1 when
  * fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1
+ * and filter_width > fmap_width
  * If filter_h = 1 and filter_w = 1, out_backprop_w * stride_h * stride_w < 4096
  *\n
  *
@@ -508,7 +509,7 @@ REG_OP(Conv2DBackpropInputD)
 /**
 *@brief Computes the Deconvolution with respect to the input.
 *@par Inputs:
- * Three 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
  * to the output of the convolution.
@@ -520,16 +521,16 @@ REG_OP(Conv2DBackpropInputD)
  * Type is int8. Reserved.\n
  *\n
  *\n
- * The following are the supported data types and data formats:
-*@verbatim
-    | Tensor    | x       | filter  | bias    | y
-    ------------|---------|---------|---------|--------
-    | Data Type | float16 | float16 | float16 | float16
-    |           |---------|---------|---------|--------
-    |           | int8    | int8    | int32   | int32
-    ------------|---------|---------|---------|--------
-    | Format    | NCHW    | NCHW    | ND      | NCHW
-@endverbatim
+ * The following are the supported data types and data formats:\n
+ *\n
+    | Tensor    | x       | filter  | bias    | y\n
+    ------------|---------|---------|---------|--------\n
+    | Data Type | float16 | float16 | float16 | float16\n
+    |           |---------|---------|---------|--------\n
+    |           | int8    | int8    | int32   | int32\n
+    ------------|---------|---------|---------|--------\n
+    | Format    | NCHW    | NCHW    | ND      | NCHW\n
+ *\n
  * For int8, a dequant or requant operator must be followed.
  *\n
  *
@@ -550,35 +551,35 @@ REG_OP(Conv2DBackpropInputD)
  * within the effective range of int8 [-128, 127]. Defaults to "0".
  *\n
  *\n
- * The following value range restrictions must be met:
-*@verbatim
-    | Name             | Field    | Scope
-    -------------------|----------|--------------
-    | x (out_backprop) | H*strideH| [1, 4096]
-    |                  | W*strideW| [1, 4096]
-    -------------------|----------|--------------
-    | Filter           | H        | [1, 255]
-    |                  | W        | [1, 255]
-    -------------------|----------|--------------
-    | y (fmap)         | H        | [1, 4096]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | Stride           | H        | [1, 63]
-    |                  | W        | [1, 63]
-    -------------------|----------|--------------
-    | Padding          | Top      | [0, 255]
-    |                  | Bottom   | [0, 255]
-    |                  | Left     | [0, 255]
-    |                  | Right    | [0, 255]
-    -------------------|----------|--------------
-    | Dilation         | H        | [1, 255]
-    |                  | W        | [1, 255]
-    -------------------|----------|--------------
-    | Offset_x         |          | [-128, 127]
-
-@endverbatim
+ * The following value range restrictions must be met:\n
+ *\n
+    | Name             | Field    | Scope\n
+    -------------------|----------|--------------\n
+    | x (out_backprop) | H*strideH| [1, 200000]\n
+    |                  | W*strideW| [1, 4096]\n
+    -------------------|----------|--------------\n
+    | Filter           | H        | [1, 255]\n
+    |                  | W        | [1, 255]\n
+    -------------------|----------|--------------\n
+    | y (fmap)         | H        | [1, 200000]\n
+    |                  | W        | [1, 4096]\n
+    -------------------|----------|--------------\n
+    | Stride           | H        | [1, 63]\n
+    |                  | W        | [1, 63]\n
+    -------------------|----------|--------------\n
+    | Padding          | Top      | [0, 255]\n
+    |                  | Bottom   | [0, 255]\n
+    |                  | Left     | [0, 255]\n
+    |                  | Right    | [0, 255]\n
+    -------------------|----------|--------------\n
+    | Dilation         | H        | [1, 255]\n
+    |                  | W        | [1, 255]\n
+    -------------------|----------|--------------\n
+    | Offset_x         |          | [-128, 127]\n
+ *\n
  * In Ascend910, fmap or out_backprop's H and W not support 1 when
  * fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1
+ * and filter_width > fmap_width
  * If filter_h = 1 and filter_w = 1, out_backprop_w * stride_h * stride_w < 4096
  *\n
  *
@@ -628,19 +629,19 @@ REG_OP(Deconvolution)
  * convolution.
  *\n
  *\n
- * The following are the supported data types and data formats:
-*@verbatim
-    | Tensor    | x       | out_backprop | y
-    ------------|---------|--------------|---------
-    | Data Type | float16 |    float16   | float16
-    |           |---------|--------------|---------
-    |           | float32 |    float32   | float32
-    |           |---------|--------------|---------
-    |           | float64 |    float64   | float64
-    |-----------|---------|--------------|---------
-    | Format    | NCHW    |     NCHW     | NCHW
-    |           | NHWC    |     NHWC     | HWCN
-@endverbatim
+ * The following are the supported data types and data formats:\n
+ *\n
+    | Tensor    | x       | out_backprop | y\n
+    ------------|---------|--------------|---------\n
+    | Data Type | float16 |    float16   | float16\n
+    |           |---------|--------------|---------\n
+    |           | float32 |    float32   | float32\n
+    |           |---------|--------------|---------\n
+    |           | float64 |    float64   | float64\n
+    |-----------|---------|--------------|---------\n
+    | Format    | NCHW    |     NCHW     | NCHW\n
+    |           | NHWC    |     NHWC     | HWCN\n
+ *\n
  * For float32 and float64 type of x and outbackprop, the actual calculation on the chip
  * is based on float16.
  *\n
@@ -658,39 +659,34 @@ REG_OP(Deconvolution)
  * @li data_format: An optional string from: "NHWC", "NCHW". Defaults to
  * "NHWC". Specify the data format of the input and output data.
  *\n
-*\n
-* The following value range restrictions must be met:
-*@verbatim
-    | Name             | Field    | Scope
-    -------------------|----------|--------------
-    | x(fmap)          | H        | [1, 4096]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | Filter Size      | H        | [1, 255]
-    |                  | W        | [1, 255]
-    -------------------|----------|--------------
-    | out_backprop     | H        | [1, 4096]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | y                | H        | [1, 4096]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | Stride           | H        | [1, 63]
-    |                  | W        | [1, 63]
-    -------------------|----------|--------------
-    | Padding          | Top      | [0, 255]
-    |                  | Bottom   | [0, 255]
-    |                  | Left     | [0, 255]
-    |                  | Right    | [0, 255]
-    -------------------|----------|--------------
-    | Dilation         | H        | [1, 255]
-    |                  | W        | [1, 255]
-
-@endverbatim
- * In Ascend910, out_backprop's H and W not support 1 when
- * fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1
  *\n
- *
+ * The following value range restrictions must be met:\n
+ *\n
+    | Name             | Field    | Scope\n
+    -------------------|----------|--------------\n
+    | x(fmap)          | H        | [1, 200000]\n
+    |                  | W        | [1, 4096]\n
+    -------------------|----------|--------------\n
+    | Filter Size      | H        | [1, 255]\n
+    |                  | W        | [1, 255]\n
+    -------------------|----------|--------------\n
+    | out_backprop     | H        | [1, 200000]\n
+    |                  | W        | [1, 4096]\n
+    -------------------|----------|--------------\n
+    | y                | H        | [1, 200000]\n
+    |                  | W        | [1, 4096]\n
+    -------------------|----------|--------------\n
+    | Stride           | H        | [1, 63]\n
+    |                  | W        | [1, 63]\n
+    -------------------|----------|--------------\n
+    | Padding          | Top      | [0, 255]\n
+    |                  | Bottom   | [0, 255]\n
+    |                  | Left     | [0, 255]\n
+    |                  | Right    | [0, 255]\n
+    -------------------|----------|--------------\n
+    | Dilation         | H        | [1, 255]\n
+    |                  | W        | [1, 255]\n
+ *\n
 *@par Outputs:
  * y: A Tensor. Has the same type as x, has the same format as filter_size.
  *\n
@@ -780,16 +776,14 @@ REG_OP(Conv2DBackpropFilterD)
 *\n
 *\n
 * The following are the supported data types and data formats:
-*@verbatim
-    | Tensor    | x       | filter  | bias    | y
-    ------------|---------|---------|---------|--------
-    | Data Type | float16 | float16 | float16 | float16
-    |           | float32 | float32 | float32 | float32
-    |           | int8    | int8    | int32   | int32
-    ------------|---------|---------|---------|--------
-    | Format    | NCHW    | NCHW    | ND      | NCHW
-    |           | NHWC    | HWCN    |         | NHWC
-@endverbatim
+*\n
+| Tensor    | x       | filter  | bias    | y       |\n
+| :-------: | :-----: | :-----: | :-----: | :-----: |\n
+| Data Type | float16 | float16 | float16 | float16 |\n
+|           | float32 | float32 | float32 | float32 |\n
+|           | int8    | int8    | int32   | int32   |\n
+| Format    | NCHW    | NCHW    | ND      | NCHW    |\n
+|           | NHWC    | HWCN    |         | NHWC    |\n
 * For float32 type, the actual calculation on the chip is based on
 * float16.
 *\n
@@ -813,35 +807,28 @@ REG_OP(Conv2DBackpropFilterD)
 *\n
 *\n
 * The following value range restrictions must be met:
-*@verbatim
-    | Name             | Field    | Scope
-    -------------------|----------|--------------
-    | Input Image Size | H        | [1, 100000]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | Filter Size      | H        | [1, 255]
-    |                  | W        | [1, 255]
-    -------------------|----------|--------------
-    | Stride           | H        | [1, 63]
-    |                  | W        | [1, 63]
-    -------------------|----------|--------------
-    | Padding          | Top      | [0, 255]
-    |                  | Bottom   | [0, 255]
-    |                  | Left     | [0, 255]
-    |                  | Right    | [0, 255]
-    -------------------|----------|--------------
-    | Dilation         | H        | [1, 255]
-    |                  | W        | [1, 255]
-    -------------------|----------|--------------
-    | Offset_x         |          | [-128, 127]
-
-@endverbatim
+*\n
+| Name             | Field    | Scope       |\n
+| :--------------: | :------: | :---------: |\n
+| Input Image Size | H        | [1, 100000] |\n
+|                  | W        | [1, 4096]   |\n
+| Filter Size      | H        | [1, 255]    |\n
+|                  | W        | [1, 255]    |\n
+| Stride           | H        | [1, 63]     |\n
+|                  | W        | [1, 63]     |\n
+| Padding          | Top      | [0, 255]    |\n
+|                  | Bottom   | [0, 255]    |\n
+|                  | Left     | [0, 255]    |\n
+|                  | Right    | [0, 255]    |\n
+| Dilation         | H        | [1, 255]    |\n
+|                  | W        | [1, 255]    |\n
+| Offset_x         | -        | [-128, 127] |\n
 * The W dimension of the input image supports cases exceeding 4096, but it may
 * cause compilation errors.
 *\n
 *
 *@par Outputs:
-*@li y: A 4D Tensor of output feature map. Has the same type as "x". With the
+* y: A 4D Tensor of output feature map. Has the same type as "x". With the
 * format "NHWC", the data is stored in the order of: [batch, out_height,
 * out_width, out_channels].
 *\n
@@ -956,16 +943,13 @@ REG_OP(Conv2DCompress)
 *\n
 *\n
 * The following are the supported data types and data formats:
-*@verbatim
-    | Tensor    | x       | filter  | offsets | bias     | y
-    ------------|---------|---------|---------|----------|--------
-    | Data Type | float16 | float16 | float16 | float16  | float16
-    |           |---------|---------|---------|----------|--------
-    |           | float32 | float32 | float32 | float32  | float32
-    ------------|---------|---------|---------|----------|--------
-    | Format    | NCHW    | NCHW    | NCHW    | ND       | NCHW
-    |           | NHWC    | HWCN    | NHWC    |          | NHWC
-@endverbatim
+*\n
+| Tensor    | x       | filter  | offsets | bias    | y       |\n
+| :-------: | :-----: | :-----: | :-----: | :-----: | :-----: |\n
+| Data Type | float16 | float16 | float16 | float16 | float16 |\n
+|           | float32 | float32 | float32 | float32 | float32 |\n
+| Format    | NCHW    | NCHW    | NCHW    | ND      | NCHW    |\n
+|           | NHWC    | HWCN    | NCHW    |         | NHWC    |\n
 * For float32 type, the actual convolution calculation part on the chip is
 * based on float16.
 *\n
@@ -992,19 +976,17 @@ REG_OP(Conv2DCompress)
 *\n
 *\n
 * The following value range restrictions must be met:
-*@verbatim
-    | Name              | Field  | Scope
-    --------------------|--------|----------------------------
-    | Input Image Size  | H      | [1, 100000 / filter_height]
-    |                   | W      | [1, 4096 / filter_width]
-    --------------------|--------|----------------------------
-    | Filter Size       | H      | [1, 63]
-    |                   | W      | [1, 63]
-@endverbatim
+*\n
+| Name             | Field    | Scope                       |\n
+| :--------------: | :------: | :-------------------------: |\n
+| Input Image Size | H        | [1, 100000 / filter_height] |\n
+|                  | W        | [1, 4096 / filter_width]    |\n
+| Filter Size      | H        | [1, 63]                     |\n
+|                  | W        | [1, 63]                     |\n
 *\n
 *
 *@par Outputs:
-*@li y:  A 4D Tensor of output feature map. Has the same type as "x". With the
+* y:  A 4D Tensor of output feature map. Has the same type as "x". With the
 * format "NHWC", the data is stored in the order of: [batch, out_height,
 * out_width, out_channels].
 *\n
@@ -1042,41 +1024,38 @@ REG_OP(DeformableConv2D)
 
 /**
 *@brief Computes a 3D convolution given 5D "x" and "filter" tensors.
- *@par Inputs:
+
+*@par Inputs:
  * @li x: A 5D tensor. Must be one of the following types: float16,
  * (Currently does not support int8). The format of x is NCDHW or NDHWC.
  * @li filter: A 5D tensor of the same type as "x".
  * (Currently does not support int8).
- * The format is NCDHW, NDHWC or DHWCN . \n
-
-*@par Optional input:
- * @li bias: An optional 1D tensor of the same type as "x".
- * @li offset_w: An optional 1D tensor for quantized deconvolution. Reserved . \n
+ * The format is NCDHW, NDHWC or DHWCN.
+ * @li bias: Optional. An 1D tensor of the same type as "x".
+ * @li offset_w: Optional. An 1D tensor for quantized deconvolution. Reserved. \n
 
-*@par Required Attributes:
- * @li strides: A list of 5 integers. Specifies the stride of the sliding window
+*@par Attributes:
+ * @li strides: Required. A list of 5 integers. Specifies the stride of the sliding window
  * for each dimension of "x".
  * The N and C dimensions must be 1. Has the same format as "x".
- * @li pads: A list of 6 integers.
+ * @li pads: Required. A list of 6 integers.
  * Supports only padding along the D, H and W dimensions in sequence of head,
- * tail, top, bottom, left and right . \n
-
-*@par Attributes:
- * @li groups: Number of blocked connections from input channels to output
+ * tail, top, bottom, left and right.
+ * @li dilations: Optional. A list of 5 integers. Specifies the dilation factor for each
+ * dimension of "x".
+ * @li groups: Optional. Number of blocked connections from input channels to output
  * channels.
- * @li data_format: An optional string from: "NDHWC", "NCDHW".
+ * @li data_format: Optional. An string from: "NDHWC", "NCDHW".
  * Defaults to "NDHWC". Specify the data format of the input and output data.
- * @li dilations: A list of 5 integers. Specifies the dilation factor for each
- * dimension of "x".
  * The N, C and D dimensions must be 1. Has the same format as "x".
- * @li offset_x: An optional int. Input offset, used for quantized inference.
- * Defaults to 0. Reserved . \n
+ * @li offset_x: Optional. An int. Input offset, used for quantized inference.
+ * Defaults to 0. Reserved. \n
 
 *@par Outputs:
- *y: A Tensor. Has the same type and data format as "x". \n
+ * y: A Tensor. Has the same type and data format as "x". \n
 
 *@attention Constraints:
- *The image size after padding is greater than the filter size . \n
+ * The image size after padding is greater than the filter size. \n
 
 *@par Third-party framework compatibility
  * @li Compatible with the TensorFlow operator conv3d.
@@ -1099,8 +1078,8 @@ REG_OP(Conv3D)
 
 /**
 *@brief Computes the gradients of convolution 3d with respect to the input.
+
 *@par Inputs:
- * Three inputs:
  * @li input_size: A Tensor of type int32, int64. An integer vector representing
  * the shape of input, where input is a 5-D tensor
  * [batch, depth, height, width, channels] or
@@ -1110,28 +1089,25 @@ REG_OP(Conv3D)
  * @li out_backprop: A Tensor. Must have the same type as filter.
  * 5-D with shape [batch, depth, out_height, out_width, out_channels]
  * or [batch, out_channels, depth, out_height, out_width]. Gradients with
- * respect to the output of the convolution . \n
+ * respect to the output of the convolution. \n
 
-*@par Required Attributes:
- * @li strides: A list of 5 integers. Specifies the stride of the sliding window
+*@par Attributes:
+ * @li strides: Required. A list of 5 integers. Specifies the stride of the sliding window
  * for each dimension of "out_backprop".
  * The N and C dimensions must be 1. Has the same format as "out_backprop".
- * @li pads: A list of 6 integers.
+ * @li pads: Required. A list of 6 integers.
  * Supports only padding along the D, H and W dimensions in sequence of head,
- * tail, top, bottom, left and right . \n
-
-*@par Attributes:
- * Three attributes:
- * @li groups: Number of blocked connections from input channels to output
- * channels.
- * @li data_format: An optional string from: "NDHWC", "NCDHW".
- * Defaults to "NDHWC". Specify the data format of the input and output data.
- * @li dilations: A tuple/list of 5 integers, The dilation factor for each
+ * tail, top, bottom, left and right.
+ * @li dilations: Optional. A tuple/list of 5 integers, The dilation factor for each
  * dimension of the input.
  * The N, C and D dimensions must be 1. Has the same format as "out_backprop".
+ * @li groups: Optional. Number of blocked connections from input channels to output
+ * channels.
+ * @li data_format: Optional. An string from: "NDHWC", "NCDHW".
+ * Defaults to "NDHWC". Specify the data format of the input and output data. \n
 
 *@par Outputs:
- * y: A Tensor. Has the same type as filter,and has same format as "input_size"
+ * y: A Tensor. Has the same type as filter,and has same format as "input_size". \n
 
 *@par Third-party framework compatibility
  * Compatible with Tensorflow's conv3d_backprop_input
@@ -1150,40 +1126,39 @@ REG_OP(Conv3DBackpropInput)
 
 /**
 *@brief Computes the gradients of convolution 3d with respect to the input.
+
 *@par Inputs:
- * Two inputs:
  * @li filter: A Tensor whose type is float16. The format of filter is NCDHW,
  * NDHWC or DHWCN.
  * @li out_backprop: A Tensor. Must have the same type as filter. The format is
- * NDHWC or NCDHW.  \n
+ * NDHWC or NCDHW. \n
 
-*@par Required Attributes:
- * @li strides: A list of 5 integers. Specifies the stride of the sliding window
+*@par Attributes:
+ * @li input_size: Required. A tuple/list of type int32, int64. An integer vector
+ * representing the shape of input, where input is a 5-D tensor
+ * [batch, depth, height, width, channels] or
+ * [batch, channels, depth, height, width].
+ * @li strides: Required. A list of 5 integers. Specifies the stride of the sliding window
  * for each dimension of "out_backprop".
  * The N and C dimensions must be 1. Has the same format as "out_backprop".
- * @li pads: A list of 6 integers. Supports only padding along the D, H and W
+ * @li pads: Required. A list of 6 integers. Supports only padding along the D, H and W
  * dimensions in sequence of head, tail, top, bottom, left and right.
- * @li input_size: A tuple/list of type int32, int64. An integer vector
- * representing the shape of input, where input is a 5-D tensor
- * [batch, depth, height, width, channels] or
- * [batch, channels, depth, height, width] . \n
-
-*@par Attributes:
- * Three attributes:
- * @li groups: Number of blocked connections from input channels to output
- * channels.
- * @li data_format: An optional string from: "NDHWC", "NCDHW".
- * Defaults to "NDHWC". Specify the data format of the input and output data.
- * @li dilations: A tuple/list of 5 integers, The dilation factor for each
+ * @li dilations: Optional. A tuple/list of 5 integers, The dilation factor for each
  * dimension of input.
  * The N, C and D dimensions must be 1. Has the same format as "out_backprop".
+ * @li groups: Optional. Number of blocked connections from input channels to output
+ * channels.
+ * @li data_format: Optional. An string from: "NDHWC", "NCDHW".
+ * Defaults to "NDHWC". Specify the data format of the input and output data. \n
+
 *@par Outputs:
- * y: A Tensor. Has the same type and data format as "out_backprop".
+ * y: A Tensor. Has the same type and data format as "out_backprop". \n
+
 *@par Third-party framework compatibility
- * Compatible with Tensorflow's conv3d_backprop_input
+ * Compatible with Tensorflow's conv3d_backprop_input. \n
 
 *@par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DBackpropInput instead.
+ * Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DBackpropInput instead.
 */
 REG_OP(Conv3DBackpropInputD)
     .INPUT(filter, TensorType({DT_FLOAT16}))
@@ -1242,8 +1217,8 @@ REG_OP(LSTM)
 
 /**
 *@brief Computes the gradients of convolution3D with respect to the filter
+
 *@par Inputs:
- * Three inputs:
  * @li x: A Tensor. Must be one of the following types: float16, float32.
  * Currently does not support double.
  * 5-D with shape [batch, in_depth, in_height, in_width, in_channels]
@@ -1258,26 +1233,23 @@ REG_OP(LSTM)
  * or [batch, out_channels, out_depth, out_height, out_width].
  * Gradients with respect to the output of the convolution. \n
 
-*@par Required Attributes:
- * @li strides: A tuple/list of 5 integers. Specifies the stride of the sliding
+*@par Attributes:
+ * @li strides: Required. A tuple/list of 5 integers. Specifies the stride of the sliding
  * window for each dimension of "x". The N and C dimensions must be 1.
  * Has the same format as "x".
- * @li pads: A tuple/list of 6 integers, [front, back, top, bottom, left, right]
- * pads on feature map . \n
-
-*@par Attributes:
- * Three attributes:
- * @li dilations: A tuple/list of 5 integers, The dilation factor for each
+ * @li pads: Required. A tuple/list of 6 integers, [front, back, top, bottom, left, right]
+ * pads on feature map.
+ * @li dilations: Optional. A tuple/list of 5 integers, The dilation factor for each
  * dimension of input.
  * The N, C and D dimensions must be 1. Has the same format as "x".
- * @li groups: Number of blocked connections from input channels to output
+ * @li groups: Optional. Number of blocked connections from input channels to output
  * channels.
- * @li data_format: An optional string from: "NDHWC", "NCDHW".
- * Defaults to "NDHWC". Specify the data format of the input and output data.
+ * @li data_format: Optional. An string from: "NDHWC", "NCDHW".
+ * Defaults to "NDHWC". Specify the data format of the input and output data. \n
 
 *@par Outputs:
- * y: A Tensor that has the same type as "x"
- * and the format is NDHWC, NCDHW or DHWCN.
+ * y: A Tensor that has the same type as "x" and the format is NDHWC, NCDHW or DHWCN. \n
+
 *@par Third-party framework compatibility
  * Compatible with Tensorflow's conv3d_backprop_filter
 */
@@ -1295,8 +1267,8 @@ REG_OP(Conv3DBackpropFilter)
 
 /**
 *@brief Computes the gradients of convolution with respect to the filter.
+
 *@par Inputs:
- * Two inputs:
  * @li x: A Tensor of type float16.
  * 5-D with shape [batch, in_depth, in_height, in_width, in_channels]
  * or [batch, in_channels, in_depth, in_height, in_width].
@@ -1305,37 +1277,34 @@ REG_OP(Conv3DBackpropFilter)
  * or [batch, out_channels, out_depth, out_height, out_width].
  * Gradients with respect to the output of the convolution. \n
 
-*@par Required Attributes:
- * @li filter_size: A tuple/list of type integers. An integer vector
+*@par Attributes:
+ * @li filter_size: Required. A tuple/list of type integers. An integer vector
  * representing the tensor shape of filter, where filter is a 5-D tensor
  * [filter_depth, filter_height, filter_width, in_channels, out_channels],
  * [out_channels, filter_depth, filter_height, filter_width, in_channels]
  * or [out_channels, in_channels, filter_depth, filter_height, filter_width].
- * @li strides: A tuple/list of 5 integers. Specifies the stride of the sliding
+ * @li strides: Required. A tuple/list of 5 integers. Specifies the stride of the sliding
  * window for each dimension of "x".
  * The N and C dimensions must be 1. Has the same format as "x".
- * @li pads: A tuple/list of 6 integers, [front, back, top, bottom, left, right]
- * pads on feature map. \n
-
-*@par Attributes:
- * Three attributes:
- * @li dilations: A tuple/list of 5 integers, The dilation factor for each
+ * @li pads: Required. A tuple/list of 6 integers, [front, back, top, bottom, left, right]
+ * pads on feature map.
+ * @li dilations: Optional. A tuple/list of 5 integers, The dilation factor for each
  * dimension of input.
  * The N, C and D dimensions must be 1. Has the same format as "x".
- * @li groups: Number of blocked connections from input channels to output
+ * @li groups: Optional. Number of blocked connections from input channels to output
  * channels.
- * @li data_format: An optional string from: "NDHWC", "NCDHW".
- * Defaults to "NDHWC". Specify the data format of the input and output data.
+ * @li data_format: Optional. An optional string from: "NDHWC", "NCDHW".
+ * Defaults to "NDHWC". Specify the data format of the input and output data. \n
 
 *@par Outputs:
- * y: A Tensor of type float32 and the format is NDHWC, NCDHW or DHWCN.
+ * y: A Tensor of type float32 and the format is NDHWC, NCDHW or DHWCN. \n
+
 *@par Third-party framework compatibility
- * Compatible with Tensorflow's conv3d_backprop_filter
+ * Compatible with Tensorflow's conv3d_backprop_filter. \n
+
 *@par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DBackpropFilter instead.
+ * Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DBackpropFilter instead.
 */
-
-
 REG_OP(Conv3DBackpropFilterD)
     .INPUT(x, TensorType({DT_FLOAT16}))
     .INPUT(out_backprop, TensorType({DT_FLOAT16}))
@@ -1350,37 +1319,32 @@ REG_OP(Conv3DBackpropFilterD)
 
 /**
 *@brief Computes the transpose of convolution 3d with respect to the input.
+
 *@par Inputs:
- * Three inputs:
  * @li input_size: A Tensor of type int32. An integer vector representing the
  * shape of input.
  * @li x: A Tensor of type float16, currently does not support int8. The format
  * is NDHWC or NCDHW.
  * @li filter: A Tensor of type float16, currently does not support int8.
  * The format is NDHWC, NCDHW or DHWCN.
+ * @li bias: Optional. An optional 1D tensor of the same type as "x". Reserved.
+ * @li offset_w: Optional. An optional 1D tensor for quantized deconvolution. Reserved. \n
 
-*@par Optional input:
- * Two optional inputs
- * @li bias: An optional 1D tensor of the same type as "x". Reserved.
- * @li offset_w: An optional 1D tensor for quantized deconvolution. Reserved . \n
-
-*@par Required Attributes:
- * @li strides: A tuple/list of 5 integers. Specifies the stride of the sliding
+*@par Attributes:
+ * @li strides: Required. A tuple/list of 5 integers. Specifies the stride of the sliding
  * window for each dimension of "x".
  * The N and C dimensions must be 1. Has the same format as "x".
- * @li pads: A tuple/list of 6 integers
-
-*@par Attributes:
- * Five attributes:
- * @li groups: Number of blocked connections from input channels to output
- * channels.
- * @li dilations: A tuple/list of 5 integers,
+ * @li pads: Required. A tuple/list of 6 integers.
+ * @li dilations: Optional. A tuple/list of 5 integers,
  * The dilation factor for each dimension of input.
  * The N, C and D dimensions must be 1. Has the same format as "x".
- * @li data_format: An optional string from: "NDHWC", "NCDHW".
+ * @li groups: Optional. Number of blocked connections from input channels to output
+ * channels.
+ * @li data_format: Optional. An string from: "NDHWC", "NCDHW".
  * Defaults to "NDHWC". Specify the data format of the input and output data.
- * @li output_padding: The size will be added in the output shape.
- * @li offset_x: Input offset_x value. Reserved.
+ * @li output_padding: Optional. The size will be added in the output shape.
+ * @li offset_x: Optional. Input offset_x value. Reserved. \n
+
 *@par Outputs:
  * y: A Tensor. Has the same type and format as "x".
 */
@@ -1402,39 +1366,37 @@ REG_OP(Conv3DTranspose)
 
 /**
 *@brief Computes the transpose of convolution 3d with respect to the input.
+
 *@par Inputs:
  * @li x: A Tensor of type float16, currently does not support int8.
  * The format is NDHWC or NCDHW.
  * @li filter: A Tensor of type float16, currently does not support int8.
  * The format is NDHWC, NCDHW or DHWCN.
+ * @li bias: Optional. An 1D tensor of the same type as "x". Reserved.
+ * @li offset_w: Optional. An 1D tensor for quantized deconvolution. Reserved. \n
 
-*@par Optional inputs:
- * @li bias: An optional 1D tensor of the same type as "x". Reserved.
- * @li offset_w: An optional 1D tensor for quantized deconvolution. Reserved . \n
-
-*@par Required Attributes:
- * @li input_size: A tuple/list of type int32.
- * An integer vector representing the shape of input
- * @li strides: A tuple/list of 5 integers.
+*@par Attributes:
+ * @li input_size: Required. A tuple/list of type int32.
+ * An integer vector representing the shape of input.
+ * @li strides: Required. A tuple/list of 5 integers.
  * Specifies the stride of the sliding window for each dimension of "x".
  * The N and C dimensions must be 1. Has the same format as "x".
- * @li pads: A tuple/list of 6 integers . \n
-
-*@par Attributes:
- * Five attributes:
- * @li dilations: A tuple/list of 5 integers, The dilation factor for each
+ * @li pads: Required. A tuple/list of 6 integers.
+ * @li dilations: Optional. A tuple/list of 5 integers, The dilation factor for each
  * dimension of input.
  * The N, C and D dimensions must be 1. Has the same format as "x".
- * @li groups: Number of blocked connections from input channels to output
+ * @li groups: Optional. Number of blocked connections from input channels to output
  * channels.
- * @li data_format: An optional string from: "NDHWC", "NCDHW".
+ * @li data_format: Optional. An optional string from: "NDHWC", "NCDHW".
  * Defaults to "NDHWC". Specify the data format of the input and output data.
- * @li output_padding: The size will be added in the output shape.
- * @li offset_x: Input offset_x value. Reserved.
+ * @li output_padding: Optional. The size will be added in the output shape.
+ * @li offset_x: Optional. Input offset_x value. Reserved. \n
+
 *@par Outputs:
- * y: A Tensor. Has the same type and format as "x".
+ * y: A Tensor. Has the same type and format as "x". \n
+
 *@par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DTranspose instead.
+ * Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DTranspose instead.
 */
 REG_OP(Conv3DTransposeD)
     .INPUT(x, TensorType({DT_FLOAT16}))
@@ -1469,17 +1431,17 @@ REG_OP(Conv3DTransposeD)
  * @li offset_w: An optional 1D tensor for quantized inference. Reserved.
  *\n
  *\n
- * The following are the supported data types and data formats:
-*@verbatim
-    | Tensor    | x       | filter  | bias    | y
-    ------------|---------|---------|---------|--------
-    | Data Type | float16 | float16 | float16 | float16
-    |           |---------|---------|---------|--------
-    |           | int8    | int8    | int32   | int32
-    ------------|---------|---------|---------|--------
-    | Format    | NCHW    | NCHW    | ND      | NCHW
-    |           | NHWC    | HWCN    |         | NHWC
-@endverbatim
+ * The following are the supported data types and data formats:\n
+ *\n
+    | Tensor    | x       | filter  | bias    | y\n
+    ------------|---------|---------|---------|--------\n
+    | Data Type | float16 | float16 | float16 | float16\n
+    |           |---------|---------|---------|--------\n
+    |           | int8    | int8    | int32   | int32\n
+    ------------|---------|---------|---------|--------\n
+    | Format    | NCHW    | NCHW    | ND      | NCHW\n
+    |           | NHWC    | HWCN    |         | NHWC\n
+ *\n
  * For int8, a dequant or requant operator must be followed.
  *\n
  *
@@ -1504,38 +1466,38 @@ REG_OP(Conv3DTransposeD)
  * within the effective range of int8 [-128, 127]. Defaults to "0".
  *\n
  *\n
- * The following value range restrictions must be met:
-*@verbatim
-    | Name             | Field    | Scope
-    -------------------|----------|--------------
-    | input_size       | H        | [1, 4096]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | x (out_backprop) | H*strideH| [1, 4096]
-    |                  | W*strideW| [1, 4096]
-    -------------------|----------|--------------
-    | filter           | H        | [1, 255]
-    |                  | W        | [1, 255]
-    -------------------|----------|--------------
-    | y (fmap)         | H        | [1, 4096]
-    |                  | W        | [1, 4096]
-    -------------------|----------|--------------
-    | Stride           | H        | [1, 63]
-    |                  | W        | [1, 63]
-    -------------------|----------|--------------
-    | Padding          | Top      | [0, 255]
-    |                  | Bottom   | [0, 255]
-    |                  | Left     | [0, 255]
-    |                  | Right    | [0, 255]
-    -------------------|----------|--------------
-    | Dilation         | H        | [1, 255]
-    |                  | W        | [1, 255]
-    -------------------|----------|--------------
-    | Offset_x         |          | [-128, 127]
-
-@endverbatim
+ * The following value range restrictions must be met:\n
+ *\n
+    | Name             | Field    | Scope\n
+    -------------------|----------|--------------\n
+    | input_size       | H        | [1, 200000]\n
+    |                  | W        | [1, 4096]\n
+    -------------------|----------|--------------\n
+    | x (out_backprop) | H*strideH| [1, 200000]\n
+    |                  | W*strideW| [1, 4096]\n
+    -------------------|----------|--------------\n
+    | filter           | H        | [1, 255]\n
+    |                  | W        | [1, 255]\n
+    -------------------|----------|--------------\n
+    | y (fmap)         | H        | [1, 200000]\n
+    |                  | W        | [1, 4096]\n
+    -------------------|----------|--------------\n
+    | Stride           | H        | [1, 63]\n
+    |                  | W        | [1, 63]\n
+    -------------------|----------|--------------\n
+    | Padding          | Top      | [0, 255]\n
+    |                  | Bottom   | [0, 255]\n
+    |                  | Left     | [0, 255]\n
+    |                  | Right    | [0, 255]\n
+    -------------------|----------|--------------\n
+    | Dilation         | H        | [1, 255]\n
+    |                  | W        | [1, 255]\n
+    -------------------|----------|--------------\n
+    | Offset_x         |          | [-128, 127]\n
+ *\n
  * In Ascend910, fmap or out_backprop's H and W not support 1 when
  * fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1
+ * and filter_width > fmap_width
  * If filter_h = 1 and filter_w = 1, out_backprop_w * stride_h * stride_w < 4096
  *\n
  *

third_party/fwkacllib/inc/ops/nn_detect_ops.h

@@ -417,7 +417,7 @@ REG_OP(PSROIPooling)
 *@brief Returns detection result . \n
 
 *@par Inputs:
-* Four inputs, including:
+* Five inputs, including:
 *@li rois: An NCHW tensor of type floa16 or float32, output from operator proposal_d at the preceding layer, used as the input of operator FSRDetectionOutput.
 *@li bbox_delta: An NCHWC0 tensor of type floa16 or float32, specifying the prediction offset, used to update the coordinates [x1, y1, x2, y2] of each ROI.
 *@li score: An NCHWC0 tensor of type floa16 or float32, specifying the probability of each class. Class 0 is the background class.
@@ -474,7 +474,6 @@ REG_OP(FSRDetectionOutput)
 *@li code_type: An optional int32, specify the code type. Defaults to 1(only supports 2). The corner is 1, center_size is 2, corner_size is 3
 *@li keep_top_k: An optional int32, specify the topk value after nms. Defaults to -1
 *@li confidence_threshold: An optional float32, specify the topk filter threshold. Only consider detections with confidence greater than the threshold
-*@li kernel_name: An optional string, specifying the operator name. Defaults to "ssd_detection_output".
 *@par Outputs:
 *@li out_boxnum: A tensor of type int32, specifying the number of output boxes.
 *@li y: A tensor of type float16 or float32 with shape [batch,keep_top_k, 8], describing the information of each output box.
@@ -989,26 +988,26 @@ REG_OP(SPP)
 * feature map . \n
 
 *@attention Constraints:
-*@li For the feature map input:
-(1) If pooled_h = pooled_w = 2, the feature map size must not exceed 50.
-(2) If pooled_h = pooled_w = 3, the feature map size must not exceed 60.
-(3) If pooled_h = pooled_w = 4, the feature map size must not exceed 70.
-(4) If pooled_h = pooled_w = 5, the feature map size must not exceed 70.
-(5) If pooled_h = pooled_w = 6, the feature map size must not exceed 80.
-(6) If pooled_h = pooled_w = 7, the feature map size must not exceed 80.
-(7) If pooled_h = pooled_w = 8, the feature map size must not exceed 80.
-(8) If pooled_h = pooled_w = 9, the feature map size must not exceed 70.
-(9) If pooled_h = pooled_w = 10, the feature map size must not exceed 70.
-(10) If pooled_h = pooled_w = 11, the feature map size must not exceed 70.
-(11) If pooled_h = pooled_w = 12, the feature map size must not exceed 70.
-(12) If pooled_h = pooled_w = 13, the feature map size must not exceed 70.
-(13) If pooled_h = pooled_w = 14, the feature map size must not exceed 70.
-(14) If pooled_h = pooled_w = 15, the feature map size must not exceed 70.
-(15) If pooled_h = pooled_w = 16, the feature map size must not exceed 70.
-(16) If pooled_h = pooled_w = 17, the feature map size must not exceed 50.
-(17) If pooled_h = pooled_w = 18, the feature map size must not exceed 40.
-(18) If pooled_h = pooled_w = 19, the feature map size must not exceed 40.
-(19) If pooled_h = pooled_w = 20, the feature map size must not exceed 40.
+* For the feature map input:
+*@li If pooled_h = pooled_w = 2, the feature map size must not exceed 50.
+*@li If pooled_h = pooled_w = 3, the feature map size must not exceed 60.
+*@li If pooled_h = pooled_w = 4, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 5, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 6, the feature map size must not exceed 80.
+*@li If pooled_h = pooled_w = 7, the feature map size must not exceed 80.
+*@li If pooled_h = pooled_w = 8, the feature map size must not exceed 80.
+*@li If pooled_h = pooled_w = 9, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 10, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 11, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 12, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 13, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 14, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 15, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 16, the feature map size must not exceed 70.
+*@li If pooled_h = pooled_w = 17, the feature map size must not exceed 50.
+*@li If pooled_h = pooled_w = 18, the feature map size must not exceed 40.
+*@li If pooled_h = pooled_w = 19, the feature map size must not exceed 40.
+*@li If pooled_h = pooled_w = 20, the feature map size must not exceed 40.
 *@par Third-party framework compatibility
 * It is a custom operator. It has no corresponding operator in Caffe.
 */
@@ -1429,9 +1428,9 @@ REG_OP(NormalizeBBox)
 * @li anchors: A Tensor. Must be int32.
 *
 *@par Attributes:
-* @li scales: optional, listfloat, .
+* @li scales: optional, listfloat.
 * @li decode_clip: optional, float, threahold of decode process.
-* @li reversed_boxes: optional, bool,.
+* @li reversed_boxes: optional, bool.
 *
 *@par Outputs:
 * y: A Tensor. Must have the same type as box_predictions.

third_party/fwkacllib/inc/ops/nn_norm_ops.h

@@ -127,9 +127,10 @@ REG_OP(SoftmaxGrad)
 *@brief Computes the sigmoid cross entropy loss of "predict" and "target" . \n
 
 *@par Inputs:
-* Two inputs, including:
+* Three inputs, including:
 *@li predict: A multi-dimensional Tensor of type float16 or float32, specifying the predictive value.
-*@li target: A multi-dimensional Tensor of type float16 or float32, specifying the target value . \n
+*@li target: A multi-dimensional Tensor of type float16 or float32, specifying the target value .
+*@li dout:A multi-dimensional Tensor of float16 or float32,specifying the gradient transferred from the upper layer. \n
 
 *@par Outputs:
 *loss: Sigmoid cross entropy between the predictive value and target value. Has the same dimensions as "predict" . \n
@@ -1198,13 +1199,11 @@ REG_OP(INInferV2D)
 * @li epsilon: An attribute of type Float. \n
 
 * @par Outputs:
-*Three outputs, including:
+* Three outputs, including:
 * @li y: A Tensor. Has the same type as "x". \n
 * @li mean: A Tensor. Has the same type as "x". \n
 * @li variance: A Tensor. Has the same type as "x". \n
 
-* @par Third-party framework compatibility
-* Can be used by onnx InstanceNormalization
 */
 REG_OP(InstanceNorm)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
@@ -1218,24 +1217,22 @@ REG_OP(InstanceNorm)
     .OP_END_FACTORY_REG(InstanceNorm)
 
 /**
-*@brief InstanceNormGrad operator interface implementation.
+* @brief InstanceNormGrad operator interface implementation.
 
-*@par Inputs:
-*Five inputs, including:
+* @par Inputs:
+* Five inputs, including:
 * @li dy: A Tensor. Must be one of the following types: float16, float32.
 * @li x: A Tensor. Must be one of the following types: float16, float32.
 * @li variance: A Tensor. Must be one of the following types: float16, float32.
 * @li mean: A Tensor. Must be one of the following types: float16, float32.
 * @li gamma: A Tensor. Must be one of the following types: float16, float32 . \n
 
-*@par Outputs:
-*Three outputs, including:
+* @par Outputs:
+* Three outputs, including:
 * @li pd_x: A Tensor. Must be one of the following types: float16, float32.
 * @li pd_gamma: A Tensor. Must be one of the following types: float16, float32.
 * @li pd_beta: A Tensor. Must be one of the following types: float16, float32.
 
-*@par Restrictions:
-*Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
 */
 REG_OP(InstanceNormGrad)
     .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16}))
@@ -1249,58 +1246,6 @@ REG_OP(InstanceNormGrad)
     .OP_END_FACTORY_REG(InstanceNormGrad)
 
 /**
-*@brief InstanceNormXBackprop operator interface implementation.
-
-*@par Inputs:
-*Five inputs, including:
-* @li dy: A Tensor. Must be one of the following types: float16, float32.
-* @li x: A Tensor. Must be one of the following types: float16, float32.
-* @li variance: A Tensor. Must be one of the following types: float16, float32.
-* @li mean: A Tensor. Must be one of the following types: float16, float32.
-* @li gamma: A Tensor. Must be one of the following types: float16, float32 . \n
-
-*@par Outputs:
-*Two outputs, including:
-* @li pd_x: A Tensor. Must be one of the following types: float16, float32.
-* @li res_for_gamma: A Tensor. Must be one of the following types: float32.
-
-*@par Restrictions:
-*Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
-*/
-REG_OP(InstanceNormXBackprop)
-    .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .INPUT(variance, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .INPUT(mean, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .INPUT(gamma, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .OUTPUT(pd_x, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .OUTPUT(res_for_gamma, TensorType({DT_FLOAT}))
-    .OP_END_FACTORY_REG(InstanceNormXBackprop)
-
-/**
-*@brief InstanceNormBetaGammaBackprop operator interface implementation.
-
-*@par Inputs:
-*Two inputs, including:
-* @li dy: A Tensor. Must be one of the following types: float16, float32.
-* @li res_for_gamma: A Tensor. Must be one of the following types: float32.\n
-
-*@par Outputs:
-*Two outputs, including:
-* @li pd_gamma: A Tensor. Must be one of the following types: float16, float32.
-* @li pd_beta: A Tensor. Must be one of the following types: float16, float32.
-
-*@par Restrictions:
-*Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
-*/
-REG_OP(InstanceNormBetaGammaBackprop)
-    .INPUT(dy, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .INPUT(res_for_gamma, TensorType({DT_FLOAT}))
-    .OUTPUT(pd_gamma, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .OUTPUT(pd_beta, TensorType({DT_FLOAT, DT_FLOAT16}))
-    .OP_END_FACTORY_REG(InstanceNormBetaGammaBackprop)
-
-/**
 * @brief Computes Kl_div_loss_grad or Kl_div_loss_backward. \n
 
 * @par Inputs:

third_party/fwkacllib/inc/ops/nn_pooling_ops.h

@@ -50,6 +50,7 @@ namespace ge {
 *dilation[2]: An optional int32, specifying the left dilation. Defaults to "1".
 *dilation[3]: An optional int32, specifying the right dilation. Defaults to "1".
 *@li ceil_mode: An optional int32, either "0" (ceil mode) or "1" (floor mode). Defaults to "0".
+*@li data_format: An optional string, Specify the data format of the input and output data. With the default format "NCHW".
 *@par Outputs:
 *y: An NCHW tensor of type float16, float32, int32.
 *@attention Constraints:
@@ -635,7 +636,8 @@ REG_OP(MaxPoolV2)
 *@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 . \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".
@@ -645,7 +647,7 @@ REG_OP(MaxPoolV2)
  * 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" .
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator MaxPoolWithArgmax.
@@ -710,14 +712,15 @@ REG_OP(MaxPoolGradWithArgmax)
 *@brief Performs transform mask to argmax . \n
 
 *@par Inputs:
-* Two input:
-*x: An NC1HWC0 Tensor of type float16.
-*mask: An NC1HWC0 Tensor of type uint16 . \n
+* Two inputs:
+*@li x: An NC1HWC0 Tensor of type float16.
+*@li mask: An NC1HWC0 Tensor of type uint16 . \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 . \n
+*@li padding: A required string. No default value .
+*@li originshape:A required list of int8, int16, int32, or int64 values, No default value. \n
 
 *@par Outputs:
 *argmax: An NC1HWC0 Tensor of type int32 . \n
@@ -931,11 +934,11 @@ REG_OP(AvgPoolV2GradD)
     .OP_END_FACTORY_REG(AvgPoolV2GradD)
 
 /**
-*@brief :upsample the layer
+*@brief upsample the layer, similar to the nearest-neighbor difference scaling algorithm.
 
 *@par Inputs:
 * one input, including:
-*@li x: A tensor of type float16 or float32.
+* 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".
@@ -1419,7 +1422,7 @@ REG_OP(MaxPoolV3)
 * the floor function will be used. Default False \n
 
 * @par Outputs:
-* y: A mutable tensor. Has the same shape and type as "x1" . \n
+* out_grad: A mutable tensor. Has the same shape and type as "x1" . \n
 
 * @attention Constraints:
 * @li Computing gradients of global pooling is not supported, which means

third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h

@@ -28,8 +28,8 @@ namespace ge {
 *@brief Computes the for the gelu of "x" . \n
 
 *@par Inputs:
-*Two inputs, including:
-* @li x: A Tensor. Must be one of the following types: float16, float32
+*One input, including:
+*x: A Tensor. Must be one of the following types: float16, float32
 
 *@par Outputs:
 *y: A Tensor. Has the same type as "x".
@@ -66,8 +66,8 @@ REG_OP(GeluGrad)
 *@brief Computes the for the fast_gelu of "x" . \n
 
 *@par Inputs:
-*Two inputs, including:
-* @li x: A Tensor. Must be one of the following types: float16, float32
+*One input, including:
+*x: A Tensor. Must be one of the following types: float16, float32
 
 *@par Outputs:
 *y: A Tensor. Has the same type as "x".
@@ -83,7 +83,7 @@ REG_OP(FastGelu)
 *@brief Computes the gradient for the fast_gelu of "x" . \n
 
 *@par Inputs:
-*Three inputs, including:
+*Two 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" . \n
 

third_party/fwkacllib/inc/ops/random_ops.h

@@ -505,15 +505,15 @@ REG_OP(RandomChoiceWithMask)
 
 *@par Inputs:
 *Inputs including:
-* @li x: A required Tensor. Must be one of the following types:
-         float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64 . \n
+* x: A required Tensor. Must be one of the following types:
+     float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64 . \n
 
 *@par Attributes:
-*@li group: A required int32, specifying the number of groups to split the channel dimension into. Defaults to "1" . \n
+* group: A required int32, specifying the number of groups to split the channel dimension into. Defaults to "1" . \n
 
 *@par Outputs:
-*y: A required Tensor. Has same type and shape as "x". Must be one of the following types:
-    float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64 . \n
+* y: A required Tensor. Has same type and shape as "x". Must be one of the following types:
+     float16, float32, int8, uint8, int16, uint16, int32, uint32, int64, uint64 . \n
 
 *@attention Constraints:
 *@li "group" must be greater than 0 and must evenly divide the channel dimension size.

third_party/fwkacllib/inc/ops/reduce_ops.h

@@ -576,7 +576,7 @@ REG_OP(ReduceAll)
 *@li axis: A mutable Tensor. The dimensions to reduce . \n
 
 *@par Attributes:
-*@li keep_dims: A bool. If true, retains reduced dimensions with length 1. Defaults to "False" . \n
+*keep_dims: A bool. If true, retains reduced dimensions with length 1. Defaults to "False" . \n
 
 *@par Outputs:
 *y: A Tensor. Has the same type and format as input "x" . \n
@@ -967,9 +967,9 @@ REG_OP(EuclideanNormD)
 Defaults to "0.00001" . \n
 
 *@par Outputs:
-*y: A Tensor of type float16 or float32 for the normalized "x".
-*batch_mean: A Tensor of type float32 for the result mean.
-*batch_ variance: A Tensor of type float32 for the result variance . \n
+*@li y: A Tensor of type float16 or float32 for the normalized "x".
+*@li batch_mean: A Tensor of type float32 for the result mean.
+*@li batch_ variance: A Tensor of type float32 for the result variance . \n
 
 *@attention Constraints:
 *For Ascend 310, the result accuracy fails to reach 0.001 due to the square root instruction.
@@ -987,7 +987,7 @@ REG_OP(INInferV2)
     .OP_END_FACTORY_REG(INInferV2)
 
 /**
-*@brief Performs reduced instance normalization . \n
+*@brief Performs reduce instance normalization. \n
 
 *@par Inputs:
 *x: A Tensor of type float16 or float32. \n
@@ -1008,32 +1008,31 @@ REG_OP(INTrainingReduceV2)
 
 
 /**
-*@brief Performs update instance normalization . \n
+*@brief Performs update instance normalization. \n
 
 *@par Inputs:
-* Seven inputs, including: (NC1HWC0supported)
+* Seven inputs, including:
 *@li x: A Tensor of type float16 or float32.
 *@li sum: A Tensor of type float32 for the output of operator INTrainingReduceV2.
 *@li square_sum: A Tensor of type float32 for the output of operator INTrainingReduceV2.
 *@li gamma: A Tensor of type float32, for the scaling gamma.
 *@li beta: A Tensor of type float32, for the scaling beta.
 *@li mean: A Tensor of type float32, for the updated mean.
-*@li variance: A Tensor of type float32, for the updated variance . \n
+*@li variance: A Tensor of type float32, for the updated variance. \n
 
 *@par Attributes:
 *@li momentum: A required float32, specifying the momentum to update mean and var.
-*@li epsilon: A required float32, specifying the small value added to variance to avoid dividing by zero . \n
+*@li epsilon: A required float32, specifying the small value added to variance to avoid dividing by zero. \n
 
 *@par Outputs:
 * Three outputs
 *@li y: A Tensor of type float16 or float32, for normalized "x".
 *@li batch_mean: A Tensor of type float32, for the updated mean.
-*@li batch_variance: A Tensor of type float32, for the updated variance . \n
+*@li batch_variance: A Tensor of type float32, for the updated variance. \n
 
 *@attention Constraints:
-*@li This operator is a InstanceNorm fusion operator for updating the moving averages for training.
+* This operator is a InstanceNorm fusion operator for updating the moving averages for training.
 * This operator is used in conjunction with INTrainingReduceV2.
-*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction.
 */
 REG_OP(INTrainingUpdateV2)
     .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
@@ -1052,6 +1051,80 @@ REG_OP(INTrainingUpdateV2)
 
 
 /**
+*@brief Performs the backpropagation of InstanceNorm. \n
+
+*@par Inputs:
+* Seven inputs, including:
+*@li dy: A Tensor of type float16 or float32.
+*@li x: A Tensor of type float16 or float32.
+*@li variance: A Tensor of type float32, for the variance of "x".
+*@li mean: A Tensor of type float32, for the mean of "x".
+*@li res_gamma: A Tensor of type float32.
+*@li res_beta: A Tensor of type float32.
+*@li gamma: A Tensor of type float32. \n
+
+*@par Outputs:
+*pd_x: A Tensor of type float16 or float32, for the offset of "x". \n
+
+*@attention Constraints:
+* The preceding layer of this operator must be INTrainingUpdateGrad. \n
+*/
+REG_OP(INTrainingReduceGrad)
+    .INPUT(dy, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(variance, TensorType({DT_FLOAT}))
+    .INPUT(mean, TensorType({DT_FLOAT}))
+    .INPUT(res_gamma, TensorType({DT_FLOAT}))
+    .INPUT(res_beta, TensorType({DT_FLOAT}))
+    .INPUT(gamma, TensorType({DT_FLOAT}))
+    .OUTPUT(pd_x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .OP_END_FACTORY_REG(INTrainingReduceGrad)
+
+/**
+*@brief Performs the backpropagation of InstanceNorm. \n
+
+*@par Inputs:
+* Four inputs, including:
+*@li dy: A Tensor of type float16 or float32, for the gradient.
+*@li x: A Tensor of type float16 or float32.
+*@li variance: A Tensor of type float32, for the variance of "x".
+*@li mean: A Tensor of type float32, for the mean of "x". \n
+
+*@par Outputs:
+*@li res_gamma: A Tensor of type float32.
+*@li res_beta: A Tensor of type float32. \n
+
+*/
+REG_OP(INTrainingUpdateGrad)
+    .INPUT(dy, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(variance, TensorType({DT_FLOAT}))
+    .INPUT(mean, TensorType({DT_FLOAT}))
+    .OUTPUT(res_gamma, TensorType({DT_FLOAT}))
+    .OUTPUT(res_beta, TensorType({DT_FLOAT}))
+    .OP_END_FACTORY_REG(INTrainingUpdateGrad)
+
+/**
+*@brief Performs the backpropagation of InstanceNorm. \n
+
+*@par Inputs:
+* Two inputs, including:
+*@li res_gamma: A Tensor of type float32.
+*@li res_beta: A Tensor of type float32. \n
+
+*@par Outputs:
+*@li pd_gamma: A Tensor of type float32.
+*@li pd_beta: A Tensor of type float32. \n
+
+*/
+REG_OP(INTrainingUpdateGradGammaBeta)
+    .INPUT(res_gamma, TensorType({DT_FLOAT}))
+    .INPUT(res_beta, TensorType({DT_FLOAT}))
+    .OUTPUT(pd_gamma, TensorType({DT_FLOAT}))
+    .OUTPUT(pd_beta, TensorType({DT_FLOAT}))
+    .OP_END_FACTORY_REG(INTrainingUpdateGradGammaBeta)
+
+/**
 *@brief Performs reduced group normalization . \n
 
 *@par Inputs:
@@ -1063,7 +1136,7 @@ REG_OP(INTrainingUpdateV2)
 
 
 *@par Attributes:
-*@li num_groups: Int, specifying the num of groups. required, same to GNTrainingUpdate . \n
+*num_groups: Int, specifying the num of groups. required, same to GNTrainingUpdate . \n
 
 *@attention Constraints:
 * This operator is a GroupNorm fusion operator for updating the moving averages for training.
@@ -1081,7 +1154,7 @@ REG_OP(GNTrainingReduce)
 *@brief Performs update group normalization . \n
 
 *@par Inputs:
-* Eight inputs, including: (NCHW NHWC supported)
+* Seven inputs, including: (NCHW NHWC supported)
 *@li x: A Tensor of type float16 or float32.
 *@li sum: A 5D Tensor of type float32,
 shape is [N, G, 1, 1, 1] for NCHW, [N, 1, 1, G, 1] for NHWC

third_party/fwkacllib/inc/ops/rnn.h

@@ -491,7 +491,6 @@ REG_OP(DynamicLSTMV2)
 *ten inputs: \n
 *@li w:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
 *@li init_c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
-*@li h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
 *@li c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
 *@li dy:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
 *@li dh:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
@@ -508,6 +507,7 @@ REG_OP(DynamicLSTMV2)
 *@li dx:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
 *@li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
 *@li dc_prev:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ.
+*@li dgate:A 4D Tensor. Must be one of the following types: float16. The format must be FRACTAL_NZ.
 */
 REG_OP(LSTMInputGrad)
     .INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT}))
@@ -571,13 +571,13 @@ REG_OP(DynamicLSTMGradCell)
   .INPUT(f, TensorType({DT_FLOAT16, DT_FLOAT}))
   .INPUT(o, TensorType({DT_FLOAT16, DT_FLOAT}))
   .INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT}))
-  .INPUT(mask, TensorType({DT_FLOAT16, DT_FLOAT}))
   .INPUT(t_state, TensorType({DT_INT32, DT_INT32}))
+  .INPUT(mask, TensorType({DT_FLOAT16, DT_FLOAT}))
   .OUTPUT(dgate, TensorType({DT_FLOAT16, DT_FLOAT}))
   .OUTPUT(dct_1, TensorType({DT_FLOAT16, DT_FLOAT}))
-  .ATTR(forget_bias, Float, 1)
-  .ATTR(activation, String, "")
-  .ATTR(direction, String, "Forward")
+  .ATTR(forget_bias, Float, 1.0)
+  .ATTR(activation, String, "tanh")
+  .ATTR(direction, String, "UNIDIRECTIONAL")
   .ATTR(gate_order, String, "ijfo")
   .OP_END_FACTORY_REG(DynamicLSTMGradCell)
 

third_party/fwkacllib/inc/ops/selection_ops.h

@@ -1968,17 +1968,14 @@ REG_OP(WriteSelect)
     .OP_END_FACTORY_REG(WriteSelect)
 
 /**
-*@brief Read data by stride . \n
+*@brief Read data by stride.
 
 *@par Inputs:
-*One input:
-*x: A Tensor. Must be one of the following types: float16, int8 . \n
-
-*@par Attributes:
-*@li axis: A required int32, specifying the index of axis to read by stride . \n
+*x: A Tensor. Must be one of the following types: float16, int8. \n
 
 *@par Attributes:
-*@li stride: A required int32, specifying the value of reading stride . \n
+*@li axis: A required int32, specifying the index of axis to read by stride. \n
+*@li stride: A required int32, specifying the value of reading stride. \n
 
 *@par Outputs:
 *y: A Tensor of the same type as "x".
@@ -1991,16 +1988,14 @@ REG_OP(StridedRead)
     .OP_END_FACTORY_REG(StridedRead)
 
 /**
-*@brief: Write data by stride . \n
+*@brief Write data by stride.
 
 *@par Inputs:
-*x: A Tensor. Must be one of the following types: float16, int8 . \n
-
-*@par Attributes:
-*@li axis: A required int32, specifying the index of axis to write by stride . \n
+*x: A Tensor. Must be one of the following types: float16, int8. \n
 
 *@par Attributes:
-*@li stride: A required int32, specifying the value of writing stride . \n
+*@li axis: A required int32, specifying the index of axis to write by stride. \n
+*@li stride: A required int32, specifying the value of writing stride. \n
 
 *@par Outputs:
 *y: A Tensor. Has the same type as "x".

third_party/fwkacllib/inc/ops/split_combination_ops.h

@@ -161,14 +161,11 @@ REG_OP(SplitVD)
 /**
 *@brief Concatenates a list of N tensors along the first dimension.
 *@par Inputs:
-* Two inputs, including:
-* @li values: A list of Tensors. Must be one of the following types: int8, int16, int32,
+* One input, including:
+* values: A list of Tensors. Must be one of the following types: int8, int16, int32,
 *     int64, uint8, uint16, uint32, uint64, float16, float32.
 *     Tensors to be concatenated. All must have size 1 in the first dimension and same shape.
-*     It's a dynamic input.
-* @li shape: A Tensor of the same type as "x".
-* The final shape of the result. Should be equal to the shapes of any input
-* but with the number of input values in the first dimension . \n
+*     It's a dynamic input. \n
 
 *@par Attributes:
 * @li shape: A required list of ints.

third_party/fwkacllib/inc/ops/transformation_ops.h

@@ -909,7 +909,7 @@ output shape would be [max(ngram_indexes) + 1]. If input shape is [N, C], this o
 *@li either pool_strings or pool_int64s attributes must be present but not both.
 */
 
-REG_OP(TfidVectorizer)
+REG_OP(TfIdfVectorizer)
     .INPUT(input, TensorType({DT_INT32, DT_INT64, DT_STRING}))
     .OUTPUT(output, TensorType({DT_FLOAT}))
     .REQUIRED_ATTR(max_gram_length, Int)
@@ -921,7 +921,7 @@ REG_OP(TfidVectorizer)
     .ATTR(pool_int64s, ListInt, {})
     .ATTR(pool_strings, ListString, {})
     .ATTR(weights, ListFloat, {})
-    .OP_END_FACTORY_REG(TfidVectorizer)
+    .OP_END_FACTORY_REG(TfIdfVectorizer)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_TRANSFORMATION_OPS_H_

third_party/fwkacllib/inc/runtime/rt_ffts.h

@@ -181,5 +181,4 @@ RTS_API rtError_t rtFftsTaskLaunch(rtFftsTaskInfo_t *fftsTaskInfo, rtStream_t st
 #if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE)
 }
 #endif
-
 #endif // __CCE_RUNTIME_FFTS_H

third_party/fwkacllib/inc/runtime/rt_model.h

@@ -50,7 +50,6 @@ typedef enum tagModelTaskType {
     RT_MODEL_TASK_STREAM_LABEL_SWITCH_BY_INDEX,
     RT_MODEL_TASK_STREAM_LABEL_GOTO,
     RT_MODEL_TASK_MODEL_EXIT,
-    RT_MODEL_TASK_FFTS_TASK,
     RT_MODEL_TASK_ALL_KERNEL,
     RT_MODEL_TASK_PROFILER_TRACE_EX,
     RT_MODEL_TASK_FFTS_TASK,