diff --git a/inc/external/acl/acl.h b/inc/external/acl/acl.h index 8d261201..a53d029d 100644 --- a/inc/external/acl/acl.h +++ b/inc/external/acl/acl.h @@ -26,9 +26,9 @@ extern "C" { #endif // Current version is 1.0.0 -#define ACL_MAJOR_VERSION 1 -#define ACL_MINOR_VERSION 0 -#define ACL_PATCH_VERSION 0 +#define ACL_MAJOR_VERSION 1 +#define ACL_MINOR_VERSION 0 +#define ACL_PATCH_VERSION 0 /** * @ingroup AscendCL @@ -72,11 +72,11 @@ ACL_FUNC_VISIBILITY aclError aclrtGetVersion(int32_t *majorVersion, int32_t *min * * @retval null for failed * @retval OtherValues success - */ +*/ ACL_FUNC_VISIBILITY const char *aclGetRecentErrMsg(); #ifdef __cplusplus } #endif -#endif // INC_EXTERNAL_ACL_ACL_H_ +#endif // INC_EXTERNAL_ACL_ACL_H_ diff --git a/inc/external/acl/acl_base.h b/inc/external/acl/acl_base.h index 64d4bd81..417a80c8 100644 --- a/inc/external/acl/acl_base.h +++ b/inc/external/acl/acl_base.h @@ -136,49 +136,50 @@ static const int ACL_ERROR_PROFILING_FAILURE = 500005; #define ACL_UNKNOWN_RANK 0xFFFFFFFFFFFFFFFE typedef enum { - ACL_DT_UNDEFINED = -1, - ACL_FLOAT = 0, - ACL_FLOAT16 = 1, - ACL_INT8 = 2, - ACL_INT32 = 3, - ACL_UINT8 = 4, - ACL_INT16 = 6, - ACL_UINT16 = 7, - ACL_UINT32 = 8, - ACL_INT64 = 9, - ACL_UINT64 = 10, - ACL_DOUBLE = 11, - ACL_BOOL = 12, - ACL_STRING = 13, + ACL_DT_UNDEFINED = -1, + ACL_FLOAT = 0, + ACL_FLOAT16 = 1, + ACL_INT8 = 2, + ACL_INT32 = 3, + ACL_UINT8 = 4, + ACL_INT16 = 6, + ACL_UINT16 = 7, + ACL_UINT32 = 8, + ACL_INT64 = 9, + ACL_UINT64 = 10, + ACL_DOUBLE = 11, + ACL_BOOL = 12, + ACL_STRING = 13, } aclDataType; typedef enum { - ACL_FORMAT_UNDEFINED = -1, - ACL_FORMAT_NCHW = 0, - ACL_FORMAT_NHWC = 1, - ACL_FORMAT_ND = 2, - ACL_FORMAT_NC1HWC0 = 3, - ACL_FORMAT_FRACTAL_Z = 4, - ACL_FORMAT_NC1HWC0_C04 = 12, - ACL_FORMAT_NDHWC = 27, - ACL_FORMAT_FRACTAL_NZ = 29, - ACL_FORMAT_NCDHW = 30, - ACL_FORMAT_NDC1HWC0 = 32, - ACL_FRACTAL_Z_3D = 33 + ACL_FORMAT_UNDEFINED = -1, + ACL_FORMAT_NCHW = 0, + ACL_FORMAT_NHWC = 1, + ACL_FORMAT_ND = 2, + ACL_FORMAT_NC1HWC0 = 3, + ACL_FORMAT_FRACTAL_Z = 4, + ACL_FORMAT_NC1HWC0_C04 = 12, + ACL_FORMAT_NDHWC = 27, + ACL_FORMAT_FRACTAL_NZ = 29, + ACL_FORMAT_NCDHW = 30, + ACL_FORMAT_NDC1HWC0 = 32, + ACL_FRACTAL_Z_3D = 33 } aclFormat; typedef enum { - ACL_DEBUG = 0, - ACL_INFO = 1, - ACL_WARNING = 2, - ACL_ERROR = 3, + ACL_DEBUG = 0, + ACL_INFO = 1, + ACL_WARNING = 2, + ACL_ERROR = 3, } aclLogLevel; typedef enum { - ACL_MEMTYPE_DEVICE = 0, - ACL_MEMTYPE_HOST = 1, + ACL_MEMTYPE_DEVICE = 0, + ACL_MEMTYPE_HOST = 1, } aclMemType; + /** * @ingroup AscendCL * @brief Converts data of type aclFloat16 to data of type float @@ -311,7 +312,9 @@ ACL_FUNC_VISIBILITY size_t aclDataTypeSize(aclDataType dataType); * @retval aclTensorDesc pointer. * @retval nullptr if param is invalid or run out of memory */ -ACL_FUNC_VISIBILITY aclTensorDesc *aclCreateTensorDesc(aclDataType dataType, int numDims, const int64_t *dims, +ACL_FUNC_VISIBILITY aclTensorDesc *aclCreateTensorDesc(aclDataType dataType, + int numDims, + const int64_t *dims, aclFormat format); /** @@ -333,7 +336,8 @@ ACL_FUNC_VISIBILITY void aclDestroyTensorDesc(const aclTensorDesc *desc); * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclSetTensorShapeRange(aclTensorDesc *desc, size_t dimsCount, +ACL_FUNC_VISIBILITY aclError aclSetTensorShapeRange(aclTensorDesc* desc, + size_t dimsCount, int64_t dimsRange[][ACL_TENSOR_SHAPE_RANGE_NUM]); /** @@ -430,7 +434,9 @@ ACL_FUNC_VISIBILITY aclError aclGetTensorDescDimV2(const aclTensorDesc *desc, si * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclGetTensorDescDimRange(const aclTensorDesc *desc, size_t index, size_t dimRangeNum, +ACL_FUNC_VISIBILITY aclError aclGetTensorDescDimRange(const aclTensorDesc *desc, + size_t index, + size_t dimRangeNum, int64_t *dimRange); /** @@ -467,7 +473,7 @@ ACL_FUNC_VISIBILITY const char *aclGetTensorDescName(aclTensorDesc *desc); * @retval OtherValues Failure */ ACL_FUNC_VISIBILITY aclError aclTransTensorDescFormat(const aclTensorDesc *srcDesc, aclFormat dstFormat, - aclTensorDesc **dstDesc); + aclTensorDesc **dstDesc); /** * @ingroup AscendCL @@ -555,7 +561,7 @@ ACL_FUNC_VISIBILITY aclError aclSetTensorOriginShape(aclTensorDesc *desc, int nu * * @retval null for failed. * @retval OtherValues success. - */ +*/ ACL_FUNC_VISIBILITY aclTensorDesc *aclGetTensorDescByIndex(aclTensorDesc *desc, size_t index); /** @@ -566,7 +572,7 @@ ACL_FUNC_VISIBILITY aclTensorDesc *aclGetTensorDescByIndex(aclTensorDesc *desc, * * @retval null for failed * @retval OtherValues success - */ +*/ ACL_FUNC_VISIBILITY void *aclGetTensorDescAddress(const aclTensorDesc *desc); /** @@ -618,7 +624,7 @@ ACL_FUNC_VISIBILITY aclError aclSetTensorPlaceMent(aclTensorDesc *desc, aclMemTy * @param ... [IN] the value of current log */ ACL_FUNC_VISIBILITY void aclAppLog(aclLogLevel logLevel, const char *func, const char *file, uint32_t line, - const char *fmt, ...); + const char *fmt, ...); /** * @ingroup AscendCL @@ -626,13 +632,14 @@ ACL_FUNC_VISIBILITY void aclAppLog(aclLogLevel logLevel, const char *func, const * * @retval null for failed * @retval OtherValues success - */ +*/ ACL_FUNC_VISIBILITY const char *aclrtGetSocName(); -#define ACL_APP_LOG(level, fmt, ...) aclAppLog(level, __FUNCTION__, __FILE__, __LINE__, fmt, ##__VA_ARGS__) +#define ACL_APP_LOG(level, fmt, ...) \ + aclAppLog(level, __FUNCTION__, __FILE__, __LINE__, fmt, ##__VA_ARGS__) #ifdef __cplusplus } #endif -#endif // INC_EXTERNAL_ACL_ACL_BASE_H_ +#endif // INC_EXTERNAL_ACL_ACL_BASE_H_ diff --git a/inc/external/acl/acl_mdl.h b/inc/external/acl/acl_mdl.h index 2bf85e29..1721929e 100644 --- a/inc/external/acl/acl_mdl.h +++ b/inc/external/acl/acl_mdl.h @@ -27,19 +27,19 @@ extern "C" { #endif -#define ACL_MAX_DIM_CNT 128 -#define ACL_MAX_TENSOR_NAME_LEN 128 -#define ACL_MAX_BATCH_NUM 128 -#define ACL_MAX_HW_NUM 128 -#define ACL_MAX_SHAPE_COUNT 128 -#define ACL_INVALID_NODE_INDEX 0xFFFFFFFF - -#define ACL_MDL_LOAD_FROM_FILE 1 -#define ACL_MDL_LOAD_FROM_FILE_WITH_MEM 2 -#define ACL_MDL_LOAD_FROM_MEM 3 -#define ACL_MDL_LOAD_FROM_MEM_WITH_MEM 4 -#define ACL_MDL_LOAD_FROM_FILE_WITH_Q 5 -#define ACL_MDL_LOAD_FROM_MEM_WITH_Q 6 +#define ACL_MAX_DIM_CNT 128 +#define ACL_MAX_TENSOR_NAME_LEN 128 +#define ACL_MAX_BATCH_NUM 128 +#define ACL_MAX_HW_NUM 128 +#define ACL_MAX_SHAPE_COUNT 128 +#define ACL_INVALID_NODE_INDEX 0xFFFFFFFF + +#define ACL_MDL_LOAD_FROM_FILE 1 +#define ACL_MDL_LOAD_FROM_FILE_WITH_MEM 2 +#define ACL_MDL_LOAD_FROM_MEM 3 +#define ACL_MDL_LOAD_FROM_MEM_WITH_MEM 4 +#define ACL_MDL_LOAD_FROM_FILE_WITH_Q 5 +#define ACL_MDL_LOAD_FROM_MEM_WITH_Q 6 #define ACL_DYNAMIC_TENSOR_NAME "ascend_mbatch_shape_data" #define ACL_DYNAMIC_AIPP_NAME "ascend_dynamic_aipp_data" @@ -52,123 +52,123 @@ typedef struct aclAippExtendInfo aclAippExtendInfo; typedef struct aclmdlConfigHandle aclmdlConfigHandle; typedef enum { - ACL_YUV420SP_U8 = 1, - ACL_XRGB8888_U8, - ACL_RGB888_U8, - ACL_YUV400_U8, - ACL_NC1HWC0DI_FP16, - ACL_NC1HWC0DI_S8, - ACL_ARGB8888_U8, - ACL_YUYV_U8, - ACL_YUV422SP_U8, - ACL_AYUV444_U8, - ACL_RAW10, - ACL_RAW12, - ACL_RAW16, - ACL_RAW24, - ACL_AIPP_RESERVED = 0xffff, + ACL_YUV420SP_U8 = 1, + ACL_XRGB8888_U8, + ACL_RGB888_U8, + ACL_YUV400_U8, + ACL_NC1HWC0DI_FP16, + ACL_NC1HWC0DI_S8, + ACL_ARGB8888_U8, + ACL_YUYV_U8, + ACL_YUV422SP_U8, + ACL_AYUV444_U8, + ACL_RAW10, + ACL_RAW12, + ACL_RAW16, + ACL_RAW24, + ACL_AIPP_RESERVED = 0xffff, } aclAippInputFormat; typedef enum { - ACL_MDL_PRIORITY_INT32 = 0, - ACL_MDL_LOAD_TYPE_SIZET, - ACL_MDL_PATH_PTR, /**< pointer to model load path with deep copy */ - ACL_MDL_MEM_ADDR_PTR, /**< pointer to model memory with shallow copy */ - ACL_MDL_MEM_SIZET, - ACL_MDL_WEIGHT_ADDR_PTR, /**< pointer to weight memory of model with shallow copy */ - ACL_MDL_WEIGHT_SIZET, - ACL_MDL_WORKSPACE_ADDR_PTR, /**< pointer to worksapce memory of model with shallow copy */ - ACL_MDL_WORKSPACE_SIZET, - ACL_MDL_INPUTQ_NUM_SIZET, - ACL_MDL_INPUTQ_ADDR_PTR, /**< pointer to inputQ with shallow copy */ - ACL_MDL_OUTPUTQ_NUM_SIZET, - ACL_MDL_OUTPUTQ_ADDR_PTR /**< pointer to outputQ with shallow copy */ + ACL_MDL_PRIORITY_INT32 = 0, + ACL_MDL_LOAD_TYPE_SIZET, + ACL_MDL_PATH_PTR, /**< pointer to model load path with deep copy */ + ACL_MDL_MEM_ADDR_PTR, /**< pointer to model memory with shallow copy */ + ACL_MDL_MEM_SIZET, + ACL_MDL_WEIGHT_ADDR_PTR, /**< pointer to weight memory of model with shallow copy */ + ACL_MDL_WEIGHT_SIZET, + ACL_MDL_WORKSPACE_ADDR_PTR, /**< pointer to worksapce memory of model with shallow copy */ + ACL_MDL_WORKSPACE_SIZET, + ACL_MDL_INPUTQ_NUM_SIZET, + ACL_MDL_INPUTQ_ADDR_PTR, /**< pointer to inputQ with shallow copy */ + ACL_MDL_OUTPUTQ_NUM_SIZET, + ACL_MDL_OUTPUTQ_ADDR_PTR /**< pointer to outputQ with shallow copy */ } aclmdlConfigAttr; typedef enum { - ACL_DATA_WITHOUT_AIPP = 0, - ACL_DATA_WITH_STATIC_AIPP, - ACL_DATA_WITH_DYNAMIC_AIPP, - ACL_DYNAMIC_AIPP_NODE + ACL_DATA_WITHOUT_AIPP = 0, + ACL_DATA_WITH_STATIC_AIPP, + ACL_DATA_WITH_DYNAMIC_AIPP, + ACL_DYNAMIC_AIPP_NODE } aclmdlInputAippType; typedef struct aclmdlIODims { - char name[ACL_MAX_TENSOR_NAME_LEN]; /**< tensor name */ - size_t dimCount; /**< dim array count */ - int64_t dims[ACL_MAX_DIM_CNT]; /**< dim data array */ + char name[ACL_MAX_TENSOR_NAME_LEN]; /**< tensor name */ + size_t dimCount; /**< dim array count */ + int64_t dims[ACL_MAX_DIM_CNT]; /**< dim data array */ } aclmdlIODims; typedef struct aclAippDims { - aclmdlIODims srcDims; /**< input dims before model transform */ - size_t srcSize; /**< input size before model transform */ - aclmdlIODims aippOutdims; /**< aipp output dims */ - size_t aippOutSize; /**< aipp output size */ + aclmdlIODims srcDims; /**< input dims before model transform */ + size_t srcSize; /**< input size before model transform */ + aclmdlIODims aippOutdims; /**< aipp output dims */ + size_t aippOutSize; /**< aipp output size */ } aclAippDims; typedef struct aclmdlBatch { - size_t batchCount; /**< batch array count */ - uint64_t batch[ACL_MAX_BATCH_NUM]; /**< batch data array */ + size_t batchCount; /**< batch array count */ + uint64_t batch[ACL_MAX_BATCH_NUM]; /**< batch data array */ } aclmdlBatch; typedef struct aclmdlHW { - size_t hwCount; /**< height&width array count */ - uint64_t hw[ACL_MAX_HW_NUM][2]; /**< height&width data array */ + size_t hwCount; /**< height&width array count */ + uint64_t hw[ACL_MAX_HW_NUM][2]; /**< height&width data array */ } aclmdlHW; typedef struct aclAippInfo { - aclAippInputFormat inputFormat; - int32_t srcImageSizeW; - int32_t srcImageSizeH; - int8_t cropSwitch; - int32_t loadStartPosW; - int32_t loadStartPosH; - int32_t cropSizeW; - int32_t cropSizeH; - int8_t resizeSwitch; - int32_t resizeOutputW; - int32_t resizeOutputH; - int8_t paddingSwitch; - int32_t leftPaddingSize; - int32_t rightPaddingSize; - int32_t topPaddingSize; - int32_t bottomPaddingSize; - int8_t cscSwitch; - int8_t rbuvSwapSwitch; - int8_t axSwapSwitch; - int8_t singleLineMode; - int32_t matrixR0C0; - int32_t matrixR0C1; - int32_t matrixR0C2; - int32_t matrixR1C0; - int32_t matrixR1C1; - int32_t matrixR1C2; - int32_t matrixR2C0; - int32_t matrixR2C1; - int32_t matrixR2C2; - int32_t outputBias0; - int32_t outputBias1; - int32_t outputBias2; - int32_t inputBias0; - int32_t inputBias1; - int32_t inputBias2; - int32_t meanChn0; - int32_t meanChn1; - int32_t meanChn2; - int32_t meanChn3; - float minChn0; - float minChn1; - float minChn2; - float minChn3; - float varReciChn0; - float varReciChn1; - float varReciChn2; - float varReciChn3; - aclFormat srcFormat; - aclDataType srcDatatype; - size_t srcDimNum; - size_t shapeCount; - aclAippDims outDims[ACL_MAX_SHAPE_COUNT]; - aclAippExtendInfo *aippExtend; /**< reserved parameters, current version needs to be null */ + aclAippInputFormat inputFormat; + int32_t srcImageSizeW; + int32_t srcImageSizeH; + int8_t cropSwitch; + int32_t loadStartPosW; + int32_t loadStartPosH; + int32_t cropSizeW; + int32_t cropSizeH; + int8_t resizeSwitch; + int32_t resizeOutputW; + int32_t resizeOutputH; + int8_t paddingSwitch; + int32_t leftPaddingSize; + int32_t rightPaddingSize; + int32_t topPaddingSize; + int32_t bottomPaddingSize; + int8_t cscSwitch; + int8_t rbuvSwapSwitch; + int8_t axSwapSwitch; + int8_t singleLineMode; + int32_t matrixR0C0; + int32_t matrixR0C1; + int32_t matrixR0C2; + int32_t matrixR1C0; + int32_t matrixR1C1; + int32_t matrixR1C2; + int32_t matrixR2C0; + int32_t matrixR2C1; + int32_t matrixR2C2; + int32_t outputBias0; + int32_t outputBias1; + int32_t outputBias2; + int32_t inputBias0; + int32_t inputBias1; + int32_t inputBias2; + int32_t meanChn0; + int32_t meanChn1; + int32_t meanChn2; + int32_t meanChn3; + float minChn0; + float minChn1; + float minChn2; + float minChn3; + float varReciChn0; + float varReciChn1; + float varReciChn2; + float varReciChn3; + aclFormat srcFormat; + aclDataType srcDatatype; + size_t srcDimNum; + size_t shapeCount; + aclAippDims outDims[ACL_MAX_SHAPE_COUNT]; + aclAippExtendInfo *aippExtend; /**< reserved parameters, current version needs to be null */ } aclAippInfo; /** @@ -292,7 +292,8 @@ ACL_FUNC_VISIBILITY aclError aclmdlAddDatasetBuffer(aclmdlDataset *dataset, aclD * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclmdlSetDatasetTensorDesc(aclmdlDataset *dataset, aclTensorDesc *tensorDesc, +ACL_FUNC_VISIBILITY aclError aclmdlSetDatasetTensorDesc(aclmdlDataset *dataset, + aclTensorDesc *tensorDesc, size_t index); /** @@ -354,7 +355,8 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFile(const char *modelPath, uint32_t * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMem(const void *model, size_t modelSize, uint32_t *modelId); +ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMem(const void *model, size_t modelSize, + uint32_t *modelId); /** * @ingroup AscendCL @@ -376,8 +378,9 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMem(const void *model, size_t modelSi * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFileWithMem(const char *modelPath, uint32_t *modelId, void *workPtr, - size_t workSize, void *weightPtr, size_t weightSize); +ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFileWithMem(const char *modelPath, + uint32_t *modelId, void *workPtr, size_t workSize, + void *weightPtr, size_t weightSize); /** * @ingroup AscendCL @@ -400,9 +403,9 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFileWithMem(const char *modelPath, ui * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMemWithMem(const void *model, size_t modelSize, uint32_t *modelId, - void *workPtr, size_t workSize, void *weightPtr, - size_t weightSize); +ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMemWithMem(const void *model, size_t modelSize, + uint32_t *modelId, void *workPtr, size_t workSize, + void *weightPtr, size_t weightSize); /** * @ingroup AscendCL @@ -437,8 +440,8 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromFileWithQ(const char *modelPath, uint * @retval OtherValues Failure */ ACL_FUNC_VISIBILITY aclError aclmdlLoadFromMemWithQ(const void *model, size_t modelSize, uint32_t *modelId, - const uint32_t *inputQ, size_t inputQNum, const uint32_t *outputQ, - size_t outputQNum); + const uint32_t *inputQ, size_t inputQNum, + const uint32_t *outputQ, size_t outputQNum); /** * @ingroup AscendCL @@ -468,8 +471,8 @@ ACL_FUNC_VISIBILITY aclError aclmdlExecute(uint32_t modelId, const aclmdlDataset * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem | * aclmdlLoadFromMemWithMem */ -ACL_FUNC_VISIBILITY aclError aclmdlExecuteAsync(uint32_t modelId, const aclmdlDataset *input, aclmdlDataset *output, - aclrtStream stream); +ACL_FUNC_VISIBILITY aclError aclmdlExecuteAsync(uint32_t modelId, const aclmdlDataset *input, + aclmdlDataset *output, aclrtStream stream); /** * @ingroup AscendCL @@ -644,7 +647,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlGetCurOutputDims(const aclmdlDesc *modelDesc, * @param modelDesc [IN] model description * @param opName [IN] op name * @param attr [IN] attr name - * + * * @retval the attr value */ ACL_FUNC_VISIBILITY const char *aclmdlGetOpAttr(aclmdlDesc *modelDesc, const char *opName, const char *attr); @@ -856,11 +859,11 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPInputFormat(aclmdlAIPP *aippParmsSet, * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCscParams(aclmdlAIPP *aippParmsSet, int8_t csc_switch, int16_t cscMatrixR0C0, - int16_t cscMatrixR0C1, int16_t cscMatrixR0C2, int16_t cscMatrixR1C0, - int16_t cscMatrixR1C1, int16_t cscMatrixR1C2, int16_t cscMatrixR2C0, - int16_t cscMatrixR2C1, int16_t cscMatrixR2C2, +*/ +ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCscParams(aclmdlAIPP *aippParmsSet, int8_t csc_switch, + int16_t cscMatrixR0C0, int16_t cscMatrixR0C1, int16_t cscMatrixR0C2, + int16_t cscMatrixR1C0, int16_t cscMatrixR1C1, int16_t cscMatrixR1C2, + int16_t cscMatrixR2C0, int16_t cscMatrixR2C1, int16_t cscMatrixR2C2, uint8_t cscOutputBiasR0, uint8_t cscOutputBiasR1, uint8_t cscOutputBiasR2, uint8_t cscInputBiasR0, uint8_t cscInputBiasR1, uint8_t cscInputBiasR2); @@ -876,7 +879,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCscParams(aclmdlAIPP *aippParmsSet, in * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPRbuvSwapSwitch(aclmdlAIPP *aippParmsSet, int8_t rbuvSwapSwitch); /** @@ -890,7 +893,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPRbuvSwapSwitch(aclmdlAIPP *aippParmsSe * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPAxSwapSwitch(aclmdlAIPP *aippParmsSet, int8_t axSwapSwitch); /** @@ -905,7 +908,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPAxSwapSwitch(aclmdlAIPP *aippParmsSet, * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPSrcImageSize(aclmdlAIPP *aippParmsSet, int32_t srcImageSizeW, int32_t srcImageSizeH); @@ -925,10 +928,14 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPSrcImageSize(aclmdlAIPP *aippParmsSet, * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPScfParams(aclmdlAIPP *aippParmsSet, int8_t scfSwitch, int32_t scfInputSizeW, - int32_t scfInputSizeH, int32_t scfOutputSizeW, - int32_t scfOutputSizeH, uint64_t batchIndex); +*/ +ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPScfParams(aclmdlAIPP *aippParmsSet, + int8_t scfSwitch, + int32_t scfInputSizeW, + int32_t scfInputSizeH, + int32_t scfOutputSizeW, + int32_t scfOutputSizeH, + uint64_t batchIndex); /** * @ingroup AscendCL @@ -946,9 +953,13 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPScfParams(aclmdlAIPP *aippParmsSet, in * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCropParams(aclmdlAIPP *aippParmsSet, int8_t cropSwitch, int32_t cropStartPosW, - int32_t cropStartPosH, int32_t cropSizeW, int32_t cropSizeH, +*/ +ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCropParams(aclmdlAIPP *aippParmsSet, + int8_t cropSwitch, + int32_t cropStartPosW, + int32_t cropStartPosH, + int32_t cropSizeW, + int32_t cropSizeH, uint64_t batchIndex); /** @@ -967,7 +978,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPCropParams(aclmdlAIPP *aippParmsSet, i * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPaddingParams(aclmdlAIPP *aippParmsSet, int8_t paddingSwitch, int32_t paddingSizeTop, int32_t paddingSizeBottom, int32_t paddingSizeLeft, int32_t paddingSizeRight, @@ -988,10 +999,13 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPaddingParams(aclmdlAIPP *aippParmsSet * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMean(aclmdlAIPP *aippParmsSet, int16_t dtcPixelMeanChn0, - int16_t dtcPixelMeanChn1, int16_t dtcPixelMeanChn2, - int16_t dtcPixelMeanChn3, uint64_t batchIndex); +*/ +ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMean(aclmdlAIPP *aippParmsSet, + int16_t dtcPixelMeanChn0, + int16_t dtcPixelMeanChn1, + int16_t dtcPixelMeanChn2, + int16_t dtcPixelMeanChn3, + uint64_t batchIndex); /** * @ingroup AscendCL @@ -1008,10 +1022,13 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMean(aclmdlAIPP *aippParmsSet, * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMin(aclmdlAIPP *aippParmsSet, float dtcPixelMinChn0, - float dtcPixelMinChn1, float dtcPixelMinChn2, - float dtcPixelMinChn3, uint64_t batchIndex); +*/ +ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMin(aclmdlAIPP *aippParmsSet, + float dtcPixelMinChn0, + float dtcPixelMinChn1, + float dtcPixelMinChn2, + float dtcPixelMinChn3, + uint64_t batchIndex); /** * @ingroup AscendCL @@ -1028,10 +1045,13 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPDtcPixelMin(aclmdlAIPP *aippParmsSet, * @retval OtherValues Failure * * @see aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPixelVarReci(aclmdlAIPP *aippParmsSet, float dtcPixelVarReciChn0, - float dtcPixelVarReciChn1, float dtcPixelVarReciChn2, - float dtcPixelVarReciChn3, uint64_t batchIndex); +*/ +ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPixelVarReci(aclmdlAIPP *aippParmsSet, + float dtcPixelVarReciChn0, + float dtcPixelVarReciChn1, + float dtcPixelVarReciChn2, + float dtcPixelVarReciChn3, + uint64_t batchIndex); /** * @ingroup AscendCL @@ -1047,8 +1067,10 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPPixelVarReci(aclmdlAIPP *aippParmsSet, * * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem | * aclmdlLoadFromMemWithMem | aclmdlGetInputIndexByName | aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlSetInputAIPP(uint32_t modelId, aclmdlDataset *dataset, size_t index, +*/ +ACL_FUNC_VISIBILITY aclError aclmdlSetInputAIPP(uint32_t modelId, + aclmdlDataset *dataset, + size_t index, const aclmdlAIPP *aippParmsSet); /** @@ -1065,8 +1087,10 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetInputAIPP(uint32_t modelId, aclmdlDataset * * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem | * aclmdlLoadFromMemWithMem | aclmdlGetInputIndexByName | aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPByInputIndex(uint32_t modelId, aclmdlDataset *dataset, size_t index, +*/ +ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPByInputIndex(uint32_t modelId, + aclmdlDataset *dataset, + size_t index, const aclmdlAIPP *aippParmsSet); /** @@ -1084,8 +1108,10 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetAIPPByInputIndex(uint32_t modelId, aclmdlD * * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem | * aclmdlLoadFromMemWithMem | aclmdlGetInputIndexByName | aclmdlCreateAIPP - */ -ACL_FUNC_VISIBILITY aclError aclmdlGetAippType(uint32_t modelId, size_t index, aclmdlInputAippType *type, +*/ +ACL_FUNC_VISIBILITY aclError aclmdlGetAippType(uint32_t modelId, + size_t index, + aclmdlInputAippType *type, size_t *dynamicAttachedDataIndex); /** @@ -1102,7 +1128,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlGetAippType(uint32_t modelId, size_t index, a * * @see aclmdlLoadFromFile | aclmdlLoadFromMem | aclmdlLoadFromFileWithMem | * aclmdlLoadFromMemWithMem | aclmdlGetInputIndexByName - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlGetFirstAippInfo(uint32_t modelId, size_t index, aclAippInfo *aippinfo); /** @@ -1121,11 +1147,10 @@ ACL_FUNC_VISIBILITY aclError aclmdlGetFirstAippInfo(uint32_t modelId, size_t ind * * @retval ACL_SUCCESS The function is successfully executed * @retval OtherValues Failure - */ -ACL_FUNC_VISIBILITY aclError aclmdlCreateAndGetOpDesc(uint32_t deviceId, uint32_t streamId, uint32_t taskId, - char *opName, size_t opNameLen, aclTensorDesc **inputDesc, - size_t *numInputs, aclTensorDesc **outputDesc, - size_t *numOutputs); +*/ +ACL_FUNC_VISIBILITY aclError aclmdlCreateAndGetOpDesc(uint32_t deviceId, uint32_t streamId, + uint32_t taskId, char *opName, size_t opNameLen, aclTensorDesc **inputDesc, size_t *numInputs, + aclTensorDesc **outputDesc, size_t *numOutputs); /** * @ingroup AscendCL @@ -1133,7 +1158,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlCreateAndGetOpDesc(uint32_t deviceId, uint32_ * * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlInitDump(); /** @@ -1144,7 +1169,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlInitDump(); * * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlSetDump(const char *dumpCfgPath); /** @@ -1153,7 +1178,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlSetDump(const char *dumpCfgPath); * * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlFinalizeDump(); /** @@ -1165,7 +1190,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlFinalizeDump(); * * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure - */ +*/ ACL_FUNC_VISIBILITY aclError aclmdlLoadWithConfig(const aclmdlConfigHandle *handle, uint32_t *modelId); /** @@ -1175,7 +1200,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlLoadWithConfig(const aclmdlConfigHandle *hand * @retval the aclmdlConfigHandle pointer * * @see aclmdlDestroyConfigHandle - */ +*/ ACL_FUNC_VISIBILITY aclmdlConfigHandle *aclmdlCreateConfigHandle(); /** @@ -1204,7 +1229,7 @@ ACL_FUNC_VISIBILITY aclError aclmdlDestroyConfigHandle(aclmdlConfigHandle *handl * @retval OtherValues Failure */ ACL_FUNC_VISIBILITY aclError aclmdlSetConfigOpt(aclmdlConfigHandle *handle, aclmdlConfigAttr attr, - const void *attrValue, size_t valueSize); + const void *attrValue, size_t valueSize); /** * @ingroup AscendCL @@ -1222,4 +1247,4 @@ ACL_FUNC_VISIBILITY const char *aclmdlGetTensorRealName(const aclmdlDesc *modelD } #endif -#endif // INC_EXTERNAL_ACL_ACL_MODEL_H_ +#endif // INC_EXTERNAL_ACL_ACL_MODEL_H_ diff --git a/inc/external/acl/acl_op.h b/inc/external/acl/acl_op.h index d2e59bfb..b1be0d6e 100644 --- a/inc/external/acl/acl_op.h +++ b/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_ diff --git a/inc/external/acl/acl_op_compiler.h b/inc/external/acl/acl_op_compiler.h index d9d1b3da..353d2a1a 100644 --- a/inc/external/acl/acl_op_compiler.h +++ b/inc/external/acl/acl_op_compiler.h @@ -24,22 +24,28 @@ extern "C" { #endif -typedef enum aclCompileType { ACL_COMPILE_SYS, ACL_COMPILE_UNREGISTERED } aclopCompileType; +typedef enum aclCompileType { + ACL_COMPILE_SYS, + ACL_COMPILE_UNREGISTERED +} aclopCompileType; typedef enum { - ACL_PRECISION_MODE, - ACL_AICORE_NUM, - ACL_AUTO_TUNE_MODE, - ACL_OP_SELECT_IMPL_MODE, - ACL_OPTYPELIST_FOR_IMPLMODE, - ACL_OP_DEBUG_LEVEL, - ACL_DEBUG_DIR, - ACL_OP_COMPILER_CACHE_MODE, - ACL_OP_COMPILER_CACHE_DIR, - ACL_OP_PERFORMANCE_MODE + ACL_PRECISION_MODE, + ACL_AICORE_NUM, + ACL_AUTO_TUNE_MODE, + ACL_OP_SELECT_IMPL_MODE, + ACL_OPTYPELIST_FOR_IMPLMODE, + ACL_OP_DEBUG_LEVEL, + ACL_DEBUG_DIR, + ACL_OP_COMPILER_CACHE_MODE, + ACL_OP_COMPILER_CACHE_DIR, + ACL_OP_PERFORMANCE_MODE } aclCompileOpt; -typedef enum aclCompileFlag { ACL_OP_COMPILE_DEFAULT, ACL_OP_COMPILE_FUZZ } aclOpCompileFlag; +typedef enum aclCompileFlag { + ACL_OP_COMPILE_DEFAULT, + ACL_OP_COMPILE_FUZZ +} aclOpCompileFlag; /** * @ingroup AscendCL @@ -59,10 +65,15 @@ typedef enum aclCompileFlag { ACL_OP_COMPILE_DEFAULT, ACL_OP_COMPILE_FUZZ } aclO * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclopCompile(const char *opType, int numInputs, const aclTensorDesc *const inputDesc[], - int numOutputs, const aclTensorDesc *const outputDesc[], - const aclopAttr *attr, aclopEngineType engineType, - aclopCompileType compileFlag, const char *opPath); +ACL_FUNC_VISIBILITY aclError aclopCompile(const char *opType, + int numInputs, + const aclTensorDesc *const inputDesc[], + int numOutputs, + const aclTensorDesc *const outputDesc[], + const aclopAttr *attr, + aclopEngineType engineType, + aclopCompileType compileFlag, + const char *opPath); /** * @ingroup AscendCL @@ -85,10 +96,11 @@ ACL_FUNC_VISIBILITY aclError aclopCompile(const char *opType, int numInputs, con * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclopCompileAndExecute( - const char *opType, int numInputs, const aclTensorDesc *const inputDesc[], const aclDataBuffer *const inputs[], - int numOutputs, const aclTensorDesc *const outputDesc[], aclDataBuffer *const outputs[], const aclopAttr *attr, - aclopEngineType engineType, aclopCompileType compileFlag, const char *opPath, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError aclopCompileAndExecute(const char *opType, + int numInputs, const aclTensorDesc *const inputDesc[], const aclDataBuffer *const inputs[], + int numOutputs, const aclTensorDesc *const outputDesc[], aclDataBuffer *const outputs[], + const aclopAttr *attr, aclopEngineType engineType, aclopCompileType compileFlag, + const char *opPath, aclrtStream stream); /** * @ingroup AscendCL @@ -118,4 +130,4 @@ ACL_FUNC_VISIBILITY aclError aclopSetCompileFlag(aclOpCompileFlag flag); } #endif -#endif // INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_ +#endif // INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_ diff --git a/inc/external/acl/acl_prof.h b/inc/external/acl/acl_prof.h index 3784d8c6..93bc3a9b 100644 --- a/inc/external/acl/acl_prof.h +++ b/inc/external/acl/acl_prof.h @@ -23,24 +23,24 @@ extern "C" { #endif -#define ACL_PROF_ACL_API 0x0001 -#define ACL_PROF_TASK_TIME 0x0002 -#define ACL_PROF_AICORE_METRICS 0x0004 -#define ACL_PROF_AICPU 0x0008 +#define ACL_PROF_ACL_API 0x0001 +#define ACL_PROF_TASK_TIME 0x0002 +#define ACL_PROF_AICORE_METRICS 0x0004 +#define ACL_PROF_AICPU 0x0008 /** * @deprecated please use aclprofGetOpTypeLen and aclprofGetOpTNameLen instead */ -#define ACL_PROF_MAX_OP_NAME_LEN 257 -#define ACL_PROF_MAX_OP_TYPE_LEN 65 +#define ACL_PROF_MAX_OP_NAME_LEN 257 +#define ACL_PROF_MAX_OP_TYPE_LEN 65 typedef enum { - ACL_AICORE_ARITHMETIC_UTILIZATION = 0, - ACL_AICORE_PIPE_UTILIZATION = 1, - ACL_AICORE_MEMORY_BANDWIDTH = 2, - ACL_AICORE_L0B_AND_WIDTH = 3, - ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4, - ACL_AICORE_NONE = 0xFF + ACL_AICORE_ARITHMETIC_UTILIZATION = 0, + ACL_AICORE_PIPE_UTILIZATION = 1, + ACL_AICORE_MEMORY_BANDWIDTH = 2, + ACL_AICORE_L0B_AND_WIDTH = 3, + ACL_AICORE_RESOURCE_CONFLICT_RATIO = 4, + ACL_AICORE_NONE = 0xFF } aclprofAicoreMetrics; typedef struct aclprofConfig aclprofConfig; @@ -101,8 +101,7 @@ ACL_FUNC_VISIBILITY aclError aclprofStart(const aclprofConfig *profilerConfig); * @see aclprofDestroyConfig */ ACL_FUNC_VISIBILITY aclprofConfig *aclprofCreateConfig(uint32_t *deviceIdList, uint32_t deviceNums, - aclprofAicoreMetrics aicoreMetrics, - aclprofAicoreEvents *aicoreEvents, uint64_t dataTypeConfig); + aclprofAicoreMetrics aicoreMetrics, aclprofAicoreEvents *aicoreEvents, uint64_t dataTypeConfig); /** * @ingroup AscendCL @@ -142,7 +141,8 @@ ACL_FUNC_VISIBILITY aclError aclprofStop(const aclprofConfig *profilerConfig); * * @see aclprofModelUnSubscribe */ -ACL_FUNC_VISIBILITY aclError aclprofModelSubscribe(uint32_t modelId, const aclprofSubscribeConfig *profSubscribeConfig); +ACL_FUNC_VISIBILITY aclError aclprofModelSubscribe(uint32_t modelId, + const aclprofSubscribeConfig *profSubscribeConfig); /** * @ingroup AscendCL @@ -170,7 +170,7 @@ ACL_FUNC_VISIBILITY aclError aclprofModelUnSubscribe(uint32_t modelId); * @see aclprofDestroySubscribeConfig */ ACL_FUNC_VISIBILITY aclprofSubscribeConfig *aclprofCreateSubscribeConfig(int8_t timeInfoSwitch, - aclprofAicoreMetrics aicoreMetrics, void *fd); + aclprofAicoreMetrics aicoreMetrics, void *fd); /** * @ingroup AscendCL @@ -222,7 +222,7 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpNum(const void *opInfo, size_t opInfoLe * @retval OtherValues Failure */ ACL_FUNC_VISIBILITY aclError aclprofGetOpTypeLen(const void *opInfo, size_t opInfoLen, uint32_t index, - size_t *opTypeLen); + size_t *opTypeLen); /** * @ingroup AscendCL @@ -237,8 +237,8 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpTypeLen(const void *opInfo, size_t opIn * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclprofGetOpType(const void *opInfo, size_t opInfoLen, uint32_t index, char *opType, - size_t opTypeLen); +ACL_FUNC_VISIBILITY aclError aclprofGetOpType(const void *opInfo, size_t opInfoLen, uint32_t index, + char *opType, size_t opTypeLen); /** * @ingroup AscendCL @@ -253,7 +253,7 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpType(const void *opInfo, size_t opInfoL * @retval OtherValues Failure */ ACL_FUNC_VISIBILITY aclError aclprofGetOpNameLen(const void *opInfo, size_t opInfoLen, uint32_t index, - size_t *opNameLen); + size_t *opNameLen); /** * @ingroup AscendCL @@ -268,8 +268,8 @@ ACL_FUNC_VISIBILITY aclError aclprofGetOpNameLen(const void *opInfo, size_t opIn * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclprofGetOpName(const void *opInfo, size_t opInfoLen, uint32_t index, char *opName, - size_t opNameLen); +ACL_FUNC_VISIBILITY aclError aclprofGetOpName(const void *opInfo, size_t opInfoLen, uint32_t index, + char *opName, size_t opNameLen); /** * @ingroup AscendCL @@ -326,4 +326,4 @@ 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_ diff --git a/inc/external/acl/acl_rt.h b/inc/external/acl/acl_rt.h index 5ee70724..3c777ecc 100644 --- a/inc/external/acl/acl_rt.h +++ b/inc/external/acl/acl_rt.h @@ -28,63 +28,63 @@ extern "C" { #define ACL_EVENT_TIME_LINE 0x00000008u typedef enum aclrtRunMode { - ACL_DEVICE, - ACL_HOST, + ACL_DEVICE, + ACL_HOST, } aclrtRunMode; typedef enum aclrtTsId { - ACL_TS_ID_AICORE = 0, - ACL_TS_ID_AIVECTOR = 1, - ACL_TS_ID_RESERVED = 2, + ACL_TS_ID_AICORE = 0, + ACL_TS_ID_AIVECTOR = 1, + ACL_TS_ID_RESERVED = 2, } aclrtTsId; typedef enum aclrtEventStatus { - ACL_EVENT_STATUS_COMPLETE = 0, - ACL_EVENT_STATUS_NOT_READY = 1, - ACL_EVENT_STATUS_RESERVED = 2, + ACL_EVENT_STATUS_COMPLETE = 0, + ACL_EVENT_STATUS_NOT_READY = 1, + ACL_EVENT_STATUS_RESERVED = 2, } aclrtEventStatus; typedef enum aclrtCallbackBlockType { - ACL_CALLBACK_NO_BLOCK, - ACL_CALLBACK_BLOCK, + ACL_CALLBACK_NO_BLOCK, + ACL_CALLBACK_BLOCK, } aclrtCallbackBlockType; typedef enum aclrtMemcpyKind { - ACL_MEMCPY_HOST_TO_HOST, - ACL_MEMCPY_HOST_TO_DEVICE, - ACL_MEMCPY_DEVICE_TO_HOST, - ACL_MEMCPY_DEVICE_TO_DEVICE, + ACL_MEMCPY_HOST_TO_HOST, + ACL_MEMCPY_HOST_TO_DEVICE, + ACL_MEMCPY_DEVICE_TO_HOST, + ACL_MEMCPY_DEVICE_TO_DEVICE, } aclrtMemcpyKind; typedef enum aclrtMemMallocPolicy { - ACL_MEM_MALLOC_HUGE_FIRST, - ACL_MEM_MALLOC_HUGE_ONLY, - ACL_MEM_MALLOC_NORMAL_ONLY, - ACL_MEM_MALLOC_HUGE_FIRST_P2P, - ACL_MEM_MALLOC_HUGE_ONLY_P2P, - ACL_MEM_MALLOC_NORMAL_ONLY_P2P, + ACL_MEM_MALLOC_HUGE_FIRST, + ACL_MEM_MALLOC_HUGE_ONLY, + ACL_MEM_MALLOC_NORMAL_ONLY, + ACL_MEM_MALLOC_HUGE_FIRST_P2P, + ACL_MEM_MALLOC_HUGE_ONLY_P2P, + ACL_MEM_MALLOC_NORMAL_ONLY_P2P, } aclrtMemMallocPolicy; typedef enum aclrtMemAttr { - ACL_DDR_MEM, - ACL_HBM_MEM, - ACL_DDR_MEM_HUGE, - ACL_DDR_MEM_NORMAL, - ACL_HBM_MEM_HUGE, - ACL_HBM_MEM_NORMAL, - ACL_DDR_MEM_P2P_HUGE, - ACL_DDR_MEM_P2P_NORMAL, - ACL_HBM_MEM_P2P_HUGE, - ACL_HBM_MEM_P2P_NORMAL, + ACL_DDR_MEM, + ACL_HBM_MEM, + ACL_DDR_MEM_HUGE, + ACL_DDR_MEM_NORMAL, + ACL_HBM_MEM_HUGE, + ACL_HBM_MEM_NORMAL, + ACL_DDR_MEM_P2P_HUGE, + ACL_DDR_MEM_P2P_NORMAL, + ACL_HBM_MEM_P2P_HUGE, + ACL_HBM_MEM_P2P_NORMAL, } aclrtMemAttr; typedef enum aclrtGroupAttr { - ACL_GROUP_AICORE_INT, - ACL_GROUP_AIV_INT, - ACL_GROUP_AIC_INT, - ACL_GROUP_SDMANUM_INT, - ACL_GROUP_ASQNUM_INT, - ACL_GROUP_GROUPID_INT + ACL_GROUP_AICORE_INT, + ACL_GROUP_AIV_INT, + ACL_GROUP_AIC_INT, + ACL_GROUP_SDMANUM_INT, + ACL_GROUP_ASQNUM_INT, + ACL_GROUP_GROUPID_INT } aclrtGroupAttr; typedef struct tagRtGroupInfo aclrtGroupInfo; @@ -487,7 +487,7 @@ ACL_FUNC_VISIBILITY aclError aclrtRecordEvent(aclrtEvent event, aclrtStream stre */ ACL_FUNC_VISIBILITY aclError aclrtResetEvent(aclrtEvent event, aclrtStream stream); -/** + /** * @ingroup AscendCL * @brief Queries an event's status * @@ -549,7 +549,9 @@ ACL_FUNC_VISIBILITY aclError aclrtEventElapsedTime(float *ms, aclrtEvent start, * * @see aclrtFree | acldvppMalloc | aclrtMallocCached */ -ACL_FUNC_VISIBILITY aclError aclrtMalloc(void **devPtr, size_t size, aclrtMemMallocPolicy policy); +ACL_FUNC_VISIBILITY aclError aclrtMalloc(void **devPtr, + size_t size, + aclrtMemMallocPolicy policy); /** * @ingroup AscendCL @@ -572,7 +574,9 @@ ACL_FUNC_VISIBILITY aclError aclrtMalloc(void **devPtr, size_t size, aclrtMemMal * * @see aclrtFree | aclrtMalloc */ -ACL_FUNC_VISIBILITY aclError aclrtMallocCached(void **devPtr, size_t size, aclrtMemMallocPolicy policy); +ACL_FUNC_VISIBILITY aclError aclrtMallocCached(void **devPtr, + size_t size, + aclrtMemMallocPolicy policy); /** * @ingroup AscendCL @@ -663,7 +667,10 @@ ACL_FUNC_VISIBILITY aclError aclrtFreeHost(void *hostPtr); * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclrtMemcpy(void *dst, size_t destMax, const void *src, size_t count, +ACL_FUNC_VISIBILITY aclError aclrtMemcpy(void *dst, + size_t destMax, + const void *src, + size_t count, aclrtMemcpyKind kind); /** @@ -710,31 +717,38 @@ ACL_FUNC_VISIBILITY aclError aclrtMemset(void *devPtr, size_t maxCount, int32_t * * @see aclrtSynchronizeStream */ -ACL_FUNC_VISIBILITY aclError aclrtMemcpyAsync(void *dst, size_t destMax, const void *src, size_t count, - aclrtMemcpyKind kind, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError aclrtMemcpyAsync(void *dst, + size_t destMax, + const void *src, + size_t count, + aclrtMemcpyKind kind, + aclrtStream stream); /** - * @ingroup AscendCL - * @brief Asynchronous initialize memory - * and set contents of memory to specified value async - * - * @par Function +* @ingroup AscendCL +* @brief Asynchronous initialize memory +* and set contents of memory to specified value async +* +* @par Function * The memory to be initialized is on the Host or device side, * and the system determines whether * it is host or device according to the address * - * @param devPtr [IN] destination address pointer - * @param maxCount [IN] Max length of destination address memory - * @param value [IN] set value - * @param count [IN] the number of byte to set - * @param stream [IN] asynchronized task stream - * - * @retval ACL_SUCCESS The function is successfully executed. - * @retval OtherValues Failure - * - * @see aclrtSynchronizeStream - */ -ACL_FUNC_VISIBILITY aclError aclrtMemsetAsync(void *devPtr, size_t maxCount, int32_t value, size_t count, +* @param devPtr [IN] destination address pointer +* @param maxCount [IN] Max length of destination address memory +* @param value [IN] set value +* @param count [IN] the number of byte to set +* @param stream [IN] asynchronized task stream +* +* @retval ACL_SUCCESS The function is successfully executed. +* @retval OtherValues Failure +* +* @see aclrtSynchronizeStream +*/ +ACL_FUNC_VISIBILITY aclError aclrtMemsetAsync(void *devPtr, + size_t maxCount, + int32_t value, + size_t count, aclrtStream stream); /** @@ -880,8 +894,11 @@ ACL_FUNC_VISIBILITY aclError aclrtGetAllGroupInfo(aclrtGroupInfo *groupInfo); * * @see aclrtGetGroupCount | aclrtGetAllGroupInfo */ -ACL_FUNC_VISIBILITY aclError aclrtGetGroupInfoDetail(const aclrtGroupInfo *groupInfo, int32_t groupIndex, - aclrtGroupAttr attr, void *attrValue, size_t valueLen, +ACL_FUNC_VISIBILITY aclError aclrtGetGroupInfoDetail(const aclrtGroupInfo *groupInfo, + int32_t groupIndex, + aclrtGroupAttr attr, + void *attrValue, + size_t valueLen, size_t *paramRetSize); /** @@ -955,4 +972,5 @@ ACL_FUNC_VISIBILITY aclError aclrtSetOpWaitTimeout(uint32_t timeout); } #endif -#endif // INC_EXTERNAL_ACL_ACL_RT_H_ +#endif // INC_EXTERNAL_ACL_ACL_RT_H_ + diff --git a/inc/external/acl/acl_tdt.h b/inc/external/acl/acl_tdt.h index c357518d..61995121 100644 --- a/inc/external/acl/acl_tdt.h +++ b/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_ + diff --git a/inc/external/acl/error_codes/rt_error_codes.h b/inc/external/acl/error_codes/rt_error_codes.h index 1c196c48..c9a40432 100644 --- a/inc/external/acl/error_codes/rt_error_codes.h +++ b/inc/external/acl/error_codes/rt_error_codes.h @@ -23,84 +23,84 @@ 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_VECTOR_CORE_TIMEOUT = 507034; // vector core timeout -static const int32_t ACL_ERROR_RT_VECTOR_CORE_EXCEPTION = 507035; // vector core exception -static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_EXCEPTION = 507036; // vector core trap exception +static const int32_t ACL_ERROR_RT_INTERNAL_ERROR = 507000; // runtime internal error +static const int32_t ACL_ERROR_RT_TS_ERROR = 507001; // ts internel error +static const int32_t ACL_ERROR_RT_STREAM_TASK_FULL = 507002; // task full in stream +static const int32_t ACL_ERROR_RT_STREAM_TASK_EMPTY = 507003; // task empty in stream +static const int32_t ACL_ERROR_RT_STREAM_NOT_COMPLETE = 507004; // stream not complete +static const int32_t ACL_ERROR_RT_END_OF_SEQUENCE = 507005; // end of sequence +static const int32_t ACL_ERROR_RT_EVENT_NOT_COMPLETE = 507006; // event not complete +static const int32_t ACL_ERROR_RT_CONTEXT_RELEASE_ERROR = 507007; // context release error +static const int32_t ACL_ERROR_RT_SOC_VERSION = 507008; // soc version error +static const int32_t ACL_ERROR_RT_TASK_TYPE_NOT_SUPPORT = 507009; // task type not support +static const int32_t ACL_ERROR_RT_LOST_HEARTBEAT = 507010; // ts lost heartbeat +static const int32_t ACL_ERROR_RT_MODEL_EXECUTE = 507011; // model execute failed +static const int32_t ACL_ERROR_RT_REPORT_TIMEOUT = 507012; // report timeout +static const int32_t ACL_ERROR_RT_SYS_DMA = 507013; // sys dma error +static const int32_t ACL_ERROR_RT_AICORE_TIMEOUT = 507014; // aicore timeout +static const int32_t ACL_ERROR_RT_AICORE_EXCEPTION = 507015; // aicore exception +static const int32_t ACL_ERROR_RT_AICORE_TRAP_EXCEPTION = 507016; // aicore trap exception +static const int32_t ACL_ERROR_RT_AICPU_TIMEOUT = 507017; // aicpu timeout +static const int32_t ACL_ERROR_RT_AICPU_EXCEPTION = 507018; // aicpu exception +static const int32_t ACL_ERROR_RT_AICPU_DATADUMP_RSP_ERR = 507019; // aicpu datadump response error +static const int32_t ACL_ERROR_RT_AICPU_MODEL_RSP_ERR = 507020; // aicpu model operate response error +static const int32_t ACL_ERROR_RT_PROFILING_ERROR = 507021; // profiling error +static const int32_t ACL_ERROR_RT_IPC_ERROR = 507022; // ipc error +static const int32_t ACL_ERROR_RT_MODEL_ABORT_NORMAL = 507023; // model abort normal +static const int32_t ACL_ERROR_RT_KERNEL_UNREGISTERING = 507024; // kernel unregistering +static const int32_t ACL_ERROR_RT_RINGBUFFER_NOT_INIT = 507025; // ringbuffer not init +static const int32_t ACL_ERROR_RT_RINGBUFFER_NO_DATA = 507026; // ringbuffer no data +static const int32_t ACL_ERROR_RT_KERNEL_LOOKUP = 507027; // kernel lookup error +static const int32_t ACL_ERROR_RT_KERNEL_DUPLICATE = 507028; // kernel register duplicate +static const int32_t ACL_ERROR_RT_DEBUG_REGISTER_FAIL = 507029; // debug register failed +static const int32_t ACL_ERROR_RT_DEBUG_UNREGISTER_FAIL = 507030; // debug unregister failed +static const int32_t ACL_ERROR_RT_LABEL_CONTEXT = 507031; // label not in current context +static const int32_t ACL_ERROR_RT_PROGRAM_USE_OUT = 507032; // program register num use out +static const int32_t ACL_ERROR_RT_DEV_SETUP_ERROR = 507033; // device setup error +static const int32_t ACL_ERROR_RT_VECTOR_CORE_TIMEOUT = 507034; // vector core timeout +static const int32_t ACL_ERROR_RT_VECTOR_CORE_EXCEPTION = 507035; // vector core exception +static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_EXCEPTION = 507036; // vector core trap exception -static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899; // drv internal error -static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR = 507900; // aicpu internal error -static const int32_t ACL_ERROR_RT_SOCKET_CLOSE = 507901; // hdc disconnect +static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899; // drv internal error +static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR = 507900; // aicpu internal error +static const int32_t ACL_ERROR_RT_SOCKET_CLOSE = 507901; // hdc disconnect #ifdef __cplusplus } #endif -#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__ +#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__ diff --git a/inc/external/acl/ops/acl_cblas.h b/inc/external/acl/ops/acl_cblas.h index 3d81eb2b..a2bd8c61 100644 --- a/inc/external/acl/ops/acl_cblas.h +++ b/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_ diff --git a/inc/external/acl/ops/acl_dvpp.h b/inc/external/acl/ops/acl_dvpp.h index dcaa3936..90dc70e8 100644 --- a/inc/external/acl/ops/acl_dvpp.h +++ b/inc/external/acl/ops/acl_dvpp.h @@ -53,109 +53,123 @@ typedef void (*aclvencCallback)(acldvppPicDesc *input, acldvppStreamDesc *output // Supported Pixel Format enum acldvppPixelFormat { - PIXEL_FORMAT_YUV_400 = 0, // 0 - PIXEL_FORMAT_YUV_SEMIPLANAR_420 = 1, // 1 - PIXEL_FORMAT_YVU_SEMIPLANAR_420 = 2, // 2 - PIXEL_FORMAT_YUV_SEMIPLANAR_422 = 3, // 3 - PIXEL_FORMAT_YVU_SEMIPLANAR_422 = 4, // 4 - PIXEL_FORMAT_YUV_SEMIPLANAR_444 = 5, // 5 - PIXEL_FORMAT_YVU_SEMIPLANAR_444 = 6, // 6 - PIXEL_FORMAT_YUYV_PACKED_422 = 7, // 7 - PIXEL_FORMAT_UYVY_PACKED_422 = 8, // 8 - PIXEL_FORMAT_YVYU_PACKED_422 = 9, // 9 - PIXEL_FORMAT_VYUY_PACKED_422 = 10, // 10 - PIXEL_FORMAT_YUV_PACKED_444 = 11, // 11 - PIXEL_FORMAT_RGB_888 = 12, // 12 - PIXEL_FORMAT_BGR_888 = 13, // 13 - PIXEL_FORMAT_ARGB_8888 = 14, // 14 - PIXEL_FORMAT_ABGR_8888 = 15, // 15 - PIXEL_FORMAT_RGBA_8888 = 16, // 16 - PIXEL_FORMAT_BGRA_8888 = 17, // 17 - PIXEL_FORMAT_YUV_SEMI_PLANNER_420_10BIT = 18, // 18 - PIXEL_FORMAT_YVU_SEMI_PLANNER_420_10BIT = 19, // 19 - PIXEL_FORMAT_YVU_PLANAR_420 = 20, // 20 - PIXEL_FORMAT_YVU_PLANAR_422, - PIXEL_FORMAT_YVU_PLANAR_444, - PIXEL_FORMAT_RGB_444 = 23, - PIXEL_FORMAT_BGR_444, - PIXEL_FORMAT_ARGB_4444, - PIXEL_FORMAT_ABGR_4444, - PIXEL_FORMAT_RGBA_4444, - PIXEL_FORMAT_BGRA_4444, - PIXEL_FORMAT_RGB_555, - PIXEL_FORMAT_BGR_555, - PIXEL_FORMAT_RGB_565, - PIXEL_FORMAT_BGR_565, - PIXEL_FORMAT_ARGB_1555, - PIXEL_FORMAT_ABGR_1555, - PIXEL_FORMAT_RGBA_1555, - PIXEL_FORMAT_BGRA_1555, - PIXEL_FORMAT_ARGB_8565, - PIXEL_FORMAT_ABGR_8565, - PIXEL_FORMAT_RGBA_8565, - PIXEL_FORMAT_BGRA_8565, - PIXEL_FORMAT_RGB_BAYER_8BPP = 50, - PIXEL_FORMAT_RGB_BAYER_10BPP, - PIXEL_FORMAT_RGB_BAYER_12BPP, - PIXEL_FORMAT_RGB_BAYER_14BPP, - PIXEL_FORMAT_RGB_BAYER_16BPP, - PIXEL_FORMAT_BGR_888_PLANAR = 70, - PIXEL_FORMAT_HSV_888_PACKAGE, - PIXEL_FORMAT_HSV_888_PLANAR, - PIXEL_FORMAT_LAB_888_PACKAGE, - PIXEL_FORMAT_LAB_888_PLANAR, - PIXEL_FORMAT_S8C1, - PIXEL_FORMAT_S8C2_PACKAGE, - PIXEL_FORMAT_S8C2_PLANAR, - PIXEL_FORMAT_S16C1, - PIXEL_FORMAT_U8C1, - PIXEL_FORMAT_U16C1, - PIXEL_FORMAT_S32C1, - PIXEL_FORMAT_U32C1, - PIXEL_FORMAT_U64C1, - PIXEL_FORMAT_S64C1, - PIXEL_FORMAT_YUV_SEMIPLANAR_440 = 1000, - PIXEL_FORMAT_YVU_SEMIPLANAR_440, - PIXEL_FORMAT_FLOAT32, - PIXEL_FORMAT_BUTT, - PIXEL_FORMAT_UNKNOWN = 10000 + PIXEL_FORMAT_YUV_400 = 0, // 0 + PIXEL_FORMAT_YUV_SEMIPLANAR_420 = 1, // 1 + PIXEL_FORMAT_YVU_SEMIPLANAR_420 = 2, // 2 + PIXEL_FORMAT_YUV_SEMIPLANAR_422 = 3, // 3 + PIXEL_FORMAT_YVU_SEMIPLANAR_422 = 4, // 4 + PIXEL_FORMAT_YUV_SEMIPLANAR_444 = 5, // 5 + PIXEL_FORMAT_YVU_SEMIPLANAR_444 = 6, // 6 + PIXEL_FORMAT_YUYV_PACKED_422 = 7, // 7 + PIXEL_FORMAT_UYVY_PACKED_422 = 8, // 8 + PIXEL_FORMAT_YVYU_PACKED_422 = 9, // 9 + PIXEL_FORMAT_VYUY_PACKED_422 = 10, // 10 + PIXEL_FORMAT_YUV_PACKED_444 = 11, // 11 + PIXEL_FORMAT_RGB_888 = 12, // 12 + PIXEL_FORMAT_BGR_888 = 13, // 13 + PIXEL_FORMAT_ARGB_8888 = 14, // 14 + PIXEL_FORMAT_ABGR_8888 = 15, // 15 + PIXEL_FORMAT_RGBA_8888 = 16, // 16 + PIXEL_FORMAT_BGRA_8888 = 17, // 17 + PIXEL_FORMAT_YUV_SEMI_PLANNER_420_10BIT = 18, // 18 + PIXEL_FORMAT_YVU_SEMI_PLANNER_420_10BIT = 19, // 19 + PIXEL_FORMAT_YVU_PLANAR_420 = 20, // 20 + PIXEL_FORMAT_YVU_PLANAR_422, + PIXEL_FORMAT_YVU_PLANAR_444, + PIXEL_FORMAT_RGB_444 = 23, + PIXEL_FORMAT_BGR_444, + PIXEL_FORMAT_ARGB_4444, + PIXEL_FORMAT_ABGR_4444, + PIXEL_FORMAT_RGBA_4444, + PIXEL_FORMAT_BGRA_4444, + PIXEL_FORMAT_RGB_555, + PIXEL_FORMAT_BGR_555, + PIXEL_FORMAT_RGB_565, + PIXEL_FORMAT_BGR_565, + PIXEL_FORMAT_ARGB_1555, + PIXEL_FORMAT_ABGR_1555, + PIXEL_FORMAT_RGBA_1555, + PIXEL_FORMAT_BGRA_1555, + PIXEL_FORMAT_ARGB_8565, + PIXEL_FORMAT_ABGR_8565, + PIXEL_FORMAT_RGBA_8565, + PIXEL_FORMAT_BGRA_8565, + PIXEL_FORMAT_RGB_BAYER_8BPP = 50, + PIXEL_FORMAT_RGB_BAYER_10BPP, + PIXEL_FORMAT_RGB_BAYER_12BPP, + PIXEL_FORMAT_RGB_BAYER_14BPP, + PIXEL_FORMAT_RGB_BAYER_16BPP, + PIXEL_FORMAT_BGR_888_PLANAR = 70, + PIXEL_FORMAT_HSV_888_PACKAGE, + PIXEL_FORMAT_HSV_888_PLANAR, + PIXEL_FORMAT_LAB_888_PACKAGE, + PIXEL_FORMAT_LAB_888_PLANAR, + PIXEL_FORMAT_S8C1, + PIXEL_FORMAT_S8C2_PACKAGE, + PIXEL_FORMAT_S8C2_PLANAR, + PIXEL_FORMAT_S16C1, + PIXEL_FORMAT_U8C1, + PIXEL_FORMAT_U16C1, + PIXEL_FORMAT_S32C1, + PIXEL_FORMAT_U32C1, + PIXEL_FORMAT_U64C1, + PIXEL_FORMAT_S64C1, + PIXEL_FORMAT_YUV_SEMIPLANAR_440 = 1000, + PIXEL_FORMAT_YVU_SEMIPLANAR_440, + PIXEL_FORMAT_FLOAT32, + PIXEL_FORMAT_BUTT, + PIXEL_FORMAT_UNKNOWN = 10000 }; // Stream Format -enum acldvppStreamFormat { H265_MAIN_LEVEL = 0, H264_BASELINE_LEVEL, H264_MAIN_LEVEL, H264_HIGH_LEVEL }; +enum acldvppStreamFormat { + H265_MAIN_LEVEL = 0, + H264_BASELINE_LEVEL, + H264_MAIN_LEVEL, + H264_HIGH_LEVEL +}; // Supported Channel Mode -enum acldvppChannelMode { DVPP_CHNMODE_VPC = 1, DVPP_CHNMODE_JPEGD = 2, DVPP_CHNMODE_JPEGE = 4 }; +enum acldvppChannelMode { + DVPP_CHNMODE_VPC = 1, + DVPP_CHNMODE_JPEGD = 2, + DVPP_CHNMODE_JPEGE = 4 +}; // Supported Border Type -enum acldvppBorderType { BORDER_CONSTANT = 0, BORDER_REPLICATE, BORDER_REFLECT, BORDER_REFLECT_101 }; +enum acldvppBorderType { + BORDER_CONSTANT = 0, + BORDER_REPLICATE, + BORDER_REFLECT, + BORDER_REFLECT_101 +}; // Venc parameter type enum aclvencChannelDescParamType { - ACL_VENC_THREAD_ID_UINT64 = 0, - ACL_VENC_CALLBACK_PTR, - ACL_VENC_PIXEL_FORMAT_UINT32, - ACL_VENC_ENCODE_TYPE_UINT32, - ACL_VENC_PIC_WIDTH_UINT32, - ACL_VENC_PIC_HEIGHT_UINT32, - ACL_VENC_KEY_FRAME_INTERVAL_UINT32, - ACL_VENC_BUF_ADDR_PTR, - ACL_VENC_BUF_SIZE_UINT32, - ACL_VENC_RC_MODE_UINT32, - ACL_VENC_SRC_RATE_UINT32, - ACL_VENC_MAX_BITRATE_UINT32, - ACL_VENC_MAX_IP_PROP_UINT32 + ACL_VENC_THREAD_ID_UINT64 = 0, + ACL_VENC_CALLBACK_PTR, + ACL_VENC_PIXEL_FORMAT_UINT32, + ACL_VENC_ENCODE_TYPE_UINT32, + ACL_VENC_PIC_WIDTH_UINT32, + ACL_VENC_PIC_HEIGHT_UINT32, + ACL_VENC_KEY_FRAME_INTERVAL_UINT32, + ACL_VENC_BUF_ADDR_PTR, + ACL_VENC_BUF_SIZE_UINT32, + ACL_VENC_RC_MODE_UINT32, + ACL_VENC_SRC_RATE_UINT32, + ACL_VENC_MAX_BITRATE_UINT32, + ACL_VENC_MAX_IP_PROP_UINT32 }; // Jpeg picture format enum acldvppJpegFormat { - ACL_JPEG_CSS_444 = 0, - ACL_JPEG_CSS_422, - ACL_JPEG_CSS_420, - ACL_JPEG_CSS_GRAY, - ACL_JPEG_CSS_440, - ACL_JPEG_CSS_411, - ACL_JPEG_CSS_UNKNOWN = 1000 + ACL_JPEG_CSS_444 = 0, + ACL_JPEG_CSS_422, + ACL_JPEG_CSS_420, + ACL_JPEG_CSS_GRAY, + ACL_JPEG_CSS_440, + ACL_JPEG_CSS_411, + ACL_JPEG_CSS_UNKNOWN = 1000 }; /** @@ -509,7 +523,9 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetPicDescRetCode(const acldvppPicDesc *picD * @retval null for failed. * @retval other success */ -ACL_FUNC_VISIBILITY acldvppRoiConfig *acldvppCreateRoiConfig(uint32_t left, uint32_t right, uint32_t top, +ACL_FUNC_VISIBILITY acldvppRoiConfig *acldvppCreateRoiConfig(uint32_t left, + uint32_t right, + uint32_t top, uint32_t bottom); /** @@ -588,7 +604,10 @@ ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfigBottom(acldvppRoiConfig *config, * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfig(acldvppRoiConfig *config, uint32_t left, uint32_t right, uint32_t top, +ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfig(acldvppRoiConfig *config, + uint32_t left, + uint32_t right, + uint32_t top, uint32_t bottom); /** @@ -1077,8 +1096,7 @@ ACL_FUNC_VISIBILITY aclError aclvencSetChannelDescMaxBitRate(aclvencChannelDesc * @retval ACL_SUCCESS for success, other for failure */ ACL_FUNC_VISIBILITY aclError aclvencSetChannelDescParam(aclvencChannelDesc *channelDesc, - aclvencChannelDescParamType paramType, size_t length, - const void *param); + aclvencChannelDescParamType paramType, size_t length, const void *param); /** * @ingroup AscendCL @@ -1227,8 +1245,7 @@ ACL_FUNC_VISIBILITY uint32_t aclvencGetChannelDescMaxBitRate(const aclvencChanne * @retval ACL_SUCCESS for success, other for failure */ ACL_FUNC_VISIBILITY aclError aclvencGetChannelDescParam(const aclvencChannelDesc *channelDesc, - aclvencChannelDescParamType paramType, size_t length, - size_t *paramRetSize, void *param); + aclvencChannelDescParamType paramType, size_t length, size_t *paramRetSize, void *param); /** * @ingroup AscendCL @@ -1528,7 +1545,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecDestroyFrameConfig(aclvdecFrameConfig *vdecF * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data, uint32_t size, uint32_t *width, uint32_t *height, +ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data, + uint32_t size, + uint32_t *width, + uint32_t *height, int32_t *components); /** @@ -1545,8 +1565,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data, uint32_t * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data, uint32_t size, uint32_t *width, - uint32_t *height, int32_t *components, +ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data, + uint32_t size, + uint32_t *width, + uint32_t *height, + int32_t *components, acldvppJpegFormat *format); /** @@ -1561,7 +1584,8 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data, uint32_ * @retval OtherValues Failure */ ACL_FUNC_VISIBILITY aclError acldvppJpegPredictEncSize(const acldvppPicDesc *inputDesc, - const acldvppJpegeConfig *config, uint32_t *size); + const acldvppJpegeConfig *config, + uint32_t *size); /** * @ingroup AscendCL @@ -1575,8 +1599,10 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegPredictEncSize(const acldvppPicDesc *inp * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data, uint32_t dataSize, - acldvppPixelFormat outputPixelFormat, uint32_t *decSize); +ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data, + uint32_t dataSize, + acldvppPixelFormat outputPixelFormat, + uint32_t *decSize); /** * @ingroup AscendCL @@ -1591,8 +1617,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data, uint32_ * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data, uint32_t dataSize, uint32_t *width, - uint32_t *height, int32_t *components); +ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data, + uint32_t dataSize, + uint32_t *width, + uint32_t *height, + int32_t *components); /** * @ingroup AscendCL @@ -1606,8 +1635,10 @@ ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data, uint32_t d * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppPngPredictDecSize(const void *data, uint32_t dataSize, - acldvppPixelFormat outputPixelFormat, uint32_t *decSize); +ACL_FUNC_VISIBILITY aclError acldvppPngPredictDecSize(const void *data, + uint32_t dataSize, + acldvppPixelFormat outputPixelFormat, + uint32_t *decSize); /** * @ingroup AscendCL @@ -1671,8 +1702,10 @@ ACL_FUNC_VISIBILITY aclError acldvppDestroyChannel(acldvppChannelDesc *channelDe * @see acldvppCreateChannel | acldvppCreatePicDesc * | acldvppCreateResizeConfig */ -ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc, - acldvppPicDesc *outputDesc, acldvppResizeConfig *resizeConfig, +ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + acldvppResizeConfig *resizeConfig, aclrtStream stream); /** @@ -1708,8 +1741,10 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDe * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc, - acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea, +ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + acldvppRoiConfig *cropArea, aclrtStream stream); /** @@ -1746,9 +1781,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc, - acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea, - acldvppResizeConfig *resizeConfig, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + acldvppRoiConfig *cropArea, + acldvppResizeConfig *resizeConfig, + aclrtStream stream); + /** * @ingroup AscendCL @@ -1772,9 +1811,12 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *chann * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig */ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAsync(acldvppChannelDesc *channelDesc, - acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, - uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs, - acldvppRoiConfig *cropAreas[], aclrtStream stream); + acldvppBatchPicDesc *srcBatchPicDescs, + uint32_t *roiNums, + uint32_t size, + acldvppBatchPicDesc *dstBatchPicDescs, + acldvppRoiConfig *cropAreas[], + aclrtStream stream); /** * @ingroup AscendCL @@ -1799,10 +1841,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAsync(acldvppChannelDesc *channe * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateDvppConfig */ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeAsync(acldvppChannelDesc *channelDesc, - acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, - uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs, + acldvppBatchPicDesc *srcBatchPicDescs, + uint32_t *roiNums, + uint32_t size, + acldvppBatchPicDesc *dstBatchPicDescs, acldvppRoiConfig *cropAreas[], - acldvppResizeConfig *resizeConfig, aclrtStream stream); + acldvppResizeConfig *resizeConfig, + aclrtStream stream); /** * @ingroup AscendCL @@ -1825,9 +1870,12 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeAsync(acldvppChannelDesc * * * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateRoiConfig */ -ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc, - acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea, - acldvppRoiConfig *pasteArea, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + acldvppRoiConfig *cropArea, + acldvppRoiConfig *pasteArea, + aclrtStream stream); /** * @ingroup AscendCL @@ -1851,10 +1899,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *cha * * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig */ -ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc, - acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea, +ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + acldvppRoiConfig *cropArea, acldvppRoiConfig *pasteArea, - acldvppResizeConfig *resizeConfig, aclrtStream stream); + acldvppResizeConfig *resizeConfig, + aclrtStream stream); /** * @ingroup AscendCL @@ -1879,11 +1930,14 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc * * * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig */ -ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc *channelDesc, - acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, - uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs, - acldvppRoiConfig *cropAreas[], - acldvppRoiConfig *pasteAreas[], aclrtStream stream); + ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc *channelDesc, + acldvppBatchPicDesc *srcBatchPicDescs, + uint32_t *roiNums, + uint32_t size, + acldvppBatchPicDesc *dstBatchPicDescs, + acldvppRoiConfig *cropAreas[], + acldvppRoiConfig *pasteAreas[], + aclrtStream stream); /** * @ingroup AscendCL @@ -1909,10 +1963,16 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc * * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig */ -ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync( - acldvppChannelDesc *channelDesc, acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, uint32_t size, - acldvppBatchPicDesc *dstBatchPicDescs, acldvppRoiConfig *cropAreas[], acldvppRoiConfig *pasteAreas[], - acldvppResizeConfig *resizeConfig, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync(acldvppChannelDesc *channelDesc, + acldvppBatchPicDesc *srcBatchPicDescs, + uint32_t *roiNums, + uint32_t size, + acldvppBatchPicDesc *dstBatchPicDescs, + acldvppRoiConfig *cropAreas[], + acldvppRoiConfig *pasteAreas[], + acldvppResizeConfig *resizeConfig, + aclrtStream stream); + /** * @ingroup AscendCL @@ -1940,8 +2000,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync( * * @see acldvppCreateChannel | acldvppCreatePicDesc */ -ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelDesc, const void *data, uint32_t size, - acldvppPicDesc *outputDesc, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelDesc, + const void *data, + uint32_t size, + acldvppPicDesc *outputDesc, + aclrtStream stream); /** * @ingroup AscendCL @@ -1959,8 +2022,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelD * * @see acldvppCreateChannel | acldvppCreateJpegeConfig */ -ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc, - const void *data, uint32_t *size, acldvppJpegeConfig *config, +ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *inputDesc, + const void *data, + uint32_t *size, + acldvppJpegeConfig *config, aclrtStream stream); /** @@ -1978,8 +2044,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelD * * @see acldvppCreateChannel | acldvppCreatePicDesc */ -ACL_FUNC_VISIBILITY aclError acldvppPngDecodeAsync(acldvppChannelDesc *channelDesc, const void *data, uint32_t size, - acldvppPicDesc *outputDesc, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppPngDecodeAsync(acldvppChannelDesc *channelDesc, + const void *data, + uint32_t size, + acldvppPicDesc *outputDesc, + aclrtStream stream); /** * @ingroup AscendCL @@ -2034,8 +2103,11 @@ ACL_FUNC_VISIBILITY aclError aclvdecDestroyChannel(aclvdecChannelDesc *channelDe * * @see aclvdecCreateChannel | acldvppCreateStreamDesc | acldvppCreatePicDesc */ -ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc, acldvppStreamDesc *input, - acldvppPicDesc *output, aclvdecFrameConfig *config, void *userData); +ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc, + acldvppStreamDesc *input, + acldvppPicDesc *output, + aclvdecFrameConfig *config, + void *userData); /** * @ingroup AscendCL @@ -2054,8 +2126,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc, a * * @see aclvdecCreateChannel | acldvppCreateStreamDesc | acldvppCreatePicDesc | aclvdecSendFrame */ -ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channelDesc, acldvppStreamDesc *input, - aclvdecFrameConfig *config, void *userData); +ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channelDesc, + acldvppStreamDesc *input, + aclvdecFrameConfig *config, + void *userData); /** * @ingroup AscendCL @@ -2076,8 +2150,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channel * * @see acldvppCreateChannel | acldvppCreatePicDesc */ -ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc, - acldvppPicDesc *outputDesc, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + aclrtStream stream); /** * @ingroup AscendCL @@ -2099,8 +2175,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *cha * * @see acldvppCreateChannel | acldvppCreatePicDesc */ -ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc, - acldvppPicDesc *outputDesc, void *reserve, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + void *reserve, + aclrtStream stream); /** * @ingroup AscendCL @@ -2112,7 +2191,8 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelD * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppSetChannelDescMode(acldvppChannelDesc *channelDesc, uint32_t mode); +ACL_FUNC_VISIBILITY aclError acldvppSetChannelDescMode(acldvppChannelDesc *channelDesc, + uint32_t mode); /** * @ingroup AscendCL @@ -2147,7 +2227,8 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetResizeConfigInterpolation(const acldvppRe * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError aclvdecSetChannelDescOutMode(aclvdecChannelDesc *channelDesc, uint32_t outMode); +ACL_FUNC_VISIBILITY aclError aclvdecSetChannelDescOutMode(aclvdecChannelDesc *channelDesc, + uint32_t outMode); /** * @ingroup AscendCL @@ -2244,7 +2325,9 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetLutMapDims(const acldvppLutMap *lutMap); * @retval ACL_SUCCESS The function is successfully executed. * @retval OtherValues Failure */ -ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap, uint32_t dim, uint8_t **data, +ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap, + uint32_t dim, + uint8_t **data, uint32_t *len); /** * @ingroup AscendCL @@ -2262,8 +2345,10 @@ ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap, u * @see acldvppCreateChannel|acldvppCreatePicDesc|acldvppCreateLutMap */ ACL_FUNC_VISIBILITY aclError acldvppVpcEqualizeHistAsync(const acldvppChannelDesc *channelDesc, - const acldvppPicDesc *inputDesc, acldvppPicDesc *outputDesc, - const acldvppLutMap *lutMap, aclrtStream stream); + const acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + const acldvppLutMap *lutMap, + aclrtStream stream); /** * @ingroup AscendCL @@ -2284,7 +2369,8 @@ ACL_FUNC_VISIBILITY acldvppBorderConfig *acldvppCreateBorderConfig(); * * @retval ACL_SUCCESS for success, other for failure */ -ACL_FUNC_VISIBILITY aclError acldvppSetBorderConfigValue(acldvppBorderConfig *borderConfig, uint32_t index, +ACL_FUNC_VISIBILITY aclError acldvppSetBorderConfigValue(acldvppBorderConfig *borderConfig, + uint32_t index, double value); /** @@ -2429,8 +2515,10 @@ ACL_FUNC_VISIBILITY aclError acldvppDestroyBorderConfig(acldvppBorderConfig *bor * @see acldvppCreateChannel|acldvppCreatePicDesc|acldvppCreateBorderConfig */ ACL_FUNC_VISIBILITY aclError acldvppVpcMakeBorderAsync(const acldvppChannelDesc *channelDesc, - const acldvppPicDesc *inputDesc, acldvppPicDesc *outputDesc, - const acldvppBorderConfig *borderConfig, aclrtStream stream); + const acldvppPicDesc *inputDesc, + acldvppPicDesc *outputDesc, + const acldvppBorderConfig *borderConfig, + aclrtStream stream); /** * @ingroup AscendCL @@ -2447,8 +2535,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcMakeBorderAsync(const acldvppChannelDesc * * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateHist */ -ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *srcPicDesc, - acldvppHist *hist, void *reserve, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channelDesc, + acldvppPicDesc *srcPicDesc, + acldvppHist *hist, + void *reserve, + aclrtStream stream); /** * @ingroup AscendCL @@ -2457,7 +2548,7 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channel * @retval null for failed. * @retval OtherValues success. */ -ACL_FUNC_VISIBILITY acldvppHist *acldvppCreateHist(); +ACL_FUNC_VISIBILITY acldvppHist* acldvppCreateHist(); /** * @ingroup AscendCL @@ -2514,7 +2605,7 @@ ACL_FUNC_VISIBILITY aclError acldvppGetHistData(acldvppHist *hist, uint32_t dim, * * @see acldvppCreateHist | acldvppVpcCalcHistAsync */ -ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist *hist); +ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist* hist); /** * @ingroup AscendCL @@ -2533,6 +2624,7 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist *hist); */ ACL_FUNC_VISIBILITY aclError acldvppClearHist(acldvppHist *hist); + /** * @ingroup AscendCL * @brief dvpp vpc batch crop, resize config and make border. @@ -2556,13 +2648,18 @@ ACL_FUNC_VISIBILITY aclError acldvppClearHist(acldvppHist *hist); * * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig */ -ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeMakeBorderAsync( - acldvppChannelDesc *channelDesc, acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, uint32_t size, - acldvppBatchPicDesc *dstBatchPicDescs, acldvppRoiConfig *cropAreas[], acldvppBorderConfig *borderCfgs[], - acldvppResizeConfig *resizeConfig, aclrtStream stream); +ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeMakeBorderAsync(acldvppChannelDesc *channelDesc, + acldvppBatchPicDesc *srcBatchPicDescs, + uint32_t *roiNums, + uint32_t size, + acldvppBatchPicDesc *dstBatchPicDescs, + acldvppRoiConfig *cropAreas[], + acldvppBorderConfig *borderCfgs[], + acldvppResizeConfig *resizeConfig, + aclrtStream stream); #ifdef __cplusplus } #endif -#endif // INC_EXTERNAL_ACL_OPS_ACL_DVPP_H_ +#endif // INC_EXTERNAL_ACL_OPS_ACL_DVPP_H_ diff --git a/inc/external/acl/ops/acl_fv.h b/inc/external/acl/ops/acl_fv.h index 27dc367a..40cd50cb 100644 --- a/inc/external/acl/ops/acl_fv.h +++ b/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_ diff --git a/inc/external/hccl/hccl.h b/inc/external/hccl/hccl.h index 8261adc4..969d855a 100644 --- a/inc/external/hccl/hccl.h +++ b/inc/external/hccl/hccl.h @@ -27,7 +27,7 @@ #ifdef __cplusplus extern "C" { -#endif // __cplusplus +#endif // __cplusplus /** * @brief Initialize HCCL. @@ -66,15 +66,14 @@ extern HcclResult HcclCommInitRootInfo(uint32_t nRanks, const HcclRootInfo *root * @param sendBuf A pointer identifying the input data address of the operator. * @param recvBuf A pointer identifying the output data address of the operator. * @param count An integer(u64) identifying the number of the output data. - * @param dataType The data type of the operator, must be one of the following types: int8, int16, int32, float16, - * float32. + * @param dataType The data type of the operator, must be one of the following types: int8, int16, int32, float16, float32. * @param op The reduction type of the operator, must be one of the following types: sum, min, max, prod. * @param comm A pointer identifying the communication resource based on. * @param stream A pointer identifying the stream information. - * @return HcclResult + * @return HcclResult */ -extern HcclResult HcclAllReduce(void *sendBuf, void *recvBuf, uint64_t count, HcclDataType dataType, HcclReduceOp op, - HcclComm comm, aclrtStream stream); +extern HcclResult HcclAllReduce(void *sendBuf, void *recvBuf, uint64_t count, HcclDataType dataType, +HcclReduceOp op, HcclComm comm, aclrtStream stream); /** * @brief Broadcast operator. @@ -85,10 +84,10 @@ extern HcclResult HcclAllReduce(void *sendBuf, void *recvBuf, uint64_t count, Hc * @param root An integer(u32) identifying the the root rank in the operator. * @param comm A pointer identifying the communication resource based on * @param stream A pointer identifying the stream information. - * @return HcclResult + * @return HcclResult */ -extern HcclResult HcclBroadcast(void *buf, uint64_t count, HcclDataType dataType, uint32_t root, HcclComm comm, - aclrtStream stream); +extern HcclResult HcclBroadcast(void *buf, uint64_t count, HcclDataType dataType, uint32_t root, HcclComm comm, +aclrtStream stream); /** * @brief ReduceScatter operator. @@ -100,10 +99,10 @@ extern HcclResult HcclBroadcast(void *buf, uint64_t count, HcclDataType dataType * @param op The reduction type of the operator, must be one of the following types: sum, min, max, prod. * @param comm A pointer identifying the communication resource based on. * @param stream A pointer identifying the stream information. - * @return HcclResult + * @return HcclResult */ -extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvCount, HcclDataType dataType, - HcclReduceOp op, HcclComm comm, aclrtStream stream); +extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvCount, HcclDataType dataType, +HcclReduceOp op, HcclComm comm, aclrtStream stream); /** * @brief AllGather operator. @@ -114,16 +113,16 @@ extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvC * @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. * @param comm A pointer identifying the communication resource based on. * @param stream A pointer identifying the stream information. - * @return HcclResult + * @return HcclResult */ -extern HcclResult HcclAllGather(void *sendBuf, void *recvBuf, uint64_t sendCount, HcclDataType dataType, HcclComm comm, - aclrtStream stream); +extern HcclResult HcclAllGather(void *sendBuf, void *recvBuf, uint64_t sendCount, HcclDataType dataType, +HcclComm comm, aclrtStream stream); /** * @brief Get the rank size of this comm. * * @param comm A pointer identifying the communication resource based on. * @param rankSize A pointer identifying the rank size. - * @return HcclResult + * @return HcclResult */ extern HcclResult HcclGetRankSize(HcclComm comm, uint32_t *rankSize); @@ -132,7 +131,7 @@ extern HcclResult HcclGetRankSize(HcclComm comm, uint32_t *rankSize); * * @param comm A pointer identifying the communication resource based on. * @param rankSize A pointer identifying the rank id. - * @return HcclResult + * @return HcclResult */ extern HcclResult HcclGetRankId(HcclComm comm, uint32_t *rank); /** @@ -140,7 +139,7 @@ extern HcclResult HcclGetRankId(HcclComm comm, uint32_t *rank); * * @param comm A pointer identifying the communication resource based on. * @param stream A pointer identifying the stream information. - * @return HcclResult + * @return HcclResult */ extern HcclResult HcclBarrier(HcclComm comm, aclrtStream stream); @@ -155,5 +154,5 @@ extern HcclResult HcclCommDestroy(HcclComm comm); #ifdef __cplusplus } -#endif // __cplusplus -#endif // HCCL_H_ +#endif // __cplusplus +#endif // HCCL_H_ diff --git a/inc/external/hccl/hccl_types.h b/inc/external/hccl/hccl_types.h index 0e832396..50a64795 100644 --- a/inc/external/hccl/hccl_types.h +++ b/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_ diff --git a/inc/external/runtime/rt_error_codes.h b/inc/external/runtime/rt_error_codes.h index 1c196c48..c9a40432 100644 --- a/inc/external/runtime/rt_error_codes.h +++ b/inc/external/runtime/rt_error_codes.h @@ -23,84 +23,84 @@ 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_VECTOR_CORE_TIMEOUT = 507034; // vector core timeout -static const int32_t ACL_ERROR_RT_VECTOR_CORE_EXCEPTION = 507035; // vector core exception -static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_EXCEPTION = 507036; // vector core trap exception +static const int32_t ACL_ERROR_RT_INTERNAL_ERROR = 507000; // runtime internal error +static const int32_t ACL_ERROR_RT_TS_ERROR = 507001; // ts internel error +static const int32_t ACL_ERROR_RT_STREAM_TASK_FULL = 507002; // task full in stream +static const int32_t ACL_ERROR_RT_STREAM_TASK_EMPTY = 507003; // task empty in stream +static const int32_t ACL_ERROR_RT_STREAM_NOT_COMPLETE = 507004; // stream not complete +static const int32_t ACL_ERROR_RT_END_OF_SEQUENCE = 507005; // end of sequence +static const int32_t ACL_ERROR_RT_EVENT_NOT_COMPLETE = 507006; // event not complete +static const int32_t ACL_ERROR_RT_CONTEXT_RELEASE_ERROR = 507007; // context release error +static const int32_t ACL_ERROR_RT_SOC_VERSION = 507008; // soc version error +static const int32_t ACL_ERROR_RT_TASK_TYPE_NOT_SUPPORT = 507009; // task type not support +static const int32_t ACL_ERROR_RT_LOST_HEARTBEAT = 507010; // ts lost heartbeat +static const int32_t ACL_ERROR_RT_MODEL_EXECUTE = 507011; // model execute failed +static const int32_t ACL_ERROR_RT_REPORT_TIMEOUT = 507012; // report timeout +static const int32_t ACL_ERROR_RT_SYS_DMA = 507013; // sys dma error +static const int32_t ACL_ERROR_RT_AICORE_TIMEOUT = 507014; // aicore timeout +static const int32_t ACL_ERROR_RT_AICORE_EXCEPTION = 507015; // aicore exception +static const int32_t ACL_ERROR_RT_AICORE_TRAP_EXCEPTION = 507016; // aicore trap exception +static const int32_t ACL_ERROR_RT_AICPU_TIMEOUT = 507017; // aicpu timeout +static const int32_t ACL_ERROR_RT_AICPU_EXCEPTION = 507018; // aicpu exception +static const int32_t ACL_ERROR_RT_AICPU_DATADUMP_RSP_ERR = 507019; // aicpu datadump response error +static const int32_t ACL_ERROR_RT_AICPU_MODEL_RSP_ERR = 507020; // aicpu model operate response error +static const int32_t ACL_ERROR_RT_PROFILING_ERROR = 507021; // profiling error +static const int32_t ACL_ERROR_RT_IPC_ERROR = 507022; // ipc error +static const int32_t ACL_ERROR_RT_MODEL_ABORT_NORMAL = 507023; // model abort normal +static const int32_t ACL_ERROR_RT_KERNEL_UNREGISTERING = 507024; // kernel unregistering +static const int32_t ACL_ERROR_RT_RINGBUFFER_NOT_INIT = 507025; // ringbuffer not init +static const int32_t ACL_ERROR_RT_RINGBUFFER_NO_DATA = 507026; // ringbuffer no data +static const int32_t ACL_ERROR_RT_KERNEL_LOOKUP = 507027; // kernel lookup error +static const int32_t ACL_ERROR_RT_KERNEL_DUPLICATE = 507028; // kernel register duplicate +static const int32_t ACL_ERROR_RT_DEBUG_REGISTER_FAIL = 507029; // debug register failed +static const int32_t ACL_ERROR_RT_DEBUG_UNREGISTER_FAIL = 507030; // debug unregister failed +static const int32_t ACL_ERROR_RT_LABEL_CONTEXT = 507031; // label not in current context +static const int32_t ACL_ERROR_RT_PROGRAM_USE_OUT = 507032; // program register num use out +static const int32_t ACL_ERROR_RT_DEV_SETUP_ERROR = 507033; // device setup error +static const int32_t ACL_ERROR_RT_VECTOR_CORE_TIMEOUT = 507034; // vector core timeout +static const int32_t ACL_ERROR_RT_VECTOR_CORE_EXCEPTION = 507035; // vector core exception +static const int32_t ACL_ERROR_RT_VECTOR_CORE_TRAP_EXCEPTION = 507036; // vector core trap exception -static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899; // drv internal error -static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR = 507900; // aicpu internal error -static const int32_t ACL_ERROR_RT_SOCKET_CLOSE = 507901; // hdc disconnect +static const int32_t ACL_ERROR_RT_DRV_INTERNAL_ERROR = 507899; // drv internal error +static const int32_t ACL_ERROR_RT_AICPU_INTERNAL_ERROR = 507900; // aicpu internal error +static const int32_t ACL_ERROR_RT_SOCKET_CLOSE = 507901; // hdc disconnect #ifdef __cplusplus } #endif -#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__ +#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__ diff --git a/third_party/fwkacllib/inc/ops/control_flow_ops.h b/third_party/fwkacllib/inc/ops/control_flow_ops.h index c0b6ad72..9a0e0cd5 100644 --- a/third_party/fwkacllib/inc/ops/control_flow_ops.h +++ b/third_party/fwkacllib/inc/ops/control_flow_ops.h @@ -387,12 +387,12 @@ REG_OP(ControlTrigger) *@par Inputs: * Three inputs, including: -*@li x: One dimensional tensore of type int32, specifying queried shape, max size is 8. -*@li data_seq: One dimensional tensore of type int32, specifying the mapped table is queried. -*@li level_index: One dimensional tensore of type int32, specifying secondary index. \n +*@li x: One dimensional tensor of type int32, specifying queried shape, max size is 128. +*@li data_seq: One dimensional tensor of type int32, specifying the mapped table is queried. +*@li level_index: One dimensional tensor of type int32, specifying secondary index. \n *@par Outputs: -*@li y: A Tensor with shape [batch, 8], of type int32, specifying index of shape in the map. +*@li y: A Tensor with shape [8], of type int32, specifying index of shape in the map. *@par Third-party framework compatibility * It is a custom operator. It has no corresponding operator in Caffe. */ diff --git a/third_party/fwkacllib/inc/ops/ctc_ops.h b/third_party/fwkacllib/inc/ops/ctc_ops.h index c6a265cc..e907b828 100644 --- a/third_party/fwkacllib/inc/ops/ctc_ops.h +++ b/third_party/fwkacllib/inc/ops/ctc_ops.h @@ -137,6 +137,87 @@ REG_OP(CTCBeamSearchDecoder) .OUTPUT(log_probability, TensorType({DT_FLOAT, DT_DOUBLE})) .OP_END_FACTORY_REG(CTCBeamSearchDecoder) +/** +*@brief The Connectionist Temporal Classification loss. + +*@par Inputs: +*@li log_probs: Tensor of size (T, N, C), where T =input length, N =batch size, + and C = number of classes (including blank). + It represent the logarithmized probabilities of the outputs. +*@li targets: Tensor of size (N, S), where S= max target length. + It represent the target sequences. +*@li input_lengths: Tuple or tensor of size (N). It represent the lengths of the inputs. +*@li target_lengths: Tuple or tensor of size (N). It represent lengths of the targets. + +*@par Outputs: +*@li neg_log_likelihood: A loss value which is differentiable with respect to each input node. +*@li log_alpha: The probability of possible trace of input to target. + +*@par Attributes: +*@li blank : Blank label. Default 0. +*@li reduction: Specifies the reduction to apply to the output. Default: 'mean'. +*@li zero_infinity : Whether to zero infinite losses and the associated gradients. + +*@par Third-party framework compatibility +* Compatible with Pytorch CTCLoss operator. + +*@par Restrictions: +*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. +*/ +REG_OP(CTCLossV2) + .INPUT(log_probs, TensorType({DT_FLOAT, DT_DOUBLE})) + .INPUT(targets, TensorType({DT_INT32, DT_INT64})) + .INPUT(input_lengths, TensorType({DT_INT32, DT_INT64})) + .INPUT(target_lengths, TensorType({DT_INT32, DT_INT64})) + .OUTPUT(neg_log_likelihood, TensorType({DT_FLOAT, DT_DOUBLE})) + .OUTPUT(log_alpha, TensorType({DT_FLOAT, DT_DOUBLE})) + .ATTR(blank, Int, 0) + .ATTR(reduction, String, "mean") + .ATTR(zero_infinity, Bool, false) + .OP_END_FACTORY_REG(CTCLossV2) + +/** +*@brief The Connectionist Temporal Classification loss grad. + +*@par Inputs: +*@li grad_out: Gradient renewal coefficient. Tensor of size (N), where N = batch size. +*@li log_probs: Tensor of size (T, N, C), where T =input length, N =batch size, + and C = number of classes (including blank). + It represent the logarithmized probabilities of the outputs. +*@li targets: Tensor of size (N, S), where S= max target length. + It represent the target sequences. +*@li input_lengths: Tuple or tensor of size (N). It represent the lengths of the inputs. +*@li target_lengths: Tuple or tensor of size (N). It represent lengths of the targets. +*@li neg_log_likelihood: A loss value which is differentiable with respect to each input node. +*@li log_alpha: The probability of possible trace of input to target. + +*@par Outputs: +*@li grad: Tensor of size (T, N, C), The grad of Connectionist Temporal Classification loss. + +*@par Attributes: +*@li blank : Blank label. Default 0. +*@li reduction: Specifies the reduction to apply to the output. Default: 'mean'. +*@li zero_infinity : Whether to zero infinite losses and the associated gradients. + +*@par Third-party framework compatibility +* Compatible with Pytorch CTCLoss operator. + +*@par Restrictions: +*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. +*/ +REG_OP(CTCLossV2Grad) + .INPUT(grad_out, TensorType({DT_FLOAT, DT_DOUBLE})) + .INPUT(log_probs, TensorType({DT_FLOAT, DT_DOUBLE})) + .INPUT(targets, TensorType({DT_INT32, DT_INT64})) + .INPUT(input_lengths, TensorType({DT_INT32, DT_INT64})) + .INPUT(target_lengths, TensorType({DT_INT32, DT_INT64})) + .INPUT(neg_log_likelihood, TensorType({DT_FLOAT, DT_DOUBLE})) + .INPUT(log_alpha, TensorType({DT_FLOAT, DT_DOUBLE})) + .OUTPUT(grad, TensorType({DT_FLOAT, DT_DOUBLE})) + .ATTR(blank, Int, 0) + .ATTR(reduction, String, "mean") + .ATTR(zero_infinity, Bool, false) + .OP_END_FACTORY_REG(CTCLossV2Grad) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_CTC_OPS_H_ \ No newline at end of file diff --git a/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h b/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h index 7850536d..c8df447d 100644 --- a/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/elewise_calculation_ops.h @@ -1039,7 +1039,7 @@ REG_OP(BesselI1e) * y = log_base(shift + scale * x), with "base" > 0. \n * @par Inputs: -* @li x: A Tensor of type complex64, complex128, float16, float32 or double. \n +* x: A Tensor of type complex64, complex128, float16, float32 or double. \n * @par Attributes: * @li base: An optional float32, specifying the base "e". Defaults to "-1.0" @@ -1084,7 +1084,7 @@ REG_OP(Log) * uint8, int8, uint16, int16, int32, int64, complex64, complex128. \n * @attention Constraints: -* @li "x1" and "x2" have incompatible shapes or types. \n +* "x1" and "x2" have incompatible shapes or types. \n * @par Third-party framework compatibility * Compatible with the TensorFlow operator Multiply. @@ -3415,7 +3415,7 @@ REG_OP(Addcdiv) .INPUT(input_data, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(value, TensorType({ DT_FLOAT16, DT_FLOAT, DT_INT32 })) + .INPUT(value, TensorType({ DT_FLOAT16, DT_FLOAT, DT_INT32})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OP_END_FACTORY_REG(Addcdiv) @@ -3468,25 +3468,6 @@ REG_OP(AxpyV2) .OP_END_FACTORY_REG(AxpyV2) /** -* @brief Computes the result of x1 * x2. - -* @par Inputs: -* @li x1: An ND tensor of type float16, float32, int32. -* @li x2: An ND tensor of type float16, float32, int32. \n - -* @par Outputs: -* @li y: Same shape and type as the largest ND tensor in x1 x2. \n - -* @par Third-party framework compatibility -* Compatible with the Pytorch operator muls. -*/ -REG_OP(PtMuls) - .INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) - .INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) - .OP_END_FACTORY_REG(PtMuls) - -/** * @brief Computes the result of x1 - x2. * @par Inputs: diff --git a/third_party/fwkacllib/inc/ops/globalavgpool.h b/third_party/fwkacllib/inc/ops/globalavgpool.h new file mode 100644 index 00000000..06f03d30 --- /dev/null +++ b/third_party/fwkacllib/inc/ops/globalavgpool.h @@ -0,0 +1,49 @@ +/** + * Copyright 2019 Huawei Technologies Co., Ltd + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/*! + * \file globalavgpool.h + * \brief + */ +#ifndef OPS_BUILT_IN_OP_PROTO_INC_GLOBALAVERAGEPOOL_H_ +#define OPS_BUILT_IN_OP_PROTO_INC_GLOBALAVERAGEPOOL_H_ + +#include "graph/operator_reg.h" + +namespace ge { +/** +*@brief GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. +This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor \n + +*@par Inputs: +*@li x: Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), +where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. +For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. + +*@par Outputs: +*y: Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. +The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1 + +*@par Restrictions: +*Warning: This operator can be integrated only by configuring INSERT_OP_FILE of aclgrphBuildModel. Please do not use it directly. +*/ +REG_OP(GlobalAveragePool) + .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .OP_END_FACTORY_REG(GlobalAveragePool) +} // namespace ge + +#endif // OPS_BUILT_IN_OP_PROTO_INC_GLOBALAVGPOOL_H_ \ No newline at end of file diff --git a/third_party/fwkacllib/inc/ops/image_ops.h b/third_party/fwkacllib/inc/ops/image_ops.h index 71f1b9e1..6909345a 100644 --- a/third_party/fwkacllib/inc/ops/image_ops.h +++ b/third_party/fwkacllib/inc/ops/image_ops.h @@ -1868,5 +1868,73 @@ REG_OP(IMGWarpOffsets) .INPUT(offsets, TensorType({DT_FLOAT, DT_INT32})) .OUTPUT(warp_images, TensorType({DT_UINT8, DT_FLOAT16, DT_FLOAT})) .OP_END_FACTORY_REG(IMGWarpOffsets) + +/** +*@brief This operation samples 3d input x by using interpolation based on flow field grid, + which is usually gennerated by affine_grid. + +*@par Inputs: +*@li x: 5-D Tensor with shape `[batch, channels, depth, height, width]`. +*@li grid: flow field grid, 5-D Tensor with shape `[batch, depth, height, width, 2]`. + +*@par Attributes: +*@li interpolation_mode: An optional string specifying the interpolation method. +*@li padding_mode: An optional string specifying the pad method. +*@li align_corners: An optional bool. If "true", the centers of the corner + pixels of the input and output tensors are aligned. Defaults to "false" . + +*@par Outputs: +*y: Returns 5-D Tensor with the same dtype as `x`. + +*@par Third-party framework compatibility +*Compatible with pytorch GridSampler3D operator. + +*@par Restrictions: +*Warning:THIS FUNCTION IS EXPERIMENTAL. Please do not use. +*/ +REG_OP(GridSampler3D) + .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .INPUT(grid, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .ATTR(interpolation_mode, String, "bilinear") + .ATTR(padding_mode, String, "zeros") + .ATTR(align_corners, Bool, false) + .OP_END_FACTORY_REG(GridSampler3D) + +/** +*@brief Computes the gradients of GridSampler3D. + +*@par Inputs: +*@li grad: 5-D Tensor with shape `[batch, channels, depth, height, width]`. +*@li x: 5-D Tensor with shape `[batch, channels, depth, height, width]`. +*@li grid: flow field grid, 5-D Tensor with shape `[batch, depth, height, width, 2]`. + +*@par Attributes: +*@li interpolation_mode: An optional string specifying the interpolation method. +*@li padding_mode: An optional string specifying the pad method. +*@li align_corners: An optional bool. If "true", the centers of the corner + pixels of the input and output tensors are aligned. Defaults to "false" . + +*@par Outputs: +*dx: Returns 5-D Tensor with the same dtype and shape as `x`. +*dgrid: Returns 5-D Tensor with the same dtype and shape as `grid`. + +*@par Third-party framework compatibility +*Compatible with pytorch GridSampler3DGrad operator. + +*@par Restrictions: +*Warning:THIS FUNCTION IS EXPERIMENTAL. Please do not use. +*/ +REG_OP(GridSampler3DGrad) + .INPUT(grad, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .INPUT(grid, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .OUTPUT(dx, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .OUTPUT(dgrid, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) + .ATTR(interpolation_mode, String, "bilinear") + .ATTR(padding_mode, String, "zeros") + .ATTR(align_corners, Bool, false) + .OP_END_FACTORY_REG(GridSampler3DGrad) + } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_IMAGE_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/linalg_ops.h b/third_party/fwkacllib/inc/ops/linalg_ops.h index 532a641d..69c77bf6 100644 --- a/third_party/fwkacllib/inc/ops/linalg_ops.h +++ b/third_party/fwkacllib/inc/ops/linalg_ops.h @@ -340,9 +340,9 @@ form square matrices. \n */ REG_OP(SelfAdjointEig) - .INPUT(x, TensorType({ DT_DOUBLE, DT_FLOAT })) - .OUTPUT(eigen_value, TensorType({ DT_DOUBLE, DT_FLOAT })) - .OUTPUT(eigen_vector, TensorType({ DT_DOUBLE, DT_FLOAT })) + .INPUT(x, TensorType({ DT_DOUBLE, DT_FLOAT, DT_COMPLEX64, DT_COMPLEX128 })) + .OUTPUT(eigen_value, TensorType({ DT_DOUBLE, DT_FLOAT, DT_COMPLEX64, DT_COMPLEX128 })) + .OUTPUT(eigen_vector, TensorType({ DT_DOUBLE, DT_FLOAT, DT_COMPLEX64, DT_COMPLEX128 })) .ATTR(compute_v, Bool, true) .OP_END_FACTORY_REG(SelfAdjointEig) diff --git a/third_party/fwkacllib/inc/ops/math_ops.h b/third_party/fwkacllib/inc/ops/math_ops.h index 4a44d744..8f11ab0f 100644 --- a/third_party/fwkacllib/inc/ops/math_ops.h +++ b/third_party/fwkacllib/inc/ops/math_ops.h @@ -818,8 +818,8 @@ REG_OP(ActsULQ) .INPUT(clamp_min, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(clamp_max, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) - .OUTPUT(clamp_min_mask, TensorType({DT_BOOL})) - .OUTPUT(clamp_max_mask, TensorType({DT_BOOL})) + .OUTPUT(clamp_min_mask, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT})) + .OUTPUT(clamp_max_mask, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT})) .OUTPUT(x_clamped_loss, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(fixed_min, Bool, false) .ATTR(num_bits, Int, 8) @@ -845,8 +845,8 @@ REG_OP(ActsULQ) REG_OP(ActsULQInputGrad) .INPUT(y_grad, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(clamp_min_mask, TensorType({DT_BOOL})) - .INPUT(clamp_max_mask, TensorType({DT_BOOL})) + .INPUT(clamp_min_mask, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT})) + .INPUT(clamp_max_mask, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT})) .OUTPUT(x_grad, TensorType({DT_FLOAT16, DT_FLOAT})) .OP_END_FACTORY_REG(ActsULQInputGrad) @@ -870,7 +870,7 @@ REG_OP(ActsULQInputGrad) REG_OP(ActULQClampMaxGrad) .INPUT(y_grad, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(clamp_max_mask, TensorType({DT_BOOL})) + .INPUT(clamp_max_mask, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT})) .INPUT(x_clamped_loss, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(clamp_max_grad, TensorType({DT_FLOAT16, DT_FLOAT})) .OP_END_FACTORY_REG(ActULQClampMaxGrad) @@ -895,7 +895,7 @@ REG_OP(ActULQClampMaxGrad) REG_OP(ActULQClampMinGrad) .INPUT(y_grad, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(clamp_min_mask, TensorType({DT_BOOL})) + .INPUT(clamp_min_mask, TensorType({DT_BOOL, DT_FLOAT16, DT_FLOAT})) .INPUT(x_clamped_loss, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(clamp_min_grad, TensorType({DT_FLOAT16, DT_FLOAT})) .OP_END_FACTORY_REG(ActULQClampMinGrad) diff --git a/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h b/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h index af02276b..b317be37 100644 --- a/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h @@ -456,6 +456,34 @@ REG_OP(TensorScatterUpdate) .OP_END_FACTORY_REG(TensorScatterUpdate) /** +*@brief Uses "updates" to update tensor "data" by "indices". \n + +*@par Inputs: +* Three inputs, including: +*@li data: An ND Tensor . \n +*Must be one of the following types: float16, float32, int32, int8, uint8 +*@li indices: An ND Tensor of type int32 or int64 +*@li updates: An Tensor. Same shape as indices. format:NCHW, NHWC . \n +*Must be one of the following types: float16, float32, int32, int8, uint8 + +*@par Attributes: +*@li axis: An optional attribute. Defaults to 0. + +*@par Outputs: +*y: A Tensor. Has the same type and format as input "data" . \n + +*@par Third-party framework compatibility +* Compatible with the ONNX operator ScatterElements. +*/ +REG_OP(ScatterElements) + .INPUT(data, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8})) + .INPUT(indices, TensorType::IndexNumberType()) + .INPUT(updates, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8})) + .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8})) + .ATTR(axis, Int, 0) + .OP_END_FACTORY_REG(ScatterElements) + +/** *@brief Adds sparse "updates" to a variable reference . \n *@par Inputs: @@ -1140,24 +1168,24 @@ REG_OP(Tril) *@par Attributes: *equation: The subscripts for the Einstein summation. \n -*tensor_size: tensor size of input \n +*N: tensor size of input \n *@par Outputs: *@li y: Sums the product of the elements of the input operands along dimensions specified using a notation based on the Einstein summation convention. \n *@attention Constraints: -*Input tensor_size must be Int. \n +*Input N must be Int. \n *@par Third-party framework compatibility *Compatible with Pytorch einsum operator. */ -REG_OP(EinSum) +REG_OP(Einsum) .DYNAMIC_INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) .REQUIRED_ATTR(equation, String) - .REQUIRED_ATTR(tensor_size, Int) - .OP_END_FACTORY_REG(EinSum) + .REQUIRED_ATTR(N, Int) + .OP_END_FACTORY_REG(Einsum) /** *@brief Returns a 2-D tensor with ones on the diagonal and zeros elsewhere. \n diff --git a/third_party/fwkacllib/inc/ops/nn_calculation_ops.h b/third_party/fwkacllib/inc/ops/nn_calculation_ops.h index 0eeeb511..98473c65 100644 --- a/third_party/fwkacllib/inc/ops/nn_calculation_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_calculation_ops.h @@ -784,16 +784,14 @@ REG_OP(Conv2DBackpropFilterD) | Tensor | x | filter | bias | y ------------|---------|---------|---------|-------- | Data Type | float16 | float16 | float16 | float16 - | |---------|---------|---------|-------- | | float32 | float32 | float32 | float32 - | |---------|---------|---------|-------- | | int8 | int8 | int32 | int32 ------------|---------|---------|---------|-------- | Format | NCHW | NCHW | ND | NCHW | | NHWC | HWCN | | NHWC @endverbatim * For float32 type, the actual calculation on the chip is based on -* float16. For int8, a dequant or requant operator must be followed. +* float16. *\n * *@par Attributes: diff --git a/third_party/fwkacllib/inc/ops/nn_detect_ops.h b/third_party/fwkacllib/inc/ops/nn_detect_ops.h index d4141e47..5fa40ad6 100644 --- a/third_party/fwkacllib/inc/ops/nn_detect_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_detect_ops.h @@ -1450,7 +1450,8 @@ REG_OP(DecodeBboxV2) * *@par Inputs: *Inputs include: -* x: A Tensor. Must be float16 or float32. +* x: A Tensor. Dtype support: flaot16, flaot, int16, int8, + uint8, int32, int64. * *@par Attributes: * @li axis: optional, int. @@ -1462,9 +1463,11 @@ REG_OP(DecodeBboxV2) * */ REG_OP(Sort) - .INPUT(x, TensorType({ DT_FLOAT16 })) - .OUTPUT(y1, TensorType({ DT_FLOAT16 })) - .OUTPUT(y2, TensorType({ DT_INT32 })) + .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT16, DT_INT8, + DT_UINT8, DT_INT32, DT_INT64})) + .OUTPUT(y1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT16, DT_INT8, + DT_UINT8, DT_INT32, DT_INT64})) + .OUTPUT(y2, TensorType({DT_INT32})) .ATTR(axis, Int, -1) .ATTR(descending, Bool, false) .OP_END_FACTORY_REG(Sort) @@ -1718,6 +1721,101 @@ REG_OP(PSROIPoolingGradV2D) .REQUIRED_ATTR(input_size, ListInt) .OP_END_FACTORY_REG(PSROIPoolingGradV2D) +/** +*@brief Generate the responsible flags of anchor in a single feature map. + +*@par Inputs: +*@li gt_bboxes: Ground truth box, 2-D Tensor with shape `[batch, 4]`. + +*@par Attributes: +*@li featmap_size: The size of feature maps, listint. +*@li strides: Stride of current level, listint. +*@li num_base_anchors: The number of base anchors. + +*@par Outputs: +*flags: The valid flags of each anchor in a single level. +*/ +REG_OP(AnchorResponseFlags) + .INPUT(gt_bboxes, TensorType({DT_FLOAT})) + .OUTPUT(flags, TensorType({DT_UINT8})) + .REQUIRED_ATTR(featmap_size, ListInt) + .REQUIRED_ATTR(strides, ListInt) + .REQUIRED_ATTR(num_base_anchors, Int) + .OP_END_FACTORY_REG(AnchorResponseFlags) + +/** +*@brief Generates bounding boxes based on yolo's "anchor" and "ground-truth" boxes. +* It is a customized mmdetection operator . \n + +*@par Inputs: +* Three inputs, including: +*@li anchor_boxes: anchor boxes generated by the yolo training set. +* A 2D Tensor of type float32 or float16 with shape (N, 4). "N" indicates the number +* of ROIs, "N" indicates the number of ROIs, and the value "4" refers to (tx, ty, tw, th). +*@li gt_bboxes: target of the transformation, e.g, ground-truth boxes. +* A 2D Tensor of type float32 or float16 with shape (N, 4). +* "N" indicates the number of ROIs, and 4 indicates "dx", "dy", "dw", and "dh" . +*@li stride: Scale for each box. +* A 1D Tensor of type int32 shape (N,). +* "N" indicates the number of ROIs. \n + +*@par Attributes: +*@li performance_mode: select performance mode, "high_precision" or "high_performance". +* select "high_precision" when input type is float32, the output tensor precision +* will be smaller than 0.0001, select "high_performance" when input type is float32, +* the ops will be best performance, but precision will be only smaller than 0.005. + +*@par Outputs: +*encoded_bboxes: Bboxes generated based on "anchor_boxes" and "gt_bboxes". Have the +* same format and type as "anchor_boxes". +* +*@attention Constraints: +* input anchor boxes only support maximum N=20480. \n +*/ +REG_OP(YoloBoxesEncode) + .INPUT(anchor_boxes, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(gt_bboxes, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(stride, TensorType({DT_INT32})) + .ATTR(performance_mode, String, "high_precision") + .OUTPUT(encoded_bboxes, TensorType({DT_FLOAT16, DT_FLOAT})) + .OP_END_FACTORY_REG(YoloBoxesEncode) + +/** +*@brief Performs Position Sensitive PS ROI Pooling Grad. + +*@par Inputs: +* Eight inputs, including: +*@li assigned_gt_inds: Tensor of type float16 or float32, shape (n, ) +*@li overlaps: A Tensor. Datatype is same as assigned_gt_inds. IOU between gt_bboxes and bboxes. shape(k, n) +*@li box_responsible_flags: A Tensor. Support uint8. Flag to indicate whether box is responsible. +*@li max_overlaps: A Tensor. Datatype is same as assigned_gt_inds. overlaps.max(axis=0). +*@li argmax_overlaps: A Tensor. Support int32. overlaps.argmax(axis=0). +*@li gt_max_overlaps: A Tensor. Datatype is same as assigned_gt_inds. overlaps.max(axis=1). +*@li gt_argmax_overlaps: A Tensor. Support int32. overlaps.argmax(axis=1). +*@li num_gts: A Tensor. Support int32. real k. shape (1, ) + +*@par Attributes: +*@li output_dim: float. IOU threshold for positive bboxes. +*@li group_size: float. minimum iou for a bbox to be considered as a positive bbox +*@li spatial_scale: bool. whether to assign all bboxes with the same highest overlap with some gt to that gt. + +*@par Outputs: +*@li assigned_gt_inds_pos: A Tensor. Support float16/float32. shape (n, ). +*/ +REG_OP(GridAssignPositive) + .INPUT(assigned_gt_inds, TensorType({ DT_FLOAT, DT_FLOAT16 })) + .INPUT(overlaps, TensorType({ DT_FLOAT, DT_FLOAT16 })) + .INPUT(box_responsible_flags, TensorType({ DT_UINT8 })) + .INPUT(max_overlaps, TensorType({ DT_FLOAT, DT_FLOAT16 })) + .INPUT(argmax_overlaps, TensorType({ DT_INT32 })) + .INPUT(gt_max_overlaps, TensorType({ DT_FLOAT, DT_FLOAT16 })) + .INPUT(gt_argmax_overlaps, TensorType({ DT_INT32 })) + .INPUT(num_gts, TensorType({ DT_INT32 })) + .OUTPUT(assigned_gt_inds_pos, TensorType({DT_FLOAT, DT_FLOAT16})) + .REQUIRED_ATTR(pos_iou_thr, Float) + .REQUIRED_ATTR(min_pos_iou, Float) + .REQUIRED_ATTR(gt_max_assign_all, Bool) + .OP_END_FACTORY_REG(GridAssignPositive) } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_NN_DETECT_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/nn_norm_ops.h b/third_party/fwkacllib/inc/ops/nn_norm_ops.h index 796e1e61..c6718c7c 100644 --- a/third_party/fwkacllib/inc/ops/nn_norm_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_norm_ops.h @@ -107,6 +107,9 @@ REG_OP(SoftmaxCrossEntropyWithLogits) * @li grad_softmax: A Tensor. Has the same shape and type as "softmax". * The format is NC1HWC0 or DN . \n +*@par Attributes: +* axes: An optional list of ints. Defaults to "{-1}" . \n + *@par Outputs: *grad_x: A Tensor. Has the same shape and type as "softmax" . \n @@ -117,6 +120,7 @@ REG_OP(SoftmaxGrad) .INPUT(softmax, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8})) .INPUT(grad_softmax, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8})) .OUTPUT(grad_x, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8})) + .ATTR(axes, ListInt, {-1}) .OP_END_FACTORY_REG(SoftmaxGrad) /** @@ -889,7 +893,7 @@ REG_OP(Scale) *@par Inputs: *One input, including: -*@li x: A Tensor. Must be 4-D shape, and only support the following types: float16, float32 . \n +*x: A Tensor. Must be 4-D shape, and only support the following types: float16, float32 . \n *@par Attributes: *@li depth_radius: An optional int32, specifying the half-width of the normalization window. Defaults to "5". diff --git a/third_party/fwkacllib/inc/ops/nn_ops.h b/third_party/fwkacllib/inc/ops/nn_ops.h index f7dd6795..49fd02fa 100644 --- a/third_party/fwkacllib/inc/ops/nn_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_ops.h @@ -104,5 +104,60 @@ REG_OP(FusedBatchNormV2) .ATTR(data_format, String, "NHWC") .ATTR(is_training, Bool, true) .OP_END_FACTORY_REG(FusedBatchNormV2) + +/** + * @brief: Large amount of data sort.First operator of TopK. + * @par Inputs: + * two input, including: + * @li input_data: A Tensor. Data to be sorted. Support float16 + * @li input_index: A Tensor. Range(0, 2048). Datatype and format is same as input_data. + * @par Attributes: + * @li k_num: Int.Number to be sorted. + * @par Outputs: + * 1 output, including: + * @li output_proposal: A Tensor. Datatype and format is same as input_data. Proposal sorted for each channel. + */ +REG_OP(SegmentSort) + .INPUT(input_data, TensorType({DT_FLOAT16})) + .INPUT(input_index, TensorType({DT_FLOAT16})) + .OUTPUT(output_proposal, TensorType({DT_FLOAT16})) + .REQUIRED_ATTR(k_num, Int) + .OP_END_FACTORY_REG(SegmentSort) + +/** + * @brief: Large amount of data sort.Second operator of TopK. + * @par Inputs: + * two input, including: + * @li input_proposal: A Tensor. Proposal sorted for each channel. Support float16 + * @par Attributes: + * @li k_num: Int.Number to be sorted. + * @par Outputs: + * 1 output, including: + * @li output_proposal: A Tensor. Datatype and format is same as input_data. Proposal sorted for each channel. + */ +REG_OP(MultiMerge) + .INPUT(input_proposal, TensorType({DT_FLOAT16})) + .OUTPUT(output_proposal, TensorType({DT_FLOAT16})) + .REQUIRED_ATTR(k_num, Int) + .OP_END_FACTORY_REG(MultiMerge) + +/** + * @brief: Large amount of data sort.Third operator of TopK. + * @par Inputs: + * two input, including: + * @li input_proposal: A Tensor. Proposal sorted for each channel. Support float16 + * @par Attributes: + * @li k_num: Int.Number to be sorted. + * @par Outputs: + * 2 output, including: + * @li output_data: A Tensor. Datatype and format is same as input_data. Data sorted. + * @li output_index: A Tensor. int32. Data index. + */ +REG_OP(SingleMerge) + .INPUT(input_proposal, TensorType({DT_FLOAT16})) + .OUTPUT(output_data, TensorType({DT_FLOAT16})) + .OUTPUT(output_index, TensorType({DT_INT32})) + .REQUIRED_ATTR(k_num, Int) + .OP_END_FACTORY_REG(SingleMerge) }// namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_NN_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/nn_pooling_ops.h b/third_party/fwkacllib/inc/ops/nn_pooling_ops.h index ef9fabb8..80a21333 100644 --- a/third_party/fwkacllib/inc/ops/nn_pooling_ops.h +++ b/third_party/fwkacllib/inc/ops/nn_pooling_ops.h @@ -227,12 +227,12 @@ REG_OP(AvgPool3DD) * @brief Computes AvgPool3DGrad function. * @par Inputs: -* @li orig_input_shape: An NDHWC tensor of type float16, float32, or double. -* @li grads: An NDHWC tensor of type int32. +* @li orig_input_shape: An NDHWC tensor of type int32. +* @li grads: An NDHWC tensor of type float16, float32, or double. * @par Attributes: -* @li ksize: List of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor. -* @li strides:List of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor. +* @li ksize: List of ints that has length 5. The size of the window for each dimension of the input tensor. +* @li strides:List of ints that has length 5. The stride of the sliding window for each dimension of the input tensor. * @li pads: List of ints, implicit zero paddings on both sides of the input. * @li ceil_mode: When true, will use ceil instead of floor in the formula to compute the output shape. * @li count_include_pad: When true, will include the zero-padding in the averaging calculation. @@ -242,6 +242,9 @@ REG_OP(AvgPool3DD) * @par Outputs: * @output: A mutable tensor with the same shape and type as "orig_input_shape". +* @attention Constraints: +* @li "ksize" is in the range [1, 255]. "strides" is in the range [1, 63] + * @par Third-party framework compatibility * @li Compatible with the TensorFlow operator AvgPoolGrad. */ @@ -269,8 +272,8 @@ REG_OP(AvgPool3DGrad) * @par Attributes: * @li orig_input_shape: List of ints that has length 5. The size of the window for each dimension of the input tensor. -* @li ksize: List of ints that has length 3. The size of the window for each dimension of the input tensor. -* @li strides:List of ints that has length 3. The stride of the sliding window for each dimension of the input tensor. +* @li ksize: List of ints that has length 5. The size of the window for each dimension of the input tensor. +* @li strides:List of ints that has length 5. The stride of the sliding window for each dimension of the input tensor. * @li pads: List of ints, implicit zero paddings on both sides of the input. * @li ceil_mode: When true, will use ceil instead of floor in the formula to compute the output shape. * @li count_include_pad: When true, will include the zero-padding in the averaging calculation. @@ -290,7 +293,7 @@ REG_OP(AvgPool3DGradD) .INPUT(grads, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(filter, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(multiplier, TensorType({DT_FLOAT16})) - .OUTPUT(output, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) + .OUTPUT(output, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(orig_input_shape, ListInt) .REQUIRED_ATTR(ksize, ListInt) .REQUIRED_ATTR(strides, ListInt) @@ -621,7 +624,7 @@ REG_OP(MaxPoolV2) *@par Inputs: * One input: -*x: An 4D Tensor. Supported type: float, double, int32, +* x: An 4D Tensor. Supported type: float, double, int32, * uint8, int16, int8, int64, uint16, half, uint32, uint64. * Must set the format, supported format list ["NCHW, NHWC"]. \n @@ -635,8 +638,8 @@ REG_OP(MaxPoolV2) *@li padding: A required string. No default value . \n *@par Outputs: -*y: A Tensor. Has the same type and format as input "x". -*argmax: A Tensor. Has the same type and format as input "x". +*@li y: A Tensor. Has the same type and format as input "x". +*@li argmax: A Tensor. Has the same type and format as input "x". *@attention Constraints: *@li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1, * ksize[1] * ksize[2] <= 255. diff --git a/third_party/fwkacllib/inc/ops/pad_ops.h b/third_party/fwkacllib/inc/ops/pad_ops.h index bacbe40d..6854c866 100644 --- a/third_party/fwkacllib/inc/ops/pad_ops.h +++ b/third_party/fwkacllib/inc/ops/pad_ops.h @@ -269,39 +269,42 @@ REG_OP(PadV3) .ATTR(paddings_contiguous, Bool, true) .OP_END_FACTORY_REG(PadV3) -/** -*@brief Pads a tensor. - -*@par Inputs: -* @li x: A Tensor. Must be one of the following types: float16, float32. -* @li paddings: A Tensor. Must be int32 type -* paddings is a required input tensor. - -*@par Attributes: -* @li constant_values: An optional int value for pad. -* @li mode: An optional string, Defaults to "constant", indicates paddings mode, -* support "constant", "reflect", "edge" -* @li paddings_contiguous: An optional bool value, Defaults to true. -* If true, paddings is arranged as [[begin0, end0], [begin1, end1], ...] -* If false, paddings is arranged as [[begin0, begin1], ..., [end0, end1], ...] - -*@par Outputs: -*y: A Tensor of the same type as "x". - -*@par Third-party framework compatibility: -* Compatible with ONNX operator Pad. - -* @par Restrictions: -* Warning: THIS FUNCTION IS DEPRECATED. Please use PadV3 instead. -*/ -REG_OP(PadV3D) - .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) - .INPUT(paddings, TensorType({DT_INT32})) - .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) - .ATTR(constant_values, Int, 0) - .ATTR(mode, String, "constant") - .ATTR(paddings_contiguous, Bool, true) - .OP_END_FACTORY_REG(PadV3D) + /** + *@brief Pads a tensor. + + *@par Inputs: + *x: A Tensor. Must be one of the following types: float16, float32, int8, uint8, int32. + + *@par Attributes: + * @li paddings: An required "vector>". + * For each dimension D of input, paddings[D, 0] indicates how many + * values to add before the contents of tensor in that dimension, + * and paddings[D, 1] indicates how many values to add after the + * contents of tensor in that dimension. + * @li constant_values: An optional int value for pad. + * @li mode: An optional string, Defaults to "constant", indicates paddings mode, + * support "constant", "reflect", "edge" + * @li paddings_contiguous: An optional bool value, Defaults to true. + * If true, paddings is arranged as [[begin0, end0], [begin1, end1], ...] + * If false, paddings is arranged as [[begin0, begin1], ..., [end0, end1], ...] + + *@par Outputs: + *y: A Tensor of the same type as "x". + + *@par Third-party framework compatibility: + * Compatible with ONNX operator Pad. + + * @par Restrictions: + * Warning: THIS FUNCTION IS DEPRECATED. Please use PadV3 instead. + */ + REG_OP(PadV3D) + .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8})) + .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8})) + .REQUIRED_ATTR(paddings, ListListInt) + .ATTR(constant_values, Int, 0) + .ATTR(mode, String, "constant") + .ATTR(paddings_contiguous, Bool, true) + .OP_END_FACTORY_REG(PadV3D) /** *@brief Create a diagonal tensor @@ -398,6 +401,36 @@ REG_OP(EmbeddingRankId) .OP_END_FACTORY_REG(EmbeddingRankId) /** +*@brief EmbeddingLocalIndex, Sort statistics index according to rank_id \n + +*@par Inputs: +* @li addr_table: A 2D tensor which last dimension must be 3. +* @li index: A tensor with data type int32, int64, uint32, uint64. + +*@par Attributes: +* @li row_memory: The size of Embedding vector in a row, the default is 320. +* @li mode: String type, currently there are two options: 'mod' and 'order' + +*@par Outputs: +* @li local_idx:Index on each server. +* @li nums:The number of local_idx found on each server. +* @li recover_idx:The sorted local_idx element is at the position corresponding +* to the original input index. + +*@par Third-party framework compatibility +* Compatible with the TensorFlow operator Diag. +*/ +REG_OP(EmbeddingLocalIndex) + .INPUT(addr_table, TensorType({DT_UINT64})) + .INPUT(index, TensorType({DT_INT64,DT_INT32,DT_UINT32,DT_UINT64})) + .OUTPUT(local_idx, TensorType({DT_INT64,DT_INT32,DT_UINT32,DT_UINT64})) + .OUTPUT(nums, TensorType({DT_INT64,DT_INT32,DT_UINT32,DT_UINT64})) + .OUTPUT(recover_idx, TensorType({DT_INT64,DT_INT32,DT_UINT32,DT_UINT64})) + .ATTR(row_memory, Int, 320) + .ATTR(mode, String, "mod") + .OP_END_FACTORY_REG(EmbeddingLocalIndex) + +/** * @brief Fill the value to a tensor has the specified shape. * @par Inputs: diff --git a/third_party/fwkacllib/inc/ops/quantize_ops.h b/third_party/fwkacllib/inc/ops/quantize_ops.h index d6eda1e6..69d5e67e 100644 --- a/third_party/fwkacllib/inc/ops/quantize_ops.h +++ b/third_party/fwkacllib/inc/ops/quantize_ops.h @@ -238,7 +238,6 @@ REG_OP(AscendRequantS16) .ATTR(dual_output, Bool, false) .ATTR(relu_flag, Bool, false) .OP_END_FACTORY_REG(AscendRequantS16) - } // namespace ge #endif // OPS_BUILT_IN_OP_PROTO_INC_QUANTIZE_OPS_H_ diff --git a/third_party/fwkacllib/inc/ops/rnn.h b/third_party/fwkacllib/inc/ops/rnn.h index 9c6a7d1b..80546860 100644 --- a/third_party/fwkacllib/inc/ops/rnn.h +++ b/third_party/fwkacllib/inc/ops/rnn.h @@ -528,6 +528,60 @@ REG_OP(LSTMInputGrad) .OP_END_FACTORY_REG(LSTMInputGrad) + +/** +*@brief: Dynamic LSTM Cell grad calculation.Calculate the gradient of gates and cell state. +*@par Inputs: +*twelve inputs: +*@li init_c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li dy:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li dh:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li dc:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li i:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li j:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li f:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li o:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li tanhct:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li mask:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. +*@li t_state:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ . \n + +*@par Attributes: +*@li forget_bias:An integer identifying the forget bias in the op. Default to 1. +*@li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported . \n +*@li direction:An string that marks the calculation sequence of the operator. Default to "Forward". +*@li gate_order:An string mark the order of output 4 gate. Default to "ijfo". + +*@par Outputs: +*two outputs: +*@li dgate:A 4D Tensor. Must be one of the following types: float16. +*@li dct_1:A 4D Tensor. Must be one of the following types: float16, float32. + +*@par Restrictions: +*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. +*/ +REG_OP(DynamicLSTMGradCell) + .INPUT(init_c, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(c, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(dc, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(i, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(j, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(f, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(o, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(tanhct, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(mask, TensorType({DT_FLOAT16, DT_FLOAT})) + .INPUT(t_state, TensorType({DT_INT32, DT_INT32})) + .OUTPUT(dgate, TensorType({DT_FLOAT16, DT_FLOAT})) + .OUTPUT(dct_1, TensorType({DT_FLOAT16, DT_FLOAT})) + .ATTR(forget_bias, Float, 1) + .ATTR(activation, String, "") + .ATTR(direction, String, "Forward") + .ATTR(gate_order, String, "ijfo") + .OP_END_FACTORY_REG(DynamicLSTMGradCell) + + /** *@brief: Basic LSTM Cell backward calculation.Calculate the gradient of input and hidden state. *@par Inputs: diff --git a/third_party/fwkacllib/inc/ops/selection_ops.h b/third_party/fwkacllib/inc/ops/selection_ops.h index f99493b7..3e67a1ea 100644 --- a/third_party/fwkacllib/inc/ops/selection_ops.h +++ b/third_party/fwkacllib/inc/ops/selection_ops.h @@ -299,8 +299,6 @@ REG_OP(GatherElements) *@par Outputs: *y: A Tensor. Has the same type as "x" . \n -*@attention Constraints: - *@par Third-party framework compatibility * Compatible with the TensorFlow operator StridedSlice. */ @@ -351,8 +349,6 @@ REG_OP(StridedSlice) *@par Outputs: *y: A Tensor. Has the same type as "x" . \n -*@attention Constraints: - *@par Third-party framework compatibility * Compatible with the TensorFlow operator StridedSlice. @@ -409,8 +405,6 @@ REG_OP(StridedSliceD) *@par Outputs: *output: A Tensor. Has the same type as "dy" . \n -*@attention Constraints: - *@par Third-party framework compatibility * Compatible with the TensorFlow operator StridedSliceGradD. @@ -468,8 +462,6 @@ REG_OP(StridedSliceGradD) *@par Outputs: *output: A Tensor has the same type as "dy" . \n -*@attention Constraints: - *@par Third-party framework compatibility * Compatible with the TensorFlow operator StridedSliceGrad. */ @@ -2107,19 +2099,19 @@ REG_OP(InplaceIndexAdd) * @brief Replace the value of X with value according to mask. * @par Inputs: * three inputs, including: -* @li x: A Tensor of dtype is float16 or float32 or int32 or int8. -* @li mask: A Tensor of dtype float16 or float32 or int32 or int8. -* @li value: A Tensor or scalar of dtype float16 or float32 or int32 or int8. \n +* @li x: A Tensor of dtype is float16 or float32 or int64 or int32 or int8. +* @li mask: A Tensor of dtype bool. +* @li value: A Tensor of dtype float16 or float32 or int64 or int32 or int8. * @par Outputs: * @li y: A tensor. Must be one of the following dtypes: -* float16, float32, int32, int8. +* float16, float32, int64, int32, int8. */ REG_OP(MaskedFill) - .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT32})) + .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT32, DT_INT64})) .INPUT(mask, TensorType({DT_BOOL})) - .INPUT(value, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT32})) - .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT32})) + .INPUT(value, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT32, DT_INT64})) + .OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT32, DT_INT64})) .OP_END_FACTORY_REG(MaskedFill) /** diff --git a/third_party/fwkacllib/inc/runtime/rt.h b/third_party/fwkacllib/inc/runtime/rt.h index fb6e2e20..10f884f2 100644 --- a/third_party/fwkacllib/inc/runtime/rt.h +++ b/third_party/fwkacllib/inc/runtime/rt.h @@ -28,5 +28,6 @@ #include "rt_model.h" #include "stream.h" #include "rt_stars.h" +#include "rt_ffts.h" #endif // __CCE_RUNTIME_RT_H__ diff --git a/third_party/fwkacllib/inc/runtime/rt_model.h b/third_party/fwkacllib/inc/runtime/rt_model.h index 8c93fbd5..a7618b45 100644 --- a/third_party/fwkacllib/inc/runtime/rt_model.h +++ b/third_party/fwkacllib/inc/runtime/rt_model.h @@ -52,6 +52,7 @@ typedef enum tagModelTaskType { RT_MODEL_TASK_MODEL_EXIT, RT_MODEL_TASK_ALL_KERNEL, RT_MODEL_TASK_PROFILER_TRACE_EX, + RT_MODEL_TASK_FFTS_TASK, } rtModelTaskType_t; typedef enum tagModelStreamType { diff --git a/third_party/fwkacllib/inc/tdt/tsd_client.h b/third_party/fwkacllib/inc/tdt/tsd_client.h index 665c8b82..36fc500e 100644 --- a/third_party/fwkacllib/inc/tdt/tsd_client.h +++ b/third_party/fwkacllib/inc/tdt/tsd_client.h @@ -107,88 +107,6 @@ TDT_LIB_EXPORT TDT_StatusT UpdateProfilingMode(const uint32_t logicDeviceId, con */ TDT_LIB_EXPORT TDT_StatusT TsdSetMsprofReporterCallback(MsprofReporterCallback callback); -/** -* @ingroup CreateCmdParameterObj -* @brief creat tsdclient func parameter obj. -* -* @par Function -* creat tsdclient func parameter obj. -* -* @param type [IN] type tdt::TsdCmdType, tsd func type. -* @param cmdParameterObj [IN] type void *, func parameter obj. -* @retval TDT_OK Success -* @retval TDT_INTERFACE_NOT_SUPPORT -* -* @par Dependency -* @li libtsdclient.so: Library to which the interface belongs. -* @li data_common.h: Header file where tdt::TsdCmdType and tdt::InputItem defined. -* @li status.h: Header file where 'TDT_StatusT' defined -*/ -TDT_StatusT CreateCmdParameterObj(tdt::TsdCmdType type, void **cmdParameterObj); - -/** -* @ingroup SetCmdParameterObjAttribute -* @brief set cmdParameterObj input value. -* -* @par Function -* set cmdParameterObj input value. -* -* @param type [IN] type tdt::TsdCmdType, tsd func type. -* @param cmdParameterObj [IN] type void *, func parameter obj. -* @param itemType [IN] type tdt::InputItem, func input type. -* @param valuePtr [IN] type const void *, input value. -* @param valueLength [IN] type int, input value length. -* @retval TDT_OK Success -* @retval TDT_INTERFACE_NOT_SUPPORT -* -* @par Dependency -* @li libtsdclient.so: Library to which the interface belongs. -* @li data_common.h: Header file where tdt::TsdCmdType and tdt::InputItem defined. -* @li status.h: Header file where 'TDT_StatusT' defined -*/ -TDT_StatusT SetCmdParameterObjAttribute(tdt::TsdCmdType type, void *cmdParameterObj, tdt::InputItem itemType, const void *valuePtr, int valueLength); - -/** -* @ingroup GetCmdParameterObjAttribute -* @brief set cmdParameterObj input value. -* -* @par Function -* set cmdParameterObj input value. -* -* @param type [IN] type tdt::TsdCmdType, tsd func type. -* @param cmdParameterObj [IN] type void *, func parameter obj. -* @param itemType [IN] type tdt::InputItem, func input type. -* @param valuePtr [IN] type const void *, input value. -* @param valueLength [IN] type int, input value length. -* @retval TDT_OK Success -* @retval TDT_INTERFACE_NOT_SUPPORT -* -* @par Dependency -* @li libtsdclient.so: Library to which the interface belongs. -* @li data_common.h: Header file where tdt::TsdCmdType and tdt::InputItem defined. -* @li status.h: Header file where 'TDT_StatusT' defined -*/ -TDT_StatusT GetCmdParameterObjAttribute(tdt::TsdCmdType type, void *cmdParameterObj, tdt::InputItem itemType, void *valuePtr, int &valueLength); - -/** -* @ingroup TsdClientCmd -* @brief creat tsdclient func parameter obj. -* -* @par Function -* creat tsdclient func parameter obj. -* -* @param type [IN] type tdt::TsdCmdType, tsd func type. -* @param cmdParameterObj [IN] type void *, func parameter obj. -* @retval TDT_OK Success -* @retval TDT_INTERFACE_NOT_SUPPORT -* -* @par Dependency -* @li libtsdclient.so: Library to which the interface belongs. -* @li data_common.h: Header file where tdt::TsdCmdType and tdt::InputItem defined. -* @li status.h: Header file where 'TDT_StatusT' defined -*/ -TDT_StatusT TsdClientCmd(tdt::TsdCmdType cmd, void *cmdParameterObj); - #ifdef __cplusplus } #endif // __cplusplus