@@ -84,9 +84,9 @@ if (ENABLE_OPEN_SRC) | |||
elseif(ENABLE_GE_COV OR ENABLE_GE_UT) | |||
add_subdirectory(tests) | |||
else() | |||
find_module(slog libalog.so ${ASCEND_ATC_DIR}) | |||
find_module(static_mmpa libmmpa.a ${ASCEND_ATC_DIR}) | |||
find_module(error_manager liberror_manager.so ${ASCEND_ATC_DIR}) | |||
find_module(slog libslog.so ${ASCEND_ATC_DIR} ${ASCEND_DRIVER_COMMON_DIR}) | |||
find_module(static_mmpa libmmpa.a ${ASCEND_ATC_DIR} ${ASCEND_RUNTIME_DIR}) | |||
find_module(error_manager liberror_manager.so ${ASCEND_ATC_DIR} ${ASCEND_RUNTIME_DIR}) | |||
if(PLATFORM STREQUAL "train") | |||
find_module(hccl libhccl.so ${ASCEND_RUNTIME_DIR}) | |||
find_module(adump_server libadump_server.a ${ASCEND_RUNTIME_DIR}) | |||
@@ -5,14 +5,10 @@ endif() | |||
include(ExternalProject) | |||
set(JSON_SRC_DIR ${CMAKE_BINARY_DIR}/opensrc/json/include) | |||
if (GE_PB_PKG) | |||
set(REQ_URL "${GE_PB_PKG}/libs/ge_nlohmann_json/include.zip") | |||
set(MD5 "0dc903888211db3a0f170304cd9f3a89") | |||
set(JSON_INCLUDE_DIR ${JSON_SRC_DIR}) | |||
#elseif (ENABLE_GITEE) | |||
# set(REQ_URL "https://gitee.com/mirrors/JSON-for-Modern-CPP/repository/archive/v3.6.1.zip") | |||
# set(MD5 "5bda78ce308e6cfcf614dcf1d5ff27a7") | |||
#set(JSON_INCLUDE_DIR "${JSON_SRC_DIR}/include") | |||
if (ENABLE_GITEE) | |||
set(REQ_URL "https://gitee.com/mirrors/JSON-for-Modern-CPP/repository/archive/v3.6.1.zip") | |||
set(MD5 "5bda78ce308e6cfcf614dcf1d5ff27a7") | |||
set(JSON_INCLUDE_DIR "${JSON_SRC_DIR}/include") | |||
else() | |||
set(REQ_URL "https://github.com/nlohmann/json/releases/download/v3.6.1/include.zip") | |||
set(MD5 "0dc903888211db3a0f170304cd9f3a89") | |||
@@ -0,0 +1,73 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_ACL_H_ | |||
#define INC_EXTERNAL_ACL_ACL_H_ | |||
#include "acl_rt.h" | |||
#include "acl_op.h" | |||
#include "acl_mdl.h" | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
// Current version is 1.0.0 | |||
#define ACL_MAJOR_VERSION 1 | |||
#define ACL_MINOR_VERSION 0 | |||
#define ACL_PATCH_VERSION 0 | |||
/** | |||
* @ingroup AscendCL | |||
* @brief acl initialize | |||
* | |||
* @par Restriction | |||
* The aclInit interface can be called only once in a process | |||
* @param configPath [IN] the config path,it can be NULL | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclInit(const char *configPath); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief acl finalize | |||
* | |||
* @par Restriction | |||
* Need to call aclFinalize before the process exits. | |||
* After calling aclFinalize,the services cannot continue to be used normally. | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclFinalize(); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief query ACL interface version | |||
* | |||
* @param majorVersion[OUT] ACL interface major version | |||
* @param minorVersion[OUT] ACL interface minor version | |||
* @param patchVersion[OUT] ACL interface patch version | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetVersion(int32_t *majorVersion, int32_t *minorVersion, int32_t *patchVersion); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_ACL_H_ |
@@ -0,0 +1,610 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_ACL_BASE_H_ | |||
#define INC_EXTERNAL_ACL_ACL_BASE_H_ | |||
#include <stdint.h> | |||
#include <stddef.h> | |||
#include "error_codes/rt_error_codes.h" | |||
#include "error_codes/ge_error_codes.h" | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
#if defined(_MSC_VER) | |||
#ifdef FUNC_VISIBILITY | |||
#define ACL_FUNC_VISIBILITY _declspec(dllexport) | |||
#else | |||
#define ACL_FUNC_VISIBILITY | |||
#endif | |||
#else | |||
#ifdef FUNC_VISIBILITY | |||
#define ACL_FUNC_VISIBILITY __attribute__((visibility("default"))) | |||
#else | |||
#define ACL_FUNC_VISIBILITY | |||
#endif | |||
#endif | |||
#ifdef __GNUC__ | |||
#define ACL_DEPRECATED __attribute__((deprecated)) | |||
#define ACL_DEPRECATED_MESSAGE(message) __attribute__((deprecated(message))) | |||
#elif defined(_MSC_VER) | |||
#define ACL_DEPRECATED __declspec(deprecated) | |||
#define ACL_DEPRECATED_MESSAGE(message) __declspec(deprecated(message)) | |||
#else | |||
#define ACL_DEPRECATED | |||
#define ACL_DEPRECATED_MESSAGE(message) | |||
#endif | |||
typedef void *aclrtStream; | |||
typedef void *aclrtEvent; | |||
typedef void *aclrtContext; | |||
typedef int aclError; | |||
typedef uint16_t aclFloat16; | |||
typedef struct aclDataBuffer aclDataBuffer; | |||
typedef struct aclTensorDesc aclTensorDesc; | |||
static const int ACL_ERROR_NONE = 0; | |||
static const int ACL_SUCCESS = 0; | |||
static const int ACL_ERROR_INVALID_PARAM = 100000; | |||
static const int ACL_ERROR_UNINITIALIZE = 100001; | |||
static const int ACL_ERROR_REPEAT_INITIALIZE = 100002; | |||
static const int ACL_ERROR_INVALID_FILE = 100003; | |||
static const int ACL_ERROR_WRITE_FILE = 100004; | |||
static const int ACL_ERROR_INVALID_FILE_SIZE = 100005; | |||
static const int ACL_ERROR_PARSE_FILE = 100006; | |||
static const int ACL_ERROR_FILE_MISSING_ATTR = 100007; | |||
static const int ACL_ERROR_FILE_ATTR_INVALID = 100008; | |||
static const int ACL_ERROR_INVALID_DUMP_CONFIG = 100009; | |||
static const int ACL_ERROR_INVALID_PROFILING_CONFIG = 100010; | |||
static const int ACL_ERROR_INVALID_MODEL_ID = 100011; | |||
static const int ACL_ERROR_DESERIALIZE_MODEL = 100012; | |||
static const int ACL_ERROR_PARSE_MODEL = 100013; | |||
static const int ACL_ERROR_READ_MODEL_FAILURE = 100014; | |||
static const int ACL_ERROR_MODEL_SIZE_INVALID = 100015; | |||
static const int ACL_ERROR_MODEL_MISSING_ATTR = 100016; | |||
static const int ACL_ERROR_MODEL_INPUT_NOT_MATCH = 100017; | |||
static const int ACL_ERROR_MODEL_OUTPUT_NOT_MATCH = 100018; | |||
static const int ACL_ERROR_MODEL_NOT_DYNAMIC = 100019; | |||
static const int ACL_ERROR_OP_TYPE_NOT_MATCH = 100020; | |||
static const int ACL_ERROR_OP_INPUT_NOT_MATCH = 100021; | |||
static const int ACL_ERROR_OP_OUTPUT_NOT_MATCH = 100022; | |||
static const int ACL_ERROR_OP_ATTR_NOT_MATCH = 100023; | |||
static const int ACL_ERROR_OP_NOT_FOUND = 100024; | |||
static const int ACL_ERROR_OP_LOAD_FAILED = 100025; | |||
static const int ACL_ERROR_UNSUPPORTED_DATA_TYPE = 100026; | |||
static const int ACL_ERROR_FORMAT_NOT_MATCH = 100027; | |||
static const int ACL_ERROR_BIN_SELECTOR_NOT_REGISTERED = 100028; | |||
static const int ACL_ERROR_KERNEL_NOT_FOUND = 100029; | |||
static const int ACL_ERROR_BIN_SELECTOR_ALREADY_REGISTERED = 100030; | |||
static const int ACL_ERROR_KERNEL_ALREADY_REGISTERED = 100031; | |||
static const int ACL_ERROR_INVALID_QUEUE_ID = 100032; | |||
static const int ACL_ERROR_REPEAT_SUBSCRIBE = 100033; | |||
static const int ACL_ERROR_STREAM_NOT_SUBSCRIBE = 100034; | |||
static const int ACL_ERROR_THREAD_NOT_SUBSCRIBE = 100035; | |||
static const int ACL_ERROR_WAIT_CALLBACK_TIMEOUT = 100036; | |||
static const int ACL_ERROR_REPEAT_FINALIZE = 100037; | |||
static const int ACL_ERROR_NOT_STATIC_AIPP = 100038; | |||
static const int ACL_ERROR_COMPILING_STUB_MODE = 100039; | |||
static const int ACL_ERROR_GROUP_NOT_SET = 100040; | |||
static const int ACL_ERROR_GROUP_NOT_CREATE = 100041; | |||
static const int ACL_ERROR_PROF_ALREADY_RUN = 100042; | |||
static const int ACL_ERROR_PROF_NOT_RUN = 100043; | |||
static const int ACL_ERROR_DUMP_ALREADY_RUN = 100044; | |||
static const int ACL_ERROR_DUMP_NOT_RUN = 100045; | |||
static const int ACL_ERROR_PROF_REPEAT_SUBSCRIBE = 148046; | |||
static const int ACL_ERROR_PROF_API_CONFLICT = 148047; | |||
static const int ACL_ERROR_INVALID_MAX_OPQUEUE_NUM_CONFIG = 148048; | |||
static const int ACL_ERROR_BAD_ALLOC = 200000; | |||
static const int ACL_ERROR_API_NOT_SUPPORT = 200001; | |||
static const int ACL_ERROR_INVALID_DEVICE = 200002; | |||
static const int ACL_ERROR_MEMORY_ADDRESS_UNALIGNED = 200003; | |||
static const int ACL_ERROR_RESOURCE_NOT_MATCH = 200004; | |||
static const int ACL_ERROR_INVALID_RESOURCE_HANDLE = 200005; | |||
static const int ACL_ERROR_FEATURE_UNSUPPORTED = 200006; | |||
static const int ACL_ERROR_PROF_MODULES_UNSUPPORTED = 200007; | |||
static const int ACL_ERROR_STORAGE_OVER_LIMIT = 300000; | |||
static const int ACL_ERROR_INTERNAL_ERROR = 500000; | |||
static const int ACL_ERROR_FAILURE = 500001; | |||
static const int ACL_ERROR_GE_FAILURE = 500002; | |||
static const int ACL_ERROR_RT_FAILURE = 500003; | |||
static const int ACL_ERROR_DRV_FAILURE = 500004; | |||
static const int ACL_ERROR_PROFILING_FAILURE = 500005; | |||
#define ACL_TENSOR_SHAPE_RANGE_NUM 2 | |||
#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, | |||
} 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 | |||
} aclFormat; | |||
typedef enum { | |||
ACL_DEBUG = 0, | |||
ACL_INFO = 1, | |||
ACL_WARNING = 2, | |||
ACL_ERROR = 3, | |||
} aclLogLevel; | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Converts data of type aclFloat16 to data of type float | |||
* | |||
* @param value [IN] Data to be converted | |||
* | |||
* @retval Transformed data | |||
*/ | |||
ACL_FUNC_VISIBILITY float aclFloat16ToFloat(aclFloat16 value); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Converts data of type float to data of type aclFloat16 | |||
* | |||
* @param value [IN] Data to be converted | |||
* | |||
* @retval Transformed data | |||
*/ | |||
ACL_FUNC_VISIBILITY aclFloat16 aclFloatToFloat16(float value); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create data of aclDataBuffer | |||
* | |||
* @param data [IN] pointer to data | |||
* @li Need to be managed by the user, | |||
* call aclrtMalloc interface to apply for memory, | |||
* call aclrtFree interface to release memory | |||
* | |||
* @param size [IN] size of data in bytes | |||
* | |||
* @retval pointer to created instance. nullptr if run out of memory | |||
* | |||
* @see aclrtMalloc | aclrtFree | |||
*/ | |||
ACL_FUNC_VISIBILITY aclDataBuffer *aclCreateDataBuffer(void *data, size_t size); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy data of aclDataBuffer | |||
* | |||
* @par Function | |||
* Only the aclDataBuffer type data is destroyed here. | |||
* The memory of the data passed in when the aclDataDataBuffer interface | |||
* is called to create aclDataBuffer type data must be released by the user | |||
* | |||
* @param dataBuffer [IN] pointer to the aclDataBuffer | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclCreateDataBuffer | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclDestroyDataBuffer(const aclDataBuffer *dataBuffer); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief update new data of aclDataBuffer | |||
* | |||
* @param dataBuffer [OUT] pointer to aclDataBuffer | |||
* @li The old data need to be released by the user, otherwise it may occur memory leak leakage | |||
* call aclGetDataBufferAddr interface to get old data address | |||
* call aclrtFree interface to release memory | |||
* | |||
* @param data [IN] pointer to new data | |||
* @li Need to be managed by the user, | |||
* call aclrtMalloc interface to apply for memory, | |||
* call aclrtFree interface to release memory | |||
* | |||
* @param size [IN] size of data in bytes | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtMalloc | aclrtFree | aclGetDataBufferAddr | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclUpdateDataBuffer(aclDataBuffer *dataBuffer, void *data, size_t size); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get data address from aclDataBuffer | |||
* | |||
* @param dataBuffer [IN] pointer to the data of aclDataBuffer | |||
* | |||
* @retval data address | |||
*/ | |||
ACL_FUNC_VISIBILITY void *aclGetDataBufferAddr(const aclDataBuffer *dataBuffer); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get data size of aclDataBuffer | |||
* | |||
* @param dataBuffer [IN] pointer to the data of aclDataBuffer | |||
* | |||
* @retval data size | |||
*/ | |||
ACL_DEPRECATED_MESSAGE("aclGetDataBufferSize is deprecated, use aclGetDataBufferSizeV2 instead") | |||
ACL_FUNC_VISIBILITY uint32_t aclGetDataBufferSize(const aclDataBuffer *dataBuffer); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get data size of aclDataBuffer to replace aclGetDataBufferSize | |||
* | |||
* @param dataBuffer [IN] pointer to the data of aclDataBuffer | |||
* | |||
* @retval data size | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t aclGetDataBufferSizeV2(const aclDataBuffer *dataBuffer); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get size of aclDataType | |||
* | |||
* @param dataType [IN] aclDataType data the size to get | |||
* | |||
* @retval size of the aclDataType | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t aclDataTypeSize(aclDataType dataType); | |||
// interfaces of tensor desc | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create data aclTensorDesc | |||
* | |||
* @param dataType [IN] Data types described by tensor | |||
* @param numDims [IN] the number of dimensions of the shape | |||
* @param dims [IN] the size of the specified dimension | |||
* @param format [IN] tensor format | |||
* | |||
* @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, | |||
aclFormat format); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy data aclTensorDesc | |||
* | |||
* @param desc [IN] pointer to the data of aclTensorDesc to destroy | |||
*/ | |||
ACL_FUNC_VISIBILITY void aclDestroyTensorDesc(const aclTensorDesc *desc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set tensor shape range for aclTensorDesc | |||
* | |||
* @param desc [OUT] pointer to the data of aclTensorDesc | |||
* @param dimsCount [IN] the number of dimensions of the shape | |||
* @param dimsRange [IN] the range of dimensions of the shape | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorShapeRange(aclTensorDesc *desc, size_t dimsCount, | |||
int64_t dimsRange[][ACL_TENSOR_SHAPE_RANGE_NUM]); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get data type specified by the tensor description | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* | |||
* @retval data type specified by the tensor description. | |||
* @retval ACL_DT_UNDEFINED if description is null | |||
*/ | |||
ACL_FUNC_VISIBILITY aclDataType aclGetTensorDescType(const aclTensorDesc *desc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get data format specified by the tensor description | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* | |||
* @retval data format specified by the tensor description. | |||
* @retval ACL_FORMAT_UNDEFINED if description is null | |||
*/ | |||
ACL_FUNC_VISIBILITY aclFormat aclGetTensorDescFormat(const aclTensorDesc *desc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get tensor size specified by the tensor description | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* | |||
* @retval data size specified by the tensor description. | |||
* @retval 0 if description is null | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t aclGetTensorDescSize(const aclTensorDesc *desc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get element count specified by the tensor description | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* | |||
* @retval element count specified by the tensor description. | |||
* @retval 0 if description is null | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t aclGetTensorDescElementCount(const aclTensorDesc *desc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get number of dims specified by the tensor description | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* | |||
* @retval number of dims specified by the tensor description. | |||
* @retval 0 if description is null | |||
* @retval ACL_UNKNOWN_RANK if the tensor dim is -2 | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t aclGetTensorDescNumDims(const aclTensorDesc *desc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get the size of the specified dim in the tensor description | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* @param index [IN] index of dims, start from 0. | |||
* | |||
* @retval dim specified by the tensor description and index. | |||
* @retval -1 if description or index is invalid | |||
*/ | |||
ACL_DEPRECATED_MESSAGE("aclGetTensorDescDim is deprecated, use aclGetTensorDescDimV2 instead") | |||
ACL_FUNC_VISIBILITY int64_t aclGetTensorDescDim(const aclTensorDesc *desc, size_t index); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get the size of the specified dim in the tensor description | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* @param index [IN] index of dims, start from 0. | |||
* @param dimSize [OUT] size of the specified dim. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclGetTensorDescDimV2(const aclTensorDesc *desc, size_t index, int64_t *dimSize); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get the range of the specified dim in the tensor description | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* @param index [IN] index of dims, start from 0. | |||
* @param dimRangeNum [IN] number of dimRange. | |||
* @param dimRange [OUT] range of the specified dim. | |||
* | |||
* @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, | |||
int64_t *dimRange); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set tensor description name | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param name [IN] tensor description name | |||
*/ | |||
ACL_FUNC_VISIBILITY void aclSetTensorDescName(aclTensorDesc *desc, const char *name); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get tensor description name | |||
* | |||
* @param desc [IN] pointer to the instance of aclTensorDesc | |||
* | |||
* @retval tensor description name. | |||
* @retval empty string if description is null | |||
*/ | |||
ACL_FUNC_VISIBILITY const char *aclGetTensorDescName(aclTensorDesc *desc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Convert the format in the source aclTensorDesc according to | |||
* the specified dstFormat to generate a new target aclTensorDesc. | |||
* The format in the source aclTensorDesc remains unchanged. | |||
* | |||
* @param srcDesc [IN] pointer to the source tensor desc | |||
* @param dstFormat [IN] destination format | |||
* @param dstDesc [OUT] pointer to the pointer to the destination tensor desc | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclTransTensorDescFormat(const aclTensorDesc *srcDesc, aclFormat dstFormat, | |||
aclTensorDesc **dstDesc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set the storage format specified by the tensor description | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param format [IN] the storage format | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_DEPRECATED_MESSAGE("aclSetTensorStorageFormat is deprecated, use aclSetTensorFormat instead") | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorStorageFormat(aclTensorDesc *desc, aclFormat format); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set the storage shape specified by the tensor description | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param numDims [IN] the number of dimensions of the shape | |||
* @param dims [IN] the size of the specified dimension | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_DEPRECATED_MESSAGE("aclSetTensorStorageShape is deprecated, use aclSetTensorShape instead") | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorStorageShape(aclTensorDesc *desc, int numDims, const int64_t *dims); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set the format specified by the tensor description | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param format [IN] the storage format | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorFormat(aclTensorDesc *desc, aclFormat format); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set the shape specified by the tensor description | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param numDims [IN] the number of dimensions of the shape | |||
* @param dims [IN] the size of the specified dimension | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorShape(aclTensorDesc *desc, int numDims, const int64_t *dims); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set the original format specified by the tensor description | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param format [IN] the storage format | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorOriginFormat(aclTensorDesc *desc, aclFormat format); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set the original shape specified by the tensor description | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param numDims [IN] the number of dimensions of the shape | |||
* @param dims [IN] the size of the specified dimension | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorOriginShape(aclTensorDesc *desc, int numDims, const int64_t *dims); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get op description info | |||
* | |||
* @param desc [IN] pointer to tensor description | |||
* @param index [IN] index of tensor | |||
* | |||
* @retval null for failed. | |||
* @retval OtherValues success. | |||
*/ | |||
ACL_FUNC_VISIBILITY aclTensorDesc *aclGetTensorDescByIndex(aclTensorDesc *desc, size_t index); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get address of tensor | |||
* | |||
* @param desc [IN] pointer to tensor description | |||
* | |||
* @retval null for failed | |||
* @retval OtherValues success | |||
*/ | |||
ACL_FUNC_VISIBILITY void *aclGetTensorDescAddress(const aclTensorDesc *desc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set the dynamic input name specified by the tensor description | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param dynamicInputName [IN] pointer to the dynamic input name | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorDynamicInput(aclTensorDesc *desc, const char *dynamicInputName); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set const data specified by the tensor description | |||
* | |||
* @param desc [OUT] pointer to the instance of aclTensorDesc | |||
* @param dataBuffer [IN] pointer to the const databuffer | |||
* @param length [IN] the length of const databuffer | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclSetTensorConst(aclTensorDesc *desc, void *dataBuffer, size_t length); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief an interface for users to output APP logs | |||
* | |||
* @param logLevel [IN] the level of current log | |||
* @param func [IN] the function where the log is located | |||
* @param file [IN] the file where the log is located | |||
* @param line [IN] Number of source lines where the log is located | |||
* @param fmt [IN] the format of current log | |||
* @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, ...); | |||
#define ACL_APP_LOG(level, fmt, ...) aclAppLog(level, __FUNCTION__, __FILE__, __LINE__, fmt, ##__VA_ARGS__) | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_ACL_BASE_H_ |
@@ -0,0 +1,504 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_ACL_OP_H_ | |||
#define INC_EXTERNAL_ACL_ACL_OP_H_ | |||
#include "acl_base.h" | |||
#include "acl_rt.h" | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
typedef struct aclopHandle aclopHandle; | |||
typedef struct aclopAttr aclopAttr; | |||
typedef struct aclopKernelDesc aclopKernelDesc; | |||
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, | |||
} aclopEngineType; | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set base directory that contains single op models | |||
* | |||
* @par Restriction | |||
* The aclopSetModelDir interface can be called only once in a process. | |||
* @param modelDir [IN] path of the directory | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetModelDir(const char *modelDir); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief load single op models from memory | |||
* | |||
* @par Restriction | |||
* The aclopLoad interface can be called more than one times in a process. | |||
* @param model [IN] address of single op models | |||
* @param modelSize [IN] size of single op models | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopLoad(const void *model, size_t modelSize); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create data of type aclopAttr | |||
* | |||
* @retval pointer to created instance. | |||
* @retval nullptr if run out of memory | |||
*/ | |||
ACL_FUNC_VISIBILITY aclopAttr *aclopCreateAttr(); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy data of typ aclopAttr | |||
* | |||
* @param attr [IN] pointer to the instance of aclopAttr | |||
*/ | |||
ACL_FUNC_VISIBILITY void aclopDestroyAttr(const aclopAttr *attr); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is bool | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param attrValue [IN] attribute value | |||
* false if attrValue is 0, true otherwise. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetAttrBool(aclopAttr *attr, const char *attrName, uint8_t attrValue); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is int64_t | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param attrValue [IN] attribute value | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetAttrInt(aclopAttr *attr, const char *attrName, int64_t attrValue); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is float | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param attrValue [IN] attribute value | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetAttrFloat(aclopAttr *attr, const char *attrName, float attrValue); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is string | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param attrValue [IN] attribute value | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetAttrString(aclopAttr *attr, const char *attrName, const char *attrValue); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is list of bools | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param numValues [IN] number of values. false if attrValue is 0, true otherwise. | |||
* @param values [IN] pointer to values | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetAttrListBool(aclopAttr *attr, const char *attrName, int numValues, | |||
const uint8_t *values); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is list of ints | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param numValues [IN] number of values | |||
* @param values [IN] pointer to values | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetAttrListInt(aclopAttr *attr, const char *attrName, int numValues, | |||
const int64_t *values); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is list of floats | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param numValues [IN] number of values | |||
* @param values [IN] pointer to values | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetAttrListFloat(aclopAttr *attr, const char *attrName, int numValues, | |||
const float *values); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is list of strings | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param numValues [IN] number of values | |||
* @param values [IN] pointer to values | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetAttrListString(aclopAttr *attr, const char *attrName, int numValues, | |||
const char **values); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set an attribute. the type of the attribute is list of list of ints | |||
* | |||
* @param attr [OUT] pointer to the instance of aclopAttr | |||
* @param attrName [IN] attribute name | |||
* @param numLists [IN] number of lists | |||
* @param numValues [IN] pointer to number of values of each list | |||
* @param values [IN] pointer to values | |||
* | |||
* @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[]); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Load and execute the specified operator asynchronously | |||
* | |||
* @par Restriction | |||
* @li The input and output organization of each operator is different, | |||
* and the application needs to organize the operator strictly | |||
* according to the operator input and output parameters when calling. | |||
* @li When the user calls aclopExecute, | |||
* the ACL finds the corresponding task according to the optype, | |||
* the description of the input tesnsor, | |||
* the description of the output tesnsor, and attr, and issues the execution. | |||
* | |||
* @param opType [IN] type of op | |||
* @param numInputs [IN] number of inputs | |||
* @param inputDesc [IN] pointer to array of input tensor descriptions | |||
* @param inputs [IN] pointer to array of input buffers | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputDesc [IN] pointer to array of output tensor descriptions | |||
* @param outputs [OUT] pointer to array of output buffers | |||
* @param attr [IN] pointer to instance of aclopAttr. | |||
* may pass nullptr if the op has no attribute | |||
* @param stream [IN] stream | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Load and execute the specified operator | |||
* The difference with aclopExecute is that aclopExecuteV2 will refresh outputDesc | |||
* | |||
* @par Restriction | |||
* @li The input and output organization of each operator is different, | |||
* and the application needs to organize the operator strictly | |||
* according to the operator input and output parameters when calling. | |||
* @li When the user calls aclopExecuteV2, | |||
* the ACL finds the corresponding task according to the optype, | |||
* the description of the input tesnsor, | |||
* the description of the output tesnsor, and attr, and issues the execution. | |||
* | |||
* @param opType [IN] type of op | |||
* @param numInputs [IN] number of inputs | |||
* @param inputDesc [IN] pointer to array of input tensor descriptions | |||
* @param inputs [IN] pointer to array of input buffers | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputDesc [IN|OUT] pointer to array of output tensor descriptions | |||
* @param outputs [OUT] pointer to array of output buffers | |||
* @param attr [IN] pointer to instance of aclopAttr. | |||
* may pass nullptr if the op has no attribute | |||
* @param stream [IN] stream | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create a instance of aclopHandle. | |||
* | |||
* @param opType [IN] type of op | |||
* @param numInputs [IN] number of inputs | |||
* @param inputDesc [IN] pointer to array of input tensor descriptions | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputDesc [IN] pointer to array of output tensor descriptions | |||
* @param opAttr [IN] pointer to instance of aclopAttr. | |||
* may pass nullptr if the op has no attribute | |||
* @param handle [OUT] pointer to the pointer to the handle | |||
* | |||
* @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, | |||
aclopHandle **handle); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy aclopHandle instance | |||
* | |||
* @param handle [IN] pointer to the instance of aclopHandle | |||
*/ | |||
ACL_FUNC_VISIBILITY void aclopDestroyHandle(aclopHandle *handle); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief execute an op with the handle. | |||
* can save op model matching cost compared with aclopExecute | |||
* | |||
* @param handle [IN] pointer to the instance of aclopHandle. | |||
* The aclopCreateHandle interface has been called | |||
* in advance to create aclopHandle type data. | |||
* @param numInputs [IN] number of inputs | |||
* @param inputs [IN] pointer to array of input buffers. | |||
* The aclCreateDataBuffer interface has been called | |||
* in advance to create aclDataBuffer type data. | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputs [OUT] pointer to array of output buffers | |||
* @param stream [IN] stream | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclopCreateHandle | aclCreateDataBuffer | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopExecWithHandle(aclopHandle *handle, int numInputs, | |||
const aclDataBuffer *const inputs[], int numOutputs, | |||
aclDataBuffer *const outputs[], aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief cast data type | |||
* | |||
* @param srcDesc [IN] source tensor desc | |||
* @param srcBuffer [IN] source tensor buffer | |||
* @param dstDesc [IN] destination tensor desc | |||
* @param dstBuffer [OUT] destination tensor buffer | |||
* @param truncate [IN] do not truncate if value is 0, truncate otherwise | |||
* @param stream [IN] stream | |||
* | |||
* @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, | |||
aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create a handle for casting datatype | |||
* | |||
* @param srcDesc [IN] source tensor desc | |||
* @param dstDesc [IN] destination tensor desc | |||
* @param truncate [IN] do not truncate if value is 0, truncate otherwise | |||
* @param handle [OUT] pointer to the pointer to the handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopCreateHandleForCast(aclTensorDesc *srcDesc, aclTensorDesc *dstDesc, uint8_t truncate, | |||
aclopHandle **handle); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create kernel | |||
* | |||
* @param opType [IN] op type | |||
* @param kernelId [IN] kernel id | |||
* @param kernelName [IN] kernel name | |||
* @param binData [IN] kernel bin data | |||
* @param binSize [IN] kernel bin size | |||
* @param enginetype [IN] enigne type | |||
* @param deallocator [IN] callback function for deallocating bin data, | |||
* null if bin data to be deallocated by caller | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclopCompile | |||
*/ | |||
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 | |||
* | |||
* @param numInputs [IN] number of inputs | |||
* @param inputDesc [IN] pointer to array of input tensor descriptions | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputDesc [IN] pointer to array of output tensor descriptions | |||
* @param opAttr [IN] pointer to instance of aclopAttr | |||
* @param aclopKernelDesc [IN] pointer to instance of aclopKernelDesc | |||
* | |||
* @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, | |||
aclopKernelDesc *aclopKernelDesc); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief register compile function | |||
* | |||
* @param opType [IN] op type | |||
* @param func [IN] compile function | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclopUnregisterCompileFunc | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopRegisterCompileFunc(const char *opType, aclopCompileFunc func); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief unregister compile function | |||
* | |||
* @param opType [IN] op type | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopUnregisterCompileFunc(const char *opType); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set kernel args | |||
* | |||
* @param kernelDesc [IN] pointer to instance of aclopKernelDesc | |||
* @param kernelId [IN] kernel id | |||
* @param blockDim [IN] block dim | |||
* @param args [IN] args | |||
* @param argSize [IN] size in bytes of args | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set workspace sizes | |||
* | |||
* @param kernelDesc [IN] pointer to instance of aclopKernelDesc | |||
* @param numWorkspaces [IN] number of workspaces | |||
* @param workspaceSizes [IN] pointer to array of sizes of workspaces | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclopSetKernelWorkspaceSizes(aclopKernelDesc *kernelDesc, int numWorkspaces, | |||
size_t *workspaceSizes); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief compile op with dynamic shape | |||
* | |||
* @param opType [IN] op type | |||
* @param numInputs [IN] number of inputs | |||
* @param inputDesc [IN] pointer to array of input tensor descriptions | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputDesc [IN] pointer to array of output tensor descriptions | |||
* @param attr [IN] pointer to instance of aclopAttr. | |||
* may pass nullptr if the op has no attribute | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief inferShape the specified operator synchronously | |||
* | |||
* @param opType [IN] type of op | |||
* @param numInputs [IN] number of inputs | |||
* @param inputDesc [IN] pointer to array of input tensor descriptions | |||
* @param inputs [IN] pointer to array of input buffers | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputDesc [OUT] pointer to array of output tensor descriptions | |||
* @param attr [IN] pointer to instance of aclopAttr. | |||
* may pass nullptr if the op has no attribute | |||
* | |||
* @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[], | |||
aclopAttr *attr); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_ACL_OP_H_ |
@@ -0,0 +1,106 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_ | |||
#define INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_ | |||
#include "acl_base.h" | |||
#include "acl_op.h" | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
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 | |||
} aclCompileOpt; | |||
/** | |||
* @ingroup AscendCL | |||
* @brief compile op | |||
* | |||
* @param opType [IN] op type | |||
* @param numInputs [IN] number of inputs | |||
* @param inputDesc [IN] pointer to array of input tensor descriptions | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputDesc [IN] pointer to array of output tensor descriptions | |||
* @param attr [IN] pointer to instance of aclopAttr. | |||
* may pass nullptr if the op has no attribute | |||
* @param engineType [IN] engine type | |||
* @param compileFlag [IN] compile flag | |||
* @param opPath [IN] path of op | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief compile and execute op | |||
* | |||
* @param opType [IN] op type | |||
* @param numInputs [IN] number of inputs | |||
* @param inputDesc [IN] pointer to array of input tensor descriptions | |||
* @param inputs [IN] pointer to array of input buffers | |||
* @param numOutputs [IN] number of outputs | |||
* @param outputDesc [IN] pointer to array of output tensor descriptions | |||
* @param outputs [IN] pointer to array of outputs buffers | |||
* @param attr [IN] pointer to instance of aclopAttr. | |||
* may pass nullptr if the op has no attribute | |||
* @param engineType [IN] engine type | |||
* @param compileFlag [IN] compile flag | |||
* @param opPath [IN] path of op | |||
* @param stream [IN] stream handle | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set compile option | |||
* | |||
* @param aclCompileOpt [IN] compile option | |||
* @param value [IN] pointer for the option value | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclSetCompileopt(aclCompileOpt opt, const char *value); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_ACL_OP_COMPILER_H_ |
@@ -0,0 +1,296 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_PROF_H_ | |||
#define INC_EXTERNAL_ACL_PROF_H_ | |||
#include "acl_base.h" | |||
#ifdef __cplusplus | |||
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_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 | |||
} aclprofAicoreMetrics; | |||
typedef struct aclprofConfig aclprofConfig; | |||
typedef struct aclprofStopConfig aclprofStopConfig; | |||
typedef struct aclprofAicoreEvents aclprofAicoreEvents; | |||
typedef struct aclprofSubscribeConfig aclprofSubscribeConfig; | |||
/** | |||
* @ingroup AscendCL | |||
* @brief profiling initialize | |||
* | |||
* @param profilerResultPath [IN] path of profiling result | |||
* @param length [IN] length of profilerResultPath | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclprofFinalize | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofInit(const char *profilerResultPath, size_t length); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief profiling finalize | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclprofInit | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofFinalize(); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Start profiling modules by profilerConfig | |||
* | |||
* @param profilerConfig [IN] config of profiling | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclprofStop | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofStart(const aclprofConfig *profilerConfig); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create data of type aclprofConfig | |||
* | |||
* @param deviceIdList [IN] list of device id | |||
* @param deviceNums [IN] number of devices | |||
* @param aicoreMetrics [IN] type of aicore metrics | |||
* @param aicoreEvents [IN] pointer to aicore events, only support NULL now | |||
* @param dataTypeConfig [IN] config modules need profiling | |||
* | |||
* @retval the aclprofConfig pointer | |||
* | |||
* @see aclprofDestroyConfig | |||
*/ | |||
ACL_FUNC_VISIBILITY aclprofConfig *aclprofCreateConfig(uint32_t *deviceIdList, uint32_t deviceNums, | |||
aclprofAicoreMetrics aicoreMetrics, | |||
aclprofAicoreEvents *aicoreEvents, uint64_t dataTypeConfig); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy data of type aclprofConfig | |||
* | |||
* @param profilerConfig [IN] config of profiling | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclprofCreateConfig | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofDestroyConfig(const aclprofConfig *profilerConfig); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief stop profiling modules by stopProfilingConfig | |||
* | |||
* @param profilerConfig [IN] pointer to stop config of profiling | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclprofStart | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofStop(const aclprofConfig *profilerConfig); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief subscribe profiling data of model | |||
* | |||
* @param modelId [IN] the model id subscribed | |||
* @param profSubscribeConfig [IN] pointer to config of model subscribe | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclprofModelUnSubscribe | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofModelSubscribe(uint32_t modelId, const aclprofSubscribeConfig *profSubscribeConfig); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief unsubscribe profiling data of model | |||
* | |||
* @param modelId [IN] the model id unsubscribed | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclprofModelSubscribe | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofModelUnSubscribe(uint32_t modelId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create subscribe config | |||
* | |||
* @param timeInfoSwitch [IN] switch whether get time info from model | |||
* @param aicoreMetrics [IN] aicore metrics | |||
* @param fd [IN] pointer to write pipe | |||
* | |||
* @retval the aclprofSubscribeConfig pointer | |||
* | |||
* @see aclprofDestroySubscribeConfig | |||
*/ | |||
ACL_FUNC_VISIBILITY aclprofSubscribeConfig *aclprofCreateSubscribeConfig(int8_t timeInfoSwitch, | |||
aclprofAicoreMetrics aicoreMetrics, void *fd); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy subscribe config | |||
* | |||
* @param profSubscribeConfig [IN] subscribe config | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclprofCreateSubscribeConfig | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofDestroySubscribeConfig(const aclprofSubscribeConfig *profSubscribeConfig); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create subscribe config | |||
* | |||
* @param opDescSize [OUT] size of op desc | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofGetOpDescSize(size_t *opDescSize); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get op number from subscription data | |||
* | |||
* @param opInfo [IN] pointer to subscription data | |||
* @param opInfoLen [IN] memory size of subscription data | |||
* @param opNumber [OUT] op number of subscription data | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclprofGetOpNum(const void *opInfo, size_t opInfoLen, uint32_t *opNumber); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get op type from subscription data | |||
* | |||
* @param opInfo [IN] pointer to subscription data | |||
* @param opInfoLen [IN] memory size of subscription data | |||
* @param index [IN] index of op array in opInfo | |||
* @param opType [OUT] obtained op type string | |||
* @param opTypeLen [IN] obtained length of op type string | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get op type from subscription data | |||
* | |||
* @param opInfo [IN] pointer to subscription data | |||
* @param opInfoLen [IN] memory size of subscription data | |||
* @param index [IN] index of op array in opInfo | |||
* @param opName [OUT] obtained op name string | |||
* @param opNameLen [IN] obtained length of op name string | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get start time of specified op from subscription data | |||
* | |||
* @param opInfo [IN] pointer to subscription data | |||
* @param opInfoLen [IN] memory size of subscription data | |||
* @param index [IN] index of op array in opInfo | |||
* | |||
* @retval start time(us) of specified op with timestamp | |||
* @retval 0 for failed | |||
*/ | |||
ACL_FUNC_VISIBILITY uint64_t aclprofGetOpStart(const void *opInfo, size_t opInfoLen, uint32_t index); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get end time of specified op from subscription data | |||
* | |||
* @param opInfo [IN] pointer to subscription data | |||
* @param opInfoLen [IN] memory size of subscription data | |||
* @param index [IN] index of op array in opInfo | |||
* | |||
* @retval end time(us) of specified op with timestamp | |||
* @retval 0 for failed | |||
*/ | |||
ACL_FUNC_VISIBILITY uint64_t aclprofGetOpEnd(const void *opInfo, size_t opInfoLen, uint32_t index); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get excution time of specified op from subscription data | |||
* | |||
* @param opInfo [IN] pointer to subscription data | |||
* @param opInfoLen [IN] memory size of subscription data | |||
* @param index [IN] index of op array in opInfo | |||
* | |||
* @retval execution time(us) of specified op with timestamp | |||
* @retval 0 for failed | |||
*/ | |||
ACL_FUNC_VISIBILITY uint64_t aclprofGetOpDuration(const void *opInfo, size_t opInfoLen, uint32_t index); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get model id from subscription data | |||
* | |||
* @param opInfo [IN] pointer to subscription data | |||
* @param opInfoLen [IN] memory size of subscription data | |||
* | |||
* @retval model id of subscription data | |||
* @retval 0 for failed | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t aclprofGetModelId(const void *opInfo, size_t opInfoLen, uint32_t index); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_PROF_H_ |
@@ -0,0 +1,932 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_ACL_RT_H_ | |||
#define INC_EXTERNAL_ACL_ACL_RT_H_ | |||
#include <stdint.h> | |||
#include <stddef.h> | |||
#include "acl_base.h" | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
typedef enum aclrtRunMode { | |||
ACL_DEVICE, | |||
ACL_HOST, | |||
} aclrtRunMode; | |||
typedef enum aclrtTsId { | |||
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, | |||
} aclrtEventStatus; | |||
typedef enum aclrtCallbackBlockType { | |||
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, | |||
} 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, | |||
} 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, | |||
} aclrtMemAttr; | |||
typedef enum aclrtGroupAttr { | |||
ACL_GROUP_AICORE_INT, | |||
ACL_GROUP_AIV_INT, | |||
ACL_GROUP_AIC_INT, | |||
ACL_GROUP_SDMANUM_INT, | |||
ACL_GROUP_ASQNUM_INT | |||
} aclrtGroupAttr; | |||
typedef struct tagRtGroupInfo aclrtGroupInfo; | |||
typedef struct rtExceptionInfo aclrtExceptionInfo; | |||
typedef void (*aclrtCallback)(void *userData); | |||
typedef void (*aclrtExceptionInfoCallback)(aclrtExceptionInfo *exceptionInfo); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set a callback function to handle exception information | |||
* | |||
* @param callback [IN] callback function to handle exception information | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSetExceptionInfoCallback(aclrtExceptionInfoCallback callback); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get task id from exception information | |||
* | |||
* @param info [IN] pointer of exception information | |||
* | |||
* @retval The task id from exception information | |||
* @retval 0xFFFFFFFF if info is null | |||
*/ | |||
ACL_FUNC_VISIBILITY uint32_t aclrtGetTaskIdFromExceptionInfo(const aclrtExceptionInfo *info); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get stream id from exception information | |||
* | |||
* @param info [IN] pointer of exception information | |||
* | |||
* @retval The stream id from exception information | |||
* @retval 0xFFFFFFFF if info is null | |||
*/ | |||
ACL_FUNC_VISIBILITY uint32_t aclrtGetStreamIdFromExceptionInfo(const aclrtExceptionInfo *info); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get thread id from exception information | |||
* | |||
* @param info [IN] pointer of exception information | |||
* | |||
* @retval The thread id of fail task | |||
* @retval 0xFFFFFFFF if info is null | |||
*/ | |||
ACL_FUNC_VISIBILITY uint32_t aclrtGetThreadIdFromExceptionInfo(const aclrtExceptionInfo *info); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get device id from exception information | |||
* | |||
* @param info [IN] pointer of exception information | |||
* | |||
* @retval The thread id of fail task | |||
* @retval 0xFFFFFFFF if info is null | |||
*/ | |||
ACL_FUNC_VISIBILITY uint32_t aclrtGetDeviceIdFromExceptionInfo(const aclrtExceptionInfo *info); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief The thread that handles the callback function on the Stream | |||
* | |||
* @param threadId [IN] thread ID | |||
* @param stream [IN] stream handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSubscribeReport(uint64_t threadId, aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Add a callback function to be executed on the host | |||
* to the task queue of the Stream | |||
* | |||
* @param fn [IN] Specify the callback function to be added | |||
* The function prototype of the callback function is: | |||
* typedef void (*aclrtCallback)(void *userData); | |||
* @param userData [IN] User data to be passed to the callback function | |||
* @param blockType [IN] callback block type | |||
* @param stream [IN] stream handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtLaunchCallback(aclrtCallback fn, void *userData, aclrtCallbackBlockType blockType, | |||
aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief After waiting for a specified time, trigger callback processing | |||
* | |||
* @par Function | |||
* The thread processing callback specified by | |||
* the aclrtSubscribeReport interface | |||
* | |||
* @param timeout [IN] timeout value | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtSubscribeReport | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtProcessReport(int32_t timeout); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Cancel thread registration, | |||
* the callback function on the specified Stream | |||
* is no longer processed by the specified thread | |||
* | |||
* @param threadId [IN] thread ID | |||
* @param stream [IN] stream handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtUnSubscribeReport(uint64_t threadId, aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create context and associates it with the calling thread | |||
* | |||
* @par Function | |||
* The following use cases are supported: | |||
* @li If you don't call the aclrtCreateContext interface | |||
* to explicitly create the context, | |||
* the system will use the default context, which is implicitly created | |||
* when the aclrtSetDevice interface is called. | |||
* @li If multiple contexts are created in a process | |||
* (there is no limit on the number of contexts), | |||
* the current thread can only use one of them at the same time. | |||
* It is recommended to explicitly specify the context of the current thread | |||
* through the aclrtSetCurrentContext interface to increase. | |||
* the maintainability of the program. | |||
* | |||
* @param context [OUT] point to the created context | |||
* @param deviceId [IN] device to create context on | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtSetDevice | aclrtSetCurrentContext | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtCreateContext(aclrtContext *context, int32_t deviceId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy context instance | |||
* | |||
* @par Function | |||
* Can only destroy context created through aclrtCreateContext interface | |||
* | |||
* @param context [IN] the context to destroy | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtCreateContext | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtDestroyContext(aclrtContext context); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set the context of the thread | |||
* | |||
* @par Function | |||
* The following scenarios are supported: | |||
* @li If the aclrtCreateContext interface is called in a thread to explicitly | |||
* create a Context (for example: ctx1), the thread's Context can be specified | |||
* without calling the aclrtSetCurrentContext interface. | |||
* The system uses ctx1 as the context of thread1 by default. | |||
* @li If the aclrtCreateContext interface is not explicitly created, | |||
* the system uses the default context as the context of the thread. | |||
* At this time, the aclrtDestroyContext interface cannot be used to release | |||
* the default context. | |||
* @li If the aclrtSetCurrentContext interface is called multiple times to | |||
* set the thread's Context, the last one prevails. | |||
* | |||
* @par Restriction | |||
* @li If the cevice corresponding to the context set for the thread | |||
* has been reset, you cannot set the context as the context of the thread, | |||
* otherwise a business exception will result. | |||
* @li It is recommended to use the context created in a thread. | |||
* If the aclrtCreateContext interface is called in thread A to create a context, | |||
* and the context is used in thread B, | |||
* the user must guarantee the execution order of tasks in the same stream | |||
* under the same context in two threads. | |||
* | |||
* @param context [IN] the current context of the thread | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtCreateContext | aclrtDestroyContext | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSetCurrentContext(aclrtContext context); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get the context of the thread | |||
* | |||
* @par Function | |||
* If the user calls the aclrtSetCurrentContext interface | |||
* multiple times to set the context of the current thread, | |||
* then the last set context is obtained | |||
* | |||
* @param context [OUT] the current context of the thread | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtSetCurrentContext | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetCurrentContext(aclrtContext *context); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Specify the device to use for the operation | |||
* implicitly create the default context and the default stream | |||
* | |||
* @par Function | |||
* The following use cases are supported: | |||
* @li Device can be specified in the process or thread. | |||
* If you call the aclrtSetDevice interface multiple | |||
* times to specify the same device, | |||
* you only need to call the aclrtResetDevice interface to reset the device. | |||
* @li The same device can be specified for operation | |||
* in different processes or threads. | |||
* @li Device is specified in a process, | |||
* and multiple threads in the process can share this device to explicitly | |||
* create a Context (aclrtCreateContext interface). | |||
* @li In multi-device scenarios, you can switch to other devices | |||
* through the aclrtSetDevice interface in the process. | |||
* | |||
* @param deviceId [IN] the device id | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtResetDevice |aclrtCreateContext | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSetDevice(int32_t deviceId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Reset the current operating Device and free resources on the device, | |||
* including the default context, the default stream, | |||
* and all streams created under the default context, | |||
* and synchronizes the interface. | |||
* If the task under the default context or stream has not been completed, | |||
* the system will wait for the task to complete before releasing it. | |||
* | |||
* @par Restriction | |||
* @li The Context, Stream, and Event that are explicitly created | |||
* on the device to be reset. Before resetting, | |||
* it is recommended to follow the following interface calling sequence, | |||
* otherwise business abnormalities may be caused. | |||
* @li Interface calling sequence: | |||
* call aclrtDestroyEvent interface to release Event or | |||
* call aclrtDestroyStream interface to release explicitly created Stream-> | |||
* call aclrtDestroyContext to release explicitly created Context-> | |||
* call aclrtResetDevice interface | |||
* | |||
* @param deviceId [IN] the device id | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtResetDevice(int32_t deviceId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get target device of current thread | |||
* | |||
* @param deviceId [OUT] the device id | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetDevice(int32_t *deviceId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get target side | |||
* | |||
* @param runMode [OUT] the run mode | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetRunMode(aclrtRunMode *runMode); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Wait for compute device to finish | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSynchronizeDevice(void); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Set Scheduling TS | |||
* | |||
* @param tsId [IN] the ts id | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSetTsDevice(aclrtTsId tsId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get total device number. | |||
* | |||
* @param count [OUT] the device number | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetDeviceCount(uint32_t *count); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create event instance | |||
* | |||
* @param event [OUT] created event | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtCreateEvent(aclrtEvent *event); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy event instance | |||
* | |||
* @par Function | |||
* Only events created through the aclrtCreateEvent interface can be | |||
* destroyed, synchronous interfaces. When destroying an event, | |||
* the user must ensure that the tasks involved in the aclrtSynchronizeEvent | |||
* interface or the aclrtStreamWaitEvent interface are completed before | |||
* they are destroyed. | |||
* | |||
* @param event [IN] event to destroy | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtCreateEvent | aclrtSynchronizeEvent | aclrtStreamWaitEvent | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtDestroyEvent(aclrtEvent event); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Record an Event in the Stream | |||
* | |||
* @param event [IN] event to record | |||
* @param stream [IN] stream handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtRecordEvent(aclrtEvent event, aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Reset an event | |||
* | |||
* @par Function | |||
* Users need to make sure to wait for the tasks in the Stream | |||
* to complete before resetting the Event | |||
* | |||
* @param event [IN] event to reset | |||
* @param stream [IN] stream handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtResetEvent(aclrtEvent event, aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Queries an event's status | |||
* | |||
* @param event [IN] event to query | |||
* @param status [OUT] event status | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtQueryEvent(aclrtEvent event, aclrtEventStatus *status); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Block Host Running, wait event to be complete | |||
* | |||
* @param event [IN] event to wait | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSynchronizeEvent(aclrtEvent event); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief computes the elapsed time between events. | |||
* | |||
* @param ms [OUT] time between start and end in ms | |||
* @param start [IN] starting event | |||
* @param end [IN] ending event | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtCreateEvent | aclrtRecordEvent | aclrtSynchronizeStream | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtEventElapsedTime(float *ms, aclrtEvent start, aclrtEvent end); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief alloc memory on device | |||
* | |||
* @par Function | |||
* alloc for size linear memory on device | |||
* and return a pointer to allocated memory by *devPtr | |||
* | |||
* @par Restriction | |||
* @li The memory requested by the aclrtMalloc interface needs to be released | |||
* through the aclrtFree interface. | |||
* @li Before calling the media data processing interface, | |||
* if you need to apply memory on the device to store input or output data, | |||
* you need to call acldvppMalloc to apply for memory. | |||
* | |||
* @param devPtr [OUT] pointer to pointer to allocated memory on device | |||
* @param size [IN] alloc memory size | |||
* @param policy [IN] memory alloc policy | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtFree | acldvppMalloc | aclrtMallocCached | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtMalloc(void **devPtr, size_t size, aclrtMemMallocPolicy policy); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief allocate memory on device with cache | |||
* | |||
* @par Function | |||
* alloc for size linear memory on device | |||
* and return a pointer to allocated memory by *devPtr | |||
* | |||
* @par Restriction | |||
* @li The memory requested by the aclrtMallocCached interface needs to be released | |||
* through the aclrtFree interface. | |||
* | |||
* @param devPtr [OUT] pointer to pointer to allocated memory on device | |||
* @param size [IN] alloc memory size | |||
* @param policy [IN] memory alloc policy | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtFree | aclrtMalloc | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtMallocCached(void **devPtr, size_t size, aclrtMemMallocPolicy policy); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief flush cache data to ddr | |||
* | |||
* @param devPtr [IN] the pointer that flush data to ddr | |||
* @param size [IN] flush size | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtMemFlush(void *devPtr, size_t size); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief invalidate cache data | |||
* | |||
* @param devPtr [IN] pointer to invalidate cache data | |||
* @param size [IN] invalidate size | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtMemInvalidate(void *devPtr, size_t size); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief free device memory | |||
* | |||
* @par Function | |||
* can only free memory allocated through the aclrtMalloc interface | |||
* | |||
* @param devPtr [IN] Pointer to memory to be freed | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtMalloc | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtFree(void *devPtr); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief alloc memory on host | |||
* | |||
* @par Restriction | |||
* @li The requested memory cannot be used in the Device | |||
* and needs to be explicitly copied to the Device. | |||
* @li The memory requested by the aclrtMallocHost interface | |||
* needs to be released through the aclrtFreeHost interface. | |||
* | |||
* @param hostPtr [OUT] pointer to pointer to allocated memory on the host | |||
* @param size [IN] alloc memory size | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtFreeHost | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtMallocHost(void **hostPtr, size_t size); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief free host memory | |||
* | |||
* @par Function | |||
* can only free memory allocated through the aclrtMallocHost interface | |||
* | |||
* @param hostPtr [IN] free memory pointer | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtMallocHost | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtFreeHost(void *hostPtr); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief synchronous memory replication between host and device | |||
* | |||
* @param dst [IN] destination address pointer | |||
* @param destMax [IN] Max length of the destination address memory | |||
* @param src [IN] source address pointer | |||
* @param count [IN] the length of byte to copy | |||
* @param kind [IN] memcpy type | |||
* | |||
* @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, | |||
aclrtMemcpyKind kind); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Initialize memory and set contents of memory to specified value | |||
* | |||
* @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] Starting address of memory | |||
* @param maxCount [IN] Max length of destination address memory | |||
* @param value [IN] Set value | |||
* @param count [IN] The length of memory | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtMemset(void *devPtr, size_t maxCount, int32_t value, size_t count); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Asynchronous memory replication between Host and Device | |||
* | |||
* @par Function | |||
* After calling this interface, | |||
* be sure to call the aclrtSynchronizeStream interface to ensure that | |||
* the task of memory replication has been completed | |||
* | |||
* @par Restriction | |||
* @li For on-chip Device-to-Device memory copy, | |||
* both the source and destination addresses must be 64-byte aligned | |||
* | |||
* @param dst [IN] destination address pointer | |||
* @param destMax [IN] Max length of destination address memory | |||
* @param src [IN] source address pointer | |||
* @param count [IN] the number of byte to copy | |||
* @param kind [IN] memcpy type | |||
* @param stream [IN] asynchronized task stream | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtSynchronizeStream | |||
*/ | |||
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 | |||
* 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, | |||
aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create stream instance | |||
* | |||
* @param stream [OUT] the created stream | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtCreateStream(aclrtStream *stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy stream instance | |||
* | |||
* @par Function | |||
* Can only destroy streams created through the aclrtCreateStream interface | |||
* | |||
* @par Restriction | |||
* Before calling the aclrtDestroyStream interface to destroy | |||
* the specified Stream, you need to call the aclrtSynchronizeStream interface | |||
* to ensure that the tasks in the Stream have been completed. | |||
* | |||
* @param stream [IN] the stream to destroy | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtCreateStream | aclrtSynchronizeStream | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtDestroyStream(aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief block the host until all tasks | |||
* in the specified stream have completed | |||
* | |||
* @param stream [IN] the stream to wait | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSynchronizeStream(aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Blocks the operation of the specified Stream until | |||
* the specified Event is completed. | |||
* Support for multiple streams waiting for the same event. | |||
* | |||
* @param stream [IN] the wait stream If using thedefault Stream, set NULL | |||
* @param event [IN] the event to wait | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtStreamWaitEvent(aclrtStream stream, aclrtEvent event); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set group | |||
* | |||
* @par Function | |||
* set the task to the corresponding group | |||
* | |||
* @param groupId [IN] group id | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtGetGroupCount | aclrtGetAllGroupInfo | aclrtGetGroupInfoDetail | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtSetGroup(int32_t groupId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get the number of group | |||
* | |||
* @par Function | |||
* get the number of group. if the number of group is zero, | |||
* it means that group is not supported or group is not created. | |||
* | |||
* @param count [OUT] the number of group | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetGroupCount(uint32_t *count); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create group information | |||
* | |||
* @retval null for failed. | |||
* @retval OtherValues success. | |||
* | |||
* @see aclrtDestroyGroupInfo | |||
*/ | |||
ACL_FUNC_VISIBILITY aclrtGroupInfo *aclrtCreateGroupInfo(); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief destroy group information | |||
* | |||
* @param groupInfo [IN] pointer to group information | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtCreateGroupInfo | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtDestroyGroupInfo(aclrtGroupInfo *groupInfo); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get all group information | |||
* | |||
* @param groupInfo [OUT] pointer to group information | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtGetGroupCount | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetAllGroupInfo(aclrtGroupInfo *groupInfo); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief get detail information of group | |||
* | |||
* @param groupInfo [IN] pointer to group information | |||
* @param groupId [IN] group index value | |||
* @param attr [IN] group attribute | |||
* @param attrValue [OUT] pointer to attribute value | |||
* @param valueLen [IN] length of attribute value | |||
* @param paramRetSize [OUT] pointer to real length of attribute value | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtGetGroupCount | aclrtGetAllGroupInfo | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetGroupInfoDetail(const aclrtGroupInfo *groupInfo, int32_t groupId, | |||
aclrtGroupAttr attr, void *attrValue, size_t valueLen, | |||
size_t *paramRetSize); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief checking whether current device and peer device support the p2p feature | |||
* | |||
* @param canAccessPeer [OUT] pointer to save the checking result | |||
* @param deviceId [IN] current device id | |||
* @param peerDeviceId [IN] peer device id | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtDeviceEnablePeerAccess | aclrtDeviceDisablePeerAccess | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtDeviceCanAccessPeer(int32_t *canAccessPeer, int32_t deviceId, int32_t peerDeviceId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief enable the peer device to support the p2p feature | |||
* | |||
* @param peerDeviceId [IN] the peer device id | |||
* @param flags [IN] reserved field, now it must be zero | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtDeviceCanAccessPeer | aclrtDeviceDisablePeerAccess | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtDeviceEnablePeerAccess(int32_t peerDeviceId, uint32_t flags); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief disable the peer device to support the p2p function | |||
* | |||
* @param peerDeviceId [IN] the peer device id | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclrtDeviceCanAccessPeer | aclrtDeviceEnablePeerAccess | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtDeviceDisablePeerAccess(int32_t peerDeviceId); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Obtain the free memory and total memory of specified attribute. | |||
* the specified memory include normal memory and huge memory. | |||
* | |||
* @param attr [IN] the memory attribute of specified device | |||
* @param free [OUT] the free memory of specified device | |||
* @param total [OUT] the total memory of specified device. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclrtGetMemInfo(aclrtMemAttr attr, size_t *free, size_t *total); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_ACL_RT_H_ |
@@ -0,0 +1,276 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_ACL_TDT_H_ | |||
#define INC_EXTERNAL_ACL_ACL_TDT_H_ | |||
#include "acl/acl_base.h" | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
enum acltdtTensorType { | |||
ACL_TENSOR_DATA_UNDEFINED = -1, | |||
ACL_TENSOR_DATA_TENSOR, | |||
ACL_TENSOR_DATA_END_OF_SEQUENCE, | |||
ACL_TENSOR_DATA_ABNORMAL | |||
}; | |||
typedef struct acltdtDataItem acltdtDataItem; | |||
typedef struct acltdtDataset acltdtDataset; | |||
typedef struct acltdtChannelHandle acltdtChannelHandle; | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get tensor type from item | |||
* | |||
* @param dataItem [IN] pointer to the data item | |||
* | |||
* @retval Tensor type. | |||
* @retval ACL_DT_UNDEFINED if dataItem is null | |||
*/ | |||
ACL_FUNC_VISIBILITY acltdtTensorType acltdtGetTensorTypeFromItem(const acltdtDataItem *dataItem); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get data type from item | |||
* | |||
* @param dataItem [IN] pointer to the data item | |||
* | |||
* @retval Data type. | |||
* @retval ACL_DT_UNDEFINED if dataItem is null | |||
*/ | |||
ACL_FUNC_VISIBILITY aclDataType acltdtGetDataTypeFromItem(const acltdtDataItem *dataItem); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get data address from item | |||
* | |||
* @param dataItem [IN] pointer to data item | |||
* | |||
* @retval null for failed | |||
* @retval OtherValues success | |||
*/ | |||
ACL_FUNC_VISIBILITY void *acltdtGetDataAddrFromItem(const acltdtDataItem *dataItem); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get data size from item | |||
* | |||
* @param dataItem [IN] pointer to data item | |||
* | |||
* @retval 0 for failed | |||
* @retval OtherValues success | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t acltdtGetDataSizeFromItem(const acltdtDataItem *dataItem); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get dim's number from item | |||
* | |||
* @param dataItem [IN] pointer to data item | |||
* | |||
* @retval 0 for failed | |||
* @retval OtherValues success | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t acltdtGetDimNumFromItem(const acltdtDataItem *dataItem); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get dims from item | |||
* | |||
* @param dataItem [IN] the struct of data item | |||
* @param dims [IN|OUT] pointer to the dims of dataTtem | |||
* @param dimNum [IN] the size of the dims | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError acltdtGetDimsFromItem(const acltdtDataItem *dataItem, int64_t *dims, size_t dimNum); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create the struct of data item | |||
* | |||
* @param tdtType [IN] Tdt tensor type | |||
* @param dims [IN] pointer of tdtDataItem's dims | |||
* @param dimNum [IN] Dim number | |||
* @param dataType [IN] Data type | |||
* @param data [IN] Data pointer | |||
* @param size [IN] Data size | |||
* | |||
* @retval null for failed | |||
* @retval OtherValues success | |||
* | |||
* @see acltdtDestroyDataItem | |||
*/ | |||
ACL_FUNC_VISIBILITY acltdtDataItem *acltdtCreateDataItem(acltdtTensorType tdtType, const int64_t *dims, size_t dimNum, | |||
aclDataType dataType, void *data, size_t size); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy the struct of data item | |||
* | |||
* @param dataItem [IN] pointer to the data item | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see acltdtCreateDataItem | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError acltdtDestroyDataItem(acltdtDataItem *dataItem); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create the tdt dataset | |||
* | |||
* @retval null for failed | |||
* @retval OtherValues success | |||
* | |||
* @see acltdtDestroyDataset | |||
*/ | |||
ACL_FUNC_VISIBILITY acltdtDataset *acltdtCreateDataset(); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy the tdt dataset | |||
* | |||
* @param dataset [IN] pointer to the dataset | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see acltdtCreateDataset | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError acltdtDestroyDataset(acltdtDataset *dataset); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get the data item | |||
* | |||
* @param dataset [IN] pointer to the dataset | |||
* @param index [IN] index of the dataset | |||
* | |||
* @retval null for failed | |||
* @retval OtherValues success | |||
* | |||
* @see acltdtAddDataItem | |||
*/ | |||
ACL_FUNC_VISIBILITY acltdtDataItem *acltdtGetDataItem(const acltdtDataset *dataset, size_t index); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get the data item | |||
* | |||
* @param dataset [OUT] pointer to the dataset | |||
* @param dataItem [IN] pointer to the data item | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see acltdtGetDataItem | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError acltdtAddDataItem(acltdtDataset *dataset, acltdtDataItem *dataItem); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Get the size of dataset | |||
* | |||
* @param dataset [IN] pointer to the dataset | |||
* | |||
* @retval 0 for failed | |||
* @retval OtherValues success | |||
*/ | |||
ACL_FUNC_VISIBILITY size_t acltdtGetDatasetSize(const acltdtDataset *dataset); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Stop the channel | |||
* | |||
* @param handle [IN] pointer to the channel handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see acltdtCreateChannel | acltdtDestroyChannel | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError acltdtStopChannel(acltdtChannelHandle *handle); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create the channel | |||
* | |||
* @param deviceId [IN] the device id | |||
* @param name [IN] the channel's name | |||
* | |||
* @retval null for failed | |||
* @retval OtherValues success | |||
* | |||
* @see acltdtStopChannel | acltdtDestroyChannel | |||
*/ | |||
ACL_FUNC_VISIBILITY acltdtChannelHandle *acltdtCreateChannel(uint32_t deviceId, const char *name); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy the channel | |||
* | |||
* @param handle [IN] pointer to the channel handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see acltdtCreateChannel | acltdtStopChannel | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError acltdtDestroyChannel(acltdtChannelHandle *handle); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Send tensor to device | |||
* | |||
* @param handle [IN] pointer to the channel handle | |||
* @param dataset [IN] pointer to the dataset | |||
* @param timeout [IN] to be reserved, now it must be -1 | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see acltdtReceiveTensor | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError acltdtSendTensor(const acltdtChannelHandle *handle, const acltdtDataset *dataset, | |||
int32_t timeout); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Receive tensor from device | |||
* | |||
* @param handle [IN] pointer to the channel handle | |||
* @param dataset [OUT] pointer to the dataset | |||
* @param timeout [IN] to be reserved, now it must be -1 | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see acltdtSendTensor | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError acltdtReceiveTensor(const acltdtChannelHandle *handle, acltdtDataset *dataset, | |||
int32_t timeout); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_ACL_TDT_H_ |
@@ -0,0 +1,61 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_GE_GE_ERROR_CODES_H_ | |||
#define INC_EXTERNAL_GE_GE_ERROR_CODES_H_ | |||
#include <stddef.h> | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
static const uint32_t ACL_ERROR_GE_PARAM_INVALID = 145000; | |||
static const uint32_t ACL_ERROR_GE_EXEC_NOT_INIT = 145001; | |||
static const uint32_t ACL_ERROR_GE_EXEC_MODEL_PATH_INVALID = 145002; | |||
static const uint32_t ACL_ERROR_GE_EXEC_MODEL_ID_INVALID = 145003; | |||
static const uint32_t ACL_ERROR_GE_EXEC_MODEL_DATA_SIZE_INVALID = 145006; | |||
static const uint32_t ACL_ERROR_GE_EXEC_MODEL_ADDR_INVALID = 145007; | |||
static const uint32_t ACL_ERROR_GE_EXEC_MODEL_QUEUE_ID_INVALID = 145008; | |||
static const uint32_t ACL_ERROR_GE_EXEC_LOAD_MODEL_REPEATED = 145009; | |||
static const uint32_t ACL_ERROR_GE_DYNAMIC_INPUT_ADDR_INVALID = 145011; | |||
static const uint32_t ACL_ERROR_GE_DYNAMIC_INPUT_LENGTH_INVALID = 145012; | |||
static const uint32_t ACL_ERROR_GE_DYNAMIC_BATCH_SIZE_INVALID = 145013; | |||
static const uint32_t ACL_ERROR_GE_AIPP_BATCH_EMPTY = 145014; | |||
static const uint32_t ACL_ERROR_GE_AIPP_NOT_EXIST = 145015; | |||
static const uint32_t ACL_ERROR_GE_AIPP_MODE_INVALID = 145016; | |||
static const uint32_t ACL_ERROR_GE_OP_TASK_TYPE_INVALID = 145017; | |||
static const uint32_t ACL_ERROR_GE_OP_KERNEL_TYPE_INVALID = 145018; | |||
static const uint32_t ACL_ERROR_GE_PLGMGR_PATH_INVALID = 145019; | |||
static const uint32_t ACL_ERROR_GE_TRANSSHAPE_FORMAT_INVALID = 145020; | |||
static const uint32_t ACL_ERROR_GE_TRANSSHAPE_SHAPE_INVALID = 145021; | |||
static const uint32_t ACL_ERROR_GE_TRANSSHAPE_DATATYPE_INVALID = 145022; | |||
static const uint32_t ACL_ERROR_GE_MEMORY_ALLOCATION = 245000; | |||
static const uint32_t ACL_ERROR_GE_MEMORY_OPERATE_FAILED = 245001; | |||
static const uint32_t ACL_ERROR_GE_INTERNAL_ERROR = 545000; | |||
static const uint32_t ACL_ERROR_GE_LOAD_MODEL = 545001; | |||
static const uint32_t ACL_ERROR_GE_EXEC_LOAD_MODEL_PARTITION_FAILED = 545002; | |||
static const uint32_t ACL_ERROR_GE_EXEC_LOAD_WEIGHT_PARTITION_FAILED = 545003; | |||
static const uint32_t ACL_ERROR_GE_EXEC_LOAD_TASK_PARTITION_FAILED = 545004; | |||
static const uint32_t ACL_ERROR_GE_EXEC_LOAD_KERNEL_PARTITION_FAILED = 545005; | |||
static const uint32_t ACL_ERROR_GE_EXEC_RELEASE_MODEL_DATA = 545006; | |||
static const uint32_t ACL_ERROR_GE_COMMAND_HANDLE = 545007; | |||
static const uint32_t ACL_ERROR_GE_GET_TENSOR_INFO = 545008; | |||
static const uint32_t ACL_ERROR_GE_UNLOAD_MODEL = 545009; | |||
#ifdef __cplusplus | |||
} // namespace ge | |||
#endif | |||
#endif // INC_EXTERNAL_GE_GE_ERROR_CODES_H_ |
@@ -0,0 +1,101 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef __INC_EXTERNEL_RT_ERROR_CODES_H__ | |||
#define __INC_EXTERNEL_RT_ERROR_CODES_H__ | |||
#include <stddef.h> | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
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_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_DRV_INTERNAL_ERROR = 507899; // drv internal error | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__ |
@@ -0,0 +1,334 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_OPS_ACL_CBLAS_H_ | |||
#define INC_EXTERNAL_ACL_OPS_ACL_CBLAS_H_ | |||
#include "acl/acl.h" | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
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; | |||
/** | |||
* @ingroup AscendCL | |||
* @brief perform the matrix-vector multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param m [IN] number of rows of matrix A | |||
* @param n [IN] number of columns of matrix A | |||
* @param alpha [IN] pointer to scalar used for multiplication. | |||
* of same type as dataTypeC | |||
* @param a [IN] pointer to matrix A | |||
* @param lda [IN] leading dimension used to store the matrix A | |||
* @param dataTypeA [IN] datatype of matrix A | |||
* @param x [IN] pointer to vector x | |||
* @param incx [IN] stride between consecutive elements of vector x | |||
* @param dataTypeX [IN] datatype of vector x | |||
* @param beta [IN] pointer to scalar used for multiplication. | |||
* of same type as dataTypeC If beta == 0, | |||
* then y does not have to be a valid input | |||
* @param y [IN|OUT] pointer to vector y | |||
* @param incy [IN] stride between consecutive elements of vector y | |||
* @param dataTypeY [IN] datatype of vector y | |||
* @param type [IN] computation type | |||
* @param stream [IN] stream | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create a handle for performing the matrix-vector multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param m [IN] number of rows of matrix A | |||
* @param n [IN] number of columns of matrix A | |||
* @param dataTypeA [IN] datatype of matrix A | |||
* @param dataTypeX [IN] datatype of vector x | |||
* @param dataTypeY [IN] datatype of vector y | |||
* @param type [IN] computation type | |||
* @param handle [OUT] pointer to the pointer to the handle | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief perform the matrix-vector multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param m [IN] number of rows of matrix A | |||
* @param n [IN] number of columns of matrix A | |||
* @param alpha [IN] pointer to scalar used for multiplication | |||
* @param a [IN] pointer to matrix A | |||
* @param lda [IN] leading dimension used to store the matrix A | |||
* @param x [IN] pointer to vector x | |||
* @param incx [IN] stride between consecutive elements of vector x | |||
* @param beta [IN] pointer to scalar used for multiplication. | |||
* If beta value == 0, | |||
* then y does not have to be a valid input | |||
* @param y [IN|OUT] pointer to vector y | |||
* @param incy [IN] stride between consecutive elements of vector y | |||
* @param type [IN] computation type | |||
* @param stream [IN] stream | |||
* @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, | |||
aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create a handle for performing the matrix-vector multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param m [IN] number of rows of matrix A | |||
* @param n [IN] number of columns of matrix A | |||
* @param type [IN] computation type | |||
* @param handle [OUT] pointer to the pointer to the handle | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForHgemv(aclTransType transA, int m, int n, aclComputeType type, | |||
aclopHandle **handle); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief perform the matrix-vector multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param m [IN] number of rows of matrix A | |||
* @param n [IN] number of columns of matrix A | |||
* @param alpha [IN] pointer to scalar used for multiplication | |||
* @param a [IN] pointer to matrix A | |||
* @param lda [IN] leading dimension used to store the matrix A | |||
* @param x [IN] pointer to vector x | |||
* @param incx [IN] stride between consecutive elements of vector x | |||
* @param beta [IN] pointer to scalar used for multiplication. | |||
* If beta value == 0, | |||
* then y does not have to be a valid input | |||
* @param y [IN|OUT] pointer to vector y | |||
* @param incy [IN] stride between consecutive elements of vector y | |||
* @param type [IN] computation type | |||
* @param stream [IN] stream | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create a handle for performing the matrix-vector multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param m [IN] number of rows of matrix A | |||
* @param n [IN] number of columns of matrix A | |||
* @param handle [OUT] pointer to the pointer to the handle | |||
* @param type [IN] computation type | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclblasCreateHandleForS8gemv(aclTransType transA, int m, int n, aclComputeType type, | |||
aclopHandle **handle); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief perform the matrix-matrix multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param transB [IN] transpose type of matrix B | |||
* @param transC [IN] transpose type of matrix C | |||
* @param m [IN] number of rows of matrix A and matrix C | |||
* @param n [IN] number of columns of matrix B and matrix C | |||
* @param k [IN] number of columns of matrix A and rows of matrix B | |||
* @param alpha [IN] pointer to scalar used for multiplication. of same type as dataTypeC | |||
* @param matrixA [IN] pointer to matrix A | |||
* @param lda [IN] leading dimension array used to store matrix A | |||
* @param dataTypeA [IN] datatype of matrix A | |||
* @param matrixB [IN] pointer to matrix B | |||
* @param ldb [IN] leading dimension array used to store matrix B | |||
* @param dataTypeB [IN] datatype of matrix B | |||
* @param beta [IN] pointer to scalar used for multiplication. | |||
* of same type as dataTypeC If beta == 0, | |||
* then matrixC does not have to be a valid input | |||
* @param matrixC [IN|OUT] pointer to matrix C | |||
* @param ldc [IN] leading dimension array used to store matrix C | |||
* @param dataTypeC [IN] datatype of matrix C | |||
* @param type [IN] computation type | |||
* @param stream [IN] stream | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create a handle for performing the matrix-matrix multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param transB [IN] transpose type of matrix B | |||
* @param transC [IN] transpose type of matrix C | |||
* @param m [IN] number of rows of matrix A and matrix C | |||
* @param n [IN] number of columns of matrix B and matrix C | |||
* @param k [IN] number of columns of matrix A and rows of matrix B | |||
* @param dataTypeA [IN] datatype of matrix A | |||
* @param dataTypeB [IN] datatype of matrix B | |||
* @param dataTypeC [IN] datatype of matrix C | |||
* @param type [IN] computation type | |||
* @param handle [OUT] pointer to the pointer to the handle | |||
* @param type [IN] computation type | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief perform the matrix-matrix multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param transB [IN] transpose type of matrix B | |||
* @param transC [IN] transpose type of matrix C | |||
* @param m [IN] number of rows of matrix A and matrix C | |||
* @param n [IN] number of columns of matrix B and matrix C | |||
* @param k [IN] number of columns of matrix A and rows of matrix B | |||
* @param alpha [IN] pointer to scalar used for multiplication | |||
* @param matrixA [IN] pointer to matrix A | |||
* @param lda [IN] leading dimension used to store the matrix A | |||
* @param matrixB [IN] pointer to matrix B | |||
* @param ldb [IN] leading dimension used to store the matrix B | |||
* @param beta [IN] pointer to scalar used for multiplication. | |||
* If beta value == 0, | |||
* then matrixC does not have to be a valid input | |||
* @param matrixC [IN|OUT] pointer to matrix C | |||
* @param ldc [IN] leading dimension used to store the matrix C | |||
* @param type [IN] computation type | |||
* @param stream [IN] stream | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create a handle for performing the matrix-matrix multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param transB [IN] transpose type of matrix B | |||
* @param transC [IN] transpose type of matrix C | |||
* @param m [IN] number of rows of matrix A and matrix C | |||
* @param n [IN] number of columns of matrix B and matrix C | |||
* @param k [IN] number of columns of matrix A and rows of matrix B | |||
* @param type [IN] computation type | |||
* @param handle [OUT] pointer to the pointer to the handle | |||
* | |||
* @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, | |||
aclopHandle **handle); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief perform the matrix-matrix multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param transB [IN] transpose type of matrix B | |||
* @param transC [IN] transpose type of matrix C | |||
* @param m [IN] number of rows of matrix A and matrix C | |||
* @param n [IN] number of columns of matrix B and matrix C | |||
* @param k [IN] number of columns of matrix A and rows of matrix B | |||
* @param alpha [IN] pointer to scalar used for multiplication | |||
* @param matrixA [IN] pointer to matrix A | |||
* @param lda [IN] leading dimension used to store the matrix A | |||
* @param matrixB [IN] pointer to matrix B | |||
* @param ldb [IN] leading dimension used to store the matrix B | |||
* @param beta [IN] pointer to scalar used for multiplication. | |||
* If beta value == 0, | |||
* then matrixC does not have to be a valid input | |||
* @param matrixC [IN|OUT] pointer to matrix C | |||
* @param ldc [IN] leading dimension used to store the matrix C | |||
* @param type [IN] computation type | |||
* @param stream [IN] stream | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief create a handle for performing the matrix-matrix multiplication | |||
* | |||
* @param transA [IN] transpose type of matrix A | |||
* @param transB [IN] transpose type of matrix B | |||
* @param transC [IN] transpose type of matrix C | |||
* @param m [IN] number of rows of matrix A and matrix C | |||
* @param n [IN] number of columns of matrix B and matrix C | |||
* @param k [IN] number of columns of matrix A and rows of matrix B | |||
* @param type [IN] computation type | |||
* @param handle [OUT] pointer to the pointer to the handle | |||
* | |||
* @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, | |||
aclopHandle **handle); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_OPS_ACL_CBLAS_H_ |
@@ -0,0 +1,353 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef INC_EXTERNAL_ACL_OPS_ACL_RETR_H_ | |||
#define INC_EXTERNAL_ACL_OPS_ACL_RETR_H_ | |||
#include "acl/acl.h" | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
typedef struct aclfvInitPara aclfvInitPara; | |||
typedef struct aclfvFeatureInfo aclfvFeatureInfo; | |||
typedef struct aclfvRepoRange aclfvRepoRange; | |||
typedef struct aclfvQueryTable aclfvQueryTable; | |||
typedef struct aclfvSearchInput aclfvSearchInput; | |||
typedef struct aclfvSearchResult aclfvSearchResult; | |||
// search operation type | |||
enum aclfvSearchType { | |||
SEARCH_1_N, // 1:N operation type | |||
SEARCH_N_M // N:M operation type | |||
}; | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create fv init param. | |||
* | |||
* @param fsNum [IN] The feature num | |||
* | |||
* @retval null for failed. | |||
* @retval OtherValues success. | |||
*/ | |||
ACL_FUNC_VISIBILITY aclfvInitPara *aclfvCreateInitPara(uint64_t fsNum); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy fv init param. | |||
* | |||
* @par Function | |||
* Can only destroy fv init param information created | |||
* through aclfvCreateInitPara interface. | |||
* | |||
* @param initPara [IN] fv init param. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclfvCreateInitPara | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvDestroyInitPara(aclfvInitPara *initPara); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set value for maxTopNumFor1N which in fv init param. | |||
* | |||
* @param initPara [IN|OUT] fv init param. | |||
* @param maxTopNumFor1N [IN] maxTopNumFor1N value for init param. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvSet1NTopNum(aclfvInitPara *initPara, uint32_t maxTopNumFor1N); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief set value for maxTopNumForNM which in fv init param. | |||
* | |||
* @param initPara [IN|OUT] fv init param. | |||
* @param maxTopNumForNM [IN] maxTopNumForNM value for init param. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvSetNMTopNum(aclfvInitPara *initPara, uint32_t maxTopNumForNM); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create fv feature info. | |||
* | |||
* @param id0 [IN] The first level library id0 | |||
* @param id1 [IN] Secondary library id1 | |||
* @param offset [IN] The offset of the first feature in the library | |||
* @param featureLen [IN] Single feature length | |||
* @param featureCount [IN] Single feature count | |||
* @param featureData [IN] Feature value list | |||
* @param featureDataLen [IN] Feature value list length | |||
* | |||
* @retval null for failed. | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy fv feature info. | |||
* | |||
* @par Function | |||
* Can only destroy fv feature info information created | |||
* through aclfvCreateFeatureInfo interface. | |||
* | |||
* @param featureInfo [IN] fv feature info. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclfvCreateFeatureInfo | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvDestroyFeatureInfo(aclfvFeatureInfo *featureInfo); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create fv repo range. | |||
* | |||
* @param id0Min [IN] id0 start value | |||
* @param id0Min [IN] id0 max | |||
* @param id1Min [IN] id0 start value | |||
* @param id1Max [IN] id1 max | |||
* | |||
* @retval null for failed. OtherValues success | |||
*/ | |||
ACL_FUNC_VISIBILITY aclfvRepoRange *aclfvCreateRepoRange(uint32_t id0Min, uint32_t id0Max, uint32_t id1Min, | |||
uint32_t id1Max); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy fv repo range. | |||
* | |||
* @par Function | |||
* Can only destroy fv repo range information created | |||
* through aclfvCreateRepoRange interface. | |||
* | |||
* @param repoRange [IN] fv repo range. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclfvCreateRepoRange | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvDestroyRepoRange(aclfvRepoRange *repoRange); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create query table. | |||
* | |||
* @param queryCnt [IN] Number of tables, the maximum number is 6 | |||
* @param tableLen [IN] Single table length, table length is 32KB | |||
* @param tableData [IN] Feature value list | |||
* @param tableDataLen [IN] The length of memory requested by the featureData pointer | |||
* | |||
* @retval null for failed. OtherValues success | |||
*/ | |||
ACL_FUNC_VISIBILITY aclfvQueryTable *aclfvCreateQueryTable(uint32_t queryCnt, uint32_t tableLen, uint8_t *tableData, | |||
uint32_t tableDataLen); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy query table. | |||
* | |||
* @par Function | |||
* Can only destroy query table information created | |||
* through aclfvCreateQueryTable interface. | |||
* | |||
* @param queryTable [IN] query table. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclfvCreateQueryTable | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvDestroyQueryTable(aclfvQueryTable *queryTable); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create search input. | |||
* | |||
* @param queryTable [IN] query table | |||
* @param repoRange [IN] query repo range | |||
* @param topk [IN] query topk | |||
* | |||
* @retval null for failed. OtherValues success | |||
*/ | |||
ACL_FUNC_VISIBILITY aclfvSearchInput *aclfvCreateSearchInput(aclfvQueryTable *queryTable, aclfvRepoRange *repoRange, | |||
uint32_t topk); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy search input. | |||
* | |||
* @par Function | |||
* Can only destroy search input information created | |||
* through aclfvCreateSearchInput interface. | |||
* | |||
* @param searchInput [IN] search input. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclfvCreateSearchInput | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvDestroySearchInput(aclfvSearchInput *searchInput); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Create search result. | |||
* | |||
* @param queryCnt [IN] Retrieve the number of features | |||
* @param resultNum [IN] The number of search results for each feature, the number is queryCnt | |||
* @param resultNumDataLen [IN] resultNum memory length | |||
* @param id0 [IN] Level 1 library id0 | |||
* @param id1 [IN] Secondary library id1 | |||
* @param resultOffset [IN] The offset of the bottom library corresponding | |||
* to each feature retrieval result, total length topK * queryCnt | |||
* @param resultDistance [IN] Distance, total length topK * queryCnt | |||
* @param dataLen [IN] The memory size requested by | |||
* id0\id1\reslutOffset\resultDistance | |||
* | |||
* @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); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief Destroy search result. | |||
* | |||
* @par Function | |||
* Can only destroy search result information created | |||
* through aclfvCreateSearchResult interface. | |||
* | |||
* @param searchResult [IN] search result. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure | |||
* | |||
* @see aclfvCreateSearchResult | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvDestroySearchResult(aclfvSearchResult *searchResult); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief fv IP initialize. | |||
* | |||
* @param initPara [IN] fv init param. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure. | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvInit(aclfvInitPara *initPara); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief release fv resources. | |||
* | |||
* @par Function | |||
* Can only release fv resources created | |||
* through aclfvInit interface. | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure. | |||
* | |||
* @see aclfvInit | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvRelease(); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief fv repo add. | |||
* | |||
* @param type [IN] repo add type | |||
* @param featureInfo [IN] add feature information | |||
* @param stream [IN] stream of task execute | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure. | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvRepoAdd(aclfvSearchType type, aclfvFeatureInfo *featureInfo, aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief fv repo del. | |||
* | |||
* @param type [IN] repo delete type | |||
* @param repoRange [IN] repo range information | |||
* @param stream [IN] stream of task execute | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure. | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvRepoDel(aclfvSearchType type, aclfvRepoRange *repoRange, aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief fv accurate del. | |||
* | |||
* @param featureInfo [IN] accurate delete feature information | |||
* @param stream [IN] stream of task execute | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure. | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvDel(aclfvFeatureInfo *featureInfo, aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief fv accurate modify. | |||
* | |||
* @param featureInfo [IN] accurate modify feature information | |||
* @param stream [IN] stream of task execute | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure. | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvModify(aclfvFeatureInfo *featureInfo, aclrtStream stream); | |||
/** | |||
* @ingroup AscendCL | |||
* @brief fv search. | |||
* | |||
* @param type [IN] search type | |||
* @param searchInput [IN] search input | |||
* @param searchRst [OUT] search result | |||
* @param stream [IN] stream of task execute | |||
* | |||
* @retval ACL_SUCCESS The function is successfully executed. | |||
* @retval OtherValues Failure. | |||
*/ | |||
ACL_FUNC_VISIBILITY aclError aclfvSearch(aclfvSearchType type, aclfvSearchInput *searchInput, | |||
aclfvSearchResult *searchRst, aclrtStream stream); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // INC_EXTERNAL_ACL_OPS_ACL_RETR_H_ |
@@ -0,0 +1,134 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
/** | |||
* @file hccl.h | |||
* @brief HCCL API | |||
*/ | |||
#ifndef HCCL_H_ | |||
#define HCCL_H_ | |||
#include <hccl/hccl_types.h> | |||
#include <acl/acl.h> | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif // __cplusplus | |||
/** | |||
* @brief Initialize HCCL. | |||
* | |||
* @param clusterInfo A string identifying the cluster info file path, include file name. | |||
* @param rank A integer identifying the identify for the rank. | |||
* @param comm A pointer identifying the initialized communication resource. | |||
* @return HcclResult | |||
* @see HcclCommDestroy() | |||
*/ | |||
extern HcclResult HcclCommInitClusterInfo(const char *clusterInfo, uint32_t rank, HcclComm *comm); | |||
/** | |||
* @brief Get hccl root info. | |||
* | |||
* @param rootInfo A pointer identifying the hccl root info. | |||
* @return HcclResult | |||
*/ | |||
extern HcclResult HcclGetRootInfo(HcclRootInfo *rootInfo); | |||
/** | |||
* @brief Initialize HCCL with root info. | |||
* | |||
* @param nRanks A integer identifying the rank size of the cluster. | |||
* @param rootInfo A struct identifying the hccl root info. | |||
* @param rank A integer identifying the identify for the rank. | |||
* @param comm A pointer identifying the initialized communication resource. | |||
* @return HcclResult | |||
* @see HcclCommDestroy() | |||
*/ | |||
extern HcclResult HcclCommInitRootInfo(uint32_t nRanks, const HcclRootInfo *rootInfo, uint32_t rank, HcclComm *comm); | |||
/** | |||
* @brief AllReduce operator. | |||
* | |||
* @param sendBuf A pointer identifying the input data address of the operator. | |||
* @param recvBuf A pointer identifying the output data address of the operator. | |||
* @param count An integer(u64) identifying the number of the output data. | |||
* @param dataType The data type of the operator, must be one of the following types: int8, int16, int32, float16, | |||
* float32. | |||
* @param op The reduction type of the operator, must be one of the following types: sum, min, max, prod. | |||
* @param comm A pointer identifying the communication resource based on. | |||
* @param stream A pointer identifying the stream information. | |||
* @return HcclResult | |||
*/ | |||
extern HcclResult HcclAllReduce(void *sendBuf, void *recvBuf, uint64_t count, HcclDataType dataType, HcclReduceOp op, | |||
HcclComm comm, aclrtStream stream); | |||
/** | |||
* @brief Broadcast operator. | |||
* | |||
* @param buf A pointer identifying the data address of the operator. | |||
* @param count An integer(u64) identifying the number of the data. | |||
* @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. | |||
* @param root An integer(u32) identifying the the root rank in the operator. | |||
* @param comm A pointer identifying the communication resource based on | |||
* @param stream A pointer identifying the stream information. | |||
* @return HcclResult | |||
*/ | |||
extern HcclResult HcclBroadcast(void *buf, uint64_t count, HcclDataType dataType, uint32_t root, HcclComm comm, | |||
aclrtStream stream); | |||
/** | |||
* @brief ReduceScatter operator. | |||
* | |||
* @param sendBuf A pointer identifying the input data address of the operator. | |||
* @param recvBuf A pointer identifying the output data address of the operator. | |||
* @param recvCount 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, 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 | |||
*/ | |||
extern HcclResult HcclReduceScatter(void *sendBuf, void *recvBuf, uint64_t recvCount, HcclDataType dataType, | |||
HcclReduceOp op, HcclComm comm, aclrtStream stream); | |||
/** | |||
* @brief AllGather operator. | |||
* | |||
* @param sendBuf A pointer identifying the input data address of the operator. | |||
* @param recvBuf A pointer identifying the output data address of the operator. | |||
* @param sendCount An integer(u64) identifying the number of the input data. | |||
* @param dataType The data type of the operator, must be one of the following types: int8, int32, float16, float32. | |||
* @param comm A pointer identifying the communication resource based on. | |||
* @param stream A pointer identifying the stream information. | |||
* @return HcclResult | |||
*/ | |||
extern HcclResult HcclAllGather(void *sendBuf, void *recvBuf, uint64_t sendCount, HcclDataType dataType, HcclComm comm, | |||
aclrtStream stream); | |||
/** | |||
* @brief Destroy HCCL comm | |||
* | |||
* @param comm A pointer identifying the communication resource targetting | |||
* @return HcclResult | |||
* @see HcclCommInitClusterInfo() | |||
*/ | |||
extern HcclResult HcclCommDestroy(HcclComm comm); | |||
#ifdef __cplusplus | |||
} | |||
#endif // __cplusplus | |||
#endif // HCCL_H_ |
@@ -0,0 +1,101 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
/** | |||
* @file hccl_types.h | |||
* @brief HCCL data type definition | |||
* | |||
*/ | |||
#ifndef HCCL_TYPES_H_ | |||
#define HCCL_TYPES_H_ | |||
#include <stdint.h> | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#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 */ | |||
} HcclResult; | |||
/** | |||
* @brief handle to HCCL communicator | |||
*/ | |||
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 */ | |||
} 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 */ | |||
} HcclDataType; | |||
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]; | |||
} HcclRootInfo; | |||
#ifdef __cplusplus | |||
} | |||
#endif // __cplusplus | |||
#endif // HCCL_TYPES_H_ |
@@ -0,0 +1,101 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef __INC_EXTERNEL_RT_ERROR_CODES_H__ | |||
#define __INC_EXTERNEL_RT_ERROR_CODES_H__ | |||
#include <stddef.h> | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
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_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_DRV_INTERNAL_ERROR = 507899; // drv internal error | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // __INC_EXTERNEL_RT_ERROR_CODES_H__ |
@@ -1,101 +0,0 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
/** | |||
* @file hccl_types.h | |||
* @brief HCCL data type definition | |||
* | |||
*/ | |||
#ifndef HCCL_TYPES_H_ | |||
#define HCCL_TYPES_H_ | |||
#include <stdint.h> | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#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 */ | |||
} HcclResult; | |||
/** | |||
* @brief handle to HCCL communicator | |||
*/ | |||
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 */ | |||
} 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 */ | |||
} HcclDataType; | |||
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]; | |||
} HcclRootInfo; | |||
#ifdef __cplusplus | |||
} | |||
#endif // __cplusplus | |||
#endif // HCCL_TYPES_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -65,6 +65,8 @@ in aipp config file, framework will auto add one input node to graph at last. \n | |||
*@par Third-party framework compatibility | |||
*Compatible with the TensorFlow operator AippData. | |||
*@par Restrictions: | |||
*Warning: This operator can be integrated only by configuring INSERT_OP_FILE of aclgrphBuildModel. Please do not use it directly. | |||
*/ | |||
REG_OP(AippData) | |||
.INPUT(data, TensorType::ALL()) | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -39,6 +39,7 @@ | |||
#include "image_ops.h" | |||
#include "internal_ops.h" | |||
#include "linalg_ops.h" | |||
#include "list_ops.h" | |||
#include "logging_ops.h" | |||
#include "lookup_ops.h" | |||
#include "math_ops.h" | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1153,6 +1153,79 @@ REG_OP(EditDistance) | |||
.OUTPUT(output, TensorType({DT_FLOAT})) | |||
.OP_END_FACTORY_REG(EditDistance) | |||
/** | |||
* @brief sort_v2. | |||
* @par Inputs: | |||
* @li x: An ND tensor of type float16. | |||
* @par Attributes: | |||
* @li axis: An optional int. The dimension to sort along. This value defaults to -1. | |||
* @li descending: An optional bool. Controls the sorting order (ascending or descending). This value defaults to False. | |||
* @par Outputs: | |||
* @li y: An ND tensor of type float16. | |||
* @attention Constraints: | |||
* @li Axis should select the last dim. | |||
* @li When the sorting data is less than 150K, it is recommended to use this tbe ops, | |||
and the descending performance is better than the ascending. | |||
* @li The upper limit of data on Ascend910 is 2000K. | |||
*/ | |||
REG_OP(SortV2) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
.ATTR(axis, Int, -1) | |||
.ATTR(descending, Bool, false) | |||
.OP_END_FACTORY_REG(SortV2) | |||
/** | |||
* @brief Expand the input tensor to a compatible shape. \n | |||
* @par Inputs: | |||
* One inputs, including: | |||
* @li x: A Tensor. Must be one of the following types: | |||
* float16, float32, int32, int8 ,uint8. \n | |||
* @li shape: A Tensor to specify the shape that the input tensor expanded to. \n | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the same type as "x", and the shape specified by input and attr shape \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the ONNX operator Expand. | |||
*/ | |||
REG_OP(Expand) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8})) | |||
.INPUT(shape, TensorType({DT_INT16, DT_INT32, DT_INT64})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8})) | |||
.OP_END_FACTORY_REG(Expand) | |||
/** | |||
* @brief Expand the input tensor to a compatible shape. \n | |||
* @par Inputs: | |||
* One inputs, including: | |||
* @li x: A Tensor. Must be one of the following types: | |||
* float16, float32, int32, int8 ,uint8. \n | |||
* @par Attributes: | |||
* @li shape: A required listInt to specify the shape that the input tensor expanded to. \n | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the same type as "x", and the shape specified by input and attr shape \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the ONNX operator Expand. | |||
*/ | |||
REG_OP(ExpandD) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8})) | |||
.REQUIRED_ATTR(shape, ListInt) | |||
.OP_END_FACTORY_REG(ExpandD) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_ARRAY_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -908,7 +908,7 @@ REG_OP(TensorArray) | |||
.OUTPUT(handle, TensorType({DT_RESOURCE})) | |||
.OUTPUT(flow, TensorType({DT_FLOAT})) | |||
.REQUIRED_ATTR(dtype, Type) | |||
.ATTR(element_shape, ListInt, ge::UNKNOWN_SHAPE) | |||
.ATTR(element_shape, ListInt, ge::UNKNOWN_RANK) | |||
.ATTR(dynamic_size, Bool, false) | |||
.ATTR(clear_after_read, Bool, true) | |||
.ATTR(identical_element_shapes, Bool, false) | |||
@@ -963,7 +963,7 @@ REG_OP(TensorArrayConcat) | |||
DT_QUINT8, DT_QINT32})) | |||
.OUTPUT(lengths, TensorType({DT_INT64})) | |||
.REQUIRED_ATTR(dtype, Type) | |||
.ATTR(element_shape_except0, ListInt, ge::UNKNOWN_SHAPE) | |||
.ATTR(element_shape_except0, ListInt, ge::UNKNOWN_RANK) | |||
.OP_END_FACTORY_REG(TensorArrayConcat) | |||
/** | |||
@@ -999,7 +999,7 @@ REG_OP(TensorArrayGather) | |||
DT_STRING, DT_COMPLEX64, DT_COMPLEX128, DT_QINT8, | |||
DT_QUINT8, DT_QINT32})) | |||
.REQUIRED_ATTR(dtype, Type) | |||
.ATTR(element_shape, ListInt, ge::UNKNOWN_SHAPE) | |||
.ATTR(element_shape, ListInt, ge::UNKNOWN_RANK) | |||
.OP_END_FACTORY_REG(TensorArrayGather) | |||
/** | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* Copyright 2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
@@ -122,7 +122,8 @@ REG_OP(MinimumGrad) | |||
*@par Inputs: | |||
*One input: | |||
*x:A Tensor. Must be one of the following types: bool, float16, float, int8, int32, uint32, uint8, | |||
int64, uint64, int16, uint16, double, complex64, complex128, qint8, quint8, qint16, quint16, qint32. \n | |||
int64, uint64, int16, uint16, double, complex64, complex128, qint8, quint8, qint16, quint16, qint32. | |||
For float32 type, the actual calculation on the chip is based on float16. \n | |||
*@par Attributes: | |||
*dst_type: An required attribute of type int32, specifying the dst data type. \n | |||
@@ -611,6 +612,15 @@ REG_OP(Log1p) | |||
*@par Outputs: | |||
*y: A Tensor. Has the same type as "x1". | |||
*@attention Constraints: | |||
*@li x2: The input data does not support 0 | |||
*@li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the | |||
*requirement of double thousandths in the mini form | |||
*@li Due to different architectures, the calculation results of this operator | |||
*on NPU and CPU may be inconsistent | |||
*@li If shape is expressed as (D1,D2... ,Dn), then D1*D2... *DN<=1000000,n<=8 | |||
*@par Third-party framework compatibility | |||
*Compatible with the TensorFlow operator Mod. | |||
*/ | |||
@@ -2042,6 +2052,15 @@ REG_OP(FloorDiv) | |||
* | |||
*@par Outputs: | |||
*y: Result remainder. | |||
*@attention Constraints: | |||
*@li x2: The input data does not support 0 | |||
*@li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the | |||
*requirement of double thousandths in the mini form | |||
*@li Due to different architectures, the calculation results of this operator | |||
*on NPU and CPU may be inconsistent | |||
*@li If shape is expressed as (D1,D2... ,Dn), then D1*D2... *DN<=1000000,n<=8 | |||
*@par Third-party framework compatibility | |||
* Compatible with the TensorFlow operator FloorMod. | |||
*/ | |||
@@ -2168,6 +2187,14 @@ REG_OP(Tan) | |||
*@par Outputs: | |||
*y: A Tensor. Has the same type as "x1". \n | |||
*@attention Constraints: | |||
*@li x2: The input data does not support 0 | |||
*@li When NUM exceeds 2048 , the accuracy of operator cannot guarantee the | |||
*requirement of double thousandths in the mini form | |||
*@li Due to different architectures, the calculation results of this operator | |||
*on NPU and CPU may be inconsistent | |||
*@li If shape is expressed as (D1,D2... ,Dn), then D1*D2... *DN<=1000000,n<=8 | |||
*@par Third-party framework compatibility | |||
*@li Compatible with the TensorFlow operator TruncateMod. | |||
*/ | |||
@@ -2829,9 +2856,9 @@ REG_OP(AdamApplyOneAssign) | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(LambApplyOptimizerAssign) | |||
.INPUT(input0, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(input1, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(input2, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(grad, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(inputv, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(inputm, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(input3, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(mul0_x, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(mul1_x, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
@@ -2842,6 +2869,8 @@ REG_OP(LambApplyOptimizerAssign) | |||
.INPUT(do_use_weight, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(weight_decay_rate, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.OUTPUT(output0, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.OUTPUT(inputv, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.OUTPUT(inputm, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.OP_END_FACTORY_REG(LambApplyOptimizerAssign) | |||
/** | |||
@@ -2873,7 +2902,8 @@ REG_OP(LambApplyWeightAssign) | |||
.INPUT(input1, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(input2, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(input3, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(input4, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.INPUT(input_param, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.OUTPUT(input_param, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
.OP_END_FACTORY_REG(LambApplyWeightAssign) | |||
/** | |||
@@ -3329,8 +3359,297 @@ REG_OP(TensorRedirect) | |||
.OUTPUT(output_x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT8, | |||
DT_INT64, DT_INT16, DT_UINT16, DT_UINT64, DT_UINT32})) | |||
.OP_END_FACTORY_REG(TensorRedirect) | |||
} // namespace ge | |||
/** | |||
* @brief Performs the element-wise division of tensor x2 by tensor x3, | |||
* multiply the result by the scalar value and add it to tensor x1 | |||
* @par Inputs: | |||
* Three inputs, including: | |||
* @li input_data: A mutable input Tensor. Must be one of the following types: | |||
* float16, float32. | |||
* @li x1: A mutable input Tensor of the same type as x1. | |||
* @li x2: A mutable input Tensor of the same type as x1. | |||
* @li value: A mutable input Tensor. Must be one of the following types: | |||
* float16, float32, int32. \n | |||
* @par Outputs: | |||
* @li y: A mutable Tensor. Has the same type as "x1". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Addcdiv. | |||
*/ | |||
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 })) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OP_END_FACTORY_REG(Addcdiv) | |||
/** | |||
* @brief Performs the element-wise multiplication of tensor x2 by tensor x3, | |||
* multiply the result by the scalar value and add it to tensor input_data | |||
* @par Inputs: | |||
* Three inputs, including: | |||
* @li input_data: A mutable input Tensor. Must be one of the following types: | |||
* float16, float32, int8, int32, uint8. | |||
* @li x1: A mutable input Tensor of the same type as x1. | |||
* @li x2: A mutable input Tensor of the same type as x1. | |||
* @li value: A tensor which includes only one element of the same type as x1. \n | |||
* @par Outputs: | |||
* @li y: A mutable output Tensor. Has the same type as "x1". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Addcmul. | |||
*/ | |||
REG_OP(Addcmul) | |||
.INPUT(input_data, TensorType({ DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT8 })) | |||
.INPUT(x1, TensorType({ DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT8 })) | |||
.INPUT(x2, TensorType({ DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT8 })) | |||
.INPUT(value, TensorType({ DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT8 })) | |||
.OUTPUT(y, TensorType({ DT_FLOAT16, DT_FLOAT, DT_INT8, DT_INT32, DT_UINT8 })) | |||
.OP_END_FACTORY_REG(Addcmul) | |||
/** | |||
* @brief Computes the result of x2 * alpha + x1. | |||
* @par Inputs: | |||
* @li x1: An ND tensor of type float16, float32, int32. | |||
* @li x2: An ND tensor of type float16, float32, int32. | |||
* @li alpha: A scalar tensor of type float16, float32. \n | |||
* @par Outputs: | |||
* @li y: An ND tensor tensor with the same shape and type as "x1". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Axpy. | |||
*/ | |||
REG_OP(AxpyV2) | |||
.INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) | |||
.INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) | |||
.INPUT(alpha, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) | |||
.OP_END_FACTORY_REG(AxpyV2) | |||
/** | |||
* @brief Computes the result of x1 + x2. | |||
* @par Inputs: | |||
* @li x1: An ND tensor of type float16, float, int32. | |||
* @li x2: An ND tensor of type float16, float, int32. \n | |||
* @par Outputs: | |||
* @li y: An ND tensor tensor with the same type as "x1". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Add. | |||
*/ | |||
REG_OP(PtAdd) | |||
.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(PtAdd) | |||
/** | |||
* @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: | |||
* @li x1: An ND tensor of type float16, float, int32. | |||
* @li x2: An ND tensor of type float16, float, int32. \n | |||
* @par Outputs: | |||
* @li y: An ND tensor tensor with the same type as "x1". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Sub. | |||
*/ | |||
REG_OP(PtSub) | |||
.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(PtSub) | |||
/** | |||
* @brief Add the partial values of two tensors in format NC1HWC0. | |||
* @par Inputs: | |||
* @li x1: A Tensor in 5HD, and must be one of the following types: float16, | |||
* float32. \n | |||
* @li x2: A Tensor of the same type as "x1", and the same shape as "x1", | |||
* except for the C1 value. \n | |||
* @par Attributes: | |||
* @li x1_c1_offset: A required int. Offset value of C1 in "x1". \n | |||
* @li x2_c1_offset: A required int. Offset value of C1 in "x2". \n | |||
* @li c1_len: A required int. C1 len of "y". The value must be less than | |||
* the difference between C1 and offset in "x1" and "x2". \n | |||
* @par Outputs: | |||
* @li y: A Tensor of the same type as "x1", and the same shape as "x1", | |||
* except for the C1 value. Record the result after adding. \n | |||
*/ | |||
REG_OP(StrideAdd) | |||
.INPUT(x1, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.INPUT(x2, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.OUTPUT(y, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.REQUIRED_ATTR(x1_c1_offset, Int) | |||
.REQUIRED_ATTR(x2_c1_offset, Int) | |||
.REQUIRED_ATTR(c1_len, Int) | |||
.OP_END_FACTORY_REG(StrideAdd) | |||
/** | |||
* @brief Compare two tensors are totally equal or not, only output a bool value" | |||
* @par Inputs: | |||
* Two inputs, including: | |||
* @li input_x: A Tensor. the first tensor. \n | |||
* @li input_y: A Tensor. the second tensor. \n | |||
* @par Outputs: | |||
* @li output_z: A Tensor. Bool type, compare result of the two inputs. \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch equal operator. \n | |||
*/ | |||
REG_OP(TensorEqual) | |||
.INPUT(input_x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8})) | |||
.INPUT(input_y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_INT8, DT_UINT8})) | |||
.OUTPUT(output_z, TensorType({DT_BOOL})) | |||
.OP_END_FACTORY_REG(TensorEqual) | |||
/** | |||
* @brief Element-wise min of each of the input tensors (with Numpy-style broadcasting support). | |||
* All inputs and outputs must have the same data type. This operator supports multidirectional | |||
* (i.e., Numpy-style) broadcasting | |||
* | |||
* @par inputs | |||
* one input including: | |||
* @li x: dynamic input A Tensor. Must be one of the following types: float32, float16, double, int32, int64 | |||
* | |||
* @par output | |||
* one output including: | |||
* @li y:A Tensor of the same type as x | |||
* | |||
*/ | |||
REG_OP(MaxN) | |||
.DYNAMIC_INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_FLOAT64, DT_INT32, DT_INT64})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_FLOAT64, DT_INT32, DT_INT64})) | |||
.OP_END_FACTORY_REG(MaxN) | |||
/** | |||
* @brief Element-wise min of each of the input tensors (with Numpy-style broadcasting support). | |||
* All inputs and outputs must have the same data type. This operator supports multidirectional | |||
* (i.e., Numpy-style) broadcasting | |||
* | |||
* @par inputs | |||
* one input including: | |||
* @li x: dynamic input A Tensor. Must be one of the following types: float32, float16, double, int32, int64 | |||
* | |||
* @par output | |||
* one output including: | |||
* @li y:A Tensor of the same type as x | |||
* | |||
*/ | |||
REG_OP(MinN) | |||
.DYNAMIC_INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_FLOAT64, | |||
DT_INT32, DT_INT64})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_FLOAT64, | |||
DT_INT32, DT_INT64})) | |||
.OP_END_FACTORY_REG(MinN) | |||
/** | |||
* @brief Calculates x * maske * value. | |||
* | |||
* @par Inputs: | |||
* @li x: An tensor of type float16 or float32, specifying the input to the data layer. | |||
* @li mask: An tensor of type int8 or float16 or float32, be same shape with x. \n | |||
* | |||
* @par Attributes: | |||
* value: A optional float. \n | |||
* | |||
* @par Outputs: | |||
* y: The output tensor of type float16 or float32. | |||
@ li y:A Tensor of the same type and shape as x | |||
* | |||
*/ | |||
REG_OP(MaskedScale) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32})) | |||
.INPUT(mask, TensorType({DT_INT8, DT_FLOAT16, DT_FLOAT32})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32})) | |||
.REQUIRED_ATTR(value, Float) | |||
.OP_END_FACTORY_REG(MaskedScale) | |||
/** | |||
* @brief Calculate the lerp function. \n | |||
* @par Inputs: | |||
* Three inputs, including: | |||
* @li start: A tensor. Must be one of the following types: | |||
* float16, float32. \n | |||
* @li end: A tensor. Must be one of the following types: | |||
* float16, float32. \n | |||
* @li weight: A tensor. Must be one of the following types: | |||
* float16, float32. \n | |||
* @par Outputs: | |||
* y: A Tensor with the same type and shape of input_x's. \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Lerp. \n | |||
*/ | |||
REG_OP(Lerp) | |||
.INPUT(start, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(end, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(weight, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OP_END_FACTORY_REG(Lerp) | |||
/** | |||
*@brief Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 | |||
*otherwise The input does not need to explicitly be a 2D vector.The "axis" attribute indicates the dimension along | |||
*which Hardmax will be performed.The output tensor has the same shape and contains the Hardmax values of the | |||
*corresponding input. | |||
* | |||
*@par inputs | |||
*one input including: | |||
*@li x: input A Tensor.Must be one of the following types:float32,float16 | |||
* | |||
*@par Attributes: | |||
*@li axis:A required int attribute that decides which dimension will be used to cal the hard_max | |||
* | |||
*@par output: | |||
*one output including: | |||
*@li y:A Tensor of the same type as x | |||
* | |||
*/ | |||
REG_OP(HardMax) | |||
.INPUT(x, TensorType({ DT_FLOAT16, DT_FLOAT })) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(axis, Int, -1) | |||
.OP_END_FACTORY_REG(HardMax) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_ELEWISE_CALCULATION_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* Copyright 2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
@@ -45,8 +45,6 @@ REG_OP(HcomAllGather) | |||
.OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16, DT_INT64, DT_UINT64})) | |||
.REQUIRED_ATTR(rank_size, Int) | |||
.REQUIRED_ATTR(group, String) | |||
.ATTR(alpha, Float, 1.0) | |||
.ATTR(beta, Float, 0.0) | |||
.OP_END_FACTORY_REG(HcomAllGather) | |||
/** | |||
@@ -77,8 +75,6 @@ REG_OP(HcomAllReduce) | |||
.REQUIRED_ATTR(group, String) | |||
.ATTR(fusion, Int, 1) | |||
.ATTR(fusion_id, Int, -1) | |||
.ATTR(alpha, Float, 1.0) | |||
.ATTR(beta, Float, 0.0) | |||
.OP_END_FACTORY_REG(HcomAllReduce) | |||
/** | |||
@@ -91,7 +87,7 @@ REG_OP(HcomAllReduce) | |||
input of this rank will be broadcast to other ranks. | |||
* @li fusion: A required integer identifying if the op need to fusion,the | |||
default value is none fusion | |||
* @li fusion: A required integer identifying the fusion id if para fusion | |||
* @li fusion_id: A required integer identifying the fusion id if para fusion | |||
is set. | |||
* @li group: A required string identifying the group name of ranks | |||
participating in the op. | |||
@@ -109,10 +105,39 @@ REG_OP(HcomBroadcast) | |||
.REQUIRED_ATTR(group, String) | |||
.ATTR(fusion, Int, 0) | |||
.ATTR(fusion_id, Int, -1) | |||
.ATTR(alpha, Float, 1.0) | |||
.ATTR(beta, Float, 0.0) | |||
.OP_END_FACTORY_REG(HcomBroadcast) | |||
/** | |||
* @brief preforms reduction from others rank to rootrank | |||
* @par Inputs: | |||
* @li root_rank: A required integer identifying the root rank in the op | |||
the reduction result will be on this root rank | |||
* x: A tensor. Must be one of the following types: int8, int16, int32, float16, | |||
float32. | |||
* @par Attributes: | |||
* @li reduction: A required string identifying the reduction operation to | |||
perform.The supported operation are: "sum", "max", "min", "prod". | |||
* @li group: A required string identifying the group name of ranks | |||
participating in the op. | |||
* @li fusion: An optional integer identifying the fusion flag of the op. | |||
0: no fusion; 1 (default): fusion; 2: fusion the ops by fusion id. | |||
* @li fusion_id: An optional integer identifying the fusion id of the op. | |||
* The HcomReduce ops with the same fusion id will be fused. | |||
* @par Outputs: | |||
* y: A Tensor. Has the same type as "x". | |||
* @attention Constraints: | |||
*"group" is limited to 128 characters. Use "hccl_world_group" | |||
as the name of a world group. | |||
*/ | |||
REG_OP(HcomReduce) | |||
.INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16})) | |||
.OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT8, DT_INT16, DT_FLOAT16})) | |||
.REQUIRED_ATTR(root_rank, Int) | |||
.REQUIRED_ATTR(reduction, String) | |||
.REQUIRED_ATTR(group, String) | |||
.ATTR(fusion, Int, 0) | |||
.ATTR(fusion_id, Int, -1) | |||
.OP_END_FACTORY_REG(HcomReduce) | |||
/** | |||
* @brief Performs reduction across all input tensors, scattering in equal | |||
blocks among ranks, each rank getting a chunk of data based on its rank | |||
@@ -139,8 +164,6 @@ REG_OP(HcomReduceScatter) | |||
.REQUIRED_ATTR(reduction, String) | |||
.REQUIRED_ATTR(group, String) | |||
.REQUIRED_ATTR(rank_size, Int) | |||
.ATTR(alpha, Float, 1.0) | |||
.ATTR(beta, Float, 0.0) | |||
.OP_END_FACTORY_REG(HcomReduceScatter) | |||
/** | |||
@@ -167,8 +190,6 @@ REG_OP(HcomSend) | |||
.REQUIRED_ATTR(group, String) | |||
.REQUIRED_ATTR(sr_tag, Int) | |||
.REQUIRED_ATTR(dest_rank, Int) | |||
.ATTR(alpha, Float, 1.0) | |||
.ATTR(beta, Float, 0.0) | |||
.OP_END_FACTORY_REG(HcomSend) | |||
/** | |||
@@ -202,8 +223,6 @@ REG_OP(HcomReceive) | |||
.REQUIRED_ATTR(src_rank, Int) | |||
.REQUIRED_ATTR(shape, ListInt) | |||
.REQUIRED_ATTR(dtype, Type) | |||
.ATTR(alpha, Float, 1.0) | |||
.ATTR(beta, Float, 0.0) | |||
.OP_END_FACTORY_REG(HcomReceive) | |||
/** | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* Copyright 2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -31,11 +31,12 @@ namespace ge { | |||
*@par Inputs: | |||
*Input images is a tensor of at least 3 dimensions. The last dimension is | |||
interpretted as channels, and must be three. Inputs include: | |||
*@li images:A Tensor of type float. Images to adjust. At least 3-D. | |||
*@li images:A Tensor of type float. Images to adjust. At least 3-D. The format | |||
must be NHWC. | |||
*@li delta:A Tensor of type float. A float delta to add to the hue . \n | |||
*@par Outputs: | |||
*y:A Tensor of type float . \n | |||
*y:A Tensor of type float. The format must be NHWC. \n | |||
*@attention Constraints: | |||
*Input images is a tensor of at least 3 dimensions. The last dimension is | |||
@@ -57,11 +58,12 @@ REG_OP(AdjustHue) | |||
*@par Inputs: | |||
*Input images is a tensor of at least 3 dimensions. The last dimension is | |||
interpretted as channels, and must be three. Inputs include: | |||
*@li images:A Tensor of type float. Images to adjust. At least 3-D. | |||
*@li images:A Tensor of type float. Images to adjust. At least 3-D. The format | |||
must be NHWC. | |||
*@li scale:A Tensor of type float. A float scale to add to the saturation . \n | |||
*@par Outputs: | |||
*y:A Tensor of type float . \n | |||
*y:A Tensor of type float. The format must be NHWC. \n | |||
*@attention Constraints: | |||
*Input images is a tensor of at least 3 dimensions. The last dimension is | |||
@@ -83,11 +85,12 @@ REG_OP(AdjustSaturation) | |||
*@par Inputs: | |||
*Input images is a tensor of at least 3 dimensions. The last 3 dimensions are | |||
interpreted as '[height, width, channels]'. Inputs include: | |||
*@li images:A Tensor of type float. Images to adjust. At least 3-D. | |||
*@li images:A Tensor of type float. Images to adjust. At least 3-D. The format | |||
must be NHWC. | |||
*@li scale:A Tensor of type float. A float multiplier for adjusting contrast . \n | |||
*@par Outputs: | |||
*y:A Tensor of type float . \n | |||
*y:A Tensor of type float. The format must be NHWC. \n | |||
*@attention Constraints: | |||
*Input images is a tensor of at least 3 dimensions. The last dimension is | |||
@@ -112,7 +115,7 @@ nearest neighbor sampling to a common output size specified by crop_size . \n | |||
*Input images must be a 4-D tensor. Inputs include: | |||
*@li images:A Tensor. Must be one of the following types:uint8, uint16, int8, | |||
int16, int32, int64, float16, float, double. A 4-D tensor of shape | |||
[batch, image_height, image_width, depth]. | |||
[batch, image_height, image_width, depth]. The format must be NHWC. | |||
*@li boxes: A Tensor of type float. A 2-D tensor of shape [num_boxes, 4]. | |||
*@li box_index: A Tensor of type int32. A 1-D tensor of shape [num_boxes] with | |||
int32 values in [0, batch). | |||
@@ -127,7 +130,7 @@ extrapolation, when applicable. | |||
NearestNeighbor . \n | |||
*@par Outputs: | |||
*y:A Tensor of type float . \n | |||
*y:A Tensor of type float. The format must be NHWC. \n | |||
*@attention Constraints: | |||
*Input images must be a 4-D tensor . \n | |||
@@ -193,7 +196,9 @@ boxes tensor . \n | |||
*@par Inputs: | |||
*Input images and grads must be a 4-D tensor. Inputs include: | |||
*@li grads: A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth]. | |||
The format must be NHWC. | |||
*@li images: A 4-D tensor of shape [batch, image_height, image_width, depth]. | |||
The format must be NHWC. | |||
Both image_height and image_width need to be positive. | |||
*@li boxes: A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor | |||
specifies the coordinates of a box in the box_ind[i] image and is specified in | |||
@@ -233,6 +238,7 @@ images tensor . \n | |||
*@par Inputs: | |||
*Input grads must be a 4-D tensor. Inputs include: | |||
*@li grads: A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth]. | |||
The format must be NHWC. | |||
*@li boxes: A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor | |||
specifies the coordinates of a box in the box_ind[i] image and is specified | |||
in normalized coordinates [y1, x1, y2, x2]. | |||
@@ -248,7 +254,8 @@ method: A string specifying the interpolation method. Only 'bilinear' is | |||
supported for now . \n | |||
*@par Outputs: | |||
*y:A 4-D tensor of shape [batch, image_height, image_width, depth] . \n | |||
*y:A 4-D tensor of shape [batch, image_height, image_width, depth]. The format | |||
must be NHWC. \n | |||
*@attention Constraints: | |||
*Input grads must be a 4-D tensor . \n | |||
@@ -273,6 +280,7 @@ REG_OP(CropAndResizeGradImage) | |||
*@par Inputs: | |||
*Input x must be a 4-D tensor. Inputs include: | |||
*@li x: A 4-D float tensor of shape [batch_size, height, width, channels]. | |||
The format must be NHWC. | |||
*@li size: A 1-D tensor of 2 elements containing the size of the glimpses to | |||
extract. The glimpse height must be specified first, following by the glimpse | |||
width. | |||
@@ -293,7 +301,7 @@ uniform_noise . \n | |||
*@par Outputs: | |||
*y:A tensor representing the glimpses [batch_size, glimpse_height, | |||
glimpse_width, channels] . \n | |||
glimpse_width, channels]. The format must be NHWC. \n | |||
*@attention Constraints: | |||
*Input x must be a 4-D tensor . \n | |||
@@ -340,7 +348,8 @@ REG_OP(HSVToRGB) | |||
*@par Inputs: | |||
*Input images must be a 4-D tensor. Inputs include: | |||
*@li images: 4-D with shape [batch, height, width, channels]. | |||
*@li images: 4-D with shape [batch, height, width, channels]. The format must | |||
be NHWC. | |||
*@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width. The new | |||
size for the images. | |||
*@li min: A Tensor of type float. | |||
@@ -354,6 +363,7 @@ the values at the corner pixels. Defaults to false. | |||
*@par Outputs: | |||
*@li resized_images: 4-D with shape [batch, new_height, new_width, channels]. | |||
The format must be NHWC. | |||
*@li y_min: A Tensor of type float. | |||
*@li y_max: A Tensor of type float . \n | |||
@@ -381,7 +391,8 @@ REG_OP(QuantizedResizeBilinear) | |||
*@par Inputs: | |||
*Input images must be a 4-D tensor. Inputs include: | |||
*@li images: 4-D with shape [batch, height, width, channels]. | |||
*@li images: 4-D with shape [batch, height, width, channels]. The format must | |||
be NHWC. | |||
*@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width. | |||
The new size for the images . \n | |||
@@ -391,7 +402,8 @@ output tensors are aligned, preserving the values at the corner pixels. | |||
Defaults to false . \n | |||
*@par Outputs: | |||
*y: 4-D with shape [batch, new_height, new_width, channels] . \n | |||
*y: 4-D with shape [batch, new_height, new_width, channels]. The format must | |||
be NHWC. \n | |||
*@attention Constraints: | |||
*Input images can be of different types but output images are always float . \n | |||
@@ -414,10 +426,10 @@ REG_OP(ResizeArea) | |||
*@par Inputs: | |||
*Input grads must be a 4-D tensor. Inputs include: | |||
*@li grads: A Tensor of type float. 4-D with shape [batch, height, width, | |||
channels]. | |||
channels]. The format must be NHWC. | |||
*@li original_image: A Tensor. Must be one of the following types: float, | |||
double. 4-D with shape [batch, orig_height, orig_width, channels], The image | |||
tensor that was resized . \n | |||
tensor that was resized. The format must be NHWC. \n | |||
*@par Attributes: | |||
*@li align_corners: An optional bool. Defaults to False. If true, the centers | |||
@@ -426,10 +438,10 @@ false. | |||
*@li half_pixel_centers: An optional bool. Defaults to False . \n | |||
*@par Outputs: | |||
*y: A Tensor. Has the same type as original_image . \n | |||
*y: A Tensor. Has the same type as original_image. The format must be NHWC. \n | |||
*@attention Constraints: | |||
*Input images can be of different types but output images are always float . \n | |||
*Input images can be of different types but output images are always float . | |||
*@par Third-party framework compatibility | |||
*Compatible with tensorflow ResizeBicubicGrad operator. | |||
@@ -448,7 +460,8 @@ REG_OP(ResizeBicubicGrad) | |||
*@par Inputs: | |||
*Input images must be a 4-D tensor. Inputs include: | |||
*@li images: 4-D with shape [batch, height, width, channels]. | |||
*@li images: 4-D with shape [batch, height, width, channels]. The format | |||
must be NHWC. | |||
*@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width. The new | |||
size for the images . \n | |||
@@ -459,10 +472,11 @@ Defaults to false. | |||
*@li half_pixel_centers: An optional bool. Defaults to False . \n | |||
*@par Outputs: | |||
*y: 4-D with shape [batch, new_height, new_width, channels] . \n | |||
*y: 4-D with shape [batch, new_height, new_width, channels]. The format | |||
must be NHWC. \n | |||
*@attention Constraints: | |||
*Input images can be of different types but output images are always float . \n | |||
*Input images can be of different types but output images are always float . | |||
*@par Third-party framework compatibility | |||
*Compatible with tensorflow ResizeBicubic operator. | |||
@@ -483,7 +497,7 @@ REG_OP(ResizeBicubic) | |||
*@par Inputs: | |||
*Input grads must be a 4-D tensor. Inputs include: | |||
*@li grads: A Tensor. Must be one of the following types: uint8, int8, int32, | |||
float16, float, double. 4-D with shape [batch, height, width, channels]. | |||
float16, float, double. Must set the format, supported format list ["NCHW, NHWC"] | |||
*@li size: A 1-D int32 Tensor of 2 elements: orig_height, orig_width. | |||
The original input size . \n | |||
@@ -550,9 +564,8 @@ REG_OP(ResizeNearestNeighborV2GradD) | |||
*@par Inputs: | |||
*Input grads must be a 4-D tensor. Inputs include: | |||
*@li grads: A Tensor of type float32. 4-D with shape [batch, height, width, | |||
channels]. | |||
*@li original_image: A Tensor. 4-D with shape [batch, orig_height, orig_width, | |||
*@li grads: A Tensor of type float32. Must set the format, supported format list ["NCHW, NHWC"] | |||
*@li original_image: A Tensor. 4-D shape. Must set the format, supported format list ["NCHW, NHWC"] | |||
channels], The image tensor that was resized . \n | |||
*@par Attributes: | |||
@@ -583,7 +596,7 @@ REG_OP(ResizeBilinearV2Grad) | |||
*@par Inputs: | |||
*Input images must be a 4-D tensor. Inputs include: | |||
*@li x: 4-D with shape [batch, height, width, channels]. | |||
*@li x: 4-D tensor. Must set the format, supported format list ["NCHW, NHWC"] | |||
*@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width. The new | |||
size for the images . \n | |||
@@ -697,7 +710,7 @@ REG_OP(SampleDistortedBoundingBoxExt2) | |||
*@par Inputs: | |||
*Input x must be a 4-D tensor. Inputs include: | |||
*@li x: 4-D with shape [batch, height, width, channels]. | |||
*@li x: 4-D tensor. Must set the format, supported format list ["NCHW, NHWC"]. | |||
*@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width. | |||
The new size for the images . \n | |||
@@ -729,12 +742,12 @@ REG_OP(ResizeNearestNeighborV2) | |||
*@par Inputs: | |||
*Input images must be a 4-D tensor. Inputs include: | |||
*@li images: A Tensor. Must be one of the following types: float. 4-D with | |||
shape [batch, height, width, depth]. A batch of images. | |||
shape [batch, height, width, depth]. A batch of images. The format must be NHWC. | |||
*@li boxes: A Tensor of type float32. 3-D with shape [batch, | |||
num_bounding_boxes, 4] containing bounding boxes . \n | |||
*@par Outputs: | |||
*A Tensor. Has the same type as images . \n | |||
*A Tensor. Has the same type as images. The format must be NHWC. \n | |||
*@attention Constraints: | |||
*Input images must be a 4-D tensor . \n | |||
@@ -1342,6 +1355,129 @@ REG_OP(SpatialTransformerD) | |||
.ATTR(use_default_theta, ListBool, {}) | |||
.OP_END_FACTORY_REG(SpatialTransformerD) | |||
} // namespace ge | |||
/** | |||
* @brief Resize the input tensor. \n | |||
currently, only support resize image tensor using nearest neighbor and linear interpolation. | |||
* @par Inputs: | |||
* Input x must be a 4-D tensor. Inputs include: \n | |||
* @li x: A Tensor. Must be one of the following types: uint8, int8, int16, \n | |||
int32, int64, float16, float, double. 4-D with shape [batch, height, width, channels] \n | |||
or shape [batch, channels, height, width]. | |||
* @li roi: A 1-D float Tensor. only takes effect when attr coordinate_transformation_mode \n | |||
is "tf_crop_and_resize" | |||
* @li scales: A 1-D float Tensor, the scale array along each dimension, Only one of \n | |||
'scales' and 'sizes' can be specified. | |||
* @li sizes: A 1-D int64 Tensor, The size of the output tensor. nly one of \n | |||
'scales' and 'sizes' can be specified. If 'size' is specified, then set scales \n | |||
to empty data (zero shape) in this operator's input list. | |||
* @par Attributes: | |||
* @li coordinate_transformation_mode: String. Defaults to half_pixel. how to transform \n | |||
the coordinate in the resized tensor to the coordinate in the original tensor. \n | |||
other optional: pytorch_half_pixel, align_corners, asymmetric, tf_half_pixel_for_nn, \n | |||
tf_crop_and_resize. | |||
* @li cubic_coeff_a: Float. Defaults to -0.75, only used in cubic interpolation. \n | |||
other optional: -0.5 | |||
* @li exclude_outside: Int. Defaults to 0, If set to 1, the weight of sampling \n | |||
locations outside the tensor will be set to 0 and the weight will be renormalized \n | |||
so that their sum is 1.0. | |||
* @li extrapolation_value: Float. Defaults to 0.0f. When coordinate_transformation_mode \n | |||
is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], \n | |||
this value is used as the corresponding output value. | |||
* @li mode: String. Defaults to nearest. Three interpolation modes: nearest (default), \n | |||
linear and cubic. | |||
* @li nearest_mode: String. Defaults to round_prefer_floor. Four modes: round_prefer_floor, \n | |||
round_prefer_ceil, floor, ceil. Only used by nearest interpolation. | |||
* @par Outputs: | |||
* y: A Tensor. Has the same type as x. | |||
* @attention Constraints: \n | |||
* Input x must be a 4-D tensor. | |||
* @par Third-party framework compatibility | |||
* Compatible with tensorflow ResizeNearestNeighborV2 operator. | |||
*/ | |||
REG_OP(Resize) | |||
.INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, | |||
DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
.INPUT(roi, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
.INPUT(scales, TensorType({DT_FLOAT})) | |||
.OPTIONAL_INPUT(sizes, TensorType({DT_INT64})) | |||
.OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, | |||
DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) | |||
.ATTR(coordinate_transformation_mode, String, "half_pixel") | |||
.ATTR(cubic_coeff_a, Float, -0.75) | |||
.ATTR(exclude_outside, Int, 0) | |||
.ATTR(extrapolation_value, Float, 0) | |||
.ATTR(mode, String, "nearest") | |||
.ATTR(nearest_mode, String, "round_prefer_floor") | |||
.OP_END_FACTORY_REG(Resize) | |||
/** | |||
*@brief Function parse image from string to int. \n | |||
*@par Inputs: | |||
*@li contents: A Tensor of type string. 0-D. The JPEG-encoded image. \n | |||
*@par Attributes: | |||
*@li channels: An optional int. Defaults to 0. Number of color channels for the decoded image. | |||
*@li ratio: An optional int. Defaults to 1. Downscaling ratio. | |||
*@li fancy_upscaling: An optional bool. Defaults to True. If true use a slower but nicer upscaling of the chroma planes | |||
*@li try_recover_truncated: An optional bool. Defaults to False. If true try to recover an image from truncated input. | |||
*@li acceptable_fraction: An optional float. Defaults to 1. The minimum required fraction of lines before a truncated input is accepted. | |||
*@li dct_method: An optional string. Defaults to "". string specifying a hint about the algorithm used for decompression. \n | |||
*@par Outputs: | |||
*image: A Tensor dtype of uint8. | |||
*/ | |||
REG_OP(DecodeJpeg) | |||
.INPUT(contents, TensorType({DT_STRING})) | |||
.OUTPUT(image, TensorType({DT_UINT8})) | |||
.ATTR(channels, Int, 0) | |||
.ATTR(ratio, Int, 1) | |||
.ATTR(fancy_upscaling, Bool, true) | |||
.ATTR(try_recover_truncated, Bool, false) | |||
.ATTR(acceptable_fraction, Float, 1.0) | |||
.ATTR(dct_method, String, "") | |||
.OP_END_FACTORY_REG(DecodeJpeg) | |||
/** | |||
*@brief Image warping using per-pixel flow vectors. \n | |||
*@par Inputs: | |||
*@li images: 4-D Tensor with shape `[batch, height, width, channels]`. | |||
*@li flow: 4-D Tensor with shape `[batch, height, width, 2]`. \n | |||
*@par Outputs: | |||
*y: Returns 4-D with the same shape and dtype as `images`. \n | |||
*/ | |||
REG_OP(DenseImageWarp) | |||
.INPUT(image, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.INPUT(flow, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OP_END_FACTORY_REG(DenseImageWarp) | |||
/** | |||
*@brief Computes the gradients of DenseImageWarp with respect to image and flow. \n | |||
*@par Inputs: | |||
*@li grad: gradients with respect to DenseImageWarp output. | |||
*@li images: 4-D Tensor with shape `[batch, height, width, channels]`. | |||
*@li flow: 4-D Tensor with shape `[batch, height, width, 2]`. \n | |||
*@par Outputs: | |||
*grad_image: Returns 4-D with the same shape and dtype as `images`. | |||
*grad_flow: Returns 4-D with the same shape and dtype as `flow`. \n | |||
*/ | |||
REG_OP(DenseImageWarpGrad) | |||
.INPUT(grad, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.INPUT(image, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.INPUT(flow, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OUTPUT(grad_image, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OUTPUT(grad_flow, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OP_END_FACTORY_REG(DenseImageWarpGrad) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_IMAGE_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -0,0 +1,230 @@ | |||
/** | |||
* 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 list_ops.h | |||
* \brief | |||
*/ | |||
#ifndef OPS_BUILT_IN_OP_PROTO_INC_LIST_OPS_H_ | |||
#define OPS_BUILT_IN_OP_PROTO_INC_LIST_OPS_H_ | |||
#include <algorithm> | |||
#include "graph/operator_reg.h" | |||
#include "graph/operator.h" | |||
namespace ge { | |||
/** | |||
*@brief Creates and returns an empty tensor list. \n | |||
*@par Inputs: | |||
*@li element_shape: A shape compatible with that of elements in the list. | |||
*@li max_num_elements: The maximum number of elements. \n | |||
*@par Attributes: | |||
*@li element_dtype: The type of elements in the list. \n | |||
*@par Outputs: | |||
*@li handle: An empty tensor list . \n | |||
*@par Third-party framework compatibility. | |||
*Compatible with tensorflow EmptyTensorList operator. | |||
*/ | |||
REG_OP(EmptyTensorList) | |||
.INPUT(element_shape, TensorType({DT_INT32,DT_INT64})) | |||
.INPUT(max_num_elements, TensorType({DT_INT32})) | |||
.OUTPUT(handle, TensorType({DT_VARIANT})) | |||
.ATTR(element_dtype, Type, DT_INT32) | |||
.OP_END_FACTORY_REG(EmptyTensorList) | |||
/** | |||
*@brief Returns a list which has the passed-in `Tensor` as last element | |||
and the other elements of the given list in `input_handle`. \n | |||
*@par Inputs: | |||
*@li input_handle: The old list. | |||
*@li tensor: The tensor to put on the list. \n | |||
*@par Attributes: | |||
*@li element_dtype: The type of elements in the list. \n | |||
*@par Outputs: | |||
*@li output_handle:A list with the elements of old list followed by tensor. \n | |||
*@par Third-party framework compatibility. | |||
*Compatible with tensorflow TensorListPushBack operator. | |||
*/ | |||
REG_OP(TensorListPushBack) | |||
.INPUT(input_handle, TensorType({DT_VARIANT})) | |||
.INPUT(tensor, TensorType({DT_FLOAT16,DT_FLOAT,DT_DOUBLE,DT_INT8, | |||
DT_INT16,DT_INT32,DT_INT64,DT_UINT8,DT_UINT16,DT_QINT8,DT_QUINT8, | |||
DT_QINT16,DT_QUINT16,DT_QINT32,DT_BOOL,DT_RESOURCE, | |||
DT_STRING,DT_COMPLEX64,DT_COMPLEX128})) | |||
.OUTPUT(output_handle, TensorType({DT_VARIANT})) | |||
.ATTR(element_dtype, Type, DT_INT32) | |||
.OP_END_FACTORY_REG(TensorListPushBack) | |||
/** | |||
*@brief The last element of the input list as well as a | |||
list with all but that element. \n | |||
*@par Inputs: | |||
*@li input_handle: The input list. | |||
*@li element_shape: A shape compatible with that of elements in the list. \n | |||
*@par Attributes: | |||
*@li element_dtype: The type of elements in the list. \n | |||
*@par Outputs: | |||
*@li output_handle:A list with the elements of the old list followed by tensor. | |||
*@li tensor:The withdrawn last element of the list. \n | |||
*@par Third-party framework compatibility. | |||
*Compatible with tensorflow TensorListPopBack operator. | |||
*/ | |||
REG_OP(TensorListPopBack) | |||
.INPUT(input_handle, TensorType({DT_VARIANT})) | |||
.INPUT(element_shape, TensorType({DT_INT32})) | |||
.OUTPUT(output_handle, TensorType({DT_VARIANT})) | |||
.OUTPUT(tensor, TensorType({DT_FLOAT16,DT_FLOAT,DT_DOUBLE,DT_INT8, | |||
DT_INT16,DT_INT32,DT_INT64,DT_UINT8,DT_UINT16,DT_QINT8,DT_QUINT8, | |||
DT_QINT16,DT_QUINT16,DT_QINT32,DT_BOOL,DT_RESOURCE, | |||
DT_STRING,DT_COMPLEX64,DT_COMPLEX128})) | |||
.ATTR(element_dtype, Type, DT_INT32) | |||
.OP_END_FACTORY_REG(TensorListPopBack) | |||
/** | |||
*@brief The number of tensors in the input tensor list. \n | |||
*@par Inputs: | |||
*@li input_handle: The input list. \n | |||
*@par Outputs: | |||
*@li length:The number of tensors in the list. \n | |||
*@par Third-party framework compatibility. | |||
*Compatible with tensorflow TensorListLength operator. | |||
*/ | |||
REG_OP(TensorListLength) | |||
.INPUT(input_handle, TensorType({DT_VARIANT})) | |||
.OUTPUT(length, TensorType({DT_INT32})) | |||
.OP_END_FACTORY_REG(TensorListLength) | |||
/** | |||
*@brief The shape of elements in the input tensor list. \n | |||
*@par Inputs: | |||
*@li input_handle: The input list. \n | |||
*@par Attributes: | |||
*@li shape_type: The type of shape in the list. \n | |||
*@par Outputs: | |||
*@li element_shape:A shape compatible with that of elements in the list. \n | |||
*@par Third-party framework compatibility. | |||
*Compatible with tensorflow TensorListElementShape operator. | |||
*/ | |||
REG_OP(TensorListElementShape) | |||
.INPUT(input_handle, TensorType({DT_VARIANT})) | |||
.OUTPUT(element_shape, TensorType({DT_INT32,DT_INT64})) | |||
.ATTR(shape_type, Type, DT_INT32) | |||
.OP_END_FACTORY_REG(TensorListElementShape) | |||
/** | |||
*@brief List of the given size with empty elements. \n | |||
*@par Inputs: | |||
*@li element_shape: A shape compatible with that of elements in the list. | |||
*@li num_elements: The number of elements to reserve. \n | |||
*@par Attributes: | |||
*@li element_dtype: The type of elements in the list. | |||
*@li shape_type: The type of shape in the list. \n | |||
*@par Outputs: | |||
*@li handle: An output tensor list . \n | |||
*@par Third-party framework compatibility. | |||
*Compatible with tensorflow TensorListReserve operator. | |||
*/ | |||
REG_OP(TensorListReserve) | |||
.INPUT(element_shape, TensorType({DT_INT32,DT_INT64})) | |||
.INPUT(num_elements, TensorType({DT_INT32})) | |||
.OUTPUT(handle, TensorType({DT_VARIANT})) | |||
.ATTR(element_dtype, Type, DT_INT32) | |||
.ATTR(shape_type, Type, DT_INT32) | |||
.OP_END_FACTORY_REG(TensorListReserve) | |||
/** | |||
*@brief Get input tensor list elements of index position. \n | |||
*@par Inputs: | |||
*@li input_handle: The input list. | |||
*@li index: A tensor of position. | |||
*@li element_shape: A shape compatible with that of elements in the list. \n | |||
*@par Attributes: | |||
*@li element_dtype: The type of elements in the list. \n | |||
*@par Outputs: | |||
*@li item: An output tensor value of index position . \n | |||
*@par Third-party framework compatibility. | |||
*Compatible with tensorflow TensorListGetItem operator. | |||
*/ | |||
REG_OP(TensorListGetItem) | |||
.INPUT(input_handle, TensorType({DT_VARIANT})) | |||
.INPUT(index, TensorType({DT_INT32})) | |||
.INPUT(element_shape, TensorType({DT_INT32})) | |||
.OUTPUT(item, TensorType({DT_FLOAT16,DT_FLOAT,DT_DOUBLE,DT_INT8, | |||
DT_INT16,DT_INT32,DT_INT64,DT_UINT8,DT_UINT16,DT_QINT8,DT_QUINT8, | |||
DT_QINT16,DT_QUINT16,DT_QINT32,DT_BOOL,DT_RESOURCE, | |||
DT_STRING,DT_COMPLEX64,DT_COMPLEX128})) | |||
.ATTR(element_dtype, Type, DT_INT32) | |||
.OP_END_FACTORY_REG(TensorListGetItem) | |||
/** | |||
*@brief Sets the index-th position of the list to contain the given tensor. \n | |||
*@par Inputs: | |||
*@li input_handle: The input list. | |||
*@li index: The position in the list to which the tensor will be assigned. | |||
*@li item: The element to be assigned to that position. \n | |||
*@par Attributes: | |||
*@li element_dtype: The type of elements in the list. \n | |||
*@par Outputs: | |||
*@li output_handle: An output tensor list . \n | |||
*@par Third-party framework compatibility. | |||
*Compatible with tensorflow TensorListSetItem operator. | |||
*/ | |||
REG_OP(TensorListSetItem) | |||
.INPUT(input_handle, TensorType({DT_VARIANT})) | |||
.INPUT(index, TensorType({DT_INT32})) | |||
.INPUT(item, TensorType({DT_FLOAT16,DT_FLOAT,DT_DOUBLE,DT_INT8, | |||
DT_INT16,DT_INT32,DT_INT64,DT_UINT8,DT_UINT16,DT_QINT8,DT_QUINT8, | |||
DT_QINT16,DT_QUINT16,DT_QINT32,DT_BOOL,DT_RESOURCE, | |||
DT_STRING,DT_COMPLEX64,DT_COMPLEX128})) | |||
.OUTPUT(output_handle, TensorType({DT_VARIANT})) | |||
.ATTR(element_dtype, Type, DT_INT32) | |||
.OP_END_FACTORY_REG(TensorListSetItem) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_LIST_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -365,6 +365,27 @@ REG_OP(GetNext) | |||
.ATTR(channel_name, String, "") | |||
.OP_END_FACTORY_REG(GetNext) | |||
/** | |||
*@brief Get dynamic dims after GetNext. \n | |||
*@par Inputs: | |||
*input: A nested structure of Tensor objects, from GetNext's output. \n | |||
*@par Attributes: | |||
*@li shape_info: GE shape_info for each inputs, -1 means unknow dim. | |||
*@li N: Inputs number. \n | |||
*@par Outputs: | |||
*dims: GE unknow dims, a vector of int64. \n | |||
*/ | |||
REG_OP(GetDynamicDims) | |||
.DYNAMIC_INPUT(input, TensorType({DT_INT32, DT_INT64})) | |||
.OUTPUT(dims, TensorType({DT_INT32, DT_INT64})) | |||
.REQUIRED_ATTR(shape_info, ListInt) | |||
.REQUIRED_ATTR(N, Int) | |||
.OP_END_FACTORY_REG(GetDynamicDims) | |||
/** | |||
*@brief End of sequence . \n | |||
@@ -710,6 +731,9 @@ REG_OP(IFMR) | |||
*@par Third-party framework compatibility | |||
*Compatible with mindspore | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(WtsARQ) | |||
@@ -741,6 +765,9 @@ REG_OP(WtsARQ) | |||
*@par Third-party framework compatibility | |||
*Compatible with mindspore | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(ActsULQ) | |||
@@ -768,6 +795,9 @@ REG_OP(ActsULQ) | |||
*@par Third-party framework compatibility | |||
*Compatible with mindspore | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(ActsULQInputGrad) | |||
@@ -790,6 +820,9 @@ REG_OP(ActsULQInputGrad) | |||
*@par Third-party framework compatibility | |||
*Compatible with mindspore | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(ActULQClampMaxGrad) | |||
@@ -812,6 +845,9 @@ REG_OP(ActULQClampMaxGrad) | |||
*@par Third-party framework compatibility | |||
*Compatible with mindspore | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(ActULQClampMinGrad) | |||
@@ -821,6 +857,33 @@ REG_OP(ActULQClampMinGrad) | |||
.OUTPUT(clamp_min_grad, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OP_END_FACTORY_REG(ActULQClampMinGrad) | |||
/** | |||
* @brief Computes Lp norm. | |||
* @par Inputs: | |||
* @li x: An ND tensor of type float16, float32. \n | |||
* | |||
* @par Attributes: | |||
* @li p: Int, "inf" or "-inf", default value is 2. | |||
* @li axes: ListInt, {} means all axes will be computed. | |||
* @li keepdim: Bool, default is false. | |||
* @li epsilon: Float, default is 1e-12. \n | |||
* @par Outputs: | |||
* @li y: An ND tensor of type float16, float32. The shape of y is depending | |||
* on axes and keepdim. \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator LpNorm. | |||
*/ | |||
REG_OP(LpNorm) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(p, Int, 2) | |||
.ATTR(axes, ListInt, {}) | |||
.ATTR(keepdim, Bool, false) | |||
.ATTR(epsilon, Float, 1e-12) | |||
.OP_END_FACTORY_REG(LpNorm) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_MATH_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* Copyright 2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
@@ -38,8 +38,8 @@ namespace ge { | |||
* float32, int32. Has format [ND, NHWC] . \n | |||
*@par Attributes: | |||
*@li transpose_a: A bool. If True, changes the shape of "x1" from [M, K] to [K, M]. | |||
*@li transpose_b: A bool. If True, changes the shape of "x2" from [M, K] to [K, M] . \n | |||
*@li transpose_x1: A bool. If True, changes the shape of "x1" from [M, K] to [K, M]. | |||
*@li transpose_x2: A bool. If True, changes the shape of "x2" from [M, K] to [K, M] . \n | |||
*@par Outputs: | |||
*y: The result matrix Tensor. 2D. Must be one of the following types: float16, | |||
@@ -70,8 +70,8 @@ REG_OP(MatMul) | |||
* float32, int32. Has format [ND, NHWC] . \n | |||
*@par Attributes: | |||
*@li transpose_a: A bool. If True, changes the shape of "x1" from [M, K] to [K, M]. | |||
*@li transpose_b: A bool. If True, changes the shape of "x2" from [M, K] to [K, M] . \n | |||
*@li transpose_x1: A bool. If True, changes the shape of "x1" from [M, K] to [K, M]. | |||
*@li transpose_x2: A bool. If True, changes the shape of "x2" from [M, K] to [K, M] . \n | |||
*@par Outputs: | |||
*y: The result matrix Tensor. 2D. Must be one of the following types: float16, | |||
@@ -156,8 +156,8 @@ REG_OP(GEMM) | |||
* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ] . \n | |||
*@par Attributes: | |||
*@li adj_x: A bool. If True, changes the shape of "x1" from [B, M, K] to [B, K, M]. | |||
*@li adj_y: A bool. If True, changes the shape of "x2" from [B, M, K] to [B, K, M] . \n | |||
*@li adj_x1: A bool. If True, changes the shape of "x1" from [B, M, K] to [B, K, M]. | |||
*@li adj_x2: A bool. If True, changes the shape of "x2" from [B, M, K] to [B, K, M] . \n | |||
*@par Outputs: | |||
*y: The result matrix Tensor. 2D or higher. Must be one of the following types: float16, | |||
@@ -175,6 +175,41 @@ REG_OP(BatchMatMul) | |||
.ATTR(adj_x2, Bool, false) | |||
.OP_END_FACTORY_REG(BatchMatMul) | |||
/** | |||
* @brief Multiplies matrix "a" by matrix "b", producing "a * b" . \n | |||
* @par Inputs: | |||
* Three inputs, including: | |||
* @li x1: A matrix Tensor. Must be one of the following types: float16, | |||
* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ]. | |||
* @li x2: A matrix Tensor. Must be one of the following types: float16, | |||
* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ] . \n | |||
* @li bias: A matrix Tensor. Must be one of the following types: float16, | |||
* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ] . \n | |||
* @par Attributes: | |||
* @li adj_x: A bool. If True, changes the shape of "x1" from [B, M, K] to [B, K, M]. | |||
* @li adj_y: A bool. If True, changes the shape of "x2" from [B, M, K] to [B, K, M] . \n | |||
* @par Outputs: | |||
* y: The result matrix Tensor. 2D or higher. Must be one of the following types: float16, | |||
* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ]. Has the same shape length as "x1" and "x2" . \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the TensorFlow operator BatchMatmul. | |||
*/ | |||
REG_OP(BatchMatMulV2) | |||
.INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32})) | |||
.INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32})) | |||
.OPTIONAL_INPUT(bias, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32})) | |||
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32})) | |||
.ATTR(adj_x1, Bool, false) | |||
.ATTR(adj_x2, Bool, false) | |||
.OP_END_FACTORY_REG(BatchMatMulV2) | |||
/** | |||
*@brief Computes half the L2 norm of a tensor without the sqrt . \n | |||
@@ -979,6 +1014,14 @@ REG_OP(MatrixDiagV2) | |||
.OUTPUT(output, TensorType::BasicType()) | |||
.OP_END_FACTORY_REG(MatrixDiagV2) | |||
REG_OP(IndexAdd) | |||
.INPUT(var, TensorType({DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16})) | |||
.INPUT(indices, TensorType({DT_INT32})) | |||
.INPUT(updates, TensorType({DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16})) | |||
.OUTPUT(var_out, TensorType({DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16})) | |||
.ATTR(axis, Int, 0) | |||
.OP_END_FACTORY_REG(IndexAdd) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -365,6 +365,25 @@ REG_OP(BiasAddGrad) | |||
* 4-D with shape [batch, out_height, out_width, out_channels] | |||
* or [batch, out_channels, out_height, out_width]. | |||
* Gradients with respect to the output of the convolution. | |||
*\n | |||
*\n | |||
* The following are the supported data types and data formats: | |||
*@verbatim | |||
| Tensor | out_bckprop | filter | y | |||
------------|-------------|---------|-------- | |||
| Data Type | float16 | float16 | float16 | |||
| |-------------|---------|-------- | |||
| | float32 | float32 | float32 | |||
| |-------------|---------|-------- | |||
| | float64 | float64 | float64 | |||
------------|-------------|---------|-------- | |||
| Format | NCHW | NCHW | NCHW | |||
| | NHWC | HWCN | NHWC | |||
@endverbatim | |||
* For float32 and float64 type, the actual calculation on the chip is based on | |||
* float16. | |||
*\n | |||
* | |||
*@par Attributes: | |||
* Five attributes: | |||
* @li strides: A tuple/list of 4 integers. The stride of the sliding window | |||
@@ -377,8 +396,52 @@ REG_OP(BiasAddGrad) | |||
* channels. | |||
* @li data_format: An optional string from: "NHWC", "NCHW". Defaults to | |||
* "NHWC". Specify the data format of the input and output data. | |||
*\n | |||
*\n | |||
* The following value range restrictions must be met: | |||
*@verbatim | |||
| Name | Field | Scope | |||
-------------------|----------|-------------- | |||
| input_size | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| Filter | H | [1, 255] | |||
| | W | [1, 255] | |||
-------------------|----------|-------------- | |||
| out_backprop | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| y(fmap) | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| Stride | H | [1, 63] | |||
| | W | [1, 63] | |||
-------------------|----------|-------------- | |||
| Padding | Top | [0, 255] | |||
| | Bottom | [0, 255] | |||
| | Left | [0, 255] | |||
| | Right | [0, 255] | |||
-------------------|----------|-------------- | |||
| Dilation | H | [1, 255] | |||
| | W | [1, 255] | |||
@endverbatim | |||
* In Ascend910, fmap or out_backprop's H and W not support 1 when | |||
* fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1 | |||
*\n | |||
* | |||
*@par Outputs: | |||
* y: A Tensor. Has the same type as filter,and has same format as input_size. | |||
*\n | |||
* out_backprop_height = (fmap_height + pad_top + pad_bottom - | |||
* (dilation_h * (filter_height - 1) + 1)) | |||
* / stride_h + 1 | |||
*\n | |||
* out_backprop_width = (fmap_width + pad_left + pad_right - | |||
* (dilation_w * (filter_width - 1) + 1)) | |||
* / stride_w + 1 | |||
*\n | |||
* | |||
*@par Third-party framework compatibility | |||
* Compatible with Tensorflow's conv2d_backprop_input | |||
*/ | |||
@@ -454,6 +517,21 @@ REG_OP(Conv2DBackpropInputD) | |||
* @li bias: An optional tensor. Must have the same type as "y". | |||
* @li offset_w: An optional 1D tensor for quantized deconvolution. | |||
* Type is int8. Reserved.\n | |||
*\n | |||
*\n | |||
* The following are the supported data types and data formats: | |||
*@verbatim | |||
| Tensor | x | filter | bias | y | |||
------------|---------|---------|---------|-------- | |||
| Data Type | float16 | float16 | float16 | float16 | |||
| |---------|---------|---------|-------- | |||
| | int8 | int8 | int32 | int32 | |||
------------|---------|---------|---------|-------- | |||
| Format | NCHW | NCHW | ND | NCHW | |||
@endverbatim | |||
* For int8, a dequant or requant operator must be followed. | |||
*\n | |||
* | |||
*@par Attributes: | |||
* Six attributes: | |||
* @li strides: A tuple or list of 2 integers. The stride of the sliding window | |||
@@ -468,8 +546,51 @@ REG_OP(Conv2DBackpropInputD) | |||
Specify the data format of the input and output data. | |||
* @li offset_x: An optional integer for quantized deconvolution. | |||
* Defaults to "0". | |||
*\n | |||
*\n | |||
* The following value range restrictions must be met: | |||
*@verbatim | |||
| Name | Field | Scope | |||
-------------------|----------|-------------- | |||
| x (out_backprop) | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| Filter | H | [1, 255] | |||
| | W | [1, 255] | |||
-------------------|----------|-------------- | |||
| y (fmap) | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| Stride | H | [1, 63] | |||
| | W | [1, 63] | |||
-------------------|----------|-------------- | |||
| Padding | Top | [0, 255] | |||
| | Bottom | [0, 255] | |||
| | Left | [0, 255] | |||
| | Right | [0, 255] | |||
-------------------|----------|-------------- | |||
| Dilation | H | [1, 255] | |||
| | W | [1, 255] | |||
-------------------|----------|-------------- | |||
| Offset_x | | [-128, 127] | |||
@endverbatim | |||
* In Ascend910, fmap or out_backprop's H and W not support 1 when | |||
* fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1 | |||
*\n | |||
* | |||
*@par Outputs: | |||
* y: A Tensor. 4D tensor with shape [batch, channels, height, width]. | |||
*\n | |||
* out_backprop_height = (fmap_height + pad_top + pad_bottom - | |||
* (dilation_h * (filter_height - 1) + 1)) | |||
* / stride_h + 1 | |||
*\n | |||
* out_backprop_width = (fmap_width + pad_left + pad_right - | |||
* (dilation_w * (filter_width - 1) + 1)) | |||
* / stride_w + 1 | |||
*\n | |||
* | |||
* When type of x is float16, the type of y must be float16. | |||
* When type of x is int8, the type of y must be int32. | |||
*/ | |||
@@ -502,6 +623,25 @@ REG_OP(Deconvolution) | |||
* [batch, out_height, out_width, out_channels] or [batch, out_channels, | |||
* out_height, out_width]. Gradients with respect to the output of the | |||
* convolution. | |||
*\n | |||
*\n | |||
* The following are the supported data types and data formats: | |||
*@verbatim | |||
| Tensor | x | out_backprop | y | |||
------------|---------|--------------|--------- | |||
| Data Type | float16 | float16 | float16 | |||
| |---------|--------------|--------- | |||
| | float32 | float32 | float32 | |||
| |---------|--------------|--------- | |||
| | float64 | float64 | float64 | |||
|-----------|---------|--------------|--------- | |||
| Format | NCHW | NCHW | NCHW | |||
| | NHWC | NHWC | HWCN | |||
@endverbatim | |||
* For float32 and float64 type of x and outbackprop, the actual calculation on the chip | |||
* is based on float16. | |||
*\n | |||
* | |||
*@par Attributes: | |||
* Five attributes: | |||
* @li strides: A tuple/list of 4 integers. The stride of the sliding window | |||
@@ -514,8 +654,52 @@ REG_OP(Deconvolution) | |||
* channels. | |||
* @li data_format: An optional string from: "NHWC", "NCHW". Defaults to | |||
* "NHWC". Specify the data format of the input and output data. | |||
*\n | |||
*\n | |||
* The following value range restrictions must be met: | |||
*@verbatim | |||
| Name | Field | Scope | |||
-------------------|----------|-------------- | |||
| x(fmap) | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| Filter Size | H | [1, 255] | |||
| | W | [1, 255] | |||
-------------------|----------|-------------- | |||
| out_backprop | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| y | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| Stride | H | [1, 63] | |||
| | W | [1, 63] | |||
-------------------|----------|-------------- | |||
| Padding | Top | [0, 255] | |||
| | Bottom | [0, 255] | |||
| | Left | [0, 255] | |||
| | Right | [0, 255] | |||
-------------------|----------|-------------- | |||
| Dilation | H | [1, 255] | |||
| | W | [1, 255] | |||
@endverbatim | |||
* In Ascend910, out_backprop's H and W not support 1 when | |||
* fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1 | |||
*\n | |||
* | |||
*@par Outputs: | |||
* y: A Tensor. Has the same type as x, has the same format as filter_size. | |||
*\n | |||
* out_backprop_height = (in_height + pad_top + pad_bottom - | |||
* (dilation_h * (filter_height - 1) + 1)) | |||
* / stride_h + 1 | |||
*\n | |||
* out_backprop_width = (in_width + pad_left + pad_right - | |||
* (dilation_w * (filter_width - 1) + 1)) | |||
* / stride_w + 1 | |||
*\n | |||
* | |||
*@par Third-party framework compatibility | |||
* Compatible with Tensorflow's conv2d_backprop_filter | |||
*/ | |||
@@ -617,8 +801,7 @@ REG_OP(Conv2DBackpropFilterD) | |||
* (top, bottom, left, right) side of the input. | |||
*@li dilations: Optional. A list of 4 integers. The dilation factor for each | |||
* dimension of input. The dimension order is determined by the data format of | |||
* "x". The N and C dimensions must be set to 1. The H and W dimensions must be | |||
* set to 1 for int8 type. Defaults to [1, 1, 1, 1]. | |||
* "x". The N and C dimensions must be set to 1. Defaults to [1, 1, 1, 1]. | |||
*@li groups: Optional. An integer of type int32. The number of blocked | |||
* connections from input channels to output channels. In_channels and | |||
* out_channels must both be divisible by "groups". Defaults to 1. | |||
@@ -652,6 +835,8 @@ REG_OP(Conv2DBackpropFilterD) | |||
| Offset_x | | [-128, 127] | |||
@endverbatim | |||
* The W dimension of the input image supports cases exceeding 4096, but it may | |||
* cause compilation errors. | |||
*\n | |||
* | |||
*@par Outputs: | |||
@@ -666,21 +851,6 @@ REG_OP(Conv2DBackpropFilterD) | |||
* out_width = (in_width + pad_left + pad_right - | |||
* (dilation_w * (filter_width - 1) + 1)) | |||
* / stride_w + 1 | |||
* | |||
*@attention Constraints: | |||
*@li The following restrictions on the output must be met: | |||
*@verbatim | |||
| Output | Restrictions | |||
----------|-------------------------------- | |||
| H == 1 | H * W(input) == H * W(filter) | |||
| W == 1 | | |||
----------|-------------------------------- | |||
| H != 1 | W(input) == W(filter) | |||
| W == 1 | Only for Ascend310 Hi3796V300CS | |||
@endverbatim | |||
* "H * W (input)" indicates the image size after padding and "H * W (filter)" | |||
* indicates the filter size after dilation."W(input)" and W(filter) indicate | |||
* the same rule on the W dimension. | |||
*\n | |||
* | |||
*@par Quantization supported or not | |||
@@ -778,7 +948,7 @@ REG_OP(Conv2DCompress) | |||
* With the format "HWCN" , the data is stored in the order of: [filter_height, | |||
* filter_width, in_channels / groups, out_channels]. | |||
*@li offsets: A 4D tensor of x-y coordinates offset and mask. With the format | |||
* "NHWC", the data is stored in the order of: [batch, in_height, in_width, | |||
* "NHWC", the data is stored in the order of: [batch, out_height, out_width, | |||
* deformable_groups * filter_height * filter_width * 3]. | |||
*@li bias: An optional 1D tensor of additive biases to the filter outputs. | |||
* The data is stored in the order of: [out_channels]. | |||
@@ -822,25 +992,12 @@ REG_OP(Conv2DCompress) | |||
*@verbatim | |||
| Name | Field | Scope | |||
--------------------|--------|---------------------------- | |||
| Input Image Size | H | [1, 100000] | |||
| | W | [1, 4096] | |||
| Input Image Size | H | [1, 100000 / filter_height] | |||
| | W | [1, 4096 / filter_width] | |||
--------------------|--------|---------------------------- | |||
| Filter Size | H | [1, 255] | |||
| | W | [1, 255] | |||
--------------------|--------|---------------------------- | |||
| Stride | H | [1, 63] | |||
| Filter Size | H | [1, 63] | |||
| | W | [1, 63] | |||
--------------------|--------|---------------------------- | |||
| Padding | Top | [0, 255] | |||
| | Bottom | [0, 255] | |||
| | Left | [0, 255] | |||
| | Right | [0, 255] | |||
------------ -------|--------|---------------------------- | |||
| Dilation | H | [1, 255] | |||
| | W | [1, 255] | |||
@endverbatim | |||
* "W(input)" indicate the image width after padding and W(filter) indicates the | |||
* filter width after dilation. | |||
*\n | |||
* | |||
*@par Outputs: | |||
@@ -855,21 +1012,7 @@ REG_OP(Conv2DCompress) | |||
* out_width = (in_width + pad_left + pad_right - | |||
* (dilation_w * (filter_width - 1) + 1)) | |||
* / stride_w + 1 | |||
* | |||
*@attention Constraints: | |||
*@li The following restrictions on the output must be met: | |||
*@verbatim | |||
| Output | Restrictions | |||
----------|-------------------------------- | |||
| H == 1 | H * W(input) == H * W(filter) | |||
| W == 1 | | |||
----------|-------------------------------- | |||
| H != 1 | W(input) == W(filter) | |||
| W == 1 | Only for Ascend310 Hi3796V300CS | |||
@endverbatim | |||
* "H * W(input)" indicates the image size after padding and "H * W(filter)" | |||
* indicates the filter size after dilation. "W(input)" and W(filter) indicate | |||
* the same rule on the W dimension. | |||
*\n | |||
* | |||
*@par Quantization supported or not | |||
*@li No | |||
@@ -920,8 +1063,8 @@ REG_OP(DeformableConv2D) | |||
* @li data_format: An optional string from: "NDHWC", "NCDHW". | |||
* Defaults to "NDHWC". Specify the data format of the input and output data. | |||
* @li dilations: A list of 5 integers. Specifies the dilation factor for each | |||
* dimension of "x", now only support [1,1,1,1,1] | |||
* The N and C dimensions must be 1. Has the same format as "x". | |||
* dimension of "x". | |||
* The N, C and D dimensions must be 1. Has the same format as "x". | |||
* @li offset_x: An optional int. Input offset, used for quantized inference. | |||
* Defaults to 0. Reserved . \n | |||
@@ -967,8 +1110,8 @@ REG_OP(Conv3D) | |||
*@par Required Attributes: | |||
* @li strides: A list of 5 integers. Specifies the stride of the sliding window | |||
* for each dimension of "x". | |||
* The N and C dimensions must be 1. Has the same format as "x". | |||
* for each dimension of "out_backprop". | |||
* The N and C dimensions must be 1. Has the same format as "out_backprop". | |||
* @li pads: A list of 6 integers. | |||
* Supports only padding along the D, H and W dimensions in sequence of head, | |||
* tail, top, bottom, left and right . \n | |||
@@ -980,10 +1123,11 @@ REG_OP(Conv3D) | |||
* @li data_format: An optional string from: "NDHWC", "NCDHW". | |||
* Defaults to "NDHWC". Specify the data format of the input and output data. | |||
* @li dilations: A tuple/list of 5 integers, The dilation factor for each | |||
* dimension of the input, now only support [1,1,1,1,1] | |||
* dimension of the input. | |||
* The N, C and D dimensions must be 1. Has the same format as "out_backprop". | |||
*@par Outputs: | |||
* y: A Tensor. Has the same type as filter,and has same format as input_size | |||
* y: A Tensor. Has the same type as filter,and has same format as "input_size" | |||
*@par Third-party framework compatibility | |||
* Compatible with Tensorflow's conv3d_backprop_input | |||
@@ -1011,8 +1155,8 @@ REG_OP(Conv3DBackpropInput) | |||
*@par Required Attributes: | |||
* @li strides: A list of 5 integers. Specifies the stride of the sliding window | |||
* for each dimension of "x". | |||
* The N and C dimensions must be 1. Has the same format as "x". | |||
* for each dimension of "out_backprop". | |||
* The N and C dimensions must be 1. Has the same format as "out_backprop". | |||
* @li pads: A list of 6 integers. Supports only padding along the D, H and W | |||
* dimensions in sequence of head, tail, top, bottom, left and right. | |||
* @li input_size: A tuple/list of type int32, int64. An integer vector | |||
@@ -1027,9 +1171,10 @@ REG_OP(Conv3DBackpropInput) | |||
* @li data_format: An optional string from: "NDHWC", "NCDHW". | |||
* Defaults to "NDHWC". Specify the data format of the input and output data. | |||
* @li dilations: A tuple/list of 5 integers, The dilation factor for each | |||
* dimension of input, now only support [1,1,1,1,1] | |||
* dimension of input. | |||
* The N, C and D dimensions must be 1. Has the same format as "out_backprop". | |||
*@par Outputs: | |||
* y: A Tensor. Has the same type and data format as out_backprop. | |||
* y: A Tensor. Has the same type and data format as "out_backprop". | |||
*@par Third-party framework compatibility | |||
* Compatible with Tensorflow's conv3d_backprop_input | |||
@@ -1072,9 +1217,7 @@ REG_OP(Conv3DBackpropInputD) | |||
* @li c_t: A optinal Tensor dtype of float16, float32. The cell state at time t . \n | |||
*@par Third-party framework compatibility: | |||
* Compatible with the Pytorch operator adds. | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
* Compatible with the Caffe operator LSTM. | |||
*/ | |||
REG_OP(LSTM) | |||
.INPUT(x, TensorType({DT_FLOAT16})) | |||
@@ -1121,14 +1264,15 @@ REG_OP(LSTM) | |||
*@par Attributes: | |||
* Three attributes: | |||
* @li dilations: A tuple/list of 5 integers, The dilation factor for each | |||
* dimension of input, now only support [1,1,1,1,1]. | |||
* dimension of input. | |||
* The N, C and D dimensions must be 1. Has the same format as "x". | |||
* @li groups: Number of blocked connections from input channels to output | |||
* channels. Reserved. | |||
* @li data_format: An optional string from: "NDHWC", "NCDHW". | |||
* Defaults to "NDHWC". Specify the data format of the input and output data. | |||
*@par Outputs: | |||
* y: A Tensor that has the same type as x | |||
* y: A Tensor that has the same type as "x" | |||
* and the format is NDHWC, NCDHW or DHWCN. | |||
*@par Third-party framework compatibility | |||
* Compatible with Tensorflow's conv3d_backprop_filter | |||
@@ -1172,7 +1316,8 @@ REG_OP(Conv3DBackpropFilter) | |||
*@par Attributes: | |||
* Three attributes: | |||
* @li dilations: A tuple/list of 5 integers, The dilation factor for each | |||
* dimension of input, now only support [1,1,1,1,1]. | |||
* dimension of input. | |||
* The N, C and D dimensions must be 1. Has the same format as "x". | |||
* @li groups: Number of blocked connections from input channels to output | |||
* channels. Reserved. | |||
* @li data_format: An optional string from: "NDHWC", "NCDHW". | |||
@@ -1226,13 +1371,14 @@ REG_OP(Conv3DBackpropFilterD) | |||
* @li groups: Number of blocked connections from input channels to output | |||
* channels. Reserved. | |||
* @li dilations: A tuple/list of 5 integers, | |||
* The dilation factor for each dimension of input, now only support [1,1,1,1,1] | |||
* The dilation factor for each dimension of input. | |||
* The N, C and D dimensions must be 1. Has the same format as "x". | |||
* @li data_format: An optional string from: "NDHWC", "NCDHW". | |||
* Defaults to "NDHWC". Specify the data format of the input and output data. | |||
* @li output_padding: The size will be added in the output shape. | |||
* @li offset_x: Input offset_x value. Reserved. | |||
*@par Outputs: | |||
* y: A Tensor. Has the same type and format as x. | |||
* y: A Tensor. Has the same type and format as "x". | |||
*/ | |||
REG_OP(Conv3DTranspose) | |||
.INPUT(input_size, TensorType({DT_INT32, DT_INT64})) | |||
@@ -1273,7 +1419,8 @@ REG_OP(Conv3DTranspose) | |||
*@par Attributes: | |||
* Five attributes: | |||
* @li dilations: A tuple/list of 5 integers, The dilation factor for each | |||
* dimension of input, now only support [1,1,1,1,1] | |||
* dimension of input. | |||
* The N, C and D dimensions must be 1. Has the same format as "x". | |||
* @li groups: Number of blocked connections from input channels to output | |||
* channels. Reserved. | |||
* @li data_format: An optional string from: "NDHWC", "NCDHW". | |||
@@ -1281,7 +1428,7 @@ REG_OP(Conv3DTranspose) | |||
* @li output_padding: The size will be added in the output shape. | |||
* @li offset_x: Input offset_x value. Reserved. | |||
*@par Outputs: | |||
* y: A Tensor. Has the same type and format as x. | |||
* y: A Tensor. Has the same type and format as "x". | |||
*@par Restrictions: | |||
* Warning: THIS FUNCTION IS DEPRECATED. Please use Conv3DTranspose instead. | |||
*/ | |||
@@ -1316,6 +1463,22 @@ REG_OP(Conv3DTransposeD) | |||
* or [out_channels, in_channel, filter_height, filter_width]. | |||
* @li bias: An optional 1D tensor of type float16 or int32. Format is "ND". | |||
* @li offset_w: An optional 1D tensor for quantized inference. Reserved. | |||
*\n | |||
*\n | |||
* The following are the supported data types and data formats: | |||
*@verbatim | |||
| Tensor | x | filter | bias | y | |||
------------|---------|---------|---------|-------- | |||
| Data Type | float16 | float16 | float16 | float16 | |||
| |---------|---------|---------|-------- | |||
| | int8 | int8 | int32 | int32 | |||
------------|---------|---------|---------|-------- | |||
| Format | NCHW | NCHW | ND | NCHW | |||
| | NHWC | HWCN | | NHWC | |||
@endverbatim | |||
* For int8, a dequant or requant operator must be followed. | |||
*\n | |||
* | |||
*@par Required Attributes: | |||
* @li strides: A required tuple/list of 4 integers. The stride of the sliding | |||
* window for H/W dimension. The index of H/W is same as data_format. | |||
@@ -1334,9 +1497,55 @@ REG_OP(Conv3DTransposeD) | |||
* to [0, 0, 0, 0]. | |||
* @li offset_x: An optional int. Input offset, used for quantized inference. | |||
* Defaults to "0". | |||
*\n | |||
*\n | |||
* The following value range restrictions must be met: | |||
*@verbatim | |||
| Name | Field | Scope | |||
-------------------|----------|-------------- | |||
| input_size | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| x (out_backprop) | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| filter | H | [1, 255] | |||
| | W | [1, 255] | |||
-------------------|----------|-------------- | |||
| y (fmap) | H | [1, 4096] | |||
| | W | [1, 4096] | |||
-------------------|----------|-------------- | |||
| Stride | H | [1, 63] | |||
| | W | [1, 63] | |||
-------------------|----------|-------------- | |||
| Padding | Top | [0, 255] | |||
| | Bottom | [0, 255] | |||
| | Left | [0, 255] | |||
| | Right | [0, 255] | |||
-------------------|----------|-------------- | |||
| Dilation | H | [1, 255] | |||
| | W | [1, 255] | |||
-------------------|----------|-------------- | |||
| Offset_x | | [-128, 127] | |||
@endverbatim | |||
* In Ascend910, fmap or out_backprop's H and W not support 1 when | |||
* fmap_h + pad_top + pad_bottom != (filter_height - 1) * dilation_h + 1 | |||
*\n | |||
* | |||
*@par Outputs: | |||
* y: A Tensor. A Tensor of type float16 or int32, and has same format as | |||
* input_size. | |||
*\n | |||
* out_backprop_height = (fmap_height + pad_top + pad_bottom - | |||
* (dilation_h * (filter_height - 1) + 1)) | |||
* / stride_h + 1 | |||
*\n | |||
* out_backprop_width = (fmap_width + pad_left + pad_right - | |||
* (dilation_w * (filter_width - 1) + 1)) | |||
* / stride_w + 1 | |||
*\n | |||
* | |||
*/ | |||
REG_OP(Conv2DTranspose) | |||
.INPUT(input_size, TensorType({DT_INT32, DT_INT64})) | |||
@@ -1405,13 +1614,13 @@ REG_OP(Conv2DTransposeD) | |||
/** | |||
*@brief Computes the deformed convolution output with the expected input | |||
*@par Inputs: | |||
* Four inputs: | |||
* Two inputs: | |||
* @li x: A Tensor of type float16,float32 | |||
* @li offsets: A Tensor of type float16,float32.Deformation offset parameter. | |||
*@par Required Attributes: | |||
* @li strides: A tuple/list of 4 integers.The stride of the sliding window for | |||
* height and width for H/W dimension. | |||
* @li pads: A tuple/list of 4 integers.Padding added to each dimension | |||
* @li pads: A tuple/list of 4 integers.Padding added to H/W dimension | |||
* of the input. | |||
* @li ksize: A tuple/list of 2 integers.kernel size. | |||
*@par Attributes: | |||
@@ -1420,6 +1629,7 @@ REG_OP(Conv2DTransposeD) | |||
* of input. Defaults to [1, 1, 1, 1] | |||
* @li data_format: An optional string from: "NCHW", "NHWC". Defaults to "NCHW". Specify the data format of the input x. | |||
* @li deformable_groups: Specify the c-axis grouping number of input x. | |||
* @li modulated: Specify version of DeformableConv2D, true means v2, false means v1 | |||
*@par Outputs: | |||
* y: A Tensor. A Tensor of type float16, float32. | |||
*/ | |||
@@ -1433,7 +1643,69 @@ REG_OP(DeformableOffsets) | |||
.ATTR(dilations, ListInt, {1, 1, 1, 1}) | |||
.ATTR(data_format, String, "NCHW") | |||
.ATTR(deformable_groups, Int, 1) | |||
.ATTR(modulated, Bool, true) | |||
.OP_END_FACTORY_REG(DeformableOffsets) | |||
/** | |||
*@brief Computes the gradients of DeformableOffsets with respect to input and offsets | |||
*@par Inputs: | |||
* Three inputs: | |||
* @li grad: A Tensor of type float16,float32. gradients with respect to DeformableOffsets output | |||
* @li x: A Tensor of type float16,float32. | |||
* @li offsets: A Tensor of type float16,float32.Deformation offset parameter. | |||
*@par Required Attributes: | |||
* @li strides: A tuple/list of 4 integers.The stride of the sliding window for | |||
* height and width for H/W dimension. | |||
* @li pads: A tuple/list of 4 integers.Padding added to H/W dimension | |||
* of the input. | |||
* @li ksize: A tuple/list of 2 integers.kernel size. | |||
*@par Attributes: | |||
* Three attributes: | |||
* @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension | |||
* of input. Defaults to [1, 1, 1, 1] | |||
* @li data_format: An optional string from: "NCHW", "NHWC". Defaults to "NCHW". Specify the data format of the input x. | |||
* @li deformable_groups: Specify the c-axis grouping number of input x. | |||
* @li modulated: Specify version of DeformableConv2D, true means v2, false means v1. | |||
*@par Outputs: | |||
* grad_x: A Tensor of type float16, float32. Gradients with respect to input_x | |||
* grad_offsets: A Tensor of type float16, float32. Gradients with respect to input_offsets | |||
*/ | |||
REG_OP(DeformableOffsetsGrad) | |||
.INPUT(grad, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(offsets, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(grad_x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(grad_offsets, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(pads, ListInt) | |||
.REQUIRED_ATTR(ksize, ListInt) | |||
.ATTR(dilations, ListInt, {1, 1, 1, 1}) | |||
.ATTR(data_format, String, "NCHW") | |||
.ATTR(deformable_groups, Int, 1) | |||
.ATTR(modulated, Bool, true) | |||
.OP_END_FACTORY_REG(DeformableOffsetsGrad) | |||
/** | |||
*@brief Computes the deformed dilation output with the expected input | |||
*@par Inputs: | |||
* One inputs: | |||
* @li x: A Tensor of type int8, float16, float32 | |||
*@par Required Attributes: | |||
* @li dilations: A tuple/list of integers. | |||
*@par Attributes: | |||
* Two attributes: | |||
* @li padding_value: default value filling in blank | |||
* @li pads: A tuple/list of integers. | |||
*@par Outputs: | |||
* y: A Tensor. A Tensor of type int8, float16, float32. | |||
*/ | |||
REG_OP(Dilation) | |||
.INPUT(x, TensorType({DT_INT8, DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_INT8, DT_FLOAT16, DT_FLOAT})) | |||
.REQUIRED_ATTR(dilations, ListInt) | |||
.ATTR(pads, ListInt, {}) | |||
.ATTR(padding_value, Float, 0.0) | |||
.OP_END_FACTORY_REG(Dilation) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_NN_CALCULATION_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1383,6 +1383,7 @@ REG_OP(DecodeWheelsTarget) | |||
*@attention Constraints: | |||
* Only computation of float16 data is supported. | |||
* Note: when the class num per image * max_size_per_class is too big, will compile fail with ERROR-insufficient memory | |||
*/ | |||
REG_OP(BatchMultiClassNonMaxSuppression) | |||
.INPUT(boxes, TensorType({DT_FLOAT16})) | |||
@@ -1485,7 +1486,10 @@ REG_OP(DecodeBboxV2) | |||
* | |||
*@par Outputs: | |||
* @li y1: A Tensor. Must have the same type as x. | |||
* @li y2: A Tensor. Indices of y1 in x.Dtype must be int32. | |||
* @li y2: A Tensor. Indices of y1 in x. Dtype must be int32. | |||
* | |||
*@attention Constraints: | |||
* The upper limit of data on the direction axis is 7040. | |||
*/ | |||
REG_OP(Sort) | |||
.INPUT(x, TensorType({ DT_FLOAT16 })) | |||
@@ -1495,6 +1499,111 @@ REG_OP(Sort) | |||
.ATTR(descending, Bool, false) | |||
.OP_END_FACTORY_REG(Sort) | |||
REG_OP(PtIou) | |||
.INPUT(bboxes, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(gtboxes, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(overlap, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(mode, String, "iou") | |||
.OP_END_FACTORY_REG(PtIou) | |||
/** | |||
*@brief Greedily selects a subset of bounding boxes in descending order of | |||
score . \n | |||
*@par Inputs: | |||
*Input boxes and scores must be float16 type. Inputs include: | |||
*@li boxes: A input tensor with shape [num_batches,spatial_dimension,4]. | |||
The single box data format is indicated by center_point_box. | |||
*@li scores: A input tensor with shape [num_batches,num_classes,spatial_dimension] | |||
*@li max_output_size: A scalar integer tensor representing the maximum number | |||
of boxes to be selected by non max suppression. | |||
*@li iou_threshold: A 0-D float tensor representing the threshold for deciding | |||
whether boxes overlap too much with respect to IOU. | |||
*@li score_threshold: A 0-D float tensor representing the threshold for | |||
deciding when to remove boxes based on score . \n | |||
*@par Attributes: | |||
*center_point_box:Integer indicate the format of the box data. | |||
The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] | |||
where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair | |||
of box corners and the coordinates can be provided as normalized | |||
(i.e., lying in the interval [0, 1]) or absolute.Mostly used for TF models. | |||
1 - the box data is supplied as [x_center, y_center, width, height]. | |||
Mostly used for Pytorch models. \n | |||
*@par Outputs: | |||
*@li selected_indices: A 2-D integer tensor of shape [M] representing the | |||
selected indices from the boxes tensor, where M <= max_output_size. \n | |||
*@attention Constraints: | |||
*Input boxes and scores must be float16 type . \n | |||
*@par Third-party framework compatibility | |||
*Compatible with onnx NonMaxSuppression operator. | |||
*/ | |||
REG_OP(NonMaxSuppressionV6) | |||
.INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(max_output_size, TensorType({DT_INT32})) | |||
.OPTIONAL_INPUT(iou_threshold, TensorType({DT_FLOAT})) | |||
.OPTIONAL_INPUT(score_threshold, TensorType({DT_FLOAT})) | |||
.OUTPUT(selected_indices, TensorType({DT_INT32})) | |||
.ATTR(center_point_box, Int, 0) | |||
.ATTR(max_boxes_size, Int, 0) | |||
.OP_END_FACTORY_REG(NonMaxSuppressionV6) | |||
/** | |||
*@brief Greedily selects a subset of bounding boxes in descending order of | |||
score . \n | |||
*@par Inputs: | |||
*Input boxes and scores must be float16 type. Inputs include: | |||
*@li boxes: A input tensor with shape [num_batches,spatial_dimension,4]. | |||
The single box data format is indicated by center_point_box. | |||
*@li scores: A input tensor with shape [num_batches,num_classes,spatial_dimension] | |||
*@li max_output_size: A scalar integer tensor representing the maximum number | |||
of boxes to be selected by non max suppression. | |||
*@li iou_threshold: A 0-D float tensor representing the threshold for deciding | |||
whether boxes overlap too much with respect to IOU. | |||
*@li score_threshold: A 0-D float tensor representing the threshold for | |||
deciding when to remove boxes based on score . \n | |||
*@li index_id: A input tensor with shape [num_batches,num_classes,spatial_dimension,3] | |||
the last dim representing (batch_id,class_id,index_id) . \n | |||
*@par Attributes: | |||
*center_point_box:Integer indicate the format of the box data. | |||
The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] | |||
where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair | |||
of box corners and the coordinates can be provided as normalized | |||
(i.e., lying in the interval [0, 1]) or absolute.Mostly used for TF models. | |||
1 - the box data is supplied as [x_center, y_center, width, height]. | |||
Mostly used for Pytorch models. \n | |||
*@par Outputs: | |||
*@li selected_indices: A 2-D integer tensor of shape [M] representing the | |||
selected indices from the boxes tensor, where M <= max_output_size. \n | |||
*@attention Constraints: | |||
*Input boxes and scores must be float16 type . \n | |||
*@par Third-party framework compatibility | |||
*Compatible with onnx NonMaxSuppression operator. | |||
*/ | |||
REG_OP(NonMaxSuppressionV7) | |||
.INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(max_output_size, TensorType({DT_INT32})) | |||
.OPTIONAL_INPUT(iou_threshold, TensorType({DT_FLOAT})) | |||
.OPTIONAL_INPUT(score_threshold, TensorType({DT_FLOAT})) | |||
.OPTIONAL_INPUT(index_id, TensorType({DT_FLOAT16})) | |||
.OUTPUT(selected_indices, TensorType({DT_INT32})) | |||
.ATTR(center_point_box, Int, 0) | |||
.ATTR(max_boxes_size, Int, 0) | |||
.OP_END_FACTORY_REG(NonMaxSuppressionV7) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_NN_DETECT_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -160,20 +160,20 @@ REG_OP(SigmoidCrossEntropyWithLogits) | |||
.OP_END_FACTORY_REG(SigmoidCrossEntropyWithLogits) | |||
/** | |||
*@brief Computes the sigmoid cross entropy loss of "predict" and "target" . \n | |||
*@brief Computes the sigmoid cross entropy loss of "predict" and "target". | |||
*@par Inputs: | |||
* four inputs, including: | |||
*@li predict: A multi-dimensional Tensor of type float16 or float32, specifying the predictive value. | |||
*@li target: A multi-dimensional Tensor of type float16 or float32, specifying the target value . \n | |||
*@li weight: An multi-dimensional Tensor, specifying the weight value. \n | |||
*@li target: A multi-dimensional Tensor of type float16 or float32, specifying the target value. | |||
*@li weight: An multi-dimensional Tensor, specifying the weight value. | |||
*@li pos_weight: An multi-dimensional Tensor, specifying the pos weight value. \n | |||
*@par Attributes: | |||
*reduction: A character string from "none", "mean", and "sum", specifying the reduction type to be applied to the output. Defaults to "mean" . \n | |||
*reduction: A character string from "none", "mean", and "sum", specifying the reduction type to be applied to the output. Defaults to "mean". \n | |||
*@par Outputs: | |||
*loss: Sigmoid cross entropy between the predictive value and target value. Has the same dimensions as "predict" . \n | |||
*loss: Sigmoid cross entropy between the predictive value and target value. Has the same dimensions as "predict". \n | |||
*@par Third-party framework compatibility | |||
* Compatible with PyTorch operator BCEWithLogitsLoss. | |||
@@ -978,6 +978,261 @@ REG_OP(InHost) | |||
.OUTPUT(variance_sqrt, TensorType({DT_FLOAT})) | |||
.ATTR(epsilon, Float, 0.00001) | |||
.OP_END_FACTORY_REG(InHost) | |||
/** | |||
* @brief perform instance normalization to x. \n | |||
* @par Inputs: | |||
* Three inputs, including: | |||
* @li x: A Tensor. Must be one of the following types: float16, float32, format is NC1HWC0. | |||
* @li gamma: A Tensor. Must be one of the following types: float16, float32, format is ND. | |||
* @li beta: A Tensor. Must be one of the following types: float16, float32, format is ND. | |||
* @par Attributes: | |||
* @li data_format: An attribute of type String \n | |||
* @li epsilon: An attribute of type Float, . \n | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the same type as "x", format is NC1HWC0. \n | |||
* @li mean: A Tensor. Has the same type as "x", format is NC1HWC0 and the shape is [N, C1, 1, 1, C0]. \n | |||
* @li variance: A Tensor. Has the same type as "x", format is NC1HWC0 and the shape is [N, C1, 1, 1, C0]. \n | |||
* @par Third-party framework compatibility | |||
* Can be used by onnx InstanceNormalization | |||
*/ | |||
REG_OP(InstanceNorm) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(gamma, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(beta, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(mean, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(variance, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.REQUIRED_ATTR(data_format, String) | |||
.REQUIRED_ATTR(epsilon, Float) | |||
.OP_END_FACTORY_REG(InstanceNorm) | |||
REG_OP(KlDivLossGrad) | |||
.INPUT(grad, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(input, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(target, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(reduction, String, "mean") | |||
.ATTR(log_target, Bool, false) | |||
.OP_END_FACTORY_REG(KlDivLossGrad) | |||
/** | |||
* @brief Computes l1_loss_grad or l1_loss_backward. \n | |||
* @par Inputs: | |||
* Three inputs, including: | |||
* @li grads: A Tensor. Must be one of the following types: float16, float32. | |||
* Required. | |||
* @li predict: A Tensor. Has the same type as "grads". Required. | |||
* @li label: A Tensor. Has the same type as "grads". Required. \n | |||
* @par Attributes: | |||
* @li reduction: An optional attribute of type String. Defaults to "mean". \n | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the same type as "x". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator L1LossGrad. | |||
*/ | |||
REG_OP(L1LossGrad) | |||
.INPUT(grads, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(predict, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(label, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(reduction, String, "mean") | |||
.OP_END_FACTORY_REG(L1LossGrad) | |||
/** | |||
* @brief Computes loss of lp, p=1,2,3.... | |||
* @par Inputs: | |||
* @li predict: An ND tensor of type float16, float32. | |||
* @li label: An ND tensor of type float16, float32. \n | |||
* @par Attributes: | |||
* @li p: A required int attribute that decides which loss to compute, now the p only can be 1 to compute l1_loss. | |||
* @li reduction: An optional string.Defaults to "mean". \n | |||
* @par Outputs: | |||
* @li y: An ND tensor tensor with the same shape and type as "predict". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator LpLoss. | |||
*/ | |||
REG_OP(LpLoss) | |||
.INPUT(predict, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(label, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.REQUIRED_ATTR(p, Int) | |||
.ATTR(reduction, String, "mean") | |||
.OP_END_FACTORY_REG(LpLoss) | |||
/** | |||
* @brief Computes gradients of mse loss. | |||
* @par Inputs: | |||
* @li predict: An ND tensor of type float16, float32. | |||
* @li label: An ND tensor of type float16, float32. | |||
* @li dout: An ND tensor of type float16, float32. \n | |||
* @par Attributes: | |||
* @li reduction: An optional string.Defaults to "mean". \n | |||
* @par Outputs: | |||
* @li y: An ND tensor tensor with the same shape and type as "predict". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator MseLossGrad. | |||
*/ | |||
REG_OP(MseLossGrad) | |||
.INPUT(predict, TensorType({DT_FLOAT32, DT_FLOAT16})) | |||
.INPUT(label, TensorType({DT_FLOAT32, DT_FLOAT16})) | |||
.INPUT(dout, TensorType({DT_FLOAT32, DT_FLOAT16})) | |||
.OUTPUT(y, TensorType({DT_FLOAT32, DT_FLOAT16})) | |||
.ATTR(reduction, String, "mean") | |||
.OP_END_FACTORY_REG(MseLossGrad) | |||
/** | |||
* @brief Computes mse loss. | |||
* @par Inputs: | |||
* two inputs, including: | |||
* @li predict: An ND Tensor of dtype float16 or float32. | |||
* @li label: An ND Tensor of dtype float16 or float32.\n | |||
* | |||
* @par Attributes: | |||
* @li reduction:An optional str from sum, none, mean, Defaults to "mean".\n | |||
* | |||
* @par Outputs: | |||
* @li y: when reduction=sum/mean, y is scale. when reduction=none, y has | |||
* same type and shape as "predict".\n | |||
*/ | |||
REG_OP(MseLoss) | |||
.INPUT(predict, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(label, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(reduction, String, "mean") | |||
.OP_END_FACTORY_REG(MseLoss) | |||
/** | |||
* @brief Calculates the reversed outputs of the function "smooth_l1_loss_v2". \n | |||
* @par Inputs: | |||
* Three Inputs, including: | |||
* @li predict: A Tensor. Must be one of the following types: | |||
* float16, float32. | |||
* @li label: A Tensor. Has the same type as "predict". | |||
* @li dout: A Tensor. Has the same type as "predict". \n | |||
* @par Attributes: | |||
* Two Attributes, including: | |||
* @li sigma: An optional float. Defaults to 1.0. \n | |||
* @li reduction: An optional string. Defaults to "mean", | |||
* Must be one of the following: "none", "mean", "sum". \n | |||
* @par Outputs: | |||
* @li gradient: A Tensor. Has the same type as "predict". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator SmoothL1LossBackward. | |||
*/ | |||
REG_OP(SmoothL1LossGradV2) | |||
.INPUT(predict, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.INPUT(label, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.INPUT(dout, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OUTPUT(gradient, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.ATTR(sigma, Float, 1.0) | |||
.ATTR(reduction, String, "mean") | |||
.OP_END_FACTORY_REG(SmoothL1LossGradV2) | |||
/** | |||
* @brief Creates a criterion that uses a squared term if the absolute | |||
* element-wise error falls below beta and an L1 term otherwise. It is | |||
* less sensitive to outliers than the MSELoss and in some cases prevents | |||
* exploding gradients. | |||
* @par Inputs: | |||
* @li predict: A multi-dimensional Tensor of type float16 or float32, | |||
* specifying the predictive value. \n | |||
* @li label: A multi-dimensional Tensor of type float16 or float32, | |||
* specifying the target value. \n | |||
* @par Attributes: | |||
* @li sigma: An optional int. Specifies the threshold of loss. Defaults | |||
* to "1.0". \n | |||
* @li reduction: An optional str. Specifies the reduction to apply to | |||
* the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, | |||
* 'mean': the sum of the output will be divided by the number of elements in | |||
* the output,'sum': the output will be summed. Default: 'mean'. \n | |||
* @par Outputs: | |||
* @li loss: Indicates the loss between the predictive value and target value. | |||
* Has the same dimensions as "predict". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator smooth_l1_loss. \n | |||
*/ | |||
REG_OP(SmoothL1LossV2) | |||
.INPUT(predict, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.INPUT(label, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.OUTPUT(loss, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.ATTR(sigma, Float, 1.0) | |||
.ATTR(reduction, String, "mean") | |||
.OP_END_FACTORY_REG(SmoothL1LossV2) | |||
/** | |||
* @brief Computes Centralization. result = x - mean(x, axes) | |||
* @par Inputs: | |||
* @li x: An ND tensor of type float16, float32. | |||
* @par Attributes: | |||
* @li axes: The dimensions to reduce. Must be one of the following types: int, list, tuple, NoneType. | |||
* Must be in the range [-rank(x), rank(x)). | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the same type as "x". \n | |||
* @par Third-party framework compatibility | |||
* custom operator \n | |||
*/ | |||
REG_OP(Centralization) | |||
.INPUT(x, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.OUTPUT(y, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.ATTR(axes, ListInt, {-1}) | |||
.OP_END_FACTORY_REG(Centralization) | |||
/** | |||
* @brief Computes gradients of sigmoid_cross_entropy_with_logits_v2. | |||
* @par Inputs: | |||
* @li predict: An ND tensor of type float16, float32. | |||
* @li target: An ND tensor of type float16, float32. | |||
* @li dout: An ND tensor of type float16, float32. | |||
* @li weight: An optional ND tensor of type float16, float32. | |||
* @li pos_weight: An optional ND tensor of type float16, float32. \n | |||
* @par Attributes: | |||
* @li reduction: An optional string.Defaults to "mean". \n | |||
* @par Outputs: | |||
* @li gradient: An ND tensor tensor with the same shape and type as "predict". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator SigmoidCrossEntropyWithLogitsGrad. | |||
*/ | |||
REG_OP(SigmoidCrossEntropyWithLogitsGradV2) | |||
.INPUT(predict, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(target, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(dout, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(weight, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(pos_weight, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(gradient, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(reduction, String, "mean") | |||
.OP_END_FACTORY_REG(SigmoidCrossEntropyWithLogitsGradV2) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_NN_NORM_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -182,6 +182,125 @@ REG_OP(AvgPool3D) | |||
.ATTR(data_format, String, "NDHWC") | |||
.OP_END_FACTORY_REG(AvgPool3D) | |||
/** | |||
*@brief Performs average pooling on the input. | |||
*@par Inputs: | |||
*@li x: A 5-D Tensor of shape [batch, depth, height, width, channels] and type float16, float32, double. | |||
*@li filter: An optional tensor of type float16, float32, double, fractal_z_3d layout. | |||
*@li multiplier: An optional tensor of float16, float32, double. | |||
*@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 pads: List of ints, implicit zero paddings on both sides of the input. | |||
*@li ceil_mode: When true, will use ceil instead of floor in the formula to compute the output shape. | |||
*@li count_include_pad: When true, will include the zero-padding in the averaging calculation. | |||
*@li divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used. | |||
*@li data_format: A string, format of input data . \n | |||
*@par Outputs: | |||
*y: The average pooled output tensor . \n | |||
*@attention Constraints: | |||
*@li "ksize" is in the range [1, 255]. "strides" is in the range [1, 63] | |||
*@par Third-party framework compatibility | |||
* Compatible with the TensorFlow operator AvgPool3D. | |||
*/ | |||
REG_OP(AvgPool3DD) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
.OPTIONAL_INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
.OPTIONAL_INPUT(multiplier, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
.REQUIRED_ATTR(ksize, ListInt) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(pads, ListInt) | |||
.ATTR(ceil_mode, Bool, false) | |||
.ATTR(count_include_pad, Bool, true) | |||
.ATTR(divisor_override, Int, 0) | |||
.ATTR(data_format, String, "NDHWC") | |||
.OP_END_FACTORY_REG(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. | |||
* @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 pads: List of ints, implicit zero paddings on both sides of the input. | |||
* @li ceil_mode: When true, will use ceil instead of floor in the formula to compute the output shape. | |||
* @li count_include_pad: When true, will include the zero-padding in the averaging calculation. | |||
* @li divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used. | |||
* @li data_format: A string, format of input data . | |||
* @par Outputs: | |||
* @output: A mutable tensor with the same shape and type as "orig_input". | |||
* @par Third-party framework compatibility | |||
* @li Compatible with the TensorFlow operator AvgPoolGrad. | |||
*/ | |||
REG_OP(AvgPool3DGrad) | |||
.INPUT(orig_input_shape, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
.INPUT(grads, TensorType({DT_INT32})) | |||
.OUTPUT(output, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
.REQUIRED_ATTR(ksize, ListInt) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(pads, ListInt) | |||
.ATTR(ceil_mode, Bool, false) | |||
.ATTR(count_include_pad, Bool, true) | |||
.ATTR(divisor_override, Int, 0) | |||
.ATTR(data_format, String, "NDHWC") | |||
.OP_END_FACTORY_REG(AvgPool3DGrad) | |||
/** | |||
* @brief Performs average pooling on the input. | |||
* @par Inputs: | |||
* @li grads: An NDHWC tensor of type float16. | |||
* @li filter: An optional tensor of type float16, fractal_z_3d layout. | |||
* @li multiplier: An optional tensor of float16. | |||
* @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 pads: List of ints, implicit zero paddings on both sides of the input. | |||
* @li ceil_mode: When true, will use ceil instead of floor in the formula to compute the output shape. | |||
* @li count_include_pad: When true, will include the zero-padding in the averaging calculation. | |||
* @li divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used. | |||
* @li data_format: A string, format of input data . \n | |||
* @par Outputs: | |||
* @output: The average pooled output tensor . \n | |||
* @attention Constraints: | |||
* @li "ksize" is in the range [1, 255]. "strides" is in the range [1, 63] | |||
* @par Third-party framework compatibility | |||
* Compatible with the TensorFlow operator AvgPool3DGradD. | |||
*/ | |||
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})) | |||
.REQUIRED_ATTR(orig_input_shape, ListInt) | |||
.REQUIRED_ATTR(ksize, ListInt) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(pads, ListInt) | |||
.ATTR(ceil_mode, Bool, false) | |||
.ATTR(count_include_pad, Bool, true) | |||
.ATTR(divisor_override, Int, 0) | |||
.ATTR(data_format, String, "NDHWC") | |||
.OP_END_FACTORY_REG(AvgPool3DGradD) | |||
/** | |||
*@brief Performs max_pool_ext2 on the input . \n | |||
@@ -308,6 +427,31 @@ REG_OP(MaxPool3D) | |||
.ATTR(data_format, String, "NDHWC") | |||
.OP_END_FACTORY_REG(MaxPool3D) | |||
/** | |||
*@brief Applies a 2D adaptive max pooling over an input signal conposed of several input planes. \n | |||
* The output is of size H x W, for any input size. | |||
* @par Inputs: | |||
* One input, including: | |||
* @li x: A Tensor. Must be one of the following data types: | |||
* float16, float32, float64. \n | |||
* @par Attributes: | |||
* @li output_size: A required list of 2 ints | |||
* specifying the size (H,W) of the output tensor. \n | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the same data type as "x" \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator AdaptiveMaxPool2d. | |||
*/ | |||
REG_OP(AdaptiveMaxPool2d) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE})) | |||
.OUTPUT(argmax, TensorType::IndexNumberType()) | |||
.REQUIRED_ATTR(output_size, ListInt) | |||
.OP_END_FACTORY_REG(AdaptiveMaxPool2d) | |||
/** | |||
* @brief Computes second-order gradients of the maxpooling3d function . \n | |||
@@ -477,8 +621,9 @@ REG_OP(MaxPoolV2) | |||
*@par Inputs: | |||
* One input: | |||
*x: An NC1HWC0 Tensor. Supported type: float, double, int32, | |||
* uint8, int16, int8, int64, uint16, half, uint32, uint64 . \n | |||
*x: An 4D Tensor. Supported type: float, double, int32, | |||
* uint8, int16, int8, int64, uint16, half, uint32, uint64. | |||
* Must set the format, supported format list ["NCHW, NHWC"]. \n | |||
*@par Attributes: | |||
*@li ksize: A required list of int8, int16, int32, or int64 values, | |||
@@ -517,10 +662,12 @@ REG_OP(MaxPoolWithArgmax) | |||
*@par Inputs: | |||
* Three inputs, including: | |||
*@li x: An NC1HWC0 tensor. Supported type: float, double, int32, | |||
*@li x: An 4d tensor. Supported type: float, double, int32, | |||
* uint8, int16, int8, int64, uint16, half, uint32, uint64. | |||
*@li grad: An NC1HWC0 tensor. Supported type: float, double, int32, | |||
* Must set the format, supported format list ["NCHW, NHWC"] | |||
*@li grad: 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"] | |||
*@li argmx: An NC1HWC0 tensor of type int32 or int64 . \n | |||
*@par Attributes: | |||
@@ -1107,7 +1254,7 @@ REG_OP(AvgPool1DD) | |||
*@par Inputs: | |||
* One input: | |||
*x: An NC1HWC0 Tensor of type float16. | |||
*x: An 4d Tensor of type float16. Must set the format, supported format list ["NCHW, NHWC"]. | |||
*@par Attributes: | |||
*@li ksize: A required list of int8, int16, int32, or int64 values, specifying the size of the window for | |||
* each dimension of the input tensor. No default value. | |||
@@ -1148,9 +1295,9 @@ REG_OP(MaxPoolWithArgmaxV2) | |||
*@par Inputs: | |||
* Three inputs, including: | |||
*@li x: An NC1HWC0 tensor of type float16. | |||
*@li grad: An NC1HWC0 tensor of type float16. | |||
*@li argmx: An NC1HWC0 tensor of type uint16 or int64 . \n | |||
*@li x: An 4d tensor of type float16. Must set the format, supported format list ["NCHW, NHWC"] | |||
*@li grad: An 4d tensor of type float16. Must set the format, supported format list ["NCHW, NHWC"] | |||
*@li argmx: An 4d tensor of type uint16 or int64. Must set the format, supported format list ["NCHW, NHWC"] \n | |||
*@par Attributes: | |||
*@li ksize: A required list of int8, int16, int32, or int64 values, specifying the size of the window for | |||
@@ -1291,5 +1438,171 @@ REG_OP(MaxPoolV3Grad) | |||
.ATTR(global_pooling, Bool, false) | |||
.ATTR(ceil_mode, Bool, false) | |||
.OP_END_FACTORY_REG(MaxPoolV3Grad) | |||
/** | |||
*@brief Performs dilation2d on the input . \n | |||
*@par Inputs: | |||
*x: A tensor of shape is 4d, format is support NHWC. | |||
*filter: A tensor of shape is 3d, the type is same with x, | |||
and the c dimension is same with x. \n | |||
*@par Attributes: | |||
*@li strides: A required list of 4 ints, specifying the stride of the sliding window. The strides of the N and C dimensions are 1. | |||
*@li rates: A required list of 4 ints. The rates of the N and C dimensions are 1. | |||
*@li padding_mode: A optional string. Defaults to "SAME", it support SAME and VALID. | |||
*@li pads: An optional list of 4 ints. | |||
*@li ceil_mode: An optional bool. Defaults to "false". Use ceil or floor to calculate the output size when padding_mode is "CALCULATED". | |||
*@li data_format: An optional string, specifying the data format of "rates" and "strides", either "NCHW" or "NHWC" (default). \n | |||
*@par Outputs: | |||
*y: The output tensor. Has the same type and format as input "x" . \n | |||
*@par Third-party framework compatibility | |||
* Compatible with the TensorFlow operator Dilation2D. | |||
*/ | |||
REG_OP(Dilation2D) | |||
.INPUT(x,TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16})) | |||
.INPUT(filter,TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16})) | |||
.OUTPUT(y,TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16})) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(rates, ListInt) | |||
.ATTR(padding_mode, String, "SAME") | |||
.ATTR(pads, ListInt, {0,0,0,0}) | |||
.ATTR(ceil_mode, Bool, false) | |||
.ATTR(data_format, String, "NHWC") | |||
.OP_END_FACTORY_REG(Dilation2D) | |||
/** | |||
* @brief Applies a 2D adaptive average pooling over | |||
* an input signal composed of several input planes. \n | |||
* @par Inputs: | |||
* One input, including: | |||
* @li x: A Tensor. Must be one of the following data types: | |||
* float16, float32. \n | |||
* @par Attributes: | |||
* @li output_size: A required list of 2 ints | |||
* specifying the size (H,W) of the output tensor. \n | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the same data type as "x" \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator AdaptiveAvgPool2d. | |||
*/ | |||
REG_OP(AdaptiveAvgPool2d) | |||
.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.REQUIRED_ATTR(output_size, ListInt) | |||
.OP_END_FACTORY_REG(AdaptiveAvgPool2d) | |||
/** | |||
* @brief Compute gradients of adaptive averagev2 pooling function. | |||
* @par Inputs: | |||
* @li input_grad: A NCHW Tensor. Must be one of the following data types: | |||
* float16, float32. | |||
* @par Attributes: | |||
* @li orig_input_shape: A required tuple or list of type int32. | |||
* @par Outputs: | |||
* @li output_grad: A tensor with the same shape and type as "orig_input_shape". | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator AdaptiveAvgPool2dGrad. | |||
*/ | |||
REG_OP(AdaptiveAvgPool2dGrad) | |||
.INPUT(input_grad, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OUTPUT(output_grad, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.REQUIRED_ATTR(orig_input_shape, ListInt) | |||
.OP_END_FACTORY_REG(AdaptiveAvgPool2dGrad) | |||
/** | |||
* @brief Performs the backpropagation of MaxPoolWithGradArgmaxV1. | |||
* @par Inputs: | |||
* Three inputs, including: | |||
* @li x: An NC1HWC0 tensor of type float16. | |||
* @li grad: An NC1HWC0 tensor of type float16. | |||
* @li argmax: An NC1HWC0 tensor of type uint16 or int64. \n | |||
* @par Attributes: | |||
* @li ksize: A required list of int8, int16, int32, or int64 values, specifying the size of the window for | |||
* each dimension of the input tensor. No default value. | |||
* @li strides: A required list of int8, int16, int32, or int64 values, specifying the stride of the sliding window for | |||
* each dimension of the input tensor. No default value. | |||
* @li pads: A required listint. \n | |||
* @par Outputs: | |||
* y: A Tensor. Has the same type and format as input "x". \n | |||
* @attention Constraints: | |||
* @li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1, ksize[1] * ksize[2] <= 255. | |||
* @li "strides" is a list that has length 4: strides[0] = 1 or strides[3] = 1 | |||
* @li "pads" is listint. | |||
* @li "ceil_mode" defaults to False. | |||
* @li "data_format" defaults to "NC1HWC0". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the TensorFlow operator MaxPoolGradWithArgmaxV1. | |||
*/ | |||
REG_OP(MaxPoolGradWithArgmaxV1) | |||
.INPUT(x, TensorType({DT_FLOAT16})) | |||
.INPUT(grad, TensorType({DT_FLOAT16})) | |||
.INPUT(argmax, TensorType({DT_UINT16})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16})) | |||
.REQUIRED_ATTR(ksize, ListInt) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(pads, ListInt) | |||
.ATTR(dtype, Int, 3) | |||
.ATTR(dilation, ListInt, {1, 1, 1, 1}) | |||
.ATTR(ceil_mode, Bool, false) | |||
.OP_END_FACTORY_REG(MaxPoolGradWithArgmaxV1) | |||
/** | |||
* @brief Performs max pooling on the input and outputs both max values and indices. | |||
* @par Inputs: | |||
* One input: | |||
* x: An NC1HWC0 Tensor of type float16. \n | |||
* @par Attributes: | |||
* @li ksize: A required list of int8, int16, int32, or int64 values, specifying the size of the window for | |||
* each dimension of the input tensor. No default value. | |||
* @li strides: A required list of int8, int16, int32, or int64 values, specifying the stride of the sliding window for | |||
* each dimension of the input tensor. No default value. | |||
* @li pads: A required string. No default value. \n | |||
* @par Outputs: | |||
* y: A Tensor. Has the same type and format as input "x". | |||
* argmax: A Tensor. type:uint16, format:NC1HWC0. \n | |||
* @attention Constraints: | |||
* @li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1, ksize[1] * ksize[2] <= 255. | |||
* @li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1, strides[1] <= 63, strides[0] >= 1, | |||
* strides[2] <= 63, strides[2] >= 1. | |||
* @li "pads" is listint. | |||
* @li "ceil_mode" defaults to False. | |||
* @li "data_format" defaults to "NC1HWC0". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the TensorFlow operator MaxPoolWithArgmaxV1. | |||
*/ | |||
REG_OP(MaxPoolWithArgmaxV1) | |||
.INPUT(x, TensorType({DT_FLOAT16})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16})) | |||
.OUTPUT(argmax, TensorType({DT_UINT16})) | |||
.REQUIRED_ATTR(ksize, ListInt) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(pads, ListInt) | |||
.ATTR(dtype, Int, 3) | |||
.ATTR(dilation, ListInt, {1, 1, 1, 1}) | |||
.ATTR(ceil_mode, Bool, false) | |||
.OP_END_FACTORY_REG(MaxPoolWithArgmaxV1) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_NN_POOLING_OPS_H |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* Copyright 2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* Copyright 2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
@@ -640,6 +640,208 @@ REG_OP(Mish) | |||
.OUTPUT(y, TensorType({ DT_FLOAT,DT_FLOAT16 })) | |||
.OP_END_FACTORY_REG(Mish) | |||
/** | |||
* @brief pytorch hardtanh_backward operator. | |||
* | |||
* @par Inputs: | |||
* 2 inputs, including: | |||
* @li result, minimum tensor of the linear region range, | |||
* datatype: float16/float32, format:ND/5HD. | |||
* @li grad, maximum tensor of the linear region range, | |||
* datatype:float16/float32, format:ND/5HD. \n | |||
* @par Attributes: | |||
* 2 attributes, including: | |||
* @li min_val, minimum value of the linear region range, datatype:float. | |||
* @li max_val, maximum value of the linear region range, datatype:float. \n | |||
* @par Outputs: | |||
* 1 output, including: | |||
* @li y, hardtanh_backward output tensor, datatype and format is same as | |||
* input result. \n | |||
* @attention Constraints: | |||
* This operator only supports dataType: float16/float32, format: ND/5HD. \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator HardtanhGrad. | |||
*/ | |||
REG_OP(HardtanhGrad) | |||
.INPUT(result, TensorType({ DT_FLOAT16, DT_FLOAT })) /* "First operand." */ | |||
.INPUT(grad, TensorType({ DT_FLOAT16, DT_FLOAT })) /* "Second operand." */ | |||
.OUTPUT(y, TensorType({ DT_FLOAT16, DT_FLOAT })) /* "Result, has same element type as two inputs" */ | |||
.ATTR(min_val, Float, -1.0) | |||
.ATTR(max_val, Float, 1.0) | |||
.OP_END_FACTORY_REG(HardtanhGrad) | |||
/** | |||
* @brief Calculates the softplus loss function with attributes of beta and threshold. \n | |||
* @par Inputs: | |||
* One inputs, including: | |||
* @li x: A mutable Tensor. Must be one of the following types: | |||
* float16, float32. \n | |||
* @par Attributes: | |||
* @li beta: An optional float. Defaults to "1.0" \n | |||
* @li threshold: An optional float. Defaults to "20.0" \n | |||
* @par Outputs: | |||
* @li y: A mutable Tensor. Has the same type as "x" \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Softplus. | |||
*/ | |||
REG_OP(SoftplusV2) | |||
.INPUT(x, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.OUTPUT(y, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.ATTR(beta, Float, 1.0) | |||
.ATTR(threshold, Float, 20.0) | |||
.OP_END_FACTORY_REG(SoftplusV2) | |||
/** | |||
* @brief Calculates the reversed outputs of the function "softplus_v2". \n | |||
* @par Inputs: | |||
* Two inputs, including: | |||
* @li input_gradients: A mutable Tensor. Must be one of the following types: | |||
* float16, float32. | |||
* @li input_features: A mutable Tensor of the same type as "input_gradients" \n | |||
* @par Attributes: | |||
* @li beta: An optional float. Defaults to "1.0" \n | |||
* @li threshold: An optional float. Defaults to "20.0" \n | |||
* @par Outputs: | |||
* @li output_backprops: A mutable Tensor. Has the same type as "input_gradients" \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator SoftplusGrad. | |||
*/ | |||
REG_OP(SoftplusV2Grad) | |||
.INPUT(input_gradients, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.INPUT(input_features, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.OUTPUT(output_backprops, TensorType({ DT_FLOAT, DT_FLOAT16 })) | |||
.ATTR(beta, Float, 1.0) | |||
.ATTR(threshold, Float, 20.0) | |||
.OP_END_FACTORY_REG(SoftplusV2Grad) | |||
/** | |||
* @brief ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) | |||
* where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise. | |||
* | |||
* @par inputs | |||
* one input including: | |||
* @li x: input A Tensor. Must be one of the following types: float32, float16 | |||
* | |||
* @par output | |||
* one output including: | |||
* @li y:A Tensor of the same type as x | |||
* | |||
*/ | |||
REG_OP(ThresholdedRelu) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(alpha, Float, 1.0) | |||
.OP_END_FACTORY_REG(ThresholdedRelu) | |||
/** | |||
* @brief Calculate the hard shrinkage function. \n | |||
* @par Inputs: | |||
* One inputs, including: | |||
* @li input_x: A tensor. Must be one of the following types: | |||
* float16, float32. \n | |||
* @par Attributes: | |||
* @li lambd: An optional float. Defaults to 0.5. \n | |||
* @par Outputs: | |||
* y: A Tensor with the same dtype and shape of input_x's. \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Hardshrink. \n | |||
*/ | |||
REG_OP(HardShrink) | |||
.INPUT(input_x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(output_y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(lambd, Float, 0.5) | |||
.OP_END_FACTORY_REG(HardShrink) | |||
/** | |||
* @brief Calculate the hard sigmoid function. \n | |||
* @par Inputs: | |||
* One inputs, including: | |||
* @li input_x: A tensor. Must be one of the following types: | |||
* float16, float32, int32. \n | |||
* @par Attributes: | |||
* @li alpha: An optional float. Defaults to 0.16666666. \n | |||
* @li beta: An optional float. Defaults to 0.5. \n | |||
* @par Outputs: | |||
* y: A Tensor with the same dtype and shape of input_x's. \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Hardsigmoid. \n | |||
*/ | |||
REG_OP(HardSigmoid) | |||
.INPUT(input_x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32})) | |||
.OUTPUT(output_y, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.ATTR(alpha, Float, 0.16666666) | |||
.ATTR(beta, Float, 0.5) | |||
.OP_END_FACTORY_REG(HardSigmoid) | |||
/** | |||
* @brief Calculate the soft shrinkage function. \n | |||
* @par Inputs: | |||
* One inputs, including: | |||
* @li input_x: A tensor. Must be one of the following types: | |||
* float16, float32. \n | |||
* @par Attributes: | |||
* @li lambd: An optional float. Defaults to 0.5. \n | |||
* @par Outputs: | |||
* y: A Tensor with the same dtype and shape of input_x's. \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator Softshrink. \n | |||
*/ | |||
REG_OP(SoftShrink) | |||
.INPUT(input_x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(output_y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(lambd, Float, 0.5) | |||
.OP_END_FACTORY_REG(SoftShrink) | |||
/** | |||
* @brief Calculate the reversed outputs of the function "soft_shrink". \n | |||
* @par Inputs: | |||
* Two inputs, including: | |||
* @li input_grad: A tensor. Must be one of the following types: | |||
* float16, float32. \n | |||
* @li input_x: A tensor of the same dtype as "input_grad". \n | |||
* @par Attributes: | |||
* @li lambd: An optional float. Defaults to 0.5. \n | |||
* @par Outputs: | |||
* y: A Tensor of the same dtype and shape as "input_graxd". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator SoftShrinkGrad. \n | |||
*/ | |||
REG_OP(SoftShrinkGrad) | |||
.INPUT(input_grad, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(input_x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(output_y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(lambd, Float, 0.5) | |||
.OP_END_FACTORY_REG(SoftShrinkGrad) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_NONLINEAR_FUC_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* Copyright 2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
@@ -161,7 +161,7 @@ REG_OP(Pad) | |||
*@brief Pads a tensor . \n | |||
*@par Inputs: | |||
*x: A Tensor. Must be one of the following types: float16, float32, int8, uint8, int32 . \n | |||
*x: A Tensor. Must be one of the following types: float16, float32, int32 . \n | |||
*@par Attributes: | |||
*paddings: An optional "vector<vector<int>>". Defaults to "{}". | |||
@@ -180,8 +180,8 @@ REG_OP(Pad) | |||
* Warning: THIS FUNCTION IS DEPRECATED. Please use Pad instead. | |||
*/ | |||
REG_OP(PadD) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8, DT_FLOAT})) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) | |||
.REQUIRED_ATTR(paddings, ListListInt) | |||
.OP_END_FACTORY_REG(PadD) | |||
@@ -213,7 +213,7 @@ REG_OP(PadV2) | |||
*@brief Pads a tensor . \n | |||
*@par Inputs: | |||
*x: A Tensor. Must be one of the following types: float16, float32, int8, uint8, int32 . \n | |||
*x: A Tensor. Must be one of the following types: float16, float32, int32 . \n | |||
*constant_values: A Tensor. Must have the same type as input. | |||
*@par Attributes: | |||
@@ -227,10 +227,7 @@ REG_OP(PadV2) | |||
*y: A Tensor of the same type as "x" . \n | |||
*@par Third-party framework compatibility: | |||
* Compatible with TensorFlow operator Pad. | |||
* | |||
* @par Restrictions: | |||
* Warning: THIS FUNCTION IS DEPRECATED. Please use Pad instead. | |||
* Compatible with TensorFlow operator PadV2. | |||
*/ | |||
REG_OP(PadV2D) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) | |||
@@ -403,5 +400,46 @@ REG_OP(EmbeddingRankId) | |||
.ATTR(mode, String, "mod") | |||
.OP_END_FACTORY_REG(EmbeddingRankId) | |||
/** | |||
* @brief Fill the value to a tensor has the specified shape. | |||
* @par Inputs: | |||
* One inputs, including: | |||
* @li dims: An Tensor, specify the shape that the value to fill. | |||
* @par Attributes: | |||
* @li value: An optional float value. Defaults to 0.0. | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the shape specify by attr shape, and full of the value specify by attr value. | |||
* @par Third-party framework compatibility | |||
* Compatible with the ONNX operator ConstantOfShape. | |||
*/ | |||
REG_OP(FillV2) | |||
.INPUT(dims, TensorType({DT_INT16, DT_INT32, DT_INT64})) | |||
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_INT16, DT_INT32, DT_INT64})) | |||
.ATTR(value, Float, 0) | |||
.OP_END_FACTORY_REG(FillV2) | |||
/** | |||
* @brief Fill the value to a tensor has the specified shape. | |||
* @par Attributes: | |||
* @li value: An optional float value. Defaults to 0.0. | |||
* @li dims: An required listInt to specify the shape that the value to fill. | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the shape specify by attr shape, and full of the value specify by attr value. | |||
* @par Third-party framework compatibility | |||
* Compatible with the ONNX operator ConstantOfShape. | |||
*/ | |||
REG_OP(FillV2D) | |||
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64})) | |||
.ATTR(value, Float, 0) | |||
.REQUIRED_ATTR(dims, ListInt) | |||
.OP_END_FACTORY_REG(FillV2D) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_PAD_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -495,6 +495,60 @@ REG_OP(ShuffleChannel) | |||
DT_UINT16, DT_INT32, DT_UINT32,DT_INT64,DT_UINT64})) | |||
.ATTR(group, Int, 1) | |||
.OP_END_FACTORY_REG(ShuffleChannel) | |||
/** | |||
* @briefGenerate a tensor of samples from a multinomial | |||
* distribution according to the probabilities of each of | |||
* the possible outcomes. | |||
* | |||
* @par inputs | |||
* one input including: | |||
* @li x:Input tensor with shape [batch_size, class_size], | |||
* where class_size is the number of all possible outcomes. | |||
* Each value along the axis zero represents the unnormalized | |||
* log-probability of each corresponding outcome in a batch. | |||
* | |||
* @par output | |||
* one output including: | |||
* @li y:Output tensor with shape [batch_size, sample_size], | |||
* where sample_size is the number of times to sample. | |||
* Each value along the axis zero represents the outcome of | |||
* the corresponding sample in a batch. | |||
* | |||
*/ | |||
REG_OP(MultinomialFuss) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_FLOAT64})) | |||
.OUTPUT(y, TensorType({DT_INT32, DT_INT64})) | |||
.ATTR(dtype, Int, 6) | |||
.ATTR(sample_size, Int, 1) | |||
.ATTR(seed, Float, 0) | |||
.OP_END_FACTORY_REG(MultinomialFuss) | |||
/** | |||
* @brief During training, randomly zeroes some of the elements of the input tensor | |||
* with probability | |||
* | |||
* @par Inputs: | |||
* @li x: A ND Tensor. Must be one of the following data types: Float, Float16 | |||
* @li seed: A ND Tensor. Must be one of the following data types: Float | |||
* | |||
* @par Attributes: | |||
* @li p: probability of an element to be zeroed | |||
* | |||
* @par Outputs: | |||
* @li y: A tensor with the same shape and type as "x". | |||
* @li mask: A tensor with the same shape and type as "x". | |||
* @li new_seed: A tensor with the same shape and type as "seed". | |||
*/ | |||
REG_OP(DropoutV2) | |||
.INPUT(x, TensorType({ DT_FLOAT16, DT_FLOAT })) | |||
.INPUT(seed, TensorType({ DT_FLOAT })) | |||
.OUTPUT(y, TensorType({ DT_FLOAT16, DT_FLOAT })) | |||
.OUTPUT(mask, TensorType({ DT_FLOAT })) | |||
.OUTPUT(seed, TensorType({ DT_FLOAT })) | |||
.REQUIRED_ATTR(p, Float) | |||
.OP_END_FACTORY_REG(DropoutV2) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_RANDOM_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* Copyright 2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
@@ -635,8 +635,8 @@ REG_OP(ReduceMin) | |||
* Warning: THIS FUNCTION IS DEPRECATED. Please use ReduceMin instead. | |||
*/ | |||
REG_OP(ReduceMinD) | |||
.INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8})) | |||
.INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8,DT_INT32})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8,DT_INT32})) | |||
.REQUIRED_ATTR(axes, ListInt) | |||
.ATTR(keep_dims, Bool, false) | |||
.OP_END_FACTORY_REG(ReduceMinD) | |||
@@ -821,7 +821,7 @@ Defaults to "0.00001" . \n | |||
*batch_ variance: A Tensor of type float32 for the result variance . \n | |||
*@attention Constraints: | |||
*For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. | |||
*For Ascend 310, the result accuracy fails to reach 0.001 due to the square root instruction. | |||
*/ | |||
REG_OP(INInferV2) | |||
.INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
@@ -882,7 +882,7 @@ REG_OP(INTrainingReduceV2) | |||
*@attention Constraints: | |||
*@li This operator is a InstanceNorm fusion operator for updating the moving averages for training. | |||
* This operator is used in conjunction with INTrainingReduceV2. | |||
*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. | |||
*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. | |||
*/ | |||
REG_OP(INTrainingUpdateV2) | |||
.INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
@@ -965,7 +965,7 @@ for the updated variance. | |||
*@attention Constraints: | |||
*@li This operator is a InstanceNorm fusion operator for updating the moving averages for training. | |||
* This operator is used in conjunction with GNTrainingUpdate. | |||
*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. | |||
*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. | |||
*/ | |||
REG_OP(GNTrainingUpdate) | |||
.INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT})) | |||
@@ -982,6 +982,41 @@ REG_OP(GNTrainingUpdate) | |||
.OUTPUT(batch_variance, TensorType({DT_FLOAT})) | |||
.OP_END_FACTORY_REG(GNTrainingUpdate) | |||
/** | |||
* @brief Calculates the standard deviation and average value of Tensors. | |||
* @par Inputs: | |||
* @li x: A Tensor. Must be one of the following types: | |||
* float16, float32. \n | |||
* @par Attributes: | |||
* Three Attributes, including: | |||
* @li dim: An optional listint, Defaults to "None". \n | |||
* @li unbiased: An optional bool. Defaults to "True". | |||
* If "True", Use Bessel Correction. | |||
* If "False", Do not use Bessel Correction. \n | |||
* @li keepdim: An optional bool. Defaults to "False". | |||
* If "True", Keep the original tensor dimension. | |||
* If "False", Do not keep the original tensor dimension. \n | |||
* @par Outputs: | |||
* Two Outputs, including: | |||
* @li y1: A Tensor. Has the same type as "x". | |||
* @li y2: A Tensor. Has the same type as "x". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator ReduceStd. | |||
*/ | |||
REG_OP(ReduceStd) | |||
.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OUTPUT(y1, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.OUTPUT(y2, TensorType({DT_FLOAT, DT_FLOAT16})) | |||
.ATTR(dim, ListInt, {}) | |||
.ATTR(unbiased, Bool, true) | |||
.ATTR(keepdim, Bool, false) | |||
.OP_END_FACTORY_REG(ReduceStd) | |||
} //namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_REDUCE_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -187,16 +187,16 @@ REG_OP(DynamicRNNGrad) | |||
*@brief: DynamicRNN calculation. | |||
*@par Inputs: | |||
*ten inputs: | |||
*@li x:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li w:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li b:A 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. | |||
*@li seq_length:A 1D Tensor. Must be one of the following types: int32. The format must be ND. | |||
*@li init_h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li init_c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li wci:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li wcf:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li wco:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li mask:A 1D Tensor. Must be one of the following types: uint8. The format must be ND . \n | |||
*@li x:A required 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li w:A required 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li b:A required 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. | |||
*@li seq_length:A optional 1D Tensor. Must be one of the following types: int32. The format must be ND. | |||
*@li init_h:A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li init_c:A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li wci:A 4D optional Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li wcf:A 4D optional Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li wco:A 4D optional Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li mask:A 1D optional Tensor. Must be one of the following types: uint8. The format must be ND . \n | |||
*@par Attributes: | |||
*@li cell_type:An string identifying the cell type in the op. Default to "LSTM". Only LSTM is currently supported. | |||
@@ -221,6 +221,8 @@ REG_OP(DynamicRNNGrad) | |||
*@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. | |||
*@par Third-party framework compatibility: | |||
* Compatible with the TF operator LSTM. | |||
*/ | |||
REG_OP(DynamicRNN) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
@@ -254,6 +256,63 @@ REG_OP(DynamicRNN) | |||
.ATTR(is_training, Bool, true) | |||
.OP_END_FACTORY_REG(DynamicRNN) | |||
/** | |||
*@brief: DynamicLSTMV2 calculation. | |||
*@par Inputs: | |||
*ten inputs: | |||
*@li x:A required 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li w:A required 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li b:A required 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. | |||
*@li cont:A required 2D Tensor. Must be one of the following types: float16, float32. The format must be ND. | |||
*@li w_xc_x_static:A optional 2D Tensor. Must be one of the following types: float16, float32. The format must be ND. | |||
*@li h0:A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li c0:A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li wci:A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li wcf:A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li wco:A optional 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li mask:A optional 1D Tensor. Must be one of the following types: uint8. The format must be ND . | |||
*@par Attributes: | |||
*@li num_output:An integer identifying the num projection in the op. Default to 0. | |||
*@li expose_hidden:An bool identifying the expose_hidden in the op. Default to flase. | |||
*@li need_output_last:An bool identifying the time major in the op. Default to true. | |||
*@li forget_bias:An float identifying the forget bias in the op. Default to 0. | |||
*@par Outputs: | |||
*eight outputs: | |||
*@li y:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li output_h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li output_c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li last_output_h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li last_output_c:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@par Third-party framework compatibility: | |||
* Compatible with the Caffe operator LSTM. | |||
*@par Restrictions: | |||
* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(DynamicLSTMV2) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(cont, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(w_xc_x_static, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(h0, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(c0, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(wci, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(wcf, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(wco, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(mask, TensorType({DT_UINT8})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(output_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(output_c, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(last_output_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(last_output_c, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(num_output, Int, 0) | |||
.ATTR(expose_hidden, Bool, false) | |||
.ATTR(need_output_last, Bool, false) | |||
.ATTR(forget_bias, Float, 0.0) | |||
.OP_END_FACTORY_REG(DynamicLSTMV2) | |||
/** | |||
*@brief: LSTMInputGrad calculation. | |||
*@par Inputs: | |||
@@ -475,9 +534,9 @@ REG_OP(BasicRNNCell) | |||
.OP_END_FACTORY_REG(BasicRNNCell) | |||
/** | |||
*@brief: DynamicGRU calculation. | |||
*@brief DynamicGRU calculation. | |||
*@par Inputs: | |||
*seven inputs: \n | |||
*seven inputs: | |||
*@li x:Must be one of the following types: float16. The format must be FRACTAL_NZ. | |||
*@li w:Must be one of the following types: float16. The format must be FRACTAL_Z. | |||
*@li b:Must be one of the following types: float16, float32. The format must be ND. | |||
@@ -497,7 +556,7 @@ REG_OP(BasicRNNCell) | |||
*@li is_training:An bool identifying is training in the op. Default to true. | |||
*@par Outputs: | |||
*five outputs: \n | |||
*five outputs: | |||
*@li y:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li output_h:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li r:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
@@ -531,9 +590,9 @@ REG_OP(DynamicGRU) | |||
.OP_END_FACTORY_REG(DynamicGRU) | |||
/** | |||
*@brief: DynamicGRUV2 calculation. | |||
*@brief DynamicGRUV2 calculation. | |||
*@par Inputs: | |||
*seven inputs: \n | |||
*seven inputs: | |||
*@li x:Must be one of the following types: float16. The format must be FRACTAL_NZ. | |||
*@li weight_input:Must be one of the following types: float16. The format must be FRACTAL_Z. | |||
*@li weight_hidden:Must be one of the following types: float16. The format must be FRACTAL_Z. | |||
@@ -555,7 +614,7 @@ REG_OP(DynamicGRU) | |||
*@li is_training:An bool identifying is training in the op. Default to true. | |||
*@par Outputs: | |||
*six outputs: \n | |||
*six outputs: | |||
*@li y:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li output_h:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li update:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
@@ -592,6 +651,68 @@ REG_OP(DynamicGRUV2) | |||
.ATTR(is_training, Bool, true) | |||
.OP_END_FACTORY_REG(DynamicGRUV2) | |||
/** | |||
*@brief DynamicGRUV2Hidden calculation. | |||
*@par Inputs: | |||
*five inputs: | |||
*@li x_weight_input:Must be one of the following types: float32. The format must be FRACTAL_NZ. | |||
*@li weight_hidden:Must be one of the following types: float16. The format must be FRACTAL_Z. | |||
*@li bias_hidden:Must be one of the following types: float16, float32. The format must be ND. | |||
*@li seq_length:Must be one of the following types: int32. The format must be ND. | |||
*@li init_h:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@par Attributes: | |||
*@li direction:An string identifying the direction in the op. Default to "UNIDIRECTIONAL". | |||
Only UNIDIRECTIONAL is currently supported. | |||
*@li cell_depth:An integer identifying the cell depth in the op. Default to 1. | |||
*@li keep_prob:An float identifying the keep prob in the op. Default to 1. | |||
*@li cell_clip:An float identifying the cell clip in the op. Default to -1. | |||
*@li num_proj:An integer identifying the num projection in the op. Default to 0. | |||
*@li time_major:An bool identifying the time major in the op. Default to true. | |||
*@li activation:An string identifying the type of activation function in the op. Default to "tanh". | |||
Only tanh is currently supported. | |||
*@li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. | |||
*@li reset_after:An bool identifying whether to apply reset gate after matrix multiplication. Default to true. | |||
*@li is_training:An bool identifying is training in the op. Default to true. | |||
*@par Outputs: | |||
*six outputs: | |||
*@li y:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li output_h:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li update:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li reset:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li new:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li hidden_new:Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(DynamicGRUV2Hidden) | |||
.INPUT(x_weight_input, TensorType({DT_FLOAT32})) | |||
.INPUT(weight_hidden, TensorType({DT_FLOAT16})) | |||
.OPTIONAL_INPUT(bias_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(seq_length, TensorType({DT_INT32})) | |||
.OPTIONAL_INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(output_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(update, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(reset, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(new, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(hidden_new, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(direction, String, "UNIDIRECTIONAL") | |||
.ATTR(cell_depth, Int, 1) | |||
.ATTR(keep_prob, Float, 1.0) | |||
.ATTR(cell_clip, Float, -1.0) | |||
.ATTR(num_proj, Int, 0) | |||
.ATTR(time_major, Bool, true) | |||
.ATTR(activation, String, "tanh") | |||
.ATTR(gate_order, String, "zrh") | |||
.ATTR(reset_after, Bool, true) | |||
.ATTR(is_training, Bool, true) | |||
.OP_END_FACTORY_REG(DynamicGRUV2Hidden) | |||
/** | |||
*@brief: DynamicGRUV2Grad calculation. | |||
*@par Inputs: | |||
@@ -618,7 +739,6 @@ REG_OP(DynamicGRUV2) | |||
*@li cell_clip:An float identifying the cell clip in the op. Default to -1. | |||
*@li num_proj:An integer identifying the num projection in the op. Default to 0. | |||
*@li time_major:An bool identifying the time major in the op. Default to true. | |||
*@li bias_type:An string identifying the type of bias_type function in the op. Default to "double_bias". | |||
*@li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. | |||
*@li reset_after:An bool identifying whether to apply reset gate after matrix multiplication. Default to true. | |||
@@ -630,6 +750,9 @@ REG_OP(DynamicGRUV2) | |||
*@li db_hidden:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li dx:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(DynamicGRUV2Grad) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
@@ -658,7 +781,6 @@ REG_OP(DynamicGRUV2Grad) | |||
.ATTR(cell_clip, Float, -1.0) | |||
.ATTR(num_proj, Int, 0) | |||
.ATTR(time_major, Bool, true) | |||
.ATTR(bias_type, String, "double_bias") | |||
.ATTR(gate_order, String, "zrh") | |||
.ATTR(reset_after, Bool, true) | |||
.OP_END_FACTORY_REG(DynamicGRUV2Grad) | |||
@@ -667,7 +789,7 @@ REG_OP(DynamicGRUV2Grad) | |||
*@brief: GRUV2HiddenGrad calculation. | |||
*@par Inputs: | |||
*nine inputs: \n | |||
*@li weight_hidden:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li dh_pre_t:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li init_h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li dy:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
@@ -678,6 +800,7 @@ REG_OP(DynamicGRUV2Grad) | |||
*@li hidden_new:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@par Attributes: | |||
*@li t_state:An Int identifying the current t state. Default to [0, 4]. | |||
*@li gate_order:An string identifying the gate order in weight and bias. Default to "zrh". "rzh" is another option. | |||
*@par Outputs: | |||
@@ -685,10 +808,12 @@ REG_OP(DynamicGRUV2Grad) | |||
*@li dh_prev:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li dgate_h:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li dnt_x:A 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. | |||
*/ | |||
REG_OP(GRUV2HiddenGrad) | |||
.INPUT(weight_hidden, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(init_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
REG_OP(GRUV2HiddenGradCell) | |||
.INPUT(dh_pre_t, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(dy, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(dh, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
@@ -699,8 +824,142 @@ REG_OP(GRUV2HiddenGrad) | |||
.OUTPUT(dh_prev, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(dgate_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(dnt_x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(t_state, Int, 0) | |||
.ATTR(gate_order, String, "zrh") | |||
.OP_END_FACTORY_REG(GRUV2HiddenGrad) | |||
.OP_END_FACTORY_REG(GRUV2HiddenGradCell) | |||
/** | |||
* @brief Calculates the reversed outputs of the function "embedding". \n | |||
* @par Inputs: | |||
* Two inputs, including: | |||
* @li grad: A mutable Tensor of word grad. Must be one of the following types: | |||
* float32. | |||
* @li indices: A mutable word index Tensor of the int32 type.\n | |||
* @par Attributes: | |||
* @li num_weights: An int attr which use to judge how many words in dict. \n | |||
* @li padding_idx: An int attr judge which word to fill zeros. Defaults to "-1". \n | |||
* @li scale_grad_by_freq: An optional bool. Defaults to "False". | |||
* If "True", "grad_weight" will be scale by word_frequency. | |||
* If "False", "grad_weight" will not be scale by word_frequency. \n | |||
* @par Outputs: | |||
* @li grad_weight: A mutable output Tensor of new word grad has the same type as "grads". \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator EmbeddingDenseGrad. | |||
*/ | |||
REG_OP(EmbeddingDenseGrad) | |||
.INPUT(grad, TensorType({ DT_FLOAT32 })) /* "First operand." */ | |||
.INPUT(indices, TensorType({ DT_INT32 })) /* "Second operand." */ | |||
.OUTPUT(y, TensorType({ DT_FLOAT32 })) /* "Result, has same element type as two inputs" */ | |||
.REQUIRED_ATTR(num_weights, Int) | |||
.ATTR(padding_idx, Int, -1) | |||
.ATTR(scale_grad_by_freq, Bool, false) | |||
.OP_END_FACTORY_REG(EmbeddingDenseGrad) | |||
/** | |||
*@brief CommonLSTM calculation. | |||
*@par Inputs: | |||
*eight inputs: \n | |||
*@li x:Each time step is a 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li w:Each direction is a 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li r:Each direction is a 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_ZN_LSTM. | |||
*@li b:An optional input. Each direction is a 1D Tensor. Must be one of the following types: float16, float32. The format must be ND. | |||
*@li sequence_lens:An optional input. A 1D Tensor.Must be one of the following types: int32. The format must be ND. | |||
*@li initial_h:An optional input. Each direction is a 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li initial_c:An optional input. Each direction is a 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li p:An optional input. Each direction is a 1D Tensor.Must be one of the following types: float16, float32. The format must be ND. | |||
*@par Attributes: | |||
*@li activation_alpha:Optional scaling values used by some activation functions. Empty is currently supported. | |||
*@li activation_beta:Optional scaling values used by some activation functions. Empty is currently supported. | |||
*@li activations:The list of activation functions. Empty is currently supported. | |||
*@li clip:An float identifying the cell clip in the op. Default to -1. | |||
*@li direction:Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward(default), reverse, or bidirectional. | |||
*@li hidden_size:Number of neurons in the hidden layer. Reserved. | |||
*@li input_forget:Couple the input and forget gates if 1. Reserved. | |||
*@par Outputs: | |||
*three outputs: \n | |||
*@li y:First dimension is time step, second dimension is direction, others is a 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li y_h:Each direction is a 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*@li y_c:Each direction is a 4D Tensor. Must be one of the following types: float16, float32. The format must be FRACTAL_NZ. | |||
*/ | |||
REG_OP(CommonLSTM) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(r, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(sequence_lens, TensorType({DT_INT32})) | |||
.OPTIONAL_INPUT(initial_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(initial_c, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(p, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y_c, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(activation_alpha, ListFloat, {}) | |||
.ATTR(activation_beta, ListFloat, {}) | |||
.ATTR(activations, ListString, {}) | |||
.ATTR(clip, Float, -1.0) | |||
.ATTR(direction, String, "forward") | |||
.REQUIRED_ATTR(hidden_size, Int) | |||
.ATTR(input_forget, Int, 0) | |||
.OP_END_FACTORY_REG(CommonLSTM) | |||
/** | |||
* @brief Common GRU calculation. | |||
* @par Inputs: | |||
* Eight inputs, including: | |||
* @li x: The input sequences packed (and pontentially padded) into on 3D Tesnor(float16). The format must be FRACTAL_NZ | |||
* @li w: The weight tensor for the gates is 3D Tensor(float16). The format must be FRACTAL_Z | |||
* @li r: The recurrence weight tesnor is 3D Tensor(float16). The format must be FRACTAL_Z | |||
* @li b: The bias tensor for the gates. The format must be ND | |||
* @li sequence_lens: Optional tensor specifying lengths of sequences(int32). The format must be ND | |||
* @li init_h: Optional initial value of the hidden(float16,float32). The format must be FRACTAL_NZ | |||
* @par Attributes: | |||
* @li activation_alpha: Optional scaling values used by some activation functions. \n | |||
* @li activation_beta: Optional scaling values used by some activation functions. \n | |||
* @li activations: A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. \n | |||
* @li clip: Cell clip threshold. \n | |||
* @li direction: Specify if the RNN is forward, reverse, or bidirectional. \n | |||
* @li hidden_size: Number of neurons in the hidden layer. \n | |||
* @li linear_before_reset: When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. \n | |||
* @par Outputs: | |||
* @li y: A Tensor that concats all the intermediate output values of the hidden(float16,float32). The format must be FRACTAL_NZ | |||
* @li y_h: The last output value of the hidden(float16,float32). The format must be FRACTAL_NZ | |||
*/ | |||
REG_OP(CommonGRU) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(w, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(r, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OPTIONAL_INPUT(sequence_lens, TensorType({DT_INT32})) | |||
.OPTIONAL_INPUT(initial_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OUTPUT(y_h, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.ATTR(activation_alpha, ListFloat, {}) | |||
.ATTR(activation_beta , ListFloat, {}) | |||
.ATTR(activations , ListString, {}) | |||
.ATTR(clip, Float, -1.0) | |||
.ATTR(direction, String, "forward") | |||
.REQUIRED_ATTR(hidden_size, Int) | |||
.ATTR(linear_before_reset , Int, 0) | |||
.OP_END_FACTORY_REG(CommonGRU) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_RNN_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -796,6 +796,34 @@ REG_OP(SliceD) | |||
.REQUIRED_ATTR(size, ListInt) | |||
.OP_END_FACTORY_REG(SliceD) | |||
/** | |||
*@brief Extracts a slice from a tensor. | |||
* This operation extracts a slice of size "size" from a tensor "x" | |||
* starting at the location specified by "begin" . \n | |||
*@par Inputs: | |||
*@li x: A Tensor. Must be one of the following types: | |||
* float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, | |||
* int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32 . \n | |||
*@par Inputs: | |||
*@li offsets: The starting location for the slice. | |||
*@par Attributes: | |||
*@li size: The tensor shape . \n | |||
*@par Outputs: | |||
*y: A Tensor. Has the same type as "x". The slice extracted from the tensor. | |||
*@par Restrictions: | |||
*Warning: THIS FUNCTION IS DEPRECATED. Please use Slice instead. | |||
*/ | |||
REG_OP(SliceDV2) | |||
.INPUT(x, TensorType::BasicType()) | |||
.INPUT(offsets, TensorType::IndexNumberType()) | |||
.OUTPUT(y, TensorType::BasicType()) | |||
.REQUIRED_ATTR(size, ListInt) | |||
.OP_END_FACTORY_REG(SliceDV2) | |||
/** | |||
* @brief Finds values and indices of the "k" largest elements for the last | |||
* dimension . \n | |||
@@ -1921,6 +1949,160 @@ REG_OP(CumulativeLogsumexpD) | |||
.ATTR(exclusive, Bool, false) | |||
.ATTR(reverse, Bool, false) | |||
.OP_END_FACTORY_REG(CumulativeLogsumexpD) | |||
/** | |||
* @brief Add updates to var according to axis and indices. | |||
* @par Inputs: | |||
* Three inputs, including: | |||
* @li var: A Tensor. Must be one of the following types: | |||
* float16, float32, int16, int32, int8, uint8. | |||
* @li indices: A Tensor of the indices, type should be int32. | |||
* @li updates: A Tensor of the same type as "var". \n | |||
* @par Attributes: | |||
* @li axis: An required int to specify the axis to perform indices add. \n | |||
* @par Outputs: | |||
* @li var: A Tensor. Same as input "var". | |||
* @par Third-party framework compatibility | |||
* Compatible with the Pytorch operator index_add_. | |||
*/ | |||
REG_OP(InplaceIndexAdd) | |||
.INPUT(var, TensorType({DT_INT16, DT_INT32, DT_INT8, | |||
DT_UINT8, DT_FLOAT32, DT_FLOAT16})) | |||
.INPUT(indices, TensorType({DT_INT32})) | |||
.INPUT(updates, TensorType({DT_INT16, DT_INT32, DT_INT8, | |||
DT_UINT8, DT_FLOAT32, DT_FLOAT16})) | |||
.OUTPUT(var, TensorType({DT_INT16, DT_INT32, DT_INT8, | |||
DT_UINT8, DT_FLOAT32, DT_FLOAT16})) | |||
.REQUIRED_ATTR(axis, Int) | |||
.OP_END_FACTORY_REG(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 | |||
* @par Outputs: | |||
* @li y: A tensor. Must be one of the following dtypes: | |||
* float16, float32, int32, int8. | |||
*/ | |||
REG_OP(MaskedFill) | |||
.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT32})) | |||
.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})) | |||
.OP_END_FACTORY_REG(MaskedFill) | |||
/** | |||
* @brief Choose the value of X with value according to mask. | |||
* @par Inputs: | |||
* two inputs, including: | |||
* @li x: A Tensor of dtype is float16 or float32. | |||
* @li mask: A Tensor of dtype is bool. \n | |||
* @par Outputs: | |||
* @li y: A tensor with the same type as x. \n | |||
* @par Third-party framework compatibility | |||
* Compatible with the Numpy operator select. | |||
* Replaces the pytorch operator masked_select in some scenarios.\n | |||
*/ | |||
REG_OP(MaskedSelectV2) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.INPUT(mask, TensorType({DT_BOOL})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) | |||
.OP_END_FACTORY_REG(MaskedSelectV2) | |||
/** | |||
* @brief Slice a tensor at its last dim, e.x. a[..., begin:end:stride]. \n | |||
* @par Inputs: | |||
* One inputs, including: | |||
* @li x: A Tensor. Must be one of the following types: float16, float32, int16, int32. | |||
* @par Attributes: | |||
* @li start: An attribute of type Int, start index of last dim. \n | |||
* @li end: An attribute of type Int, end index of last dim. \n | |||
* @li stride: An attribute of type Int, stride of slice. \n | |||
* @par Outputs: | |||
* @li y: A Tensor. Has the same type as "x". \n | |||
* @par Third-party framework compatibility | |||
* No compatibility | |||
*/ | |||
REG_OP(SliceLastDim) | |||
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_INT16, DT_INT32, DT_INT64})) | |||
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_INT16, DT_INT32, DT_INT64})) | |||
.REQUIRED_ATTR(start, Int) | |||
.REQUIRED_ATTR(end, Int) | |||
.ATTR(stride, Int, 1) | |||
.OP_END_FACTORY_REG(SliceLastDim) | |||
/** | |||
* @brief Extracts a strided slice of a tensor. Roughly speaking, this op \n | |||
* extracts a slice of size (end-begin)/stride from the given input tensor. \n | |||
* Starting at the location specified by begin the slice continues by \n | |||
* adding stride to the index until all dimensions are not less than end. \n | |||
* | |||
* @par Inputs: | |||
* Four inputs, including: | |||
* @li x: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, \n | |||
* complex64, int64, qint8, quint8, qint32, qint16, quint16, uint16, \n | |||
* complex128, float16, uint32, uint64, complex64, complex128. \n | |||
* @li begin: A Tensor of type int32 or int64, for the index of the first value to select. | |||
* | |||
* @li end: A Tensor of type int32 or int64, for the index of the last value to select. | |||
* | |||
* @li axes: A Tensor of type int32 or int64, indicate axis to be select. | |||
* | |||
* @li strides: A Tensor of type int32 or int64, for the increment. | |||
* | |||
* @par Attributes: | |||
* @li begin_mask: A Tensor of type int32. \n | |||
* A bitmask where a bit "i" being "1" means to ignore the begin \n | |||
* value and instead use the largest interval possible. | |||
* @li end_mask: A Tensor of type int32. \n | |||
* Analogous to "begin_mask". | |||
* @li ellipsis_mask: A Tensor of type int32. \n | |||
* A bitmask where bit "i" being "1" means the "i"th position \n | |||
* is actually an ellipsis. | |||
* @li new_axis_mask: A Tensor of type int32. \n | |||
* A bitmask where bit "i" being "1" means the "i"th \n | |||
* specification creates a new shape 1 dimension. | |||
* @li shrink_axis_mask: A Tensor of type int32. \n | |||
* A bitmask where bit "i" implies that the "i"th \n | |||
* specification should shrink the dimensionality. | |||
* | |||
* @par Outputs: | |||
* y: A Tensor. Has the same type as "x". | |||
* | |||
* @attention Constraints: | |||
* | |||
* @par Third-party framework compatibility | |||
* Compatible with the TensorFlow operator StridedSliceV2. | |||
*/ | |||
REG_OP(StridedSliceV2) | |||
.INPUT(x, TensorType::BasicType()) | |||
.INPUT(begin, TensorType::IndexNumberType()) | |||
.INPUT(end, TensorType::IndexNumberType()) | |||
.OPTIONAL_INPUT(axes, TensorType::IndexNumberType()) | |||
.OPTIONAL_INPUT(strides, TensorType::IndexNumberType()) | |||
.ATTR(begin_mask, Int, 0) | |||
.ATTR(end_mask, Int, 0) | |||
.ATTR(ellipsis_mask, Int, 0) | |||
.ATTR(new_axis_mask, Int, 0) | |||
.ATTR(shrink_axis_mask, Int, 0) | |||
.OUTPUT(y, TensorType::BasicType()) | |||
.OP_END_FACTORY_REG(StridedSliceV2) | |||
} // namespace ge | |||
#endif // OPS_BUILT_IN_OP_PROTO_INC_SELECTION_OPS_H_ |
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -141,7 +141,7 @@ support "NHWC/NCHW" to "NC1HWC0" and "NC1HWC0" to "NHWC/NCHW" | |||
*@par Attributes: | |||
*@li src_format: A string source data format, can be "NHWC", "NCHW", "FRACTAL_Zn" etc. | |||
*@li dst_format: A string target data format, can be "NC1HWC0", "NCHW", "FRACTAL_Zn" etc. | |||
*@li group: A required int32, default value is 1. \n | |||
*@li group: A optional int32, default value is 1. \n | |||
*@par Outputs: | |||
*dst: A Tensor dtype of all types. | |||
@@ -151,7 +151,7 @@ REG_OP(TransData) | |||
.OUTPUT(dst, TensorType::BasicType()) | |||
.REQUIRED_ATTR(src_format, String) | |||
.REQUIRED_ATTR(dst_format, String) | |||
.ATTR(group, Int, 1) | |||
.ATTR(groups, Int, 1) | |||
.OP_END_FACTORY_REG(TransData) | |||
/** | |||
@@ -357,7 +357,7 @@ REG_OP(DepthToSpace) | |||
*@brief Permutes data into spatial data blocks and then prunes them . \n | |||
*@par Inputs: | |||
*@li x: A 4D Tensor with format NHWC. | |||
*@li x: A 4D Tensor with format. Must set the format, supported format list ["NCHW, NHWC"] | |||
*@li crops: A 1D list or tuple of int32 or int64 . \n | |||
*Must be one of the following types: float16, float32 | |||
@@ -434,9 +434,10 @@ REG_OP(BatchToSpaceD) | |||
*@par Inputs: | |||
* Two inputs, including: | |||
*@li x: An NHWC Tensor. Must be one of the following types: | |||
*@li x: An 4D Tensor. Must be one of the following types: | |||
* float16, float32, double, int64, int32, uint8, uint16, uint32, uint64, int8, | |||
* int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32. | |||
* Must set the format, supported format list ["NCHW, NHWC"] | |||
*@li paddings: A 2D tensor of type int, specifying the input . \n | |||
*@par Attributes: | |||
@@ -518,7 +519,8 @@ REG_OP(Unpack) | |||
* @par Inputs: | |||
* x: A 4D Tensor with shape [batch, in_rows, in_cols, depth], Must be one of the | |||
* following types:float32, double, int32, uint8, int16, int8, int64, uint16, | |||
* float16, uint32, uint64 | |||
* float16, uint32, uint64. The inputs must have data_format with one of follows: | |||
* NHWC, NCHW. | |||
* @par Attributes: | |||
* @li ksizes: A required list or tuple. The size of the sliding window for each | |||
@@ -533,7 +535,6 @@ REG_OP(Unpack) | |||
* This is equivalent to rate in dilated (a.k.a. Atrous) convolutions. | |||
* @li padding: A required string. The type of padding algorithm to use, | |||
support "SAME" or "VALID". \n | |||
* @li data_format: A required string. The format of input, only supported NHWC. \n | |||
* @par Outputs: | |||
* y: A 4D Tensor with shape [batch, out_rows, out_cols, ksize_rows * | |||
@@ -554,7 +555,6 @@ REG_OP(ExtractImagePatches) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(rates, ListInt) | |||
.REQUIRED_ATTR(padding, String) | |||
.ATTR(data_format, String, "NHWC") | |||
.OP_END_FACTORY_REG(ExtractImagePatches) | |||
/** | |||
@@ -563,6 +563,7 @@ REG_OP(ExtractImagePatches) | |||
* @par Inputs: | |||
* x: A 5D Tensor with shape [batch, in_planes, in_rows, in_cols, depth] . \n | |||
* The inputs must have data_format with one of follows: NDHWC, NCDHW. \n | |||
* @par Attributes: | |||
* @li ksizes: A required list or tuple. The size of the sliding window for each | |||
@@ -571,7 +572,6 @@ REG_OP(ExtractImagePatches) | |||
* patches are in "x". Must be: [1, stride_planes, stride_rows, stride_cols, 1]. | |||
* @li padding: A required string. The type of padding algorithm to use , | |||
* support "SAME" or "VALID" . \n | |||
* @li data_format: An optional string. The format of input, only supported NDHWC. \n | |||
* @par Outputs: | |||
* Output: A 5D Tensor with shape [batch, out_planes, out_rows, out_cols, ksize_planes * | |||
@@ -590,7 +590,6 @@ REG_OP(ExtractVolumePatches) | |||
.REQUIRED_ATTR(ksizes, ListInt) | |||
.REQUIRED_ATTR(strides, ListInt) | |||
.REQUIRED_ATTR(padding, String) | |||
.ATTR(data_format, String, "NDHWC") | |||
.OP_END_FACTORY_REG(ExtractVolumePatches) | |||
/** | |||
@@ -1,5 +1,5 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* 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. | |||
@@ -1,12 +1,18 @@ | |||
/** | |||
* @file adx_datadump_server.h | |||
* | |||
* Copyright (c) Huawei Technologies Co., Ltd. 2020-2020. All rights reserved. | |||
* | |||
* This program is distributed in the hope that it will be useful, | |||
* but WITHOUT ANY WARRANTY; without even the implied warranty of | |||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. | |||
*/ | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#ifndef ADX_DATADUMP_SERVER_H | |||
#define ADX_DATADUMP_SERVER_H | |||
@@ -14,151 +14,99 @@ | |||
* limitations under the License. | |||
*/ | |||
#ifndef MSPROF_ENGINE_PROF_ACL_API_H_ | |||
#define MSPROF_ENGINE_PROF_ACL_API_H_ | |||
#define MSVP_MAX_DEV_NUM 64 | |||
#define MSVP_PROF_API __attribute__((visibility("default"))) | |||
#ifndef MSPROFILER_API_PROF_ACL_API_H_ | |||
#define MSPROFILER_API_PROF_ACL_API_H_ | |||
// DataTypeConfig | |||
#define PROF_ACL_API 0x0001 | |||
#define PROF_TASK_TIME 0x0002 | |||
#define PROF_AICORE_METRICS 0x0004 | |||
#define PROF_AICPU_TRACE 0x0008 | |||
#define PROF_MODEL_EXECUTE 0x0010 | |||
#define PROF_RUNTIME_API 0x0020 | |||
#define PROF_RUNTIME_TRACE 0x0040 | |||
#define PROF_SCHEDULE_TIMELINE 0x0080 | |||
#define PROF_SCHEDULE_TRACE 0x0100 | |||
#define PROF_AIVECTORCORE_METRICS 0x0200 | |||
#define PROF_SUBTASK_TIME 0x0400 | |||
#define PROF_TRAINING_TRACE 0x0800 | |||
#define PROF_HCCL_TRACE 0x1000 | |||
#define PROF_DATA_PROCESS 0x2000 | |||
#define PROF_TASK_TRACE 0x3842 | |||
#define PROF_ACL_API 0x00000001 | |||
#define PROF_TASK_TIME 0x00000002 | |||
#define PROF_AICORE_METRICS 0x00000004 | |||
#define PROF_AICPU_TRACE 0x00000008 | |||
#define PROF_MODEL_EXECUTE 0x00000010 | |||
#define PROF_RUNTIME_API 0x00000020 | |||
#define PROF_RUNTIME_TRACE 0x00000040 | |||
#define PROF_SCHEDULE_TIMELINE 0x00000080 | |||
#define PROF_SCHEDULE_TRACE 0x00000100 | |||
#define PROF_AIVECTORCORE_METRICS 0x00000200 | |||
#define PROF_SUBTASK_TIME 0x00000400 | |||
#define PROF_TRAINING_TRACE 0x00000800 | |||
#define PROF_HCCL_TRACE 0x00001000 | |||
#define PROF_TASK_TRACE 0x00001852 | |||
// system profilinig switch | |||
#define PROF_CPU 0x00010000 | |||
#define PROF_HARDWARE_MEMORY 0x00020000 | |||
#define PROF_IO 0x00040000 | |||
#define PROF_INTER_CONNECTION 0x00080000 | |||
#define PROF_DVPP 0x00100000 | |||
#define PROF_SYS_AICORE_SAMPLE 0x00200000 | |||
#define PROF_AIVECTORCORE_SAMPLE 0x00400000 | |||
#define PROF_MODEL_LOAD 0x8000000000000000 | |||
// DataTypeConfig MASK | |||
#define PROF_ACL_API_MASK 0x0001 | |||
#define PROF_TASK_TIME_MASK 0x0002 | |||
#define PROF_AICORE_METRICS_MASK 0x0004 | |||
#define PROF_AICPU_TRACE_MASK 0x0008 | |||
#define PROF_MODEL_EXECUTE_MASK 0x0010 | |||
#define PROF_RUNTIME_API_MASK 0x0020 | |||
#define PROF_RUNTIME_TRACE_MASK 0x0040 | |||
#define PROF_SCHEDULE_TIMELINE_MASK 0x0080 | |||
#define PROF_SCHEDULE_TRACE_MASK 0x0100 | |||
#define PROF_AIVECTORCORE_METRICS_MASK 0x0200 | |||
#define PROF_SUBTASK_TIME_MASK 0x0400 | |||
#define PROF_TRAINING_TRACE_MASK 0x0800 | |||
#define PROF_HCCL_TRACE_MASK 0x1000 | |||
#define PROF_DATA_PROCESS_MASK 0x2000 | |||
#define PROF_ACL_API_MASK 0x00000001 | |||
#define PROF_TASK_TIME_MASK 0x00000002 | |||
#define PROF_AICORE_METRICS_MASK 0x00000004 | |||
#define PROF_AICPU_TRACE_MASK 0x00000008 | |||
#define PROF_MODEL_EXECUTE_MASK 0x00000010 | |||
#define PROF_RUNTIME_API_MASK 0x00000020 | |||
#define PROF_RUNTIME_TRACE_MASK 0x00000040 | |||
#define PROF_SCHEDULE_TIMELINE_MASK 0x00000080 | |||
#define PROF_SCHEDULE_TRACE_MASK 0x00000100 | |||
#define PROF_AIVECTORCORE_METRICS_MASK 0x00000200 | |||
#define PROF_SUBTASK_TIME_MASK 0x00000400 | |||
#define PROF_TRAINING_TRACE_MASK 0x00000800 | |||
#define PROF_HCCL_TRACE_MASK 0x00001000 | |||
// system profilinig mask | |||
#define PROF_CPU_MASK 0x00010000 | |||
#define PROF_HARDWARE_MEMORY_MASK 0x00020000 | |||
#define PROF_IO_MASK 0x00040000 | |||
#define PROF_INTER_CONNECTION_MASK 0x00080000 | |||
#define PROF_DVPP_MASK 0x00100000 | |||
#define PROF_SYS_AICORE_SAMPLE_MASK 0x00200000 | |||
#define PROF_AIVECTORCORE_SAMPLE_MASK 0x00400000 | |||
#define PROF_MODEL_LOAD_MASK 0x8000000000000000 | |||
#include <cstdint> | |||
#include <string> | |||
/** | |||
* @name ProrErrorCode | |||
* @brief error code enum of prof_acl_apis | |||
*/ | |||
enum ProfErrorCode { | |||
PROF_ERROR_NONE = 0, // ok | |||
PROF_ERROR_PARAM_INVALID, // param invalid, for example nullptr | |||
PROF_ERROR_REPEAT_INIT, // profiling has already been inited | |||
PROF_ERROR_CONFIG_INVALID, // config invalid, for example invalid json string | |||
PROF_ERROR_DIR_NO_ACCESS, // dir is not accessable | |||
PROF_ERROR_FAILURE, // failed to init or start profiling | |||
PROF_ERROR_NOT_INITED, // profiling has not been inited | |||
PROF_ERROR_DEVICE_INVALID, // device id invalid | |||
PROF_ERROR_UNSUPPORTED, // unsupported data type or ai core metrics | |||
PROF_ERROR_REPEAT_START, // profiilng has already been started | |||
PROF_ERROR_NOT_STARTED, // profiling has not been started | |||
}; | |||
/** | |||
* @brief transfer profiling config in acl.json to sample config | |||
* @param aclCfg [IN] profiling json string from acl.json as {"switch":"on", "result_path":"/home",...} | |||
* @param sampleCfg [OUT] json string for GE as {"startCfg":[{"deviceID":"all","jobID":"1234",...}]} | |||
* @return ProfErrorCode | |||
*/ | |||
MSVP_PROF_API int32_t ProfAclCfgToSampleCfg(const std::string &aclCfg, std::string &sampleCfg); | |||
#ifndef OS_TYPE | |||
#define OS_TYPE 0 | |||
#endif // OS_TYPE | |||
/** | |||
* @name ProfInit | |||
* @brief init profiling | |||
* @param profInitCfg [IN] config of init profiling of json format | |||
* @return ProfErrorCode | |||
*/ | |||
MSVP_PROF_API int32_t ProfInit(const std::string &profInitCfg); | |||
/** | |||
* @name ProfAicoreMetrics | |||
* @brief aicore metrics enum | |||
*/ | |||
enum ProfAicoreMetrics { | |||
PROF_AICORE_ARITHMATIC_THROUGHPUT = 0, | |||
PROF_AICORE_PIPELINE = 1, | |||
PROF_AICORE_SYNCHRONIZATION = 2, | |||
PROF_AICORE_MEMORY = 3, | |||
PROF_AICORE_INTERNAL_MEMORY = 4, | |||
PROF_AICORE_STALL = 5, | |||
PROF_AICORE_EVENT = 255 | |||
}; | |||
#if (OS_TYPE != LINUX) | |||
#define MSVP_PROF_API __declspec(dllexport) | |||
#else | |||
#define MSVP_PROF_API __attribute__((visibility("default"))) | |||
#endif | |||
/** | |||
* @name ProfConfig | |||
* @brief struct of ProfStart | |||
*/ | |||
struct ProfConfig { | |||
uint32_t devNums; // length of device id list | |||
uint32_t devIdList[MSVP_MAX_DEV_NUM]; // physical device id list | |||
ProfAicoreMetrics aicoreMetrics; // aicore metric | |||
uint64_t dataTypeConfig; // data type to start profiling | |||
}; | |||
#include <cstdint> | |||
namespace Msprofiler { | |||
namespace Api { | |||
/** | |||
* @name ProfStartProfiling | |||
* @brief start profiling | |||
* @param profStartCfg [IN] config to start profiling | |||
* @return ProfErrorCode | |||
* @name ProfGetOpExecutionTime | |||
* @brief get op execution time of specific part of data | |||
* @param data [IN] data read from pipe | |||
* @param len [IN] data length | |||
* @param index [IN] index of part(op) | |||
* @return op execution time (us) | |||
*/ | |||
MSVP_PROF_API int32_t ProfStartProfiling(const ProfConfig *profStartCfg); | |||
MSVP_PROF_API uint64_t ProfGetOpExecutionTime(const void *data, uint32_t len, uint32_t index); | |||
} | |||
} | |||
/** | |||
* @name ProfStopConfig | |||
* @brief struct of ProfStop | |||
*/ | |||
struct ProfStopConfig { | |||
uint64_t padding; | |||
}; | |||
#ifdef __cplusplus | |||
extern "C" { | |||
#endif | |||
/** | |||
* @name ProfStopProfiling | |||
* @brief stop profiling | |||
* @param profStopCfg [IN] config to stop profiling | |||
* @return ProfErrorCode | |||
*/ | |||
MSVP_PROF_API int32_t ProfStopProfiling(const ProfConfig *profStopCfg); | |||
/** | |||
* @name ProfFinalize | |||
* @brief finalize profiling task | |||
* @return ProfErrorCode | |||
*/ | |||
MSVP_PROF_API int32_t ProfFinalize(); | |||
MSVP_PROF_API uint64_t ProfGetOpExecutionTime(const void *data, uint32_t len, uint32_t index); | |||
/** | |||
* @name ProfGetDataTypeConfig | |||
* @brief get dataTypeConfig started with of one device | |||
* @param deviceId [IN] deviceId to get dataTypeConfig | |||
* @param dataTypeConfig [OUT] result get | |||
* @return ProfErrorCode | |||
*/ | |||
MSVP_PROF_API int32_t ProfGetDataTypeConfig(uint32_t deviceId, uint64_t &dataTypeConfig); | |||
#ifdef __cplusplus | |||
} | |||
#endif | |||
#endif // MSPROF_ENGINE_PROF_ACL_API_H_ | |||
#endif // MSPROFILER_API_PROF_ACL_API_H_ |
@@ -16,7 +16,16 @@ | |||
#ifndef MSPROF_ENGINE_PROF_MGR_CORE_H_ | |||
#define MSPROF_ENGINE_PROF_MGR_CORE_H_ | |||
#ifndef OS_TYPE | |||
#define OS_TYPE 0 | |||
#endif // OS_TYPE | |||
#if (OS_TYPE != LINUX) | |||
#define MSVP_PROF_API __declspec(dllexport) | |||
#else | |||
#define MSVP_PROF_API __attribute__((visibility("default"))) | |||
#endif | |||
#include <string> | |||
#include <vector> | |||