update include file

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
@@ -70,4 +70,4 @@ ACL_FUNC_VISIBILITY aclError aclrtGetVersion(int32_t *majorVersion, int32_t *min
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_H_
+#endif // INC_EXTERNAL_ACL_ACL_H_

inc/external/acl/acl_base.h

@@ -111,6 +111,7 @@ static const int ACL_ERROR_DUMP_NOT_RUN = 100045;
 static const int ACL_ERROR_PROF_REPEAT_SUBSCRIBE = 148046;
 static const int ACL_ERROR_PROF_API_CONFLICT = 148047;
 static const int ACL_ERROR_INVALID_MAX_OPQUEUE_NUM_CONFIG = 148048;
+static const int ACL_ERROR_INVALID_OPP_PATH = 148049;
 
 static const int ACL_ERROR_BAD_ALLOC = 200000;
 static const int ACL_ERROR_API_NOT_SUPPORT = 200001;
@@ -134,42 +135,42 @@ 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;
 
 /**
@@ -304,7 +305,9 @@ 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);
 
 /**
@@ -326,7 +329,8 @@ 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]);
 
 /**
@@ -423,7 +427,9 @@ 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);
 
 /**
@@ -460,7 +466,7 @@ 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
@@ -548,7 +554,7 @@ 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);
 
 /**
@@ -559,7 +565,7 @@ ACL_FUNC_VISIBILITY aclTensorDesc *aclGetTensorDescByIndex(aclTensorDesc *desc,
  *
  * @retval null for failed
  * @retval OtherValues success
- */
+*/
 ACL_FUNC_VISIBILITY void *aclGetTensorDescAddress(const aclTensorDesc *desc);
 
 /**
@@ -599,12 +605,13 @@ ACL_FUNC_VISIBILITY aclError aclSetTensorConst(aclTensorDesc *desc, void *dataBu
  * @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, ...);
 
-#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"
@@ -51,123 +51,123 @@ 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;
 
 /**
@@ -339,7 +339,8 @@ 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
@@ -361,8 +362,9 @@ 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
@@ -385,9 +387,9 @@ 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
@@ -422,8 +424,8 @@ 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
@@ -453,8 +455,8 @@ 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
@@ -829,11 +831,11 @@ 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);
@@ -849,7 +851,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCscParams(aclmdlAIPP *aippParmsSet, in
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPRbuvSwapSwitch(aclmdlAIPP *aippParmsSet, int8_t rbuvSwapSwitch);
 
 /**
@@ -863,7 +865,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPRbuvSwapSwitch(aclmdlAIPP *aippParmsSe
  * @retval OtherValues Failure
  *
  * @see aclmdlCreateAIPP
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPAxSwapSwitch(aclmdlAIPP *aippParmsSet, int8_t axSwapSwitch);
 
 /**
@@ -878,7 +880,7 @@ 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);
 
@@ -898,10 +900,14 @@ 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
@@ -919,9 +925,13 @@ 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);
 
 /**
@@ -940,7 +950,7 @@ 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,
@@ -961,10 +971,13 @@ 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
@@ -981,10 +994,13 @@ 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
@@ -1001,10 +1017,13 @@ 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
@@ -1020,8 +1039,10 @@ 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);
 
 /**
@@ -1038,8 +1059,10 @@ 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);
 
 /**
@@ -1057,8 +1080,10 @@ 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);
 
 /**
@@ -1075,7 +1100,7 @@ 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);
 
 /**
@@ -1094,11 +1119,10 @@ 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
@@ -1106,7 +1130,7 @@ 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();
 
 /**
@@ -1117,7 +1141,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlInitDump();
  *
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlSetDump(const char *dumpCfgPath);
 
 /**
@@ -1126,7 +1150,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetDump(const char *dumpCfgPath);
  *
  * @retval ACL_SUCCESS The function is successfully executed.
  * @retval OtherValues Failure
- */
+*/
 ACL_FUNC_VISIBILITY aclError aclmdlFinalizeDump();
 
 /**
@@ -1138,7 +1162,7 @@ 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);
 
 /**
@@ -1148,7 +1172,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadWithConfig(const aclmdlConfigHandle *hand
  * @retval the aclmdlConfigHandle pointer
  *
  * @see aclmdlDestroyConfigHandle
- */
+*/
 ACL_FUNC_VISIBILITY aclmdlConfigHandle *aclmdlCreateConfigHandle();
 
 /**
@@ -1177,10 +1201,10 @@ 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);
 
 #ifdef __cplusplus
 }
 #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

@@ -24,18 +24,21 @@
 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_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
 } aclCompileOpt;
 
 /**
@@ -56,10 +59,15 @@ typedef enum {
  * @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
@@ -82,10 +90,11 @@ 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
@@ -103,4 +112,4 @@ ACL_FUNC_VISIBILITY aclError aclSetCompileopt(aclCompileOpt opt, const char *val
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_
+#endif // INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_

inc/external/acl/acl_prof.h

@@ -23,21 +23,21 @@
 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
 
-#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 struct aclprofConfig aclprofConfig;
@@ -98,8 +98,7 @@ 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
@@ -139,7 +138,8 @@ 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
@@ -167,7 +167,7 @@ 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
@@ -219,8 +219,8 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpNum(const void *opInfo, size_t opInfoLe
  * @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
@@ -235,8 +235,8 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpType(const void *opInfo, size_t opInfoL
  * @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
@@ -293,4 +293,4 @@ ACL_FUNC_VISIBILITY size_t aclprofGetModelId(const void *opInfo, size_t opInfoLe
 }
 #endif
 
-#endif  // INC_EXTERNAL_ACL_PROF_H_
+#endif // INC_EXTERNAL_ACL_PROF_H_

inc/external/acl/acl_rt.h

@@ -26,62 +26,62 @@ extern "C" {
 #endif
 
 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_AICORE_INT,
+    ACL_GROUP_AIV_INT,
+    ACL_GROUP_AIC_INT,
+    ACL_GROUP_SDMANUM_INT,
+    ACL_GROUP_ASQNUM_INT
 } aclrtGroupAttr;
 
 typedef struct tagRtGroupInfo aclrtGroupInfo;
@@ -472,7 +472,7 @@ 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
  *
@@ -534,7 +534,9 @@ 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
@@ -557,7 +559,9 @@ 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
@@ -648,7 +652,10 @@ 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);
 
 /**
@@ -695,31 +702,38 @@ 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);
 
 /**
@@ -865,8 +879,11 @@ ACL_FUNC_VISIBILITY aclError aclrtGetAllGroupInfo(aclrtGroupInfo *groupInfo);
  *
  * @see aclrtGetGroupCount | aclrtGetAllGroupInfo
  */
-ACL_FUNC_VISIBILITY aclError aclrtGetGroupInfoDetail(const aclrtGroupInfo *groupInfo, int32_t groupId,
-                                                     aclrtGroupAttr attr, void *attrValue, size_t valueLen,
+ACL_FUNC_VISIBILITY aclError aclrtGetGroupInfoDetail(const aclrtGroupInfo *groupInfo,
+                                                     int32_t groupId,
+                                                     aclrtGroupAttr attr,
+                                                     void *attrValue,
+                                                     size_t valueLen,
                                                      size_t *paramRetSize);
 
 /**
@@ -929,4 +946,5 @@ ACL_FUNC_VISIBILITY aclError aclrtGetMemInfo(aclrtMemAttr attr, size_t *free, si
 }
 #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/ge_error_codes.h

@@ -17,6 +17,20 @@
 #ifndef INC_EXTERNAL_GE_GE_ERROR_CODES_H_
 #define INC_EXTERNAL_GE_GE_ERROR_CODES_H_
 
+#if defined(_MSC_VER)
+#ifdef FUNC_VISIBILITY
+#define GE_FUNC_VISIBILITY _declspec(dllexport)
+#else
+#define GE_FUNC_VISIBILITY
+#endif
+#else
+#ifdef FUNC_VISIBILITY
+#define GE_FUNC_VISIBILITY __attribute__((visibility("default")))
+#else
+#define GE_FUNC_VISIBILITY
+#endif
+#endif
+
 #include <stddef.h>
 
 #ifdef __cplusplus

inc/external/acl/error_codes/rt_error_codes.h

@@ -23,79 +23,80 @@
 extern "C" {
 #endif
 
-static const int32_t ACL_RT_SUCCESS = 0;  // success
+static const int32_t ACL_RT_SUCCESS                          = 0; // success
 
-static const int32_t ACL_ERROR_RT_PARAM_INVALID = 107000;             // param invalid
-static const int32_t ACL_ERROR_RT_INVALID_DEVICEID = 107001;          // invalid device id
-static const int32_t ACL_ERROR_RT_CONTEXT_NULL = 107002;              // current context null
-static const int32_t ACL_ERROR_RT_STREAM_CONTEXT = 107003;            // stream not in current context
-static const int32_t ACL_ERROR_RT_MODEL_CONTEXT = 107004;             // model not in current context
-static const int32_t ACL_ERROR_RT_STREAM_MODEL = 107005;              // stream not in model
-static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_INVALID = 107006;   // event timestamp invalid
-static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_REVERSAL = 107007;  // event timestamp reversal
-static const int32_t ACL_ERROR_RT_ADDR_UNALIGNED = 107008;            // memory address unaligned
-static const int32_t ACL_ERROR_RT_FILE_OPEN = 107009;                 // open file failed
-static const int32_t ACL_ERROR_RT_FILE_WRITE = 107010;                // write file failed
-static const int32_t ACL_ERROR_RT_STREAM_SUBSCRIBE = 107011;          // error subscribe stream
-static const int32_t ACL_ERROR_RT_THREAD_SUBSCRIBE = 107012;          // error subscribe thread
-static const int32_t ACL_ERROR_RT_GROUP_NOT_SET = 107013;             // group not set
-static const int32_t ACL_ERROR_RT_GROUP_NOT_CREATE = 107014;          // group not create
-static const int32_t ACL_ERROR_RT_STREAM_NO_CB_REG = 107015;          // callback not register to stream
-static const int32_t ACL_ERROR_RT_INVALID_MEMORY_TYPE = 107016;       // invalid memory type
-static const int32_t ACL_ERROR_RT_INVALID_HANDLE = 107017;            // invalid handle
-static const int32_t ACL_ERROR_RT_INVALID_MALLOC_TYPE = 107018;       // invalid malloc type
+static const int32_t ACL_ERROR_RT_PARAM_INVALID              = 107000; // param invalid
+static const int32_t ACL_ERROR_RT_INVALID_DEVICEID           = 107001; // invalid device id
+static const int32_t ACL_ERROR_RT_CONTEXT_NULL               = 107002; // current context null
+static const int32_t ACL_ERROR_RT_STREAM_CONTEXT             = 107003; // stream not in current context
+static const int32_t ACL_ERROR_RT_MODEL_CONTEXT              = 107004; // model not in current context
+static const int32_t ACL_ERROR_RT_STREAM_MODEL               = 107005; // stream not in model
+static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_INVALID    = 107006; // event timestamp invalid
+static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_REVERSAL   = 107007; // event timestamp reversal
+static const int32_t ACL_ERROR_RT_ADDR_UNALIGNED             = 107008; // memory address unaligned
+static const int32_t ACL_ERROR_RT_FILE_OPEN                  = 107009; // open file failed
+static const int32_t ACL_ERROR_RT_FILE_WRITE                 = 107010; // write file failed
+static const int32_t ACL_ERROR_RT_STREAM_SUBSCRIBE           = 107011; // error subscribe stream
+static const int32_t ACL_ERROR_RT_THREAD_SUBSCRIBE           = 107012; // error subscribe thread
+static const int32_t ACL_ERROR_RT_GROUP_NOT_SET              = 107013; // group not set
+static const int32_t ACL_ERROR_RT_GROUP_NOT_CREATE           = 107014; // group not create
+static const int32_t ACL_ERROR_RT_STREAM_NO_CB_REG           = 107015; // callback not register to stream
+static const int32_t ACL_ERROR_RT_INVALID_MEMORY_TYPE        = 107016; // invalid memory type
+static const int32_t ACL_ERROR_RT_INVALID_HANDLE             = 107017; // invalid handle
+static const int32_t ACL_ERROR_RT_INVALID_MALLOC_TYPE        = 107018; // invalid malloc type
 
-static const int32_t ACL_ERROR_RT_FEATURE_NOT_SUPPORT = 207000;  // feature not support
-static const int32_t ACL_ERROR_RT_MEMORY_ALLOCATION = 207001;    // memory allocation error
-static const int32_t ACL_ERROR_RT_MEMORY_FREE = 207002;          // memory free error
-static const int32_t ACL_ERROR_RT_AICORE_OVER_FLOW = 207003;     // aicore over flow
-static const int32_t ACL_ERROR_RT_NO_DEVICE = 207004;            // no device
-static const int32_t ACL_ERROR_RT_RESOURCE_ALLOC_FAIL = 207005;  // resource alloc fail
-static const int32_t ACL_ERROR_RT_NO_PERMISSION = 207006;        // no permission
-static const int32_t ACL_ERROR_RT_NO_EVENT_RESOURCE = 207007;    // no event resource
-static const int32_t ACL_ERROR_RT_NO_STREAM_RESOURCE = 207008;   // no stream resource
-static const int32_t ACL_ERROR_RT_NO_NOTIFY_RESOURCE = 207009;   // no notify resource
-static const int32_t ACL_ERROR_RT_NO_MODEL_RESOURCE = 207010;    // no model resource
+static const int32_t ACL_ERROR_RT_FEATURE_NOT_SUPPORT        = 207000; // feature not support
+static const int32_t ACL_ERROR_RT_MEMORY_ALLOCATION          = 207001; // memory allocation error
+static const int32_t ACL_ERROR_RT_MEMORY_FREE                = 207002; // memory free error
+static const int32_t ACL_ERROR_RT_AICORE_OVER_FLOW           = 207003; // aicore over flow
+static const int32_t ACL_ERROR_RT_NO_DEVICE                  = 207004; // no device
+static const int32_t ACL_ERROR_RT_RESOURCE_ALLOC_FAIL        = 207005; // resource alloc fail
+static const int32_t ACL_ERROR_RT_NO_PERMISSION              = 207006; // no permission
+static const int32_t ACL_ERROR_RT_NO_EVENT_RESOURCE          = 207007; // no event resource
+static const int32_t ACL_ERROR_RT_NO_STREAM_RESOURCE         = 207008; // no stream resource
+static const int32_t ACL_ERROR_RT_NO_NOTIFY_RESOURCE         = 207009; // no notify resource
+static const int32_t ACL_ERROR_RT_NO_MODEL_RESOURCE          = 207010; // no model resource
 
-static const int32_t ACL_ERROR_RT_INTERNAL_ERROR = 507000;          // runtime internal error
-static const int32_t ACL_ERROR_RT_TS_ERROR = 507001;                // ts internel error
-static const int32_t ACL_ERROR_RT_STREAM_TASK_FULL = 507002;        // task full in stream
-static const int32_t ACL_ERROR_RT_STREAM_TASK_EMPTY = 507003;       // task empty in stream
-static const int32_t ACL_ERROR_RT_STREAM_NOT_COMPLETE = 507004;     // stream not complete
-static const int32_t ACL_ERROR_RT_END_OF_SEQUENCE = 507005;         // end of sequence
-static const int32_t ACL_ERROR_RT_EVENT_NOT_COMPLETE = 507006;      // event not complete
-static const int32_t ACL_ERROR_RT_CONTEXT_RELEASE_ERROR = 507007;   // context release error
-static const int32_t ACL_ERROR_RT_SOC_VERSION = 507008;             // soc version error
-static const int32_t ACL_ERROR_RT_TASK_TYPE_NOT_SUPPORT = 507009;   // task type not support
-static const int32_t ACL_ERROR_RT_LOST_HEARTBEAT = 507010;          // ts lost heartbeat
-static const int32_t ACL_ERROR_RT_MODEL_EXECUTE = 507011;           // model execute failed
-static const int32_t ACL_ERROR_RT_REPORT_TIMEOUT = 507012;          // report timeout
-static const int32_t ACL_ERROR_RT_SYS_DMA = 507013;                 // sys dma error
-static const int32_t ACL_ERROR_RT_AICORE_TIMEOUT = 507014;          // aicore timeout
-static const int32_t ACL_ERROR_RT_AICORE_EXCEPTION = 507015;        // aicore exception
-static const int32_t ACL_ERROR_RT_AICORE_TRAP_EXCEPTION = 507016;   // aicore trap exception
-static const int32_t ACL_ERROR_RT_AICPU_TIMEOUT = 507017;           // aicpu timeout
-static const int32_t ACL_ERROR_RT_AICPU_EXCEPTION = 507018;         // aicpu exception
-static const int32_t ACL_ERROR_RT_AICPU_DATADUMP_RSP_ERR = 507019;  // aicpu datadump response error
-static const int32_t ACL_ERROR_RT_AICPU_MODEL_RSP_ERR = 507020;     // aicpu model operate response error
-static const int32_t ACL_ERROR_RT_PROFILING_ERROR = 507021;         // profiling error
-static const int32_t ACL_ERROR_RT_IPC_ERROR = 507022;               // ipc error
-static const int32_t ACL_ERROR_RT_MODEL_ABORT_NORMAL = 507023;      // model abort normal
-static const int32_t ACL_ERROR_RT_KERNEL_UNREGISTERING = 507024;    // kernel unregistering
-static const int32_t ACL_ERROR_RT_RINGBUFFER_NOT_INIT = 507025;     // ringbuffer not init
-static const int32_t ACL_ERROR_RT_RINGBUFFER_NO_DATA = 507026;      // ringbuffer no data
-static const int32_t ACL_ERROR_RT_KERNEL_LOOKUP = 507027;           // kernel lookup error
-static const int32_t ACL_ERROR_RT_KERNEL_DUPLICATE = 507028;        // kernel register duplicate
-static const int32_t ACL_ERROR_RT_DEBUG_REGISTER_FAIL = 507029;     // debug register failed
-static const int32_t ACL_ERROR_RT_DEBUG_UNREGISTER_FAIL = 507030;   // debug unregister failed
-static const int32_t ACL_ERROR_RT_LABEL_CONTEXT = 507031;           // label not in current context
-static const int32_t ACL_ERROR_RT_PROGRAM_USE_OUT = 507032;         // program register num use out
-static const int32_t ACL_ERROR_RT_DEV_SETUP_ERROR = 507033;         // device setup error
+static const int32_t ACL_ERROR_RT_INTERNAL_ERROR             = 507000; // runtime internal error
+static const int32_t ACL_ERROR_RT_TS_ERROR                   = 507001; // ts internel error
+static const int32_t ACL_ERROR_RT_STREAM_TASK_FULL           = 507002; // task full in stream
+static const int32_t ACL_ERROR_RT_STREAM_TASK_EMPTY          = 507003; // task empty in stream
+static const int32_t ACL_ERROR_RT_STREAM_NOT_COMPLETE        = 507004; // stream not complete
+static const int32_t ACL_ERROR_RT_END_OF_SEQUENCE            = 507005; // end of sequence
+static const int32_t ACL_ERROR_RT_EVENT_NOT_COMPLETE         = 507006; // event not complete
+static const int32_t ACL_ERROR_RT_CONTEXT_RELEASE_ERROR      = 507007; // context release error
+static const int32_t ACL_ERROR_RT_SOC_VERSION                = 507008; // soc version error
+static const int32_t ACL_ERROR_RT_TASK_TYPE_NOT_SUPPORT      = 507009; // task type not support
+static const int32_t ACL_ERROR_RT_LOST_HEARTBEAT             = 507010; // ts lost heartbeat
+static const int32_t ACL_ERROR_RT_MODEL_EXECUTE              = 507011; // model execute failed
+static const int32_t ACL_ERROR_RT_REPORT_TIMEOUT             = 507012; // report timeout
+static const int32_t ACL_ERROR_RT_SYS_DMA                    = 507013; // sys dma error
+static const int32_t ACL_ERROR_RT_AICORE_TIMEOUT             = 507014; // aicore timeout
+static const int32_t ACL_ERROR_RT_AICORE_EXCEPTION           = 507015; // aicore exception
+static const int32_t ACL_ERROR_RT_AICORE_TRAP_EXCEPTION      = 507016; // aicore trap exception
+static const int32_t ACL_ERROR_RT_AICPU_TIMEOUT              = 507017; // aicpu timeout
+static const int32_t ACL_ERROR_RT_AICPU_EXCEPTION            = 507018; // aicpu exception
+static const int32_t ACL_ERROR_RT_AICPU_DATADUMP_RSP_ERR     = 507019; // aicpu datadump response error
+static const int32_t ACL_ERROR_RT_AICPU_MODEL_RSP_ERR        = 507020; // aicpu model operate response error
+static const int32_t ACL_ERROR_RT_PROFILING_ERROR            = 507021; // profiling error
+static const int32_t ACL_ERROR_RT_IPC_ERROR                  = 507022; // ipc error
+static const int32_t ACL_ERROR_RT_MODEL_ABORT_NORMAL         = 507023; // model abort normal
+static const int32_t ACL_ERROR_RT_KERNEL_UNREGISTERING       = 507024; // kernel unregistering
+static const int32_t ACL_ERROR_RT_RINGBUFFER_NOT_INIT        = 507025; // ringbuffer not init
+static const int32_t ACL_ERROR_RT_RINGBUFFER_NO_DATA         = 507026; // ringbuffer no data
+static const int32_t ACL_ERROR_RT_KERNEL_LOOKUP              = 507027; // kernel lookup error
+static const int32_t ACL_ERROR_RT_KERNEL_DUPLICATE           = 507028; // kernel register duplicate
+static const int32_t ACL_ERROR_RT_DEBUG_REGISTER_FAIL        = 507029; // debug register failed
+static const int32_t ACL_ERROR_RT_DEBUG_UNREGISTER_FAIL      = 507030; // debug unregister failed
+static const int32_t ACL_ERROR_RT_LABEL_CONTEXT              = 507031; // label not in current context
+static const int32_t ACL_ERROR_RT_PROGRAM_USE_OUT            = 507032; // program register num use out
+static const int32_t ACL_ERROR_RT_DEV_SETUP_ERROR            = 507033; // device setup error
 
-static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899;  // drv internal error
+static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR         = 507899; // drv internal error
+static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR       = 507900; // aicpu internal error
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // __INC_EXTERNEL_RT_ERROR_CODES_H__
+#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__

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);
 
 /**
@@ -1734,9 +1769,12 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc
  * @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
@@ -1759,9 +1797,12 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAsync(acldvppChannelDesc *channe
  *
  * @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
@@ -1786,11 +1827,14 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *cha
  *
  * @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
@@ -1818,8 +1862,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc
  *
  * @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
@@ -1837,8 +1884,11 @@ 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);
 
 /**
@@ -1856,8 +1906,11 @@ 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
@@ -1912,8 +1965,11 @@ 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
@@ -1932,8 +1988,10 @@ 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
@@ -1954,8 +2012,10 @@ 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
@@ -1977,8 +2037,11 @@ 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
@@ -1990,7 +2053,8 @@ 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
@@ -2025,7 +2089,8 @@ 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
@@ -2122,7 +2187,9 @@ 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
@@ -2140,8 +2207,10 @@ 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
@@ -2162,7 +2231,8 @@ 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);
 
 /**
@@ -2307,8 +2377,10 @@ 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
@@ -2325,8 +2397,11 @@ 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
@@ -2335,7 +2410,7 @@ 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
@@ -2392,7 +2467,7 @@ 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
@@ -2415,4 +2490,4 @@ ACL_FUNC_VISIBILITY aclError acldvppClearHist(acldvppHist *hist);
 }
 #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
@@ -350,4 +348,4 @@ 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.
@@ -114,10 +113,10 @@ extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvC
  * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32.
  * @param comm A pointer identifying the communication resource based on.
  * @param stream A pointer identifying the stream information.
- * @return HcclResult
+ * @return HcclResult 
  */
-extern HcclResult HcclAllGather(void *sendBuf, void *recvBuf, uint64_t sendCount, HcclDataType dataType, HcclComm comm,
-                                aclrtStream stream);
+extern HcclResult HcclAllGather(void *sendBuf, void *recvBuf, uint64_t sendCount, HcclDataType dataType, 
+HcclComm comm, aclrtStream stream);
 
 /**
  * @brief Destroy HCCL comm
@@ -130,5 +129,5 @@ extern HcclResult HcclCommDestroy(HcclComm comm);
 
 #ifdef __cplusplus
 }
-#endif  // __cplusplus
-#endif  // HCCL_H_
+#endif // __cplusplus
+#endif // HCCL_H_

inc/external/hccl/hccl_types.h

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

inc/external/runtime/rt_error_codes.h

@@ -23,79 +23,80 @@
 extern "C" {
 #endif
 
-static const int32_t ACL_RT_SUCCESS = 0;  // success
+static const int32_t ACL_RT_SUCCESS                          = 0; // success
 
-static const int32_t ACL_ERROR_RT_PARAM_INVALID = 107000;             // param invalid
-static const int32_t ACL_ERROR_RT_INVALID_DEVICEID = 107001;          // invalid device id
-static const int32_t ACL_ERROR_RT_CONTEXT_NULL = 107002;              // current context null
-static const int32_t ACL_ERROR_RT_STREAM_CONTEXT = 107003;            // stream not in current context
-static const int32_t ACL_ERROR_RT_MODEL_CONTEXT = 107004;             // model not in current context
-static const int32_t ACL_ERROR_RT_STREAM_MODEL = 107005;              // stream not in model
-static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_INVALID = 107006;   // event timestamp invalid
-static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_REVERSAL = 107007;  // event timestamp reversal
-static const int32_t ACL_ERROR_RT_ADDR_UNALIGNED = 107008;            // memory address unaligned
-static const int32_t ACL_ERROR_RT_FILE_OPEN = 107009;                 // open file failed
-static const int32_t ACL_ERROR_RT_FILE_WRITE = 107010;                // write file failed
-static const int32_t ACL_ERROR_RT_STREAM_SUBSCRIBE = 107011;          // error subscribe stream
-static const int32_t ACL_ERROR_RT_THREAD_SUBSCRIBE = 107012;          // error subscribe thread
-static const int32_t ACL_ERROR_RT_GROUP_NOT_SET = 107013;             // group not set
-static const int32_t ACL_ERROR_RT_GROUP_NOT_CREATE = 107014;          // group not create
-static const int32_t ACL_ERROR_RT_STREAM_NO_CB_REG = 107015;          // callback not register to stream
-static const int32_t ACL_ERROR_RT_INVALID_MEMORY_TYPE = 107016;       // invalid memory type
-static const int32_t ACL_ERROR_RT_INVALID_HANDLE = 107017;            // invalid handle
-static const int32_t ACL_ERROR_RT_INVALID_MALLOC_TYPE = 107018;       // invalid malloc type
+static const int32_t ACL_ERROR_RT_PARAM_INVALID              = 107000; // param invalid
+static const int32_t ACL_ERROR_RT_INVALID_DEVICEID           = 107001; // invalid device id
+static const int32_t ACL_ERROR_RT_CONTEXT_NULL               = 107002; // current context null
+static const int32_t ACL_ERROR_RT_STREAM_CONTEXT             = 107003; // stream not in current context
+static const int32_t ACL_ERROR_RT_MODEL_CONTEXT              = 107004; // model not in current context
+static const int32_t ACL_ERROR_RT_STREAM_MODEL               = 107005; // stream not in model
+static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_INVALID    = 107006; // event timestamp invalid
+static const int32_t ACL_ERROR_RT_EVENT_TIMESTAMP_REVERSAL   = 107007; // event timestamp reversal
+static const int32_t ACL_ERROR_RT_ADDR_UNALIGNED             = 107008; // memory address unaligned
+static const int32_t ACL_ERROR_RT_FILE_OPEN                  = 107009; // open file failed
+static const int32_t ACL_ERROR_RT_FILE_WRITE                 = 107010; // write file failed
+static const int32_t ACL_ERROR_RT_STREAM_SUBSCRIBE           = 107011; // error subscribe stream
+static const int32_t ACL_ERROR_RT_THREAD_SUBSCRIBE           = 107012; // error subscribe thread
+static const int32_t ACL_ERROR_RT_GROUP_NOT_SET              = 107013; // group not set
+static const int32_t ACL_ERROR_RT_GROUP_NOT_CREATE           = 107014; // group not create
+static const int32_t ACL_ERROR_RT_STREAM_NO_CB_REG           = 107015; // callback not register to stream
+static const int32_t ACL_ERROR_RT_INVALID_MEMORY_TYPE        = 107016; // invalid memory type
+static const int32_t ACL_ERROR_RT_INVALID_HANDLE             = 107017; // invalid handle
+static const int32_t ACL_ERROR_RT_INVALID_MALLOC_TYPE        = 107018; // invalid malloc type
 
-static const int32_t ACL_ERROR_RT_FEATURE_NOT_SUPPORT = 207000;  // feature not support
-static const int32_t ACL_ERROR_RT_MEMORY_ALLOCATION = 207001;    // memory allocation error
-static const int32_t ACL_ERROR_RT_MEMORY_FREE = 207002;          // memory free error
-static const int32_t ACL_ERROR_RT_AICORE_OVER_FLOW = 207003;     // aicore over flow
-static const int32_t ACL_ERROR_RT_NO_DEVICE = 207004;            // no device
-static const int32_t ACL_ERROR_RT_RESOURCE_ALLOC_FAIL = 207005;  // resource alloc fail
-static const int32_t ACL_ERROR_RT_NO_PERMISSION = 207006;        // no permission
-static const int32_t ACL_ERROR_RT_NO_EVENT_RESOURCE = 207007;    // no event resource
-static const int32_t ACL_ERROR_RT_NO_STREAM_RESOURCE = 207008;   // no stream resource
-static const int32_t ACL_ERROR_RT_NO_NOTIFY_RESOURCE = 207009;   // no notify resource
-static const int32_t ACL_ERROR_RT_NO_MODEL_RESOURCE = 207010;    // no model resource
+static const int32_t ACL_ERROR_RT_FEATURE_NOT_SUPPORT        = 207000; // feature not support
+static const int32_t ACL_ERROR_RT_MEMORY_ALLOCATION          = 207001; // memory allocation error
+static const int32_t ACL_ERROR_RT_MEMORY_FREE                = 207002; // memory free error
+static const int32_t ACL_ERROR_RT_AICORE_OVER_FLOW           = 207003; // aicore over flow
+static const int32_t ACL_ERROR_RT_NO_DEVICE                  = 207004; // no device
+static const int32_t ACL_ERROR_RT_RESOURCE_ALLOC_FAIL        = 207005; // resource alloc fail
+static const int32_t ACL_ERROR_RT_NO_PERMISSION              = 207006; // no permission
+static const int32_t ACL_ERROR_RT_NO_EVENT_RESOURCE          = 207007; // no event resource
+static const int32_t ACL_ERROR_RT_NO_STREAM_RESOURCE         = 207008; // no stream resource
+static const int32_t ACL_ERROR_RT_NO_NOTIFY_RESOURCE         = 207009; // no notify resource
+static const int32_t ACL_ERROR_RT_NO_MODEL_RESOURCE          = 207010; // no model resource
 
-static const int32_t ACL_ERROR_RT_INTERNAL_ERROR = 507000;          // runtime internal error
-static const int32_t ACL_ERROR_RT_TS_ERROR = 507001;                // ts internel error
-static const int32_t ACL_ERROR_RT_STREAM_TASK_FULL = 507002;        // task full in stream
-static const int32_t ACL_ERROR_RT_STREAM_TASK_EMPTY = 507003;       // task empty in stream
-static const int32_t ACL_ERROR_RT_STREAM_NOT_COMPLETE = 507004;     // stream not complete
-static const int32_t ACL_ERROR_RT_END_OF_SEQUENCE = 507005;         // end of sequence
-static const int32_t ACL_ERROR_RT_EVENT_NOT_COMPLETE = 507006;      // event not complete
-static const int32_t ACL_ERROR_RT_CONTEXT_RELEASE_ERROR = 507007;   // context release error
-static const int32_t ACL_ERROR_RT_SOC_VERSION = 507008;             // soc version error
-static const int32_t ACL_ERROR_RT_TASK_TYPE_NOT_SUPPORT = 507009;   // task type not support
-static const int32_t ACL_ERROR_RT_LOST_HEARTBEAT = 507010;          // ts lost heartbeat
-static const int32_t ACL_ERROR_RT_MODEL_EXECUTE = 507011;           // model execute failed
-static const int32_t ACL_ERROR_RT_REPORT_TIMEOUT = 507012;          // report timeout
-static const int32_t ACL_ERROR_RT_SYS_DMA = 507013;                 // sys dma error
-static const int32_t ACL_ERROR_RT_AICORE_TIMEOUT = 507014;          // aicore timeout
-static const int32_t ACL_ERROR_RT_AICORE_EXCEPTION = 507015;        // aicore exception
-static const int32_t ACL_ERROR_RT_AICORE_TRAP_EXCEPTION = 507016;   // aicore trap exception
-static const int32_t ACL_ERROR_RT_AICPU_TIMEOUT = 507017;           // aicpu timeout
-static const int32_t ACL_ERROR_RT_AICPU_EXCEPTION = 507018;         // aicpu exception
-static const int32_t ACL_ERROR_RT_AICPU_DATADUMP_RSP_ERR = 507019;  // aicpu datadump response error
-static const int32_t ACL_ERROR_RT_AICPU_MODEL_RSP_ERR = 507020;     // aicpu model operate response error
-static const int32_t ACL_ERROR_RT_PROFILING_ERROR = 507021;         // profiling error
-static const int32_t ACL_ERROR_RT_IPC_ERROR = 507022;               // ipc error
-static const int32_t ACL_ERROR_RT_MODEL_ABORT_NORMAL = 507023;      // model abort normal
-static const int32_t ACL_ERROR_RT_KERNEL_UNREGISTERING = 507024;    // kernel unregistering
-static const int32_t ACL_ERROR_RT_RINGBUFFER_NOT_INIT = 507025;     // ringbuffer not init
-static const int32_t ACL_ERROR_RT_RINGBUFFER_NO_DATA = 507026;      // ringbuffer no data
-static const int32_t ACL_ERROR_RT_KERNEL_LOOKUP = 507027;           // kernel lookup error
-static const int32_t ACL_ERROR_RT_KERNEL_DUPLICATE = 507028;        // kernel register duplicate
-static const int32_t ACL_ERROR_RT_DEBUG_REGISTER_FAIL = 507029;     // debug register failed
-static const int32_t ACL_ERROR_RT_DEBUG_UNREGISTER_FAIL = 507030;   // debug unregister failed
-static const int32_t ACL_ERROR_RT_LABEL_CONTEXT = 507031;           // label not in current context
-static const int32_t ACL_ERROR_RT_PROGRAM_USE_OUT = 507032;         // program register num use out
-static const int32_t ACL_ERROR_RT_DEV_SETUP_ERROR = 507033;         // device setup error
+static const int32_t ACL_ERROR_RT_INTERNAL_ERROR             = 507000; // runtime internal error
+static const int32_t ACL_ERROR_RT_TS_ERROR                   = 507001; // ts internel error
+static const int32_t ACL_ERROR_RT_STREAM_TASK_FULL           = 507002; // task full in stream
+static const int32_t ACL_ERROR_RT_STREAM_TASK_EMPTY          = 507003; // task empty in stream
+static const int32_t ACL_ERROR_RT_STREAM_NOT_COMPLETE        = 507004; // stream not complete
+static const int32_t ACL_ERROR_RT_END_OF_SEQUENCE            = 507005; // end of sequence
+static const int32_t ACL_ERROR_RT_EVENT_NOT_COMPLETE         = 507006; // event not complete
+static const int32_t ACL_ERROR_RT_CONTEXT_RELEASE_ERROR      = 507007; // context release error
+static const int32_t ACL_ERROR_RT_SOC_VERSION                = 507008; // soc version error
+static const int32_t ACL_ERROR_RT_TASK_TYPE_NOT_SUPPORT      = 507009; // task type not support
+static const int32_t ACL_ERROR_RT_LOST_HEARTBEAT             = 507010; // ts lost heartbeat
+static const int32_t ACL_ERROR_RT_MODEL_EXECUTE              = 507011; // model execute failed
+static const int32_t ACL_ERROR_RT_REPORT_TIMEOUT             = 507012; // report timeout
+static const int32_t ACL_ERROR_RT_SYS_DMA                    = 507013; // sys dma error
+static const int32_t ACL_ERROR_RT_AICORE_TIMEOUT             = 507014; // aicore timeout
+static const int32_t ACL_ERROR_RT_AICORE_EXCEPTION           = 507015; // aicore exception
+static const int32_t ACL_ERROR_RT_AICORE_TRAP_EXCEPTION      = 507016; // aicore trap exception
+static const int32_t ACL_ERROR_RT_AICPU_TIMEOUT              = 507017; // aicpu timeout
+static const int32_t ACL_ERROR_RT_AICPU_EXCEPTION            = 507018; // aicpu exception
+static const int32_t ACL_ERROR_RT_AICPU_DATADUMP_RSP_ERR     = 507019; // aicpu datadump response error
+static const int32_t ACL_ERROR_RT_AICPU_MODEL_RSP_ERR        = 507020; // aicpu model operate response error
+static const int32_t ACL_ERROR_RT_PROFILING_ERROR            = 507021; // profiling error
+static const int32_t ACL_ERROR_RT_IPC_ERROR                  = 507022; // ipc error
+static const int32_t ACL_ERROR_RT_MODEL_ABORT_NORMAL         = 507023; // model abort normal
+static const int32_t ACL_ERROR_RT_KERNEL_UNREGISTERING       = 507024; // kernel unregistering
+static const int32_t ACL_ERROR_RT_RINGBUFFER_NOT_INIT        = 507025; // ringbuffer not init
+static const int32_t ACL_ERROR_RT_RINGBUFFER_NO_DATA         = 507026; // ringbuffer no data
+static const int32_t ACL_ERROR_RT_KERNEL_LOOKUP              = 507027; // kernel lookup error
+static const int32_t ACL_ERROR_RT_KERNEL_DUPLICATE           = 507028; // kernel register duplicate
+static const int32_t ACL_ERROR_RT_DEBUG_REGISTER_FAIL        = 507029; // debug register failed
+static const int32_t ACL_ERROR_RT_DEBUG_UNREGISTER_FAIL      = 507030; // debug unregister failed
+static const int32_t ACL_ERROR_RT_LABEL_CONTEXT              = 507031; // label not in current context
+static const int32_t ACL_ERROR_RT_PROGRAM_USE_OUT            = 507032; // program register num use out
+static const int32_t ACL_ERROR_RT_DEV_SETUP_ERROR            = 507033; // device setup error
 
-static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899;  // drv internal error
+static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR         = 507899; // drv internal error
+static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR       = 507900; // aicpu internal error
 
 #ifdef __cplusplus
 }
 #endif
 
-#endif  // __INC_EXTERNEL_RT_ERROR_CODES_H__
+#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__

third_party/fwkacllib/inc/ops/batch_ops.h

@@ -107,11 +107,13 @@ across multiple sessions .   \n
 
 REG_OP(Unbatch)
   .INPUT(x_tensor, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
-      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE}))
+      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+      DT_COMPLEX64, DT_COMPLEX128}))
   .INPUT(index, TensorType({DT_INT64}))
   .INPUT(id, TensorType({DT_INT64}))
   .OUTPUT(y_tensor, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
-      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE}))
+      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+      DT_COMPLEX64, DT_COMPLEX128}))
   .REQUIRED_ATTR(timeout_micros, Int)
   .ATTR(container, String, "")
   .ATTR(shared_name, String, "")
@@ -146,13 +148,16 @@ across multiple sessions .   \n
 
 REG_OP(UnbatchGrad)
   .INPUT(x_input, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
-      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE}))
+      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+      DT_COMPLEX64, DT_COMPLEX128}))
   .INPUT(index, TensorType({DT_INT64}))
   .INPUT(grad, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
-      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE}))
+      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+      DT_COMPLEX64, DT_COMPLEX128}))
   .INPUT(id, TensorType({DT_INT64}))
   .OUTPUT(y_grad, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
-      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE}))
+      DT_INT32, DT_INT64, DT_BOOL, DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+      DT_COMPLEX64, DT_COMPLEX128}))
   .ATTR(container, String, "")
   .ATTR(shared_name, String, "")
   .OP_END_FACTORY_REG(UnbatchGrad)

third_party/fwkacllib/inc/ops/data_flow_ops.h

@@ -1430,6 +1430,24 @@ REG_OP(OrderedMapClear)
     .ATTR(shared_name, String, "")
     .OP_END_FACTORY_REG(OrderedMapClear)
 
+/**
+*@brief FakeQueue, support tf api FixedLengthRecordReader. \n
+
+*@par Inputs:
+*Including:
+* @li resource: A Tensor of type DT_RESOURCE.
+
+*@par Outputs:
+*handle: A Tensor of type DT_STRING ref. \n
+
+*@par Third-party framework compatibility
+*Compatible with the TensorFlow operator FakeQueue.
+*/
+REG_OP(FakeQueue)
+    .INPUT(resource, TensorType({DT_RESOURCE}))
+    .OUTPUT(handle, TensorType({DT_STRING}))
+    .OP_END_FACTORY_REG(FakeQueue)
+
 /**
 *@brief Returns the number of incomplete elements in the underlying container. \n
 
@@ -2258,6 +2276,7 @@ REG_OP(LruCache)
   .ATTR(shared_name, String, "LruCache")
   .ATTR(cache_size, Int, 100000)
   .ATTR(load_factor, Float, 1)
+  .REQUIRED_ATTR(dtype, Type)
   .OP_END_FACTORY_REG(LruCache)
 
 /**
@@ -2277,9 +2296,9 @@ REG_OP(CacheAdd)
   .INPUT(cache, TensorType({DT_RESOURCE}))
   .INPUT(ids, TensorType({DT_INT64, DT_INT32, DT_UINT64, DT_UINT32}))
   .OUTPUT(swap_in_id, TensorType({DT_INT64, DT_INT32, DT_UINT64, DT_UINT32}))
-  .OUTPUT(swap_in_idx, TensorType({DT_INT64}))
+  .OUTPUT(swap_in_idx, TensorType({DT_INT64, DT_INT32, DT_UINT64, DT_UINT32}))
   .OUTPUT(swap_out_id, TensorType({DT_INT64, DT_INT32, DT_UINT64, DT_UINT32}))
-  .OUTPUT(swap_out_idx, TensorType({DT_INT64}))
+  .OUTPUT(swap_out_idx, TensorType({DT_INT64, DT_INT32, DT_UINT64, DT_UINT32}))
   .OP_END_FACTORY_REG(CacheAdd)
 
 /**
@@ -2295,9 +2314,31 @@ REG_OP(CacheAdd)
 REG_OP(CacheRemoteIndexToLocal)
   .INPUT(cache, TensorType({DT_RESOURCE}))
   .INPUT(ids, TensorType({DT_INT64, DT_INT32, DT_UINT64, DT_UINT32}))
-  .OUTPUT(local_idx, TensorType({DT_INT64}))
+  .OUTPUT(local_idx, TensorType({DT_INT64, DT_INT32, DT_UINT64, DT_UINT32}))
   .OP_END_FACTORY_REG(CacheRemoteIndexToLocal)
 
+/**
+*@brief CacheAllToLocalIndex, get id in cache
+*@par Inputs:
+*cache: resource data
+*local_idx: id in cache.
+*@par Restrictions:
+*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
+*/
+REG_OP(CacheAllIndexToLocal)
+  .INPUT(cache, TensorType({DT_RESOURCE}))
+  .OUTPUT(local_idx, TensorType({DT_INT64, DT_INT32, DT_UINT64, DT_UINT32}))
+  .REQUIRED_ATTR(dtype, Type)
+  .OP_END_FACTORY_REG(CacheAllIndexToLocal)
+
+REG_OP(DynamicGetNext)
+  .INPUT(x, TensorType::ALL())
+  .DYNAMIC_OUTPUT(y, TensorType::ALL())
+  .ATTR(output_types, ListType, {})
+  .ATTR(output_shapes, ListListInt, {{}, {}})
+  .ATTR(_dynamic_graph_execute_mode, String, "lazy_recompile")
+  .ATTR(_getnext_inputs_shape_range, String, "")
+  .OP_END_FACTORY_REG(DynamicGetNext)
 }   // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_DATA_FLOW_OPS_H_

third_party/fwkacllib/inc/ops/elewise_calculation_ops.h

@@ -3627,6 +3627,35 @@ REG_OP(Lerp)
     .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
     .OP_END_FACTORY_REG(Lerp)
 
+/**
+*@brief Returns the num value of abs(x1-x2) > atol+rtol*abs(x2) element-wise. \n
+
+*
+*@par Inputs:
+*@li x1: A tensor. Must be one of the following types: float32, int32, uint8, int8, float16
+*@li x2: A tensor of the same type as "x1".
+*
+*@par Attributes:
+* atol: Defaults to "1e-05".
+* rtol: Defaults to "1e-03".
+*
+*@par Outputs:
+* num: A tensor of type int32.
+* diff: A tensor of type float16.
+*
+*@par Restrictions:
+*Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
+*
+*/
+REG_OP(DataCompare)
+  .INPUT(x1, TensorType({ DT_FLOAT16, DT_FLOAT,DT_INT8, DT_UINT8, DT_INT32 }))
+  .INPUT(x2, TensorType({ DT_FLOAT16, DT_FLOAT,DT_INT8, DT_UINT8, DT_INT32 }))
+  .OUTPUT(num, TensorType({DT_FLOAT}))
+  .OUTPUT(diff, TensorType({DT_FLOAT16}))
+  .ATTR(atol, Float, 1e-5)
+  .ATTR(rtol, Float, 1e-3)
+  .OP_END_FACTORY_REG(DataCompare)
+
 /**
 *@brief Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0
 *otherwise The input does not need to explicitly be a 2D vector.The "axis" attribute indicates the dimension along
@@ -3650,6 +3679,57 @@ REG_OP(HardMax)
     .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
     .ATTR(axis, Int, -1)
     .OP_END_FACTORY_REG(HardMax)
+
+/**
+* @brief Computes the dot product (inner product) of two tensors. This function does not broadcast.
+
+* @par Inputs:
+* Two inputs, including:
+* @li input_x: A Tensor. the first tensor must be 1d. \n
+* @li input_y: A Tensor. the second tensor must be 1d. \n
+
+* @par Outputs:
+* @li output: A Tensor. Result of the two inputs, must be 1d. \n
+
+* @par Third-party framework compatibility
+* Compatible with the Pytorch dot operator. \n
+*/
+REG_OP(Dot)
+    .INPUT(input_x, TensorType({DT_FLOAT, DT_FLOAT16, DT_UINT8, DT_INT8, DT_INT32}))
+    .INPUT(input_y, TensorType({DT_FLOAT, DT_FLOAT16, DT_UINT8, DT_INT8, DT_INT32}))
+    .OUTPUT(output, TensorType({DT_FLOAT, DT_FLOAT16, DT_UINT8, DT_INT8, DT_INT32}))
+    .OP_END_FACTORY_REG(Dot)
+	
+/**
+*@brief Returns a new tensor with boolean elements representing \n
+*if each element of input is “close” to the corresponding element of other \n
+
+*@par Inputs:
+*Two inputs, including:
+* @li x1: A tensor. Must be one of the following types:
+*     float16, float32, int32. \n
+* @li x2: A tensor with the same type and shape of x1's. \n
+
+*@par Attributes:
+*@li rtol: An optional float.Defaults to 1e-05. \n
+*@li atol: An optional float.Defaults to 1e-08. \n
+*@li equal_nan: An optional bool.Defaults to false. \n
+
+*@par Outputs:
+*y: A Tensor bool with the same shape of x1's. \n
+
+*@par Third-party framework compatibility
+*Compatible with the Pytorch operator isclose. \n
+*/
+REG_OP(IsClose)
+    .INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
+    .INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
+    .OUTPUT(y, TensorType({DT_BOOL}))
+    .ATTR(rtol, Float, 1e-05)
+    .ATTR(atol, Float, 1e-08)
+    .ATTR(equal_nan, Bool, false)
+    .OP_END_FACTORY_REG(IsClose)
+
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_ELEWISE_CALCULATION_OPS_H_

third_party/fwkacllib/inc/ops/hcom_ops.h

@@ -238,6 +238,15 @@ REG_OP(HcomRemoteRead)
     .REQUIRED_ATTR(dtype, Type)
     .OP_END_FACTORY_REG(HcomRemoteRead)
 
+/**
+ * @brief Performs Remote Ref Read of input tensors
+ * @par Inputs:
+ * remote: A tensor. describing the remote memory address to read: u64 remoteId, u64 addrRemote, u64 length
+ * cache_var: The local base address
+ * local_offset: Skip step length
+ * @par Outputs:
+ * cache_var: The local base address
+ */
 REG_OP(HcomRemoteRefRead)
     .INPUT(remote, TensorType({DT_UINT64}))
     .INPUT(cache_var, TensorType({DT_UINT64}))
@@ -258,6 +267,13 @@ REG_OP(HcomRemoteWrite)
     .INPUT(local, TensorType::ALL())
     .OP_END_FACTORY_REG(HcomRemoteWrite)
 
+/**
+ * @brief Performs Remote Write of input tensors
+ * @par Inputs:
+ * remote: A tensor. describing the remote memory address to write: u64 remoteId, u64 addrRemote, u64 length
+ * @par Inputs:
+ * local: A Tensor. whose value is length / size_of(Type)
+ */
 REG_OP(HcomRemoteScatterWrite)
     .INPUT(remote, TensorType({DT_INT64, DT_UINT64}))
     .INPUT(local, TensorType::ALL())

third_party/fwkacllib/inc/ops/image_ops.h

@@ -652,6 +652,62 @@ REG_OP(RGBToHSV)
 /**
 *@brief Generate a single randomly distorted bounding box for an image . \n
 
+*@par Inputs:
+*Input images must be a 4-D tensor. Inputs include:
+*@li image_size: 1-D, containing [height, width, channels].
+*@li bounding_boxes: 3-D with shape [batch, N, 4] describing the N bounding
+boxes associated with the image. \n
+
+*@par Attributes:
+*@li seed: If either seed or seed2 are set to non-zero, the random number
+generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
+*@li seed2: A second seed to avoid seed collision.
+*@li min_object_covered: The cropped area of the image must contain at least
+this fraction of any bounding box supplied. The value of this parameter should
+be non-negative. In the case of 0, the cropped area does not need to overlap
+any of the bounding boxes supplied .
+*@li aspect_ratio_range: The cropped area of the image must have an aspect
+ratio = width / height within this range.
+*@li max_attempts: Number of attempts at generating a cropped region of the
+image of the specified constraints. After max_attempts failures, return the
+entire image.
+*@li use_image_if_no_bounding_boxes: Controls behavior if no bounding boxes
+supplied. If true, assume an implicit bounding box covering the whole input.
+If false, raise an error . \n
+
+*@par Outputs:
+*@li begin: 1-D, containing [offset_height, offset_width, 0].
+*@li size: 1-D, containing [target_height, target_width, -1].
+*@li bboxes: 3-D with shape [1, 1, 4] containing the distorted bounding box . \n
+
+*@attention Constraints:
+*Input images can be of different types but output images are always float . \n
+
+*@par Third-party framework compatibility
+*Compatible with tensorflow SampleDistortedBoundingBox operator.
+*/
+
+REG_OP(SampleDistortedBoundingBox)
+    .INPUT(image_size, TensorType({ DT_UINT8, DT_INT8, DT_INT16, \
+        DT_INT32, DT_INT64 }))
+    .INPUT(bounding_boxes, TensorType({ DT_FLOAT }))
+    .OUTPUT(begin, TensorType({ DT_UINT8, DT_INT8, DT_INT16, \
+        DT_INT32, DT_INT64 }))
+    .OUTPUT(size, TensorType({ DT_UINT8, DT_INT8, DT_INT16, \
+        DT_INT32, DT_INT64 }))
+    .OUTPUT(bboxes, TensorType({ DT_FLOAT }))
+    .ATTR(seed, Int, 0)
+    .ATTR(seed2, Int, 0)
+    .ATTR(min_object_covered, Float, 0.1f)
+    .ATTR(aspect_ratio_range, ListFloat, { 0.75f, 1.33f })
+    .ATTR(area_range, ListFloat, { 0.05f, 1.0f })
+    .ATTR(max_attempts, Int, 100)
+    .ATTR(use_image_if_no_bounding_boxes, Bool, false)
+    .OP_END_FACTORY_REG(SampleDistortedBoundingBox)
+
+/**
+*@brief Generate a single randomly distorted bounding box for an image . \n
+
 *@par Inputs:
 *Input images must be a 4-D tensor. Inputs include:
 *@li image_size: 1-D, containing [height, width, channels].
@@ -1424,11 +1480,11 @@ REG_OP(Resize)
 
 *@par Attributes:
 *@li channels: An optional int. Defaults to 0. Number of color channels for the decoded image.
-*@li ratio: An optional int. Defaults to 1. Downscaling ratio. 
+*@li ratio: An optional int. Defaults to 1. Downscaling ratio.
 *@li fancy_upscaling: An optional bool. Defaults to True. If true use a slower but nicer upscaling of the chroma planes
 *@li try_recover_truncated: An optional bool. Defaults to False. If true try to recover an image from truncated input.
 *@li acceptable_fraction: An optional float. Defaults to 1. The minimum required fraction of lines before a truncated input is accepted.
-*@li dct_method: An optional string. Defaults to "". string specifying a hint about the algorithm used for decompression. \n 
+*@li dct_method: An optional string. Defaults to "". string specifying a hint about the algorithm used for decompression. \n
 
 *@par Outputs:
 *image: A Tensor dtype of uint8.

third_party/fwkacllib/inc/ops/linalg_ops.h

@@ -61,8 +61,8 @@ REG_OP(CholeskyGrad)
 
 *@par Inputs:
 *The input x has to be symmetric and positive definite.Inputs include:
-*x:A Tensor. Must be one of the following types: double, float32. Shape
-is [..., M, M] . \n
+*x:A Tensor. Must be one of the following types: double, float32, float16,
+complex64, complex128. Shape is [..., M, M] . \n
 
 *@par Outputs:
 *y:A Tensor. Has the same type as x . \n
@@ -76,8 +76,10 @@ form square matrices.
 */
 
 REG_OP(Cholesky)
-    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, \
+        DT_FLOAT16, DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, \
+        DT_FLOAT16, DT_COMPLEX64, DT_COMPLEX128}))
     .OP_END_FACTORY_REG(Cholesky)
 
 /**
@@ -87,8 +89,8 @@ of one or more square matrices . \n
 *@par Inputs:
 *The input x is a tensor of shape [N, M, M] whose inner-most 2 dimensions
 form square matrices. Inputs include:
-*x:A Tensor. Must be one of the following types: double, float32. Shape is
-[..., M, M] . \n
+*x:A Tensor. Must be one of the following types: double, float32,
+complex64, complex128. Shape is [..., M, M] . \n
 
 *@par Outputs:
 *@li y:A Tensor. Has the same type as x.
@@ -103,9 +105,9 @@ form square matrices. \n
 */
 
 REG_OP(LogMatrixDeterminant)
-    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(sign, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(sign, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
     .OP_END_FACTORY_REG(LogMatrixDeterminant)
 
 /**
@@ -114,8 +116,8 @@ REG_OP(LogMatrixDeterminant)
 *@par Inputs:
 *The input x is a tensor of shape [N, M, M] whose inner-most 2 dimensions
 form square matrices. Inputs include:
-*x:A Tensor. Must be one of the following types: double, float32. Shape is
-[..., M, M] . \n
+*x:A Tensor. Must be one of the following types: double, float32, complex64,
+complex128. Shape is [..., M, M] . \n
 
 *@par Outputs:
 *y:A Tensor. Has the same type as x . \n
@@ -129,8 +131,8 @@ form square matrices.
 */
 
 REG_OP(MatrixDeterminant)
-    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
     .OP_END_FACTORY_REG(MatrixDeterminant)
 
 /**
@@ -140,8 +142,7 @@ their adjoints (conjugate transposes) . \n
 *@par Inputs:
 *The input x is a tensor of shape [..., M, M] whose inner-most 2 dimensions
 form square matrices. Inputs include:
-*x:A Tensor. Must be one of the following types: double, float. Shape is
-[..., M, M] . \n
+*x:A Tensor of input. Shape is [..., M, M] . \n
 
 *@par Attributes:
 *adjoint:An optional bool. Defaults to False.Boolean indicating whether to
@@ -159,8 +160,10 @@ form square matrices.  \n
 */
 
 REG_OP(MatrixInverse)
-    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+        DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+        DT_COMPLEX64, DT_COMPLEX128}))
     .ATTR(adjoint, Bool, false)
     .OP_END_FACTORY_REG(MatrixInverse)
 
@@ -169,8 +172,7 @@ REG_OP(MatrixInverse)
 
 *@par Inputs:
 *The input rhs must have the same type as matrix. Inputs include:
-*@li matrix:A Tensor. Must be one of the following types: double, float.
-Shape is [..., M, M].
+*@li matrix:A Tensor of input. Shape is [..., M, M].
 *@li rhs:A Tensor. Must have the same type as matrix. Shape is [..., M, K] . \n
 
 *@par Attributes:
@@ -189,9 +191,9 @@ dimensions form square matrices.  \n
 */
 
 REG_OP(MatrixSolve)
-    .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
     .ATTR(adjoint, Bool, false)
     .OP_END_FACTORY_REG(MatrixSolve)
 
@@ -221,8 +223,10 @@ dimensions form square matrices.  \n
 */
 
 REG_OP(MatrixSolveLs)
-    .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+        DT_COMPLEX64, DT_COMPLEX128}))
+    .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+        DT_COMPLEX64, DT_COMPLEX128}))
     .INPUT(l2, TensorType({DT_DOUBLE}))
     .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
     .ATTR(fast, Bool, true)
@@ -234,8 +238,7 @@ matrices by backsubstitution . \n
 
 *@par Inputs:
 *The input rhs must have the same type as matrix. Inputs include:
-*@li matrix: A Tensor. Must be one of the following types: double, float.
-Shape is [..., M, M].
+*@li matrix: A Tensor. Shape is [..., M, M].
 *@li rhs:A Tensor. Must have the same type as matrix. Shape is [..., M, K] . \n
 
 *@par Attributes:
@@ -256,9 +259,12 @@ dimensions form square matrices.  \n
 */
 
 REG_OP(MatrixTriangularSolve)
-    .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+        DT_COMPLEX64, DT_COMPLEX128}))
+    .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+        DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_FLOAT16, \
+        DT_COMPLEX64, DT_COMPLEX128}))
     .ATTR(lower, Bool, true)
     .ATTR(adjoint, Bool, false)
     .OP_END_FACTORY_REG(MatrixTriangularSolve)
@@ -268,8 +274,7 @@ REG_OP(MatrixTriangularSolve)
 
 *@par Inputs:
 *The input shape of x must be [..., M, N]. Inputs include:
-*x:A Tensor whose shape is [..., M, N]. Must be one of the following types:
-double, float . \n
+*x:A Tensor whose shape is [..., M, N]. \n
 
 *@par Attributes:
 *full_matrices: An optional bool. Defaults to False. If true, compute
@@ -289,9 +294,12 @@ dimensions form matrices of size [M, N].  \n
 */
 
 REG_OP(Qr)
-    .INPUT(x, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
-    .OUTPUT(q, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
-    .OUTPUT(r, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
+    .INPUT(x, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
+        DT_COMPLEX64, DT_COMPLEX128 }))
+    .OUTPUT(q, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
+        DT_COMPLEX64, DT_COMPLEX128 }))
+    .OUTPUT(r, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE, \
+        DT_COMPLEX64, DT_COMPLEX128 }))
     .ATTR(full_matrices, Bool, false)
     .OP_END_FACTORY_REG(Qr)
 
@@ -384,8 +392,8 @@ of the rows encoded as a list of indices in `0..M-1`. Shape is `[..., M]` . \n
 */
 
 REG_OP(Lu)
-    .INPUT(input, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(lu, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(input, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(lu, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
     .OUTPUT(p, TensorType({DT_INT32, DT_INT64}))
     .REQUIRED_ATTR(output_idx_type, Type)
     .OP_END_FACTORY_REG(Lu)
@@ -404,8 +412,8 @@ y: Shape is `[..., M, M]` . \n
 */
 
 REG_OP(MatrixSquareRoot)
-    .INPUT(input, TensorType({DT_FLOAT, DT_DOUBLE}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(input, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
     .OP_END_FACTORY_REG(MatrixSquareRoot)
 
 /**

third_party/fwkacllib/inc/ops/list_ops.h

@@ -192,7 +192,7 @@ REG_OP(TensorListGetItem)
     .INPUT(element_shape, TensorType({DT_INT32}))
     .OUTPUT(item, TensorType({DT_FLOAT16,DT_FLOAT,DT_DOUBLE,DT_INT8,
         DT_INT16,DT_INT32,DT_INT64,DT_UINT8,DT_UINT16,DT_QINT8,DT_QUINT8,
-        DT_QINT16,DT_QUINT16,DT_QINT32,DT_BOOL,DT_RESOURCE,
+        DT_QINT16,DT_QUINT16,DT_QINT32,DT_BOOL,
         DT_STRING,DT_COMPLEX64,DT_COMPLEX128}))
     .ATTR(element_dtype, Type, DT_INT32)
     .OP_END_FACTORY_REG(TensorListGetItem)

third_party/fwkacllib/inc/ops/math_ops.h

@@ -222,6 +222,24 @@ REG_OP(Bucketize)
     .REQUIRED_ATTR(boundaries, ListFloat)
     .OP_END_FACTORY_REG(Bucketize)
 
+/**
+*@brief Returns a new tensor with the truncated integer values of the elements of input. \n
+
+*@par Inputs:
+*One inputs, including:
+*   @li input_x: A tensor. Must be one of the following types: float16, float32, int8, uint8, int32. \n
+
+*@par Outputs:
+*y: A tensor with the same type and shape of input_x \n
+
+*@par Third-party framework compatibility
+*Compatible with the Pytorch operator Trunc. \n
+*/
+REG_OP(Trunc)
+    .INPUT(input_x, TensorType({DT_FLOAT16,DT_FLOAT, DT_INT8, DT_INT32, DT_UINT8}))
+    .OUTPUT(output_y, TensorType({DT_FLOAT16,DT_FLOAT, DT_INT8, DT_INT32, DT_UINT8}))
+    .OP_END_FACTORY_REG(Trunc)
+	
 /**
 *@brief Computes the sum along sparse segments of a tensor . \n
 
@@ -645,6 +663,7 @@ REG_OP(NLLLoss)
     .OUTPUT(y, TensorType({DT_FLOAT}))
     .OUTPUT(total_weight, TensorType({DT_FLOAT}))
     .ATTR(reduction, String, "mean")
+    .ATTR(ignore_index, Int, -100)
     .OP_END_FACTORY_REG(NLLLoss)
 
 /**
@@ -674,6 +693,7 @@ REG_OP(NLLLossGrad)
     .INPUT(total_weight, TensorType({DT_FLOAT}))
     .OUTPUT(x_grad, TensorType({DT_FLOAT}))
     .ATTR(reduction, String, "mean")
+    .ATTR(ignore_index, Int, -100)
     .OP_END_FACTORY_REG(NLLLossGrad)
 
 /**
@@ -884,6 +904,54 @@ REG_OP(LpNorm)
     .ATTR(keepdim, Bool, false)
     .ATTR(epsilon, Float, 1e-12)
     .OP_END_FACTORY_REG(LpNorm)
+
+/**
+* @brief get complex.
+
+* @par Inputs:
+* @li real: An ND tensor of type  float32. double
+* @li imag: An ND tensor of type  float32. double \n
+*
+* @par Outputs:
+* @li out: An ND tensor of type complex64, complex128 \n
+*/
+REG_OP(Complex)
+    .INPUT(real, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .INPUT(imag, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .OUTPUT(out, TensorType({DT_COMPLEX64, DT_COMPLEX128}))
+    .ATTR(Tout, Type, DT_COMPLEX64)
+    .OP_END_FACTORY_REG(Complex)
+
+/**
+* @brief  deal complex.
+
+* @par Inputs:
+* @li input: An ND tensor of type complex64, complex128 \n
+*
+* @par Outputs:
+* @li output: An ND tensor of type float32. double \n
+*/
+REG_OP(Imag)
+    .INPUT(input, TensorType({DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(output, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .ATTR(Tout, Type, DT_FLOAT)
+    .OP_END_FACTORY_REG(Imag)
+
+/**
+* @brief  deal complex.
+
+* @par Inputs:
+* @li input: An ND tensor of type complex64, complex128 \n
+*
+* @par Outputs:
+* @li output: An ND tensor of type float32. double \n
+*/
+REG_OP(Angle)
+    .INPUT(input, TensorType({DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(output, TensorType({DT_FLOAT, DT_DOUBLE}))
+    .ATTR(Tout, Type, DT_FLOAT)
+    .OP_END_FACTORY_REG(Angle)
+
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_MATH_OPS_H_

third_party/fwkacllib/inc/ops/matrix_calculation_ops.h

@@ -1022,6 +1022,27 @@ REG_OP(IndexAdd)
     .ATTR(axis, Int, 0)
     .OP_END_FACTORY_REG(IndexAdd)
 
+/**
+*@brief: Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices input \n
+
+*@par Inputs:
+* Two inputs, including:
+*@li x: A Tensor. Must be one of the following types:
+*    float16, float32, double, int32, uint8, int16, int8, complex64, int64,
+*    qint8, quint8, qint32, uint16, complex128, uint32, uint64.
+*@li diagonal:(int, optional) – the diagonal to consider。\n
+
+*@par Outputs:
+*y: A Tensor. Has the same type as "x" . \n
+
+*@par Third-party framework compatibility
+* Compatible with the Pytorch operator Triu.
+*/
+REG_OP(Triu)
+    .INPUT(x, TensorType::BasicType())
+    .ATTR(diagonal, Int, 0)
+    .OUTPUT(y, TensorType::BasicType())
+    .OP_END_FACTORY_REG(Triu)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_

third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h

@@ -144,6 +144,64 @@ REG_OP(BatchNorm)
 /**
 *@brief Performs batch normalization . \n
 
+*@par Inputs:
+* Five inputs, including: (NHWC, NCHW, or NC1HWC0 supported)
+*@li x: A 3D or 6D Tensor of type float16 or float32, with format NDHWC or NCDHW for 4D or NDC1HWC0 for 6D.
+*@li scale: A Tensor of type float32. Must be 1D if input "x" is with format NDHWC or NCDHW. Must be 6D
+if input "x" is with format NDC1HWC0. Specifies the scaling factor.
+*@li offset: A Tensor of type float32. Must be 3D if input "x" is with format NDHWC or NCDHW. Must be 6D
+if input "x" is with format NC1HWC0. Specifies the offset.
+*@li mean: A Tensor of type float32. Must be 3D if input "x" is with format NDHWC or NCDHW. Must be 6D
+if input "x" is with format NC1HWC0. Specifies the mean used for inference. Must be "None" if the
+operation is used for training.
+*@li variance: A Tensor of type float32. Must be 3D if input "x" is with format NHWC or NCHW. Must be
+5D if input "x" is with format NC1HWC0. Specifies the variance used for inference. Must be "None"
+if the operation is used for training . \n
+
+*@par Attributes:
+*@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. Defaults to "0.0001".
+*@li data_format: An optional string, specifying the format of "x". Defaults to "NHWC".
+*@li is_training: An optional bool, specifying if the operation is used for training or inference. Defaults to "True" . \n
+
+*@par Outputs:
+* Five outputs, including: (NHWC, NCHW, or NC1HWC0 supported)
+*@li y: A 3D or 6D Tensor of type float16 or float32 for the normalized "x", with format NDHWC or NCDHW for 4D or NDC1HWC0 for 6D.
+*@li batch_mean: A Tensor of type float32. Must be 3D if input "x" is with format NDHWC or NCDHW. Must be 6D
+if input "x" is with format NDC1HWC0. Specifies the mean of "x".
+*@li batch_variance: A Tensor of type float32. Must be 1D if input "x" is with format NDHWC or NCDHW.
+Must be 6D if input "x" is with format NDC1HWC0. Specifies the variance of "x".
+*@li reserve_space_1: An optional Tensor of type float32. Must be 1D if input "x" is with format NDHWC or NCDHW.
+Must be 6D if input "x" is with format NDC1HWC0. Specifies the mean of "x" for gradient computation. Pass "None" to skip this output.
+*@li reserve_space_2: An optional Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW.
+Must be 6D if input "x" is with format NDC1HWC0. Specifies the variance of "x" for gradient computation. Pass "None" to skip this output . \n
+
+*@attention Constraints:
+*@li If the operation is used for inference and outputs "reserve_space_1" and "reserve_space_2" are available,
+then "reserve_space_1" has the same value as "mean" and "reserve_space_2" has the same value as "variance".
+*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction . \n
+
+*@par Third-party framework compatibility
+*@li Compatible with the TensorFlow operator fused_batch_norm.
+*@li Compatible with the TensorFlow operator fused_batch_norm_v2.
+*/
+REG_OP(BatchNorm3D)
+    .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(scale, TensorType({DT_FLOAT}))
+    .INPUT(offset, TensorType({DT_FLOAT}))
+    .OPTIONAL_INPUT(mean, TensorType({DT_FLOAT}))
+    .OPTIONAL_INPUT(variance, TensorType({DT_FLOAT}))
+    .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .OUTPUT(batch_mean, TensorType({DT_FLOAT}))
+    .OUTPUT(batch_variance, TensorType({DT_FLOAT}))
+    .OUTPUT(reserve_space_1, TensorType({DT_FLOAT}))
+    .OUTPUT(reserve_space_2, TensorType({DT_FLOAT}))
+    .ATTR(epsilon, Float, 0.0001)
+    .ATTR(data_format, String, "NCDHW")
+    .ATTR(is_training, Bool, true)
+    .OP_END_FACTORY_REG(BatchNorm3D)
+/**
+*@brief Performs batch normalization . \n
+
 *@par Inputs:
 * Five inputs, including: (NHWC or NCHW supported)
 *@li x: A 4D Tensor of type float16 or float32.
@@ -242,6 +300,52 @@ REG_OP(BatchNormGrad)
 /**
 *@brief Performs the backpropagation of BatchNorm . \n
 
+*@par Inputs:
+* Five inputs, including:
+*@li y_backprop: A 3D or 6D Tensor of type float16 or float32, with format NDHWC, NCDHW, or NDC1HWC0, for the gradient.
+*@li x: A 3D or 6D Tensor of type float16 or float32, with format NDHWC, NCDHW, or NDC1HWC0.
+*@li scale: A 3D or 6D Tensor of type float32, with format NDHWC, NCDHW, or NDC1HWC0.
+*@li reserve_space_1: A 3D or 6D Tensor of type float32, with format NDHWC, NCDHW, or NC1HWC0. It is an output of BatchNorm.
+*@li reserve_space_2: A 3D or 6D Tensor of type float32, with format NDHWC, NCDHW, or NC1HWC0. It is an output of BatchNorm . \n
+
+*@par Attributes:
+*@li epsilon: An optional float32. Defaults to "0.0001". A small float number added to the variance of "x".
+*@li data_format: An optional string. Defaults to "NCDHW".
+*@li is_training: An optional bool. Defaults to "true". Specifies the operation is for training (default) or inference . \n
+
+*@par Outputs:
+*@li x_backprop: A Tensor of type float16 or float32, with format NHWC, NCHW, or NC1HWC0, for the offset of "x".
+*@li scale_backprop: A Tensor of type float32, with format NDHWC, NCDHW, or NDC1HWC0, for the offset of "scale".
+*@li *offset_backprop: A Tensor of type float32, with format NDHWC, NCDHW, or NDC1HWC0, for the offset of "offset".
+*@li *reserve_space_4: A Tensor of type float32, with shape NDHWC, NCDHW, or NDC1HWC0. Pass "None" to skip this output.
+*@li *reserve_space_5: A Tensor of type float32, with shape NDHWC, NCDHW, or NDC1HWC0. Pass "None" to skip this output . \n
+
+*@attention Constraints:
+* The preceding layer of this operator must be operator BatchNorm . \n
+
+*@see BatchNorm
+*@par Third-party framework compatibility
+* Compatible with the TensorFlow operators FusedBatchNormGradV2 and FusedBatchNorm3DGrad.
+*/
+REG_OP(BatchNorm3DGrad)
+    .INPUT(y_backprop, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(scale, TensorType({DT_FLOAT}))
+    .INPUT(reserve_space_1, TensorType({DT_FLOAT}))
+    .INPUT(reserve_space_2, TensorType({DT_FLOAT}))
+    .OUTPUT(x_backprop, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .OUTPUT(scale_backprop, TensorType({DT_FLOAT}))
+    .OUTPUT(offset_backprop, TensorType({DT_FLOAT}))
+    .OUTPUT(reserve_space_4, TensorType({DT_FLOAT}))
+    .OUTPUT(reserve_space_5, TensorType({DT_FLOAT}))
+    .ATTR(epsilon, Float, 0.0001)
+    .ATTR(data_format, String, "NCDHW")
+    .ATTR(is_training, Bool, true)
+    .OP_END_FACTORY_REG(BatchNorm3DGrad)
+
+/**
+*@brief Performs the backpropagation of BatchNorm . \n
+
 *@par Inputs:
 * Five inputs, including:
 *@li y_backprop: A 4D Tensor of type float16 or float32, with format NHWC or NCHW, for the gradient.

third_party/fwkacllib/inc/ops/nn_calculation_ops.h

@@ -1059,7 +1059,7 @@ REG_OP(DeformableConv2D)
 
 *@par Attributes:
  * @li groups: Number of blocked connections from input channels to output
- * channels. Reserved.
+ * 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 list of 5 integers. Specifies the dilation factor for each
@@ -1119,7 +1119,7 @@ REG_OP(Conv3D)
 *@par Attributes:
  * Three attributes:
  * @li groups: Number of blocked connections from input channels to output
- * channels. Reserved.
+ * 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
@@ -1167,7 +1167,7 @@ REG_OP(Conv3DBackpropInput)
 *@par Attributes:
  * Three attributes:
  * @li groups: Number of blocked connections from input channels to output
- * channels. Reserved.
+ * 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
@@ -1267,7 +1267,7 @@ REG_OP(LSTM)
  * 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
- * channels. Reserved.
+ * channels.
  * @li data_format: An optional string from: "NDHWC", "NCDHW".
  * Defaults to "NDHWC". Specify the data format of the input and output data.
 
@@ -1319,7 +1319,7 @@ REG_OP(Conv3DBackpropFilter)
  * 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
- * channels. Reserved.
+ * channels.
  * @li data_format: An optional string from: "NDHWC", "NCDHW".
  * Defaults to "NDHWC". Specify the data format of the input and output data.
 
@@ -1369,7 +1369,7 @@ REG_OP(Conv3DBackpropFilterD)
 *@par Attributes:
  * Five attributes:
  * @li groups: Number of blocked connections from input channels to output
- * channels. Reserved.
+ * channels.
  * @li dilations: 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".
@@ -1422,7 +1422,7 @@ REG_OP(Conv3DTranspose)
  * 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
- * channels. Reserved.
+ * channels.
  * @li data_format: 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.
@@ -1624,7 +1624,7 @@ REG_OP(Conv2DTransposeD)
  * of the input.
  * @li ksize: A tuple/list of 2 integers.kernel size.
 *@par Attributes:
- * Three attributes:
+ * Four attributes:
  * @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension
  * of input.  Defaults to [1, 1, 1, 1]
  * @li data_format: An optional string from: "NCHW", "NHWC". Defaults to "NCHW". Specify the data format of the input x.

third_party/fwkacllib/inc/ops/nn_detect_ops.h

@@ -968,8 +968,9 @@ REG_OP(SPP)
 * Three inputs, including:
 *@li x: An NC1HWC0 tensor of type float16 or float32, describing the feature
 * map.
-*@li rois: A tensor of type float16 or float32, with shape
+*@li rois: A tensor of type float16 or float32, with 3D shape
 * [batch, 5, roi_max_num], describing the RIOs.
+* roi_max_num must be less than or equal to 6000 and must be divided by 16.
 *@li roi_actual_num: A  optional tensor of type int32, with shape [batch, 8], specifying
 * the number of ROIs per batch . \n
 
@@ -1604,6 +1605,50 @@ REG_OP(NonMaxSuppressionV7)
     .ATTR(max_boxes_size, Int, 0)
     .OP_END_FACTORY_REG(NonMaxSuppressionV7)
 
+/**
+*@brief Obtains the ROI feature matrix from the feature map list. It is a customized fused operator for mmdetection. \n
+
+*@par Inputs:
+* Three inputs, including:
+*@li features: A 5HD Tensor list of type float32 or float16.
+*@li rois: ROI position. A 2D Tensor of float32 or float16 with shape (N, 5). "N" indicates the number of ROIs,
+* the value "5" indicates the indexes of images where the ROIs are located, "x0", "y0", "x1", and "y1".
+
+*@par Attributes:
+*@li finest_scale: A optional attribute of type int, specifying the scale of calculate levels of "rois".
+*@li roi_scale_factor: A optional attribute of type float32, specifying the rescaling of "rois" coordinates.
+*@li spatial_scale: A optional attribute of type list float32, specifying the scaling ratio of "features"
+* to the original image.
+*@li pooled_height: A optional attribute of type int32, specifying the H dimension.
+*@li pooled_width: A optional attribute of type int32, specifying the W dimension.
+*@li sample_num: An optional attribute of type int32, specifying the horizontal and vertical sampling frequency
+* of each output. If this attribute is set to "0", the sampling frequency is equal to the rounded up value of "rois",
+* which is a floating point number. Defaults to "0".
+*@li pool_mode: An optional attribute of type string to indicate pooling mode. Defaults to "avg" . \n
+*@li aligned: An optional attribute of type bool, specifying the align to corner. Defaults to true . \n
+
+*@par Outputs:
+* output: Outputs the feature sample of each ROI position. The format is 5HD Tensor of type float32 or float16.
+* The axis N is the number of input ROIs. Axes H, W, and C are consistent with the values of "pooled_height",
+* "pooled_width", and "features", respectively.
+
+*@par Third-party framework compatibility
+*Compatible with mmdetection SingleRoIExtractor operator.
+*/
+REG_OP(RoiExtractor)
+    .DYNAMIC_INPUT(features, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(rois, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .ATTR(finest_scale, Int, 56)
+    .ATTR(roi_scale_factor, Float, 0)
+    .ATTR(spatial_scale, ListFloat, { 1.f/4, 1.f/8, 1.f/16, 1.f/32 })
+    .ATTR(pooled_height, Int, 7)
+    .ATTR(pooled_width, Int, 7)
+    .ATTR(sample_num, Int, 0)
+    .ATTR(pool_mode, String, "avg")
+    .ATTR(aligned, Bool, true)
+    .OP_END_FACTORY_REG(RoiExtractor)
+
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_NN_DETECT_OPS_H_

third_party/fwkacllib/inc/ops/nn_norm_ops.h

@@ -1233,6 +1233,47 @@ REG_OP(SigmoidCrossEntropyWithLogitsGradV2)
     .OUTPUT(gradient, TensorType({DT_FLOAT16, DT_FLOAT}))
     .ATTR(reduction, String, "mean")
     .OP_END_FACTORY_REG(SigmoidCrossEntropyWithLogitsGradV2)
+/**
+ * @brief Calculate the PoissonNllLoss function. 
+ *        target∼Poisson(input)loss(input,target)=input−target∗log(input)+log(target!) \n
+
+ * @par Inputs:
+ * Two inputs, including:
+ * @li input_x: A tensor. Must be one of the following types:
+ *     float16, float32. \n
+ * 
+ * @par Inputs:
+ * @li target: A tensor. Must be one of the following types:
+ *     float16, float32. \n
+
+ * @par Attributes:
+ * four Attributes, including:
+ * @li log_input: An optional bool. Defaults to "True" \n
+ * 
+ *  @par Attributes:
+ * @li full: An optional bool. Defaults to "False" \n
+ * 
+ *  @par Attributes:
+ * @li eps: An optional float. Defaults to "1e-8" \n
+ * 
+ *  @par Attributes:
+ * @li reduction: An optional string. Defaults to "mean" \n
+
+ * @par Outputs:
+ * loss: A Tensor has same element type as two inputs. \n
+
+ * @par Third-party framework compatibility
+ * Compatible with the Pytorch operator PoissonNllLoss. \n
+ */
+REG_OP(PoissonNllLoss)
+    .INPUT(input_x, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(target, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OUTPUT(loss, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .ATTR(log_input, Bool, true)
+    .ATTR(full, Bool, false)
+    .ATTR(eps, Float, 1e-8)
+    .ATTR(reduction, String, "mean")
+    .OP_END_FACTORY_REG(PoissonNllLoss)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_NN_NORM_OPS_H_

third_party/fwkacllib/inc/ops/nn_ops.h

@@ -20,7 +20,34 @@
  */
 #ifndef OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_
 #define OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_
-
+#include "graph/operator_reg.h"
 #include "nn_pooling_ops.h"
 
+namespace ge {
+/**
+* @brief Says whether the targets are in the top "k" predictions . \n
+
+* @par Inputs:
+* Three inputs, including:
+* @li predictions: A 2D Tensor of type float32. A "batch_size * classes" tensor.
+* @li targets: A 1D Tensor of type IndexNumberType. A batch_size tensor of class ids.
+* @li k: A 1D Tensor of the same type as "targets".
+* Specifies the number of top elements to look at for computing precision . \n
+
+* @par Outputs:
+* precision: A Tensor of type bool . \n
+
+* @attention Constraints:
+* @li targets must be non-negative tensor.
+
+* @par Third-party framework compatibility
+* @li Compatible with the TensorFlow operator InTopKV2.
+*/
+REG_OP(InTopKV2)
+    .INPUT(predictions, TensorType({DT_FLOAT}))
+    .INPUT(targets, TensorType(IndexNumberType))
+    .INPUT(k, TensorType({IndexNumberType}))
+    .OUTPUT(precision, TensorType({DT_BOOL}))
+    .OP_END_FACTORY_REG(InTopKV2)
+}// namespace ge
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_

third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h

@@ -223,7 +223,29 @@ REG_OP(Relu6Grad)
     .INPUT(features, TensorType::RealNumberType())
     .OUTPUT(backprops, TensorType::RealNumberType())
     .OP_END_FACTORY_REG(Relu6Grad)
-
+/**
+*@brief Calculate the elu_grad_v2 function. 
+*Applies the element-wise function:
+* Computes the backward for the elu: if x>0, 1; otherwise elu() + alpha .
+*@par Inputs:
+*One inputs, including:
+* @li grads: A tensor. Must be one of the following types:
+*     float16, float32. 
+* @li activations: A tensor. Must be one of the following types:
+*     float16, float32. 
+*
+*@par Outputs:
+*y: A Tensor with the same type and shape of grads's.
+* 
+*@par Attributes:
+*@li alpha: scalar parameter, default value = 1.0
+*/	
+REG_OP(EluGradV2)
+    .INPUT(grads, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .INPUT(activations, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16}))
+    .ATTR(alpha, Float, 1.0)
+    .OP_END_FACTORY_REG(EluGradV2)
 /**
 * @brief Compute sigmoid of "x" element-wise . \n
 
@@ -842,6 +864,26 @@ REG_OP(SoftShrinkGrad)
      .OUTPUT(output_y, TensorType({DT_FLOAT16, DT_FLOAT}))
      .ATTR(lambd, Float, 0.5)
      .OP_END_FACTORY_REG(SoftShrinkGrad)
+	 
+/**
+*@brief Calculate -ln(1+e^(-x)). \n
+
+*@par Inputs:
+*One inputs, including:
+* @li x: A tensor. Must be one of the following types:
+*       float16, float32. \n
+
+*@par Outputs:
+*One outputs, including:
+* @li y: A tensor with the same type and shape of x's. \n
+
+*@par Third-party framework compatibility
+*Compatible with the Pytorch operator LogSigmoid. \n
+*/
+REG_OP(LogSigmoid)
+    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) /* "input:x" */
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))  /* "output:y" */
+    .OP_END_FACTORY_REG(LogSigmoid)
 } // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_NONLINEAR_FUC_OPS_H_

third_party/fwkacllib/inc/ops/reduce_ops.h

@@ -37,7 +37,7 @@ namespace ge {
 *@attention Constraints:
 * This operator is a BatchNorm fusion operator for updating the moving
 * averages for training.
-* This operator is used in conjunction with BNTrainingUpdate.
+* This operator is used in conjunction with BNTrainingReduce.
 */
 REG_OP(BNTrainingReduce)
     .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
@@ -45,6 +45,27 @@ REG_OP(BNTrainingReduce)
     .OUTPUT(square_sum, TensorType({DT_FLOAT}))
     .OP_END_FACTORY_REG(BNTrainingReduce)
 
+/**
+*@brief Performs reduced batch normalization . \n
+
+*@par Inputs:
+*x: A 6D Tensor of type float16 or float32, with format NDC1HWC0 . \n
+
+*@par Outputs:
+*@li sum: A 3D Tensor of type float32 for SUM reduced "x".
+*@li square_sum: A 3D Tensor of type float32 for SUMSQ reduced "x" . \n
+
+*@attention Constraints:
+* This operator is a BatchNorm fusion operator for updating the moving
+* averages for training.
+* This operator is used in conjunction with BN3DTrainingReduce.
+*/
+REG_OP(BN3DTrainingReduce)
+    .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .OUTPUT(sum, TensorType({DT_FLOAT}))
+    .OUTPUT(square_sum, TensorType({DT_FLOAT}))
+    .OP_END_FACTORY_REG(BN3DTrainingReduce)
+
 /**
 *@brief Performs the backpropagation of BatchNorm . \n
 
@@ -88,6 +109,49 @@ REG_OP(BNTrainingReduceGrad)
     .ATTR(epsilon, Float, 0.0001)
     .OP_END_FACTORY_REG(BNTrainingReduceGrad)
 
+/**
+*@brief Performs the backpropagation of BatchNorm . \n
+
+*@par Inputs:
+* Seven inputs, including:
+*@li grads: A 6D Tensor of type float16 or float32, with format NDC1HWC0, for
+* the gradient.
+*@li x: A 6D Tensor of type float16 or float32, with format NDC1HWC0.
+*@li diff_scale: A 6D Tensor of type float32, with format NDC1HWC0,
+* for the mean of "x".
+*@li diff_offset: A 6D Tensor of type float32, with format NDC1HWC0,
+* for the variance of "x".
+*@li scale: A 6D Tensor of type float32, with format NDC1HWC0.
+*@li batch_mean: A 6D Tensor of type float32, with format NDC1HWC0,
+* for the mean of "x".
+*@li batch_variance: A 6D Tensor of type float32, with format NDC1HWC0,
+* for the variance of "x" . \n
+
+*@par Attributes:
+*epsilon: An optional float32. Defaults to "0.0001". A small float number
+* added to the variance of "x" . \n
+
+*@par Outputs:
+*y: A Tensor of type float16 or float32, with format NDC1HWC0, for the offset
+* of "x" . \n
+
+*@attention Constraints:
+* The preceding layer of this operator must be BN3DTrainingReduceGrad . \n
+
+*@see BN3DTrainingReduceGrad
+*/
+REG_OP(BN3DTrainingReduceGrad)
+    .INPUT(grads, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(diff_scale, TensorType({DT_FLOAT}))
+    .INPUT(diff_offset, TensorType({DT_FLOAT}))
+    .INPUT(scale, TensorType({DT_FLOAT}))
+    .INPUT(batch_mean, TensorType({DT_FLOAT}))
+    .INPUT(batch_variance, TensorType({DT_FLOAT}))
+    .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .ATTR(epsilon, Float, 0.0001)
+    .OP_END_FACTORY_REG(BN3DTrainingReduceGrad)
+
 /**
 *@brief Performs reduced batch normalization . \n
 
@@ -120,7 +184,7 @@ REG_OP(BNTrainingReduceGrad)
 *@attention Constraints:
 *@li This operator is a BatchNorm fusion operator for updating the moving
 averages for training.
-*This operator is used in conjunction with BNTrainingReduce.
+*This operator is used in conjunction with BNTrainingUpdate.
 *@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square
 * root instruction.
 */
@@ -141,6 +205,59 @@ REG_OP(BNTrainingUpdate)
     .OUTPUT(batch_variance, TensorType({DT_FLOAT}))
     .OP_END_FACTORY_REG(BNTrainingUpdate)
 
+/**
+*@brief Performs reduced batch normalization . \n
+
+*@par Inputs:
+* Seven inputs, including: (NDC1HWC0 supported)
+*@li x: A 6D Tensor of type float16 or float32.
+*@li sum: A 6D Tensor of type float32 for the output of operator
+* BN3DTrainingUpdate.
+*@li square_sum: A 6D Tensor of type float32 for the output of operator
+* BN3DTrainingUpdate.
+*@li scale: A 6D Tensor of type float32, for the scaling factor.
+*@li offset: A 6D Tensor of type float32, for the scaling offset.
+*@li mean: A 6D Tensor of type float32, for the updated mean.
+*@li variance: A 6D Tensor of type float32, for the updated variance . \n
+
+*@par Attributes:
+*@li epsilon: A required float32, specifying the small value added to variance
+* to avoid dividing by zero.
+*@li factor: A required float32, specifying the weight for updating the mean
+* and variance . \n
+
+*@par Outputs:
+* Five outputs, including: (NDC1HWC0 supported)
+*@li y: A 6D Tensor of type float16 or float32, for normalized "x".
+*@li mean: A 6D Tensor of type float32, for the updated mean.
+*@li variance: A 6D Tensor of type float32, for the updated variance.
+*@li batch_mean: A 6D Tensor of type float32, for the mean of "x".
+*@li batch_variance: A 6D Tensor of type float32, for the variance of "x" . \n
+
+*@attention Constraints:
+*@li This operator is a BatchNorm fusion operator for updating the moving
+averages for training.
+*This operator is used in conjunction with BN3DTrainingUpdate.
+*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square
+* root instruction.
+*/
+REG_OP(BN3DTrainingUpdate)
+    .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(sum, TensorType({DT_FLOAT}))
+    .INPUT(square_sum, TensorType({DT_FLOAT}))
+    .INPUT(scale, TensorType({DT_FLOAT}))
+    .INPUT(offset, TensorType({DT_FLOAT}))
+    .INPUT(mean, TensorType({DT_FLOAT}))
+    .INPUT(variance, TensorType({DT_FLOAT}))
+    .REQUIRED_ATTR(factor, Float)
+    .REQUIRED_ATTR(epsilon, Float)
+    .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .OUTPUT(mean, TensorType({DT_FLOAT}))
+    .OUTPUT(variance, TensorType({DT_FLOAT}))
+    .OUTPUT(batch_mean, TensorType({DT_FLOAT}))
+    .OUTPUT(batch_variance, TensorType({DT_FLOAT}))
+    .OP_END_FACTORY_REG(BN3DTrainingUpdate)
+
 /**
 *@brief Performs batch normalization for inference . \n
 
@@ -284,6 +401,40 @@ REG_OP(BNTrainingUpdateGrad)
     .OUTPUT(diff_offset, TensorType({DT_FLOAT}))
     .OP_END_FACTORY_REG(BNTrainingUpdateGrad)
 
+/**
+*@brief Performs the backpropagation of BatchNorm . \n
+
+*@par Inputs:
+* Four inputs, including:
+*@li grads: A 6D Tensor of type float16 or float32, with format NDC1HWC0,
+* for the gradient.
+*@li x: A 6D Tensor of type float16 or float32, with format NDC1HWC0.
+*@li batch_mean: A 6D Tensor of type float32, with format NDC1HWC0,
+* for the mean of "x".
+*@li batch_variance: A 6D Tensor of type float32, with format NDC1HWC0,
+* for the variance of "x" . \n
+
+*@par Attributes:
+*epsilon: An optional float32. Defaults to "0.0001". A small float number
+* added to the variance of "x" . \n
+
+*@par Outputs:
+*@li diff_scale: A Tensor of type float32, with format NDC1HWC0,
+* for the offset of "scale".
+*@li diff_offset: A Tensor of type float32, with format NDC1HWC0,
+* for the offset of "offset" . \n
+
+*/
+REG_OP(BN3DTrainingUpdateGrad)
+    .INPUT(grads, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
+    .INPUT(batch_mean, TensorType({DT_FLOAT}))
+    .INPUT(batch_variance, TensorType({DT_FLOAT}))
+    .ATTR(epsilon, Float, 0.0001)
+    .OUTPUT(diff_scale, TensorType({DT_FLOAT}))
+    .OUTPUT(diff_offset, TensorType({DT_FLOAT}))
+    .OP_END_FACTORY_REG(BN3DTrainingUpdateGrad)
+
 /**
 *@brief Performs the backpropagation of BatchNorm for inference . \n
 

third_party/fwkacllib/inc/ops/selection_ops.h

@@ -857,8 +857,8 @@ REG_OP(SliceDV2)
 * @li sorted = true
 * @li It's unstable sorted indices on the platform of Ascend310
 
-* @par Third-party framework compatibility
-* @li Compatible with the TensorFlow operator TopK.
+* @par Restrictions:
+* Warning: THIS FUNCTION IS DEPRECATED. Please use TopKV2 instead.
 */
 REG_OP(TopKD)
     .INPUT(x, TensorType::RealNumberType())
@@ -883,6 +883,44 @@ REG_OP(TopKD)
 * Number of top elements to look for along the last dimension (along each row
 * for matrices) . \n
 
+* @par Attributes:
+* @li sorted: An optional bool. Defaults to true.
+* If true, the resulting "k" elements will be sorted by the values in descending
+* order.
+* @li dim: An optional int. Defaults to -1. For reserved use.
+* @li largest: An optional bool. Defaults to true. For reserved use. \n
+
+* @par Outputs:
+* @li values: A Tensor, specifying the sorted data. Has the same type as
+* "input".
+* @li indices: A Tensor of type int32, specifying the indices of sorted data . \n
+
+* @see TopK()
+* @par Third-party framework compatibility
+* @li Compatible with the TensorFlow operator TopKV2.
+*/
+REG_OP(TopKV2)
+    .INPUT(x, TensorType::RealNumberType())
+    .INPUT(k, TensorType({DT_INT32}))
+    .OUTPUT(values, TensorType::RealNumberType())
+    .OUTPUT(indices, TensorType({DT_INT32}))
+    .ATTR(sorted, Bool, true)
+    .ATTR(dim, Int, -1)
+    .ATTR(largest, Bool, true)
+    .OP_END_FACTORY_REG(TopKV2)
+
+/**
+* @brief Finds values and indices of the "k" largest elements for the last
+* dimension . \n
+
+* @par Inputs:
+* Two inputs, including:
+* @li x: A 1D or higher tensor of type BasicType, with the last dimension
+* at least "k".
+* @li k: A 0D Tensor of type int32.
+* Number of top elements to look for along the last dimension (along each row
+* for matrices) . \n
+
 * @par Attributes:
 * @li sorted: An optional bool. Defaults to true.
 * If true, the resulting "k" elements will be sorted by the values in descending
@@ -2103,6 +2141,34 @@ REG_OP(StridedSliceV2)
     .OUTPUT(y, TensorType::BasicType())
     .OP_END_FACTORY_REG(StridedSliceV2)
 
+/**
+*@brief Fills the elements of the input tensor with value val by selecting the indices in the order given in index. \n
+
+*@par Inputs:
+*Three inputs, including:
+* @li x: A tensor. Must be one of the following types:
+*     float16, float32, int32. \n
+*@li assist1: A tensor. Must be one of the following types:
+*     float16, float32, int32. \n
+*@li assist2: A tensor. Must be one of the following types:
+*     float16, float32, int32. \n
+
+* @par Attributes:
+* @li dim: A required int. Used to select the dimension of this tensor. \n
+
+*@par Outputs:
+*y: A Tensor with the same type and shape of input_x's. \n
+
+*@par Third-party framework compatibility
+*Compatible with the Pytorch operator IndexFill. \n
+*/
+REG_OP(IndexFillD)
+    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
+    .INPUT(assist1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
+    .INPUT(assist2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
+    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
+    .REQUIRED_ATTR(dim, Int)
+    .OP_END_FACTORY_REG(IndexFillD)
 } // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_SELECTION_OPS_H_

third_party/fwkacllib/inc/ops/sparse_ops.h

@@ -383,11 +383,11 @@ REG_OP(SparseFillEmptyRowsGrad)
 REG_OP(SparseTensorDenseMatMul)
     .INPUT(x1_indices, TensorType({DT_INT32, DT_INT64}))
     .INPUT(x1_values, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, \
-        DT_COMPLEXT64, DT_COMPLEX128, DT_FLOAT16}))
+        DT_COMPLEXT64, DT_COMPLEX128, DT_FLOAT16, DT_INT64}))
     .INPUT(x1_shape, TensorType({DT_INT64}))
-    .INPUT(x2, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_COMPLEXT64, \
+    .INPUT(x2, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT64, DT_INT32, DT_COMPLEXT64, \
         DT_COMPLEX128, DT_FLOAT16}))
-    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_COMPLEXT64, \
+    .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT64, DT_INT32, DT_COMPLEXT64, \
         DT_COMPLEX128, DT_FLOAT16}))
     .ATTR(adjoint_a, Bool, false)
     .ATTR(adjoint_b, Bool, false)

third_party/fwkacllib/inc/ops/spectral_ops.h

@@ -26,6 +26,24 @@
 
 namespace ge {
 
+/**
+*@brief Computes the inverse 1-dimensional discrete Fourier transform over the
+inner-most dimension of `x`. \n
+
+*@par Inputs:
+*@li x: A Tensor. Must be the following types: complex64, complex128. \n
+
+*@par Outputs:
+*@li y: A complex tensor of the same rank as `x`. \n
+
+*@par Third-party framework compatibility
+* Compatible with TensorFlow IFFT operator.
+*/
+REG_OP(IFFT)
+    .INPUT(x, TensorType({DT_COMPLEX64,DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_COMPLEX64,DT_COMPLEX128}))
+    .OP_END_FACTORY_REG(IFFT)
+
 /**
 *@brief Real-valued fast Fourier transform . \n
 
@@ -47,6 +65,84 @@ REG_OP(RFFT)
     .OUTPUT(y, TensorType({DT_COMPLEX64}))
     .OP_END_FACTORY_REG(RFFT)
 
+/**
+*@brief Inverse real-valued fast Fourier transform . \n
+
+*@par Inputs:
+*@li x: A complex64 tensor.
+*@li fft_length: An int32 tensor of shape [1]. The FFT length . \n
+
+*@par Outputs:
+*@li y: A float32 tensor of the same rank as `input`. The inner-most
+  dimension of `input` is replaced with the `fft_length` samples of its inverse
+  1D Fourier transform . \n
+
+*@par Third-party framework compatibility
+* Compatible with TensorFlow IRFFT operator.
+*/
+REG_OP(IRFFT)
+    .INPUT(x, TensorType({DT_COMPLEX64}))
+    .INPUT(fft_length, TensorType({DT_INT32}))
+    .OUTPUT(y, TensorType({DT_FLOAT}))
+    .OP_END_FACTORY_REG(IRFFT)
+
+
+/**
+*@brief 2D fast Fourier transform. \n
+
+*@par Inputs:
+*@li x: A complex64 tensor..
+
+*@par Outputs:
+*@li y: A complex64 tensor of the same shape as `input`. The inner-most 2
+  dimensions of `input` are replaced with their 2D Fourier transform.\n
+
+*@par Third-party framework compatibility
+* Compatible with TensorFlow FFT2D operator.
+*/
+REG_OP(FFT2D)
+    .INPUT(x, TensorType({DT_COMPLEX64, DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_COMPLEX64, DT_COMPLEX128}))
+    .OP_END_FACTORY_REG(FFT2D)
+
+/**
+*@brief Calculate the one-dimensional discrete Fourier transform on the
+innermost dimension of the input. \n
+
+*@par Inputs:
+*@li x: A Tensor. Must be the following types: complex64, complex128. \n
+
+*@par Outputs:
+*@li y: A complex tensor with the same shape as input. The innermost dimension
+of the input is replaced by its 1-dimensional Fourier transform. \n
+
+*@par Third-party framework compatibility
+* Compatible with TensorFlow FFT operator.
+*/
+REG_OP(FFT)
+    .INPUT(x, TensorType({DT_COMPLEX64,DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_COMPLEX64,DT_COMPLEX128}))
+    .OP_END_FACTORY_REG(FFT)
+
+/**
+*@brief Calculate the inverse 1-dimensional discrete Fourier transform on the
+innermost dimension of the input. \n
+
+*@par Inputs:
+*@li x: A Tensor. Must be the following types: complex64, complex128. \n
+
+*@par Outputs:
+*@li y: A complex tensor with the same shape as input. The innermost dimension
+of the input is replaced by its inverse two-dimensional Fourier transform. \n
+
+*@par Third-party framework compatibility
+* Compatible with TensorFlow IFFT2D operator.
+*/
+REG_OP(IFFT2D)
+    .INPUT(x, TensorType({DT_COMPLEX64,DT_COMPLEX128}))
+    .OUTPUT(y, TensorType({DT_COMPLEX64,DT_COMPLEX128}))
+    .OP_END_FACTORY_REG(IFFT2D)
+
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_SPECTRAL_OPS_H_

third_party/fwkacllib/inc/ops/split_combination_ops.h

@@ -62,8 +62,8 @@ REG_OP(Split)
 *Must be one of the following types: float16, float32, int32, int8, int16, int64, uint8, uint16, uint32, uint64
 
 *@par Attributes:
-*@li split_dim: A required int8, int16, int32, or int64. Specifies the dimension along which to split. No default value.
-*@li num_split: A required int8, int16, int32, or int64. Specifies the number of output tensors. No default value . \n
+*@li split_dim: A required int32. Specifies the dimension along which to split. No default value.
+*@li num_split: A required int32. Specifies the number of output tensors. No default value . \n
 
 *@par Outputs:
 *y:Dynamic output. A list of output tensors. Has the same type and format as "x" . \n
@@ -94,12 +94,12 @@ REG_OP(SplitD)
 *@par Inputs:
 * Three inputs, including:
 *@li x: An ND Tensor.
-*Must be one of the following types:
-*@li size_splits: A list of int8, int16, int32, or int64. Specifies a list containing the sizes of each output tensor along the split dimension.
-*@li split_dim: An int8, int16, int32, or int64. Specifies the dimension along which to split . \n
+*Must be one of the types:float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32.
+*@li size_splits: Must be one of the types:int32, int64. Specifies a list containing the sizes of each output tensor along the split dimension.
+*@li split_dim: Must be the following type:int32. Specifies the dimension along which to split . \n
 
 *@par Attributes:
-*num_split: A required int8, int16, int32, or int64. Specifies the number of output tensors. No default value . \n
+*num_split: A required int32. Specifies the number of output tensors. No default value . \n
 
 *@par Outputs:
 *y:  Dynamic output.A list of output tensors. Has the same type and format as "x" . \n
@@ -129,9 +129,9 @@ REG_OP(SplitV)
 *Must be one of the following types: float16, float32, int32, int8, int16, int64, uint8, uint16, uint32, uint64
 
 *@par Attributes:
-*@li size_splits: A required list of int8, int16, int32, or int64. Specifies a list containing the sizes of each output tensor along the split dimension.
-*@li split_dim: A required int8, int16, int32, or int64. Specifies the dimension along which to split. No default value.
-*@li num_split: A required int8, int16, int32, or int64. Specifies the number of output tensors. No default value . \n
+*@li size_splits: A required list of int32. Specifies a list containing the sizes of each output tensor along the split dimension.
+*@li split_dim: A required int32. Specifies the dimension along which to split. No default value.
+*@li num_split: A required int32. Specifies the number of output tensors. No default value . \n
 
 *@par Outputs:
 *y: Dynamic output.A list of output tensors. Has the same type and format as "x" . \n

third_party/fwkacllib/inc/ops/string_ops.h

@@ -488,7 +488,7 @@ include:
 */
 REG_OP(AsString)
     .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_FLOAT, \
-        DT_DOUBLE, DT_BOOL}))
+        DT_DOUBLE, DT_BOOL, DT_COMPLEX64, DT_COMPLEX128}))
     .OUTPUT(y, TensorType({DT_STRING}))
     .ATTR(precision, Int, -1)
     .ATTR(scientific, Bool, false)

third_party/fwkacllib/inc/runtime/config.h

@@ -46,6 +46,12 @@ typedef enum tagRtChipType {
     CHIP_END,
 } rtChipType_t;
 
+typedef enum tagRtAicpuScheType {
+    SCHEDULE_SOFTWARE = 0, /* Software Schedule */
+    SCHEDULE_SOFTWARE_OPT,
+    SCHEDULE_HARDWARE, /* HWTS Schedule */
+} rtAicpuScheType;
+
 typedef enum tagRtVersion {
     VER_BEGIN = 0,
     VER_NA = VER_BEGIN,
@@ -184,6 +190,19 @@ RTS_API rtError_t rtMemGetL2Info(rtStream_t stream, void **ptr, uint32_t *size);
  */
 RTS_API rtError_t rtGetRuntimeVersion(uint32_t *runtimeVersion);
 
+
+/**
+ * @ingroup
+ * @brief get device feature ability by device id, such as task schedule ability.
+ * @param [in] deviceId
+ * @param [in] moduleType
+ * @param [in] featureType
+ * @param [out] value
+ * @return RT_ERROR_NONE for ok
+ * @return RT_ERROR_INVALID_VALUE for error input
+ */
+RTS_API rtError_t rtGetDeviceCapability(int32_t deviceId, int32_t moduleType, int32_t featureType, int32_t *value);
+
 #if defined(__cplusplus) && !defined(COMPILE_OMG_PACKAGE)
 }
 #endif

third_party/fwkacllib/inc/runtime/dev.h

@@ -62,6 +62,11 @@ typedef enum tagRtFeatureType {
     FEATURE_TYPE_RSV
 } rtFeatureType_t;
 
+typedef enum tagRtDeviceFeatureType {
+  FEATURE_TYPE_SCHE,
+  FEATURE_TYPE_END,
+} rtDeviceFeatureType_t;
+
 typedef enum tagMemcpyInfo {
     MEMCPY_INFO_SUPPORT_ZEROCOPY = 0,
     MEMCPY_INFO_RSV

third_party/fwkacllib/inc/runtime/kernel.h

@@ -191,6 +191,14 @@ typedef void (*rtCallback_t)(void *fnData);
 #define RT_FUSION_KERNEL_DUMPFLAG (0x04)
 #define RT_KERNEL_CUSTOM_AICPU (0x08)
 
+/**
+ * @ingroup rt_kernel
+ * @brief kernel mode
+**/
+#define RT_DEFAULT_KERNEL_MODE (0x00)
+#define RT_NORMAL_KERNEL_MODE (0x01)
+#define RT_ALL_KERNEL_MODE (0x02)
+
 /**
  * @ingroup rt_kernel
  * @brief kernel L1 Fusion Dump bit flags
@@ -207,6 +215,16 @@ typedef void (*rtCallback_t)(void *fnData);
  */
 RTS_API rtError_t rtDevBinaryRegister(const rtDevBinary_t *bin, void **handle);
 
+/**
+ * @ingroup rt_kernel
+ * @brief register device binary with all kernel
+ * @param [in] bin   device binary description
+ * @param [out] handle   device binary handle
+ * @return RT_ERROR_NONE for ok
+ * @return RT_ERROR_INVALID_VALUE for error input
+ */
+RTS_API rtError_t rtRegisterAllKernel(const rtDevBinary_t *bin, void **handle);
+
 /**
  * @ingroup rt_kernel
  * @brief register fast memeory device binary
@@ -314,6 +332,23 @@ RTS_API rtError_t rtKernelConfigDump(uint32_t kind, uint32_t dumpSizePerBlock, u
 RTS_API rtError_t rtKernelLaunch(const void *stubFunc, uint32_t blockDim, void *args, uint32_t argsSize,
                                  rtSmDesc_t *smDesc, rtStream_t stream);
 
+/**
+ * @ingroup rt_kernel
+ * @brief launch kernel with handle to device
+ * @param [in] handle   program
+ * @param [in] devFunc   device function description.
+ * @param [in] blockDim   block dimentions
+ * @param [in] args   argments address for kernel function
+ * @param [in] argsSize   argements size
+ * @param [in] smDesc   shared memory description
+ * @param [in] stream   associated stream
+ * @param [in] kernelInfo   kernel info
+ * @return RT_ERROR_NONE for ok
+ * @return RT_ERROR_INVALID_VALUE for error input
+ */
+RTS_API rtError_t rtKernelLaunchWithHandle(void *handle, const void *devFunc, uint32_t blockDim, void *args, uint32_t argsSize,
+                                            rtSmDesc_t *smDesc, rtStream_t stream_, const void *kernelInfo);
+
 /**
  * @ingroup rt_kernel
  * @brief launch kernel to device

third_party/fwkacllib/inc/runtime/rt_model.h

@@ -50,6 +50,7 @@ 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_ALL_KERNEL,
 } rtModelTaskType_t;
 
 typedef enum tagModelStreamType {
@@ -127,6 +128,18 @@ typedef struct tagKernelTaskInfo {
     uint16_t *argsOffset;
 } rtKernelTaskInfo_t;
 
+typedef struct tagAllKernelTaskInfo {
+    uint16_t blockDim;
+    uint16_t argsCount;
+    uint16_t argsSize;
+    uint16_t reserved;
+    void *devfunc;
+    void *handle;
+    uint8_t *smDesc;
+    uint8_t *args;
+    uint16_t *argsOffset;
+} rtAllKernelTaskInfo_t;
+
 typedef struct tagKernelTaskInfoEx {
     uint32_t flags;
     uint32_t argsSize;
@@ -251,6 +264,7 @@ typedef struct tagTaskInfo {
     union {
         rtKernelTaskInfoEx_t kernelTaskEx;
         rtKernelTaskInfo_t kernelTask;
+        rtAllKernelTaskInfo_t allKernelTask;
         rtEventTaskInfo_t eventTask;
         rtStreamSwitchTaskInfo_t streamSwitchTask;
         rtStreamActiveTaskInfo_t streamActiveTask;

third_party/fwkacllib/inc/toolchain/tuning_tool/tune_api.h

@@ -1,72 +1,137 @@
-/**
- * @file tune_api.h
- *
- * Copyright (c) Huawei Technologies Co., Ltd. 2020-2020. All rights reserved.\n
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n
- * 描述：mstune调优接口头文件
- */
-/** @defgroup mstune mstune调优接口 */
-#ifndef TUNE_API_H
-#define TUNE_API_H
-#include <vector>
-#include <map>
-#include <string>
-#include "graph/graph.h"
-#include "ge/ge_api.h"
-
-/**
- * @ingroup mstune
- *
- * mstune status
- */
-enum MsTuneStatus {
-    MSTUNE_SUCCESS,  /** tune success */
-    MSTUNE_FAILED,   /** tune failed */
-};
-
-// Option key: for train options sets
-const std::string MSTUNE_SELF_KEY = "mstune";
-const std::string MSTUNE_GEINIT_KEY = "initialize";
-const std::string MSTUNE_GESESS_KEY = "session";
-
-/**
- * @ingroup mstune
- * @par 描述: 命令行调优
- *
- * @attention 无
- * @param  option [IN] 调优参数
- * @param  msg [OUT] 调优异常下返回信息
- * @retval #MSTUNE_SUCCESS 执行成功
- * @retval #MSTUNE_FAILED 执行失败
- * @par 依赖:
- * @li tune_api.cpp：该接口所属的开发包。
- * @li tune_api.h：该接口声明所在的头文件。
- * @see 无
- * @since
- */
-MsTuneStatus MsTuning(const std::map<std::string, std::string> &option, std::string &msg);
-
-/**
- * @ingroup mstune
- * @par 描述: 梯度调优
- *
- * @attention 无
- * @param  tuningGraph [IN] 调优图
- * @param  dependGraph [IN] 调优依赖图
- * @param  session [IN] ge连接会话
- * @param  option [IN] 参数集. 包含调优参数及ge参数
- * @retval #MSTUNE_SUCCESS 执行成功
- * @retval #MSTUNE_FAILED 执行失败
- * @par 依赖:
- * @li tune_api.cpp：该接口所属的开发包。
- * @li tune_api.h：该接口声明所在的头文件。
- * @see 无
- * @since
- */
-extern "C" MsTuneStatus MsTrainTuning(ge::Graph &tuningGraph, std::vector<ge::Graph> &dependGraph,
-    ge::Session *session, const std::map<std::string, std::map<std::string, std::string>> &option);
-
-#endif
+/**
+ * @file tune_api.h
+ *
+ * Copyright (c) Huawei Technologies Co., Ltd. 2020-2020. All rights reserved.\n
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n
+ * 描述：mstune调优接口头文件
+ */
+/** @defgroup mstune mstune调优接口 */
+#ifndef TUNE_API_H
+#define TUNE_API_H
+#include <vector>
+#include <map>
+#include <string>
+#include "graph/graph.h"
+#include "ge/ge_api.h"
+
+/**
+ * @ingroup mstune
+ *
+ * mstune status
+ */
+enum MsTuneStatus {
+    MSTUNE_SUCCESS,  /** tune success */
+    MSTUNE_FAILED,   /** tune failed */
+};
+
+// Option key: for train options sets
+const std::string MSTUNE_SELF_KEY = "mstune";
+const std::string MSTUNE_GEINIT_KEY = "initialize";
+const std::string MSTUNE_GESESS_KEY = "session";
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct RunnerInitConfig {
+    // onilne online
+    std::string profPath;
+    std::string parserPath;
+    // ncs only
+    std::vector<uint32_t> devList;
+};
+
+struct RunnerOpInfo {
+    std::string opName;
+    uint64_t opCostTime;
+    uint64_t aicoreCostTime;
+    // gradient_split only
+    std::string modelName;
+    std::string opType;
+    std::vector<uint64_t> start;
+    std::vector<uint64_t> end;
+};
+
+struct RunnerModelInfo {
+    uint64_t totalCostTime;
+};
+
+struct RunnerRunResult {
+    std::vector<RunnerModelInfo> modelInfo;
+    std::vector<RunnerOpInfo> opInfo;
+};
+
+struct RunnerResult {
+    uint64_t totalCostTime;
+    std::map<std::string, uint64_t> opCostTime;
+    std::map<std::string, uint64_t> aicoreCostTime;
+};
+
+struct RunnerDataBuf {
+    void *ptr = nullptr;
+    size_t size = 0;
+};
+
+struct AOEBufferData {
+    std::shared_ptr<uint8_t> data = nullptr;
+    uint64_t length;
+};
+
+struct RunnerConfig {
+    bool isProf;
+    uint32_t loop;
+    // offline only
+    std::vector<RunnerDataBuf> input;
+    std::vector<RunnerDataBuf> output;
+    std::string modelPath;
+    RunnerDataBuf modelData;
+    // online only
+    uint32_t devId;
+    std::vector<std::vector<ge::Tensor>> inputs;
+    std::vector<ge::Graph> dependGraph; // run graph (for training)
+};
+#ifdef __cplusplus
+}
+#endif
+
+/**
+ * @ingroup mstune
+ * @par 描述: 命令行调优
+ *
+ * @attention 无
+ * @param  option [IN] 调优参数
+ * @param  msg [OUT] 调优异常下返回信息
+ * @retval #MSTUNE_SUCCESS 执行成功
+ * @retval #MSTUNE_FAILED 执行失败
+ * @par 依赖:
+ * @li tune_api.cpp：该接口所属的开发包。
+ * @li tune_api.h：该接口声明所在的头文件。
+ * @see 无
+ * @since
+ */
+MsTuneStatus MsTuning(const std::map<std::string, std::string> &option, std::string &msg);
+
+/**
+ * @ingroup mstune
+ * @par 描述: 梯度调优
+ *
+ * @attention 无
+ * @param  tuningGraph [IN] 调优图
+ * @param  dependGraph [IN] 调优依赖图
+ * @param  session [IN] ge连接会话
+ * @param  option [IN] 参数集. 包含调优参数及ge参数
+ * @retval #MSTUNE_SUCCESS 执行成功
+ * @retval #MSTUNE_FAILED 执行失败
+ * @par 依赖:
+ * @li tune_api.cpp：该接口所属的开发包。
+ * @li tune_api.h：该接口声明所在的头文件。
+ * @see 无
+ * @since
+ */
+extern "C" MsTuneStatus MsTrainTuning(ge::Graph &tuningGraph, std::vector<ge::Graph> &dependGraph,
+    ge::Session *session, const std::map<std::string, std::map<std::string, std::string>> &option);
+
+#endif